paper_id
uint32 | title
string | authors
list | cvf_url
string | pdf_url
string | supp_url
string | bibtex
string | abstract
large_string | arxiv_id
string | comment
string | github
string | project_page
string | space_ids
list | model_ids
list | dataset_ids
list | embedding
list |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
100
|
DDB: Diffusion Driven Balancing to Address Spurious Correlations
|
[
"Aryan Yazdan Parast",
"Basim Azam",
"Naveed Akhtar"
] |
https://openaccess.thecvf.com/content/ICCV2025/html/Parast_DDB_Diffusion_Driven_Balancing_to_Address_Spurious_Correlations_ICCV_2025_paper.html
|
https://openaccess.thecvf.com/content/ICCV2025/papers/Parast_DDB_Diffusion_Driven_Balancing_to_Address_Spurious_Correlations_ICCV_2025_paper.pdf
|
https://openaccess.thecvf.com/content/ICCV2025/supplemental/Parast_DDB_Diffusion_Driven_ICCV_2025_supplemental.pdf
|
@InProceedings{Parast_2025_ICCV,
author = {Parast, Aryan Yazdan and Azam, Basim and Akhtar, Naveed},
title = {DDB: Diffusion Driven Balancing to Address Spurious Correlations},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2025},
pages = {17526-17535}
}
|
Deep neural networks trained with Empirical Risk Minimization (ERM) perform well when both training and test data come from the same domain, but they often fail to generalize to out-of-distribution samples. In image classification, these models may rely on spurious correlations that often exist between labels and irrelevant features of images, making predictions unreliable when those features do not exist. We propose a Diffusion Driven Balancing (DDB) technique to generate training samples with text-to-image diffusion models for addressing the spurious correlation problem. First, we compute the best describing token for the visual features pertaining to the causal components of samples by a textual inversion mechanism. Then, leveraging a language segmentation method and a diffusion model, we generate new samples by combining the causal component with the elements from other classes. We also meticulously prune the generated samples based on the prediction probabilities and attribution scores of the ERM model to ensure their correct composition for our objective. Finally, we retrain the ERM model on our augmented dataset. This process reduces the model's reliance on spurious correlations by learning from carefully crafted samples in which this correlation does not exist. Our experiments show that across different benchmarks, our technique achieves better worst-group accuracy than the existing state-of-the-art methods. Our code is available at https://github.com/ArianYp/DDB.
|
2503.17226
| null |
https://github.com/ArianYp/DDB
|
https://arianyp.github.io/Diffusion-Driven-Balancing/
|
[] |
[] |
[] |
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] |
101
|
Feature Purification Matters: Suppressing Outlier Propagation for Training-Free Open-Vocabulary Semantic Segmentation
|
[
"Shuo Jin",
"Siyue Yu",
"Bingfeng Zhang",
"Mingjie Sun",
"Yi Dong",
"Jimin Xiao"
] |
https://openaccess.thecvf.com/content/ICCV2025/html/Jin_Feature_Purification_Matters_Suppressing_Outlier_Propagation_for_Training-Free_Open-Vocabulary_Semantic_ICCV_2025_paper.html
|
https://openaccess.thecvf.com/content/ICCV2025/papers/Jin_Feature_Purification_Matters_Suppressing_Outlier_Propagation_for_Training-Free_Open-Vocabulary_Semantic_ICCV_2025_paper.pdf
|
https://openaccess.thecvf.com/content/ICCV2025/supplemental/Jin_Feature_Purification_Matters_ICCV_2025_supplemental.pdf
|
@InProceedings{Jin_2025_ICCV,
author = {Jin, Shuo and Yu, Siyue and Zhang, Bingfeng and Sun, Mingjie and Dong, Yi and Xiao, Jimin},
title = {Feature Purification Matters: Suppressing Outlier Propagation for Training-Free Open-Vocabulary Semantic Segmentation},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2025},
pages = {20291-20300}
}
|
Training-free open-vocabulary semantic segmentation has advanced with vision-language models like CLIP, which exhibit strong zero-shot abilities. However, CLIP's attention mechanism often wrongly emphasises specific image tokens, namely outliers, which results in irrelevant over-activation. Existing approaches struggle with these outliers that arise in intermediate layers and propagate through the model, ultimately degrading spatial perception. In this paper, we propose a Self-adaptive Feature Purifier framework (SFP) to suppress propagated outliers and enhance semantic representations for open-vocabulary semantic segmentation. Specifically, based on an in-depth analysis of attention responses between image and class tokens, we design a self-adaptive outlier mitigator to detect and mitigate outliers at each layer for propagated feature purification. In addition, we introduce a semantic-aware attention enhancer to augment attention intensity in semantically relevant regions, which strengthens the purified feature to focus on objects. Further, we introduce a hierarchical attention integrator to aggregate multi-layer attention maps to refine spatially coherent feature representations for final segmentation. Our proposed SFP enables robust outlier suppression and object-centric feature representation, leading to a more precise segmentation. Extensive experiments show that our method achieves state-of-the-art performance and surpasses existing methods by an average of 4.6% mIoU on eight segmentation benchmarks. The code will be released.
| null | null | null | null |
[] |
[] |
[] |
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] |
102
|
Motal: Unsupervised 3D Object Detection by Modality and Task-specific Knowledge Transfer
|
[
"Hai Wu",
"Hongwei Lin",
"Xusheng Guo",
"Xin Li",
"Mingming Wang",
"Cheng Wang",
"Chenglu Wen"
] |
https://openaccess.thecvf.com/content/ICCV2025/html/Wu_Motal_Unsupervised_3D_Object_Detection_by_Modality_and_Task-specific_Knowledge_ICCV_2025_paper.html
|
https://openaccess.thecvf.com/content/ICCV2025/papers/Wu_Motal_Unsupervised_3D_Object_Detection_by_Modality_and_Task-specific_Knowledge_ICCV_2025_paper.pdf
|
https://openaccess.thecvf.com/content/ICCV2025/supplemental/Wu_Motal_Unsupervised_3D_ICCV_2025_supplemental.pdf
|
@InProceedings{Wu_2025_ICCV,
author = {Wu, Hai and Lin, Hongwei and Guo, Xusheng and Li, Xin and Wang, Mingming and Wang, Cheng and Wen, Chenglu},
title = {Motal: Unsupervised 3D Object Detection by Modality and Task-specific Knowledge Transfer},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2025},
pages = {6284-6293}
}
|
The performance of unsupervised 3D object classification and bounding box regression relies heavily on the quality of initial pseudo-labels. Traditionally, the labels of classification and regression are represented by a single set of candidate boxes generated by motion or geometry heuristics. However, due to the similarity of many objects to the background in shape or lack of motion, the labels often fail to achieve high accuracy in two tasks simultaneously. Using these labels to directly train the network results in decreased detection performance. To address this challenge, we introduce Motal that performs unsupervised 3D object detection by Modality and task-specific knowledge transfer. Motal decouples the pseudo-labels into two sets of candidates, from which Motal discovers classification knowledge by motion and image appearance prior, and discovers box regression knowledge by geometry prior, respectively. Motal finally transfers all knowledge to a single student network by a TMT (Task-specific Masked Training) scheme, attaining high performance in both classification and regression. Motal can greatly enhance various unsupervised methods by about 2x mAP. For example, on the WOD test set, Motal improves the state-of-the-art CPD by 21.56% mAP L1 (from 20.54% to 42.10%) and 19.90% mAP L2 (from 18.18% to 38.08%). These achievements highlight the significance of our method.
| null | null | null | null |
[] |
[] |
[] |
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] |
103
|
MMAIF: Multi-task and Multi-degradation All-in-One for Image Fusion with Language Guidance
|
[
"Zihan Cao",
"Yu Zhong",
"Ziqi Wang",
"Liang-Jian Deng"
] |
https://openaccess.thecvf.com/content/ICCV2025/html/Cao_MMAIF_Multi-task_and_Multi-degradation_All-in-One_for_Image_Fusion_with_Language_ICCV_2025_paper.html
|
https://openaccess.thecvf.com/content/ICCV2025/papers/Cao_MMAIF_Multi-task_and_Multi-degradation_All-in-One_for_Image_Fusion_with_Language_ICCV_2025_paper.pdf
|
https://openaccess.thecvf.com/content/ICCV2025/supplemental/Cao_MMAIF_Multi-task_and_ICCV_2025_supplemental.pdf
|
@InProceedings{Cao_2025_ICCV,
author = {Cao, Zihan and Zhong, Yu and Wang, Ziqi and Deng, Liang-Jian},
title = {MMAIF: Multi-task and Multi-degradation All-in-One for Image Fusion with Language Guidance},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2025},
pages = {11744-11754}
}
|
Image fusion, a fundamental low-level vision task, aims to integrate multiple image sequences into a single output while preserving as much information as possible from the input. However, existing methods face several significant limitations: 1) requiring task- or dataset-specific models; 2) neglecting real-world image degradations (e.g., noise), which causes failure when processing degraded inputs; 3) operating in pixel space, where attention mechanisms are computationally expensive; and 4) lacking user interaction capabilities.To address these challenges, we propose a unified framework for multi-task, multi-degradation, and language-guided image fusion. Our framework includes two key components: 1) a practical degradation pipeline that simulates real-world image degradations and generates interactive prompts to guide the model; 2) an all-in-one Diffusion Transformer (DiT) operating in latent space, which fuses a clean image conditioned on both the degraded inputs and the generated prompts. Furthermore, we introduce principled modifications to the original DiT architecture to better suit the fusion task. Based on this framework, we develop two versions of the model: Regression-based and Flow Matching-based variants.Extensive qualitative and quantitative experiments demonstrate that our approach effectively addresses the aforementioned limitations and outperforms previous restoration+fusion and all-in-one pipelines.
|
2503.14944
| null |
https://github.com/294coder/MMAIF
| null |
[] |
[] |
[] |
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] |
104
|
SparseLaneSTP: Leveraging Spatio-Temporal Priors with Sparse Transformers for 3D Lane Detection
|
[
"Maximilian Pittner",
"Joel Janai",
"Mario Faigle",
"Alexandru Paul Condurache"
] |
https://openaccess.thecvf.com/content/ICCV2025/html/Pittner_SparseLaneSTP_Leveraging_Spatio-Temporal_Priors_with_Sparse_Transformers_for_3D_Lane_ICCV_2025_paper.html
|
https://openaccess.thecvf.com/content/ICCV2025/papers/Pittner_SparseLaneSTP_Leveraging_Spatio-Temporal_Priors_with_Sparse_Transformers_for_3D_Lane_ICCV_2025_paper.pdf
|
https://openaccess.thecvf.com/content/ICCV2025/supplemental/Pittner_SparseLaneSTP_Leveraging_Spatio-Temporal_ICCV_2025_supplemental.pdf
|
@InProceedings{Pittner_2025_ICCV,
author = {Pittner, Maximilian and Janai, Joel and Faigle, Mario and Condurache, Alexandru Paul},
title = {SparseLaneSTP: Leveraging Spatio-Temporal Priors with Sparse Transformers for 3D Lane Detection},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2025},
pages = {29099-29109}
}
|
3D lane detection has emerged as a critical challenge in autonomous driving, encompassing identification and localization of lane markings and the 3D road surface. Conventional 3D methods detect lanes from dense birds-eye-viewed (BEV) features, though erroneous transformations often result in a poor feature representation misaligned with the true 3D road surface. While recent sparse lane detectors have surpassed dense BEV approaches, they completely disregard valuable lane-specific priors. Furthermore, existing methods fail to utilize historic lane observations, which yield the potential to resolve ambiguities in situations of poor visibility. To address these challenges, we present SparseLaneSTP, a novel method that integrates both geometric properties of the lane structure and temporal information into a sparse lane transformer. It introduces a new lane-specific spatio-temporal attention mechanism, a continuous lane representation tailored for sparse architectures as well as temporal regularization. Identifying weaknesses of existing 3D lane datasets, we also introduce a precise and consistent 3D lane dataset using a simple yet effective auto-labeling strategy. Our experimental section proves the benefits of our contributions and demonstrates state-of-the-art performance across all detection and error metrics on existing 3D lane detection benchmarks as well as on our novel dataset.
| null | null | null | null |
[] |
[] |
[] |
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] |
105
|
MonSTeR: a Unified Model for Motion, Scene, Text Retrieval
|
[
"Luca Collorone",
"Matteo Gioia",
"Massimiliano Pappa",
"Paolo Leoni",
"Giovanni Ficarra",
"Or Litany",
"Indro Spinelli",
"Fabio Galasso"
] |
https://openaccess.thecvf.com/content/ICCV2025/html/Collorone_MonSTeR_a_Unified_Model_for_Motion_Scene_Text_Retrieval_ICCV_2025_paper.html
|
https://openaccess.thecvf.com/content/ICCV2025/papers/Collorone_MonSTeR_a_Unified_Model_for_Motion_Scene_Text_Retrieval_ICCV_2025_paper.pdf
|
https://openaccess.thecvf.com/content/ICCV2025/supplemental/Collorone_MonSTeR_a_Unified_ICCV_2025_supplemental.zip
|
@InProceedings{Collorone_2025_ICCV,
author = {Collorone, Luca and Gioia, Matteo and Pappa, Massimiliano and Leoni, Paolo and Ficarra, Giovanni and Litany, Or and Spinelli, Indro and Galasso, Fabio},
title = {MonSTeR: a Unified Model for Motion, Scene, Text Retrieval},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2025},
pages = {10940-10949}
}
|
Intention drives human movement in complex environments, but such movement can only happen if the surrounding context supports it. Despite the intuitive nature of this mechanism, existing research has not yet provided tools to evaluate the alignment between skeletal movement (motion), intention (text), and the surrounding context (scene). In this work, we introduce MonSTeR, the first MOtioN-Scene-TExt Retrieval model. Inspired by the modeling of higher-order relations, MonSTeR constructs a unified latent space by leveraging unimodal and cross-modal representations. This allows MonSTeR to capture the intricate dependencies between modalities, enabling flexible but robust retrieval across various tasks. Our results show that MonSTeR outperforms trimodal models that rely solely on unimodal representations. Furthermore, we validate the alignment of our retrieval scores with human preferences through a dedicated user study. We demonstrate the versatility of MonSTeR's latent space on zero-shot in-Scene Object Placement and Motion Captioning. Code and pre-trained models are available at github.com/colloroneluca/MonSTeR.
|
2510.03200
| null | null | null |
[] |
[] |
[] |
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] |
106
|
Semantic Causality-Aware Vision-Based 3D Occupancy Prediction
|
[
"Dubing Chen",
"Huan Zheng",
"Yucheng Zhou",
"Xianfei Li",
"Wenlong Liao",
"Tao He",
"Pai Peng",
"Jianbing Shen"
] |
https://openaccess.thecvf.com/content/ICCV2025/html/Chen_Semantic_Causality-Aware_Vision-Based_3D_Occupancy_Prediction_ICCV_2025_paper.html
|
https://openaccess.thecvf.com/content/ICCV2025/papers/Chen_Semantic_Causality-Aware_Vision-Based_3D_Occupancy_Prediction_ICCV_2025_paper.pdf
| null |
@InProceedings{Chen_2025_ICCV,
author = {Chen, Dubing and Zheng, Huan and Zhou, Yucheng and Li, Xianfei and Liao, Wenlong and He, Tao and Peng, Pai and Shen, Jianbing},
title = {Semantic Causality-Aware Vision-Based 3D Occupancy Prediction},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2025},
pages = {24878-24888}
}
|
Vision-based 3D semantic occupancy prediction is a critical task in 3D vision that integrates volumetric 3D reconstruction with semantic understanding. Existing methods, however, often rely on modular pipelines. These modules are typically optimized independently or use pre-configured inputs, leading to cascading errors. In this paper, we address this limitation by designing a novel causal loss that enables holistic, end-to-end supervision of the modular 2D-to-3D transformation pipeline. Grounded in the principle of 2D-to-3D semantic causality, this loss regulates the gradient flow from 3D voxel representations back to the 2D features. Consequently, it renders the entire pipeline differentiable, unifying the learning process and making previously non-trainable components fully learnable. Building on this principle, we propose the Semantic Causality-Aware 2D-to-3D Transformation, which comprises three components guided by our causal loss: Channel-Grouped Lifting for adaptive semantic mapping, Learnable Camera Offsets for enhanced robustness against camera perturbations, and Normalized Convolution for effective feature propagation. Extensive experiments demonstrate that our method achieves state-of-the-art performance on the Occ3D benchmark, demonstrating significant robustness to camera perturbations and improved 2D-to-3D semantic consistency.
|
2509.08388
| null | null | null |
[] |
[] |
[] |
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] |
107
|
OmniSAM: Omnidirectional Segment Anything Model for UDA in Panoramic Semantic Segmentation
|
[
"Ding Zhong",
"Xu Zheng",
"Chenfei Liao",
"Yuanhuiyi Lyu",
"Jialei Chen",
"Shengyang Wu",
"Linfeng Zhang",
"Xuming Hu"
] |
https://openaccess.thecvf.com/content/ICCV2025/html/Zhong_OmniSAM_Omnidirectional_Segment_Anything_Model_for_UDA_in_Panoramic_Semantic_ICCV_2025_paper.html
|
https://openaccess.thecvf.com/content/ICCV2025/papers/Zhong_OmniSAM_Omnidirectional_Segment_Anything_Model_for_UDA_in_Panoramic_Semantic_ICCV_2025_paper.pdf
|
https://openaccess.thecvf.com/content/ICCV2025/supplemental/Zhong_OmniSAM_Omnidirectional_Segment_ICCV_2025_supplemental.pdf
|
@InProceedings{Zhong_2025_ICCV,
author = {Zhong, Ding and Zheng, Xu and Liao, Chenfei and Lyu, Yuanhuiyi and Chen, Jialei and Wu, Shengyang and Zhang, Linfeng and Hu, Xuming},
title = {OmniSAM: Omnidirectional Segment Anything Model for UDA in Panoramic Semantic Segmentation},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2025},
pages = {23892-23901}
}
|
Segment Anything Model 2 (SAM2) has emerged as a strong base model in various pinhole imaging segmentation tasks. However, when applying it to 360^\circ domain, the significant field-of-view (FoV) gap between pinhole (70^\circ x70^\circ) and panoramic images (180^\circ x360^\circ) poses unique challenges. Two major concerns for this application includes 1) inevitable distortion and object deformation brought by the large FoV disparity between domains; 2) the lack of pixel-level semantic understanding that the original SAM2 cannot provide. To address these issues, we propose a novel OmniSAM framework, which makes the first attempt to apply SAM2 for panoramic semantic segmentation. Specifically, to bridge the first gap, OmniSAM first divides the panorama into sequences of patches. These patches are then treated as image sequences in similar manners as in video segmentation tasks. We then leverage the SAM2's memory mechanism to extract cross-patch correspondences that embeds the cross-FoV dependencies, improving feature continuity and the prediction consistency along mask boundaries. For the second gap, OmniSAM fine-tunes the pretrained image encoder and reutilize the mask decoder for semantic prediction. An FoV-based prototypical adaptation module with dynamic pseudo label update mechanism is also introduced to facilitate the alignment of memory and backbone features, thereby improving model generalization ability across different sizes of source models. Extensive experimental results demonstrate that our method outperforms the state-of-the-art methods by large margins, e.g., 79.06% (10.22%\uparrow) on SPin8-to-SPan8, 62.46% (6.58%\uparrow) on CS13-to-DP13.
|
2503.07098
| null | null | null |
[] |
[] |
[] |
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] |
108
|
Event-Driven Storytelling with Multiple Lifelike Humans in a 3D Scene
|
[
"Donggeun Lim",
"Jinseok Bae",
"Inwoo Hwang",
"Seungmin Lee",
"Hwanhee Lee",
"Young Min Kim"
] |
https://openaccess.thecvf.com/content/ICCV2025/html/Lim_Event-Driven_Storytelling_with_Multiple_Lifelike_Humans_in_a_3D_Scene_ICCV_2025_paper.html
|
https://openaccess.thecvf.com/content/ICCV2025/papers/Lim_Event-Driven_Storytelling_with_Multiple_Lifelike_Humans_in_a_3D_Scene_ICCV_2025_paper.pdf
|
https://openaccess.thecvf.com/content/ICCV2025/supplemental/Lim_Event-Driven_Storytelling_with_ICCV_2025_supplemental.pdf
|
@InProceedings{Lim_2025_ICCV,
author = {Lim, Donggeun and Bae, Jinseok and Hwang, Inwoo and Lee, Seungmin and Lee, Hwanhee and Kim, Young Min},
title = {Event-Driven Storytelling with Multiple Lifelike Humans in a 3D Scene},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2025},
pages = {11654-11664}
}
|
In this work, we propose a framework that creates a lively virtual dynamic scene with contextual motions of multiple humans. Generating multi-human contextual motion requires holistic reasoning over dynamic relationships among human-human and human-scene interactions. We adapt the power of a large language model (LLM) to digest the contextual complexity within textual input and convert the task into tangible subproblems such that we can generate multi-agent behavior beyond the scale that was not considered before. Specifically, our event generator formulates the temporal progression of a dynamic scene into a sequence of small events. Each event calls for a well-defined motion involving relevant characters and objects. Next, we synthesize the motions of characters at positions sampled based on spatial guidance. We employ a high-level module to deliver scalable yet comprehensive context, translating events into relative descriptions that enable the retrieval of precise coordinates. As the first to address this problem at scale and with diversity, we offer a benchmark to assess diverse aspects of contextual reasoning. Benchmark results and user studies show that our framework effectively captures scene context with high scalability.
|
2507.19232
|
16 pages, project page:
https://rms0329.github.io/Event-Driven-Storytelling/
| null | null |
[] |
[] |
[] |
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] |
109
|
Probabilistic Inertial Poser (ProbIP): Uncertainty-aware Human Motion Modeling from Sparse Inertial Sensors
|
[
"Min Kim",
"Younho Jeon",
"Sungho Jo"
] |
https://openaccess.thecvf.com/content/ICCV2025/html/Kim_Probabilistic_Inertial_Poser_ProbIP_Uncertainty-aware_Human_Motion_Modeling_from_Sparse_ICCV_2025_paper.html
|
https://openaccess.thecvf.com/content/ICCV2025/papers/Kim_Probabilistic_Inertial_Poser_ProbIP_Uncertainty-aware_Human_Motion_Modeling_from_Sparse_ICCV_2025_paper.pdf
|
https://openaccess.thecvf.com/content/ICCV2025/supplemental/Kim_Probabilistic_Inertial_Poser_ICCV_2025_supplemental.zip
|
@InProceedings{Kim_2025_ICCV,
author = {Kim, Min and Jeon, Younho and Jo, Sungho},
title = {Probabilistic Inertial Poser (ProbIP): Uncertainty-aware Human Motion Modeling from Sparse Inertial Sensors},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2025},
pages = {25893-25902}
}
|
Wearable Inertial Measurement Units (IMUs) allow non-intrusive motion tracking, but limited sensor placements can introduce uncertainty in capturing detailed full-body movements. Existing methods mitigate this issue by selecting more physically plausible motion patterns but do not directly address inherent uncertainties in the data. We introduce the Probabilistic Inertial Poser (ProbIP), a novel probabilistic model that transforms sparse IMU data into human motion predictions without physical constraints. ProbIP utilizes RU-Mamba blocks to predict a matrix Fisher distribution over rotations, effectively estimating both rotation matrices and associated uncertainties. To refine motion distribution through layers, our Progressive Distribution Narrowing (PDN) technique enables stable learning across a diverse range of motions. Experimental results demonstrate that ProbIP achieves state-of-the-art performance on multiple public datasets with six and fewer IMU sensors. Our contributions include the development of ProbIP with RU-Mamba blocks for probabilistic motion estimation, applying Progressive Distribution Narrowing (PDN) for uncertainty reduction, and evidence of superior results with six and reduced sensor configurations.
| null | null | null | null |
[] |
[] |
[] |
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0.011072655208408833,
-0.04828421771526337,
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] |
110
|
Bi-Level Optimization for Self-Supervised AI-Generated Face Detection
|
[
"Mian Zou",
"Nan Zhong",
"Baosheng Yu",
"Yibing Zhan",
"Kede Ma"
] |
https://openaccess.thecvf.com/content/ICCV2025/html/Zou_Bi-Level_Optimization_for_Self-Supervised_AI-Generated_Face_Detection_ICCV_2025_paper.html
|
https://openaccess.thecvf.com/content/ICCV2025/papers/Zou_Bi-Level_Optimization_for_Self-Supervised_AI-Generated_Face_Detection_ICCV_2025_paper.pdf
|
https://openaccess.thecvf.com/content/ICCV2025/supplemental/Zou_Bi-Level_Optimization_for_ICCV_2025_supplemental.pdf
|
@InProceedings{Zou_2025_ICCV,
author = {Zou, Mian and Zhong, Nan and Yu, Baosheng and Zhan, Yibing and Ma, Kede},
title = {Bi-Level Optimization for Self-Supervised AI-Generated Face Detection},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2025},
pages = {18959-18968}
}
|
AI-generated face detectors trained via supervised learning typically rely on synthesized images from specific generators, limiting their generalization to emerging generative techniques. To overcome this limitation, we introduce a self-supervised method based on bi-level optimization. In the inner loop, we pretrain a vision encoder only on photographic face images using a set of linearly weighted pretext tasks: classification of categorical exchangeable image file format (EXIF) tags, ranking of ordinal EXIF tags, and detection of artificial face manipulations. The outer loop then optimizes the relative weights of these pretext tasks to enhance the coarse-grained detection of manipulated faces, serving as a proxy task for identifying AI-generated faces. In doing so, it aligns self-supervised learning more closely with the ultimate goal of AI-generated face detection. Once pretrained, the encoder remains fixed, and AI-generated faces are detected either as anomalies under a Gaussian mixture model fitted to photographic face features or by a lightweight two-layer perceptron serving as a binary classifier. Extensive experiments demonstrate that our detectors significantly outperform existing approaches in both one-class and binary classification settings, exhibiting strong generalization to unseen generators.
|
2507.22824
| null | null | null |
[] |
[] |
[] |
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] |
111
|
RIOcc: Efficient Cross-Modal Fusion Transformer with Collaborative Feature Refinement for 3D Semantic Occupancy Prediction
|
[
"Baojie Fan",
"Xiaotian Li",
"Yuhan Zhou",
"Yuyu Jiang",
"Jiandong Tian",
"Huijie Fan"
] |
https://openaccess.thecvf.com/content/ICCV2025/html/Fan_RIOcc_Efficient_Cross-Modal_Fusion_Transformer_with_Collaborative_Feature_Refinement_for_ICCV_2025_paper.html
|
https://openaccess.thecvf.com/content/ICCV2025/papers/Fan_RIOcc_Efficient_Cross-Modal_Fusion_Transformer_with_Collaborative_Feature_Refinement_for_ICCV_2025_paper.pdf
|
https://openaccess.thecvf.com/content/ICCV2025/supplemental/Fan_RIOcc_Efficient_Cross-Modal_ICCV_2025_supplemental.pdf
|
@InProceedings{Fan_2025_ICCV,
author = {Fan, Baojie and Li, Xiaotian and Zhou, Yuhan and Jiang, Yuyu and Tian, Jiandong and Fan, Huijie},
title = {RIOcc: Efficient Cross-Modal Fusion Transformer with Collaborative Feature Refinement for 3D Semantic Occupancy Prediction},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2025},
pages = {25851-25861}
}
|
The multi-modal 3D semantic occupancy task provides a comprehensive understanding of the scene and has received considerable attention in the field of autonomous driving. However, existing methods mainly focus on processing large-scale voxels, which bring high computational costs and degrade details. Additionally, they struggle to accurately capture occluded targets and distant information. In this paper, we propose a novel LiDAR-Camera 3D semantic occupancy prediction framework called RIOcc, with collaborative feature refinement and multi-scale cross-modal fusion transformer. Specifically, RIOcc encodes multi-modal data into a unified Bird's Eye View (BEV) space, which reduces computational complexity and enhances the efficiency of feature alignment. Then, multi-scale feature processing substantially expands the receptive fields. Meanwhile, in the LiDAR branch, we design the Dual-branch Pooling (DBP) to adaptively enhance geometric features across both the Channel and Grid dimensions. In the camera branch, the Wavelet and Semantic Encoders are developed to extract high-level semantic features with abundant edge and structural information. Finally, to facilitate effective cross-modal complementarity, we develop the Deformable Dual-Attention (DDA) module. Extensive experiments demonstrate that RIOcc achieves state-of-the-art performance, with 54.2 mIoU and 25.9 mIoU on the Occ3D-nuScenes and nuScenes-Occupancy datasets, respectively.
| null | null | null | null |
[] |
[] |
[] |
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] |
112
|
Golden Noise for Diffusion Models: A Learning Framework
|
[
"Zikai Zhou",
"Shitong Shao",
"Lichen Bai",
"Shufei Zhang",
"Zhiqiang Xu",
"Bo Han",
"Zeke Xie"
] |
https://openaccess.thecvf.com/content/ICCV2025/html/Zhou_Golden_Noise_for_Diffusion_Models_A_Learning_Framework_ICCV_2025_paper.html
|
https://openaccess.thecvf.com/content/ICCV2025/papers/Zhou_Golden_Noise_for_Diffusion_Models_A_Learning_Framework_ICCV_2025_paper.pdf
|
https://openaccess.thecvf.com/content/ICCV2025/supplemental/Zhou_Golden_Noise_for_ICCV_2025_supplemental.pdf
|
@InProceedings{Zhou_2025_ICCV,
author = {Zhou, Zikai and Shao, Shitong and Bai, Lichen and Zhang, Shufei and Xu, Zhiqiang and Han, Bo and Xie, Zeke},
title = {Golden Noise for Diffusion Models: A Learning Framework},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2025},
pages = {17688-17697}
}
|
Text-to-image diffusion model is a popular paradigm that synthesizes personalized images by providing a text prompt and a random Gaussian noise. While people observe that some noises are "golden noises" that can achieve better text-image alignment and higher human preference than others, we still lack a machine learning framework to obtain those golden noises. To learn golden noises for diffusion sampling, we mainly make three contributions in this paper. First, we identify a new concept termed the noise prompt, which aims at turning a random Gaussian noise into a golden noise by adding a small desirable perturbation derived from the text prompt. Following the concept, we first formulate the noise prompt learning framework that systematically learns "prompted" golden noise associated with a text prompt for diffusion models. Second, we design a noise prompt data collection pipeline and collect a large-scale noise prompt dataset (NPD) that contains 100k pairs of random noises and golden noises with the associated text prompts. With the prepared NPD as the training dataset, we trained a small noise prompt network (NPNet) that can directly learn to transform a random noise into a golden noise. The learned golden noise perturbation can be considered as a kind of prompt for noise, as it is rich in semantic information and tailored to the given text prompt. Third, our extensive experiments demonstrate the impressive effectiveness and generalization of NPNet on improving the quality of synthesized images across various diffusion models, including SDXL, DreamShaper-xl-v2-turbo, and Hunyuan-DiT. Moreover, NPNet is a small and efficient controller that acts as a plug-and-play module with very limited additional inference and computational costs, as it just provides a golden noise instead of a random noise without accessing the original pipeline.
|
2411.09502
| null | null | null |
[] |
[
"Klayand/GoldenNoiseModel"
] |
[] |
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] |
113
|
CharaConsist: Fine-Grained Consistent Character Generation
|
[
"Mengyu Wang",
"Henghui Ding",
"Jianing Peng",
"Yao Zhao",
"Yunpeng Chen",
"Yunchao Wei"
] |
https://openaccess.thecvf.com/content/ICCV2025/html/Wang_CharaConsist_Fine-Grained_Consistent_Character_Generation_ICCV_2025_paper.html
|
https://openaccess.thecvf.com/content/ICCV2025/papers/Wang_CharaConsist_Fine-Grained_Consistent_Character_Generation_ICCV_2025_paper.pdf
| null |
@InProceedings{Wang_2025_ICCV,
author = {Wang, Mengyu and Ding, Henghui and Peng, Jianing and Zhao, Yao and Chen, Yunpeng and Wei, Yunchao},
title = {CharaConsist: Fine-Grained Consistent Character Generation},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2025},
pages = {16058-16067}
}
|
In text-to-image generation, producing a series of consistent contents that preserve the same identity is highly valuable for real-world applications. Although a few works have explored training-free methods to enhance the consistency of generated subjects, we observe that they suffer from the following problems.First, they fail to maintain consistent background details, which limits their applicability. Furthermore, when the foreground character undergoes large motion variations, inconsistencies in identity and clothing details become evident. To address these problems, we propose CharaConsist, which employs point-tracking attention and adaptive token merge along with decoupled control of the foreground and background.CharaConsist enables fine-grained consistency for both foreground and background, supporting the generation of one character in continuous shots within a fixed scene or in discrete shots across different scenes.Moreover, CharaConsist is the first consistent generation method tailored for text-to-image DiT model. Its ability to maintain fine-grained consistency, combined with the larger capacity of latest base model, enables it to produce high-quality visual outputs, broadening its applicability to a wider range of real-world scenarios.
|
2507.11533
|
ICCV 2025 accepted paper, project page:
https://murray-wang.github.io/CharaConsist/
|
https://github.com/Murray-Wang/CharaConsist
|
https://murray-wang.github.io/CharaConsist/
|
[] |
[] |
[] |
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] |
114
|
RetinexMCNet: A Memory Controller Dominated Network for Low-Light Video Enhancement Based on Retinex
|
[
"Meiao Wang",
"Xuejing Kang",
"Yaxi Lu",
"Jie Xu"
] |
https://openaccess.thecvf.com/content/ICCV2025/html/Wang_RetinexMCNet_A_Memory_Controller_Dominated_Network_for_Low-Light_Video_Enhancement_ICCV_2025_paper.html
|
https://openaccess.thecvf.com/content/ICCV2025/papers/Wang_RetinexMCNet_A_Memory_Controller_Dominated_Network_for_Low-Light_Video_Enhancement_ICCV_2025_paper.pdf
|
https://openaccess.thecvf.com/content/ICCV2025/supplemental/Wang_RetinexMCNet_A_Memory_ICCV_2025_supplemental.pdf
|
@InProceedings{Wang_2025_ICCV,
author = {Wang, Meiao and Kang, Xuejing and Lu, Yaxi and Xu, Jie},
title = {RetinexMCNet: A Memory Controller Dominated Network for Low-Light Video Enhancement Based on Retinex},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2025},
pages = {9716-9725}
}
|
Low-light video enhancement (LLVE) aims to restore videos degraded by insufficient illumination.While existing methods have demonstrated their effectiveness, they often face challenges with intra-frame noise, overexposure, and inter-frame inconsistency since they fail to exploit the temporal continuity across frames.Inspired by the progressive video understanding mechanism of human, we propose a novel end-to-end two-stage memory controller (MC) dominated network (RetinexMCNet). Specifically, we first define the overall optimization objective for Retinex-based LLVE, and accordingly design our framework.In stage one, aided by a dual-perspective Lightness-Texture Stability (LTS) loss, we perform per-frame enhancement without the MC, which uses a channel-aware Illumination Adjustment Module (IAM) and an illumination-guided Reflectance Denoising Module (RDM) based on Retinex theory to mitigate intra-frame noise and overexposure.In stage two, we activate the MC to simulate human temporal memory and integrate it with high-quality single frames for global consistency.Extensive qualitative and quantitative experiments on common low-light sRGB datasets demonstrate our method significantly outperforms state-of-the-art approaches. Code is available at xxx/xxx/xxx.
| null | null | null | null |
[] |
[] |
[] |
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] |
115
|
Learnable Fractional Reaction-Diffusion Dynamics for Under-Display ToF Imaging and Beyond
|
[
"Xin Qiao",
"Matteo Poggi",
"Xing Wei",
"Pengchao Deng",
"Yanhui Zhou",
"Stefano Mattoccia"
] |
https://openaccess.thecvf.com/content/ICCV2025/html/Qiao_Learnable_Fractional_Reaction-Diffusion_Dynamics_for_Under-Display_ToF_Imaging_and_Beyond_ICCV_2025_paper.html
|
https://openaccess.thecvf.com/content/ICCV2025/papers/Qiao_Learnable_Fractional_Reaction-Diffusion_Dynamics_for_Under-Display_ToF_Imaging_and_Beyond_ICCV_2025_paper.pdf
|
https://openaccess.thecvf.com/content/ICCV2025/supplemental/Qiao_Learnable_Fractional_Reaction-Diffusion_ICCV_2025_supplemental.pdf
|
@InProceedings{Qiao_2025_ICCV,
author = {Qiao, Xin and Poggi, Matteo and Wei, Xing and Deng, Pengchao and Zhou, Yanhui and Mattoccia, Stefano},
title = {Learnable Fractional Reaction-Diffusion Dynamics for Under-Display ToF Imaging and Beyond},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2025},
pages = {6080-6090}
}
|
Under-display ToF imaging aims to achieve accurate depth sensing through a ToF camera placed beneath a screen panel. However, transparent OLED (TOLED) layers introduce severe degradations--such as signal attenuation, multi-path interference (MPI), and temporal noise--that significantly compromise depth quality. To alleviate this drawback, we propose Learnable Fractional Reaction-Diffusion Dynamics (LFRD^2), a hybrid framework that combines the expressive power of neural networks with the interpretability of physical modeling. Specifically, we implement a time-fractional reaction-diffusion module that enables iterative depth refinement with dynamically generated differential orders, capturing long-term dependencies. In addition, we introduce an efficient continuous convolution operator via coefficient prediction and repeated differentiation to further improve restoration quality. Experiments on four benchmark datasets demonstrate the effectiveness of our approach. The code is publicly available at https://github.com/wudiqx106/LFRD2.
| null | null | null | null |
[] |
[] |
[] |
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] |
116
|
Anomaly Detection of Integrated Circuits Package Substrates Using the Large Vision Model SAIC: Dataset Construction, Methodology, and Application
|
[
"Ruiyun Yu",
"Bingyang Guo",
"Haoyuan Li"
] |
https://openaccess.thecvf.com/content/ICCV2025/html/Yu_Anomaly_Detection_of_Integrated_Circuits_Package_Substrates_Using_the_Large_ICCV_2025_paper.html
|
https://openaccess.thecvf.com/content/ICCV2025/papers/Yu_Anomaly_Detection_of_Integrated_Circuits_Package_Substrates_Using_the_Large_ICCV_2025_paper.pdf
|
https://openaccess.thecvf.com/content/ICCV2025/supplemental/Yu_Anomaly_Detection_of_ICCV_2025_supplemental.zip
|
@InProceedings{Yu_2025_ICCV,
author = {Yu, Ruiyun and Guo, Bingyang and Li, Haoyuan},
title = {Anomaly Detection of Integrated Circuits Package Substrates Using the Large Vision Model SAIC: Dataset Construction, Methodology, and Application},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2025},
pages = {22563-22574}
}
|
Anomaly detection plays a crucial role in the industrial sector, especially in ensuring the quality of integrated circuits (IC), which are critical for product reliability and performance. With increasing demands for higher quality standards, anomaly detection during the IC manufacturing process has become a significant research focus. However, the progress of IC anomaly detection is hampered by the scarcity of defective samples and the shortage of well-defined annotations. To address this challenge, this paper focuses on the research in the field of IC, especially on ceramic package substrates (CPS). We construct a systematic automated optical inspection (AOI) equipment, and based on this, collected large-scale CPS 2D images to build a novel anomaly detection dataset (CPS2D-AD), which offers copious samples with precise annotations, including category, mask, and bounding box. To the best of our knowledge, CPS2D-AD is the largest dataset in the field of IC. Meanwhile, we conduct an extensive benchmark of CPS2D-AD, intending to supplement existing research by providing a baseline for the detection and localization of anomalies in high-resolution data of ceramic package substrates. In addition, we have developed a novel large vision model, Segment Any Integrated Circuits (SAIC), by embedding-based distillation mechanism based on CPS2D-AD datasets. Our CPS2D-AD is the first open-source anomaly detection dataset about ceramic package substrates, which can be accessed at https://github.com/Bingyang0410/CPS2D-AD.
| null | null | null | null |
[] |
[] |
[] |
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] |
117
|
Refer to Any Segmentation Mask Group With Vision-Language Prompts
|
[
"Shengcao Cao",
"Zijun Wei",
"Jason Kuen",
"Kangning Liu",
"Lingzhi Zhang",
"Jiuxiang Gu",
"HyunJoon Jung",
"Liang-Yan Gui",
"Yu-Xiong Wang"
] |
https://openaccess.thecvf.com/content/ICCV2025/html/Cao_Refer_to_Any_Segmentation_Mask_Group_With_Vision-Language_Prompts_ICCV_2025_paper.html
|
https://openaccess.thecvf.com/content/ICCV2025/papers/Cao_Refer_to_Any_Segmentation_Mask_Group_With_Vision-Language_Prompts_ICCV_2025_paper.pdf
|
https://openaccess.thecvf.com/content/ICCV2025/supplemental/Cao_Refer_to_Any_ICCV_2025_supplemental.pdf
|
@InProceedings{Cao_2025_ICCV,
author = {Cao, Shengcao and Wei, Zijun and Kuen, Jason and Liu, Kangning and Zhang, Lingzhi and Gu, Jiuxiang and Jung, HyunJoon and Gui, Liang-Yan and Wang, Yu-Xiong},
title = {Refer to Any Segmentation Mask Group With Vision-Language Prompts},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2025},
pages = {21853-21863}
}
|
Recent image segmentation models have advanced to segment images into high-quality masks for visual entities, and yet they cannot provide comprehensive semantic understanding for complex queries based on both language and vision. This limitation reduces their effectiveness in applications that require user-friendly interactions driven by vision-language prompts. To bridge this gap, we introduce a novel task of omnimodal referring expression segmentation (ORES). In this task, a model produces a group of masks based on arbitrary prompts specified by text only or text plus reference visual entities. To address this new challenge, we propose a novel framework to "Refer to Any Segmentation Mask Group" (RAS), which augments segmentation models with complex multimodal interactions and comprehension via a mask-centric large multimodal model. For training and benchmarking ORES models, we create datasets MaskGroups-2M and MaskGroups-HQ to include diverse mask groups specified by text and reference entities. Through extensive evaluation, we demonstrate superior performance of RAS on our new ORES task, as well as classic referring expression segmentation (RES) and generalized referring expression segmentation (GRES) tasks. Project page: https://Ref2Any.github.io.
| null | null | null | null |
[] |
[] |
[] |
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] |
118
|
Integrating Visual Interpretation and Linguistic Reasoning for Geometric Problem Solving
|
[
"Zixian Guo",
"Ming Liu",
"Qilong Wang",
"Zhilong Ji",
"Jinfeng Bai",
"Lei Zhang",
"Wangmeng Zuo"
] |
https://openaccess.thecvf.com/content/ICCV2025/html/Guo_Integrating_Visual_Interpretation_and_Linguistic_Reasoning_for_Geometric_Problem_Solving_ICCV_2025_paper.html
|
https://openaccess.thecvf.com/content/ICCV2025/papers/Guo_Integrating_Visual_Interpretation_and_Linguistic_Reasoning_for_Geometric_Problem_Solving_ICCV_2025_paper.pdf
|
https://openaccess.thecvf.com/content/ICCV2025/supplemental/Guo_Integrating_Visual_Interpretation_ICCV_2025_supplemental.pdf
|
@InProceedings{Guo_2025_ICCV,
author = {Guo, Zixian and Liu, Ming and Wang, Qilong and Ji, Zhilong and Bai, Jinfeng and Zhang, Lei and Zuo, Wangmeng},
title = {Integrating Visual Interpretation and Linguistic Reasoning for Geometric Problem Solving},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2025},
pages = {3988-3998}
}
|
Current large vision-language models (LVLMs) typically employ a connector module to link visual features with text embeddings of large language models (LLMs) and use end-to-end training to achieve multi-modal understanding in a unified process. Well alignment needs high-quality pre-training data and a carefully designed training process. Current LVLMs face challenges when addressing complex vision-language reasoning tasks, with their reasoning capabilities notably lagging behind those of LLMs. This paper proposes a paradigm shift: instead of training end-to-end vision-language reasoning models, we advocate for developing a decoupled reasoning framework based on existing visual interpretation specialists and text-based reasoning LLMs. Our approach leverages (1) a dedicated vision-language model to transform the visual content of images into textual descriptions and (2) an LLM to perform reasoning according to the visual-derived text and the original question. This method presents a cost-efficient solution for multi-modal model development by optimizing existing models to work collaboratively, avoiding end-to-end development of vision-language models from scratch. By transforming images into language model-compatible text representations, it facilitates future low-cost and flexible upgrades to upcoming powerful LLMs. We introduce an outcome-rewarded joint-tuning strategy to optimize the cooperation between the visual interpretation and linguistic reasoning model. Evaluation results on vision-language benchmarks demonstrate that the decoupled reasoning framework outperforms recent LVLMs. Our approach yields particularly significant performance gains on visually intensive geometric mathematics problems. The code is available: https://github.com/guozix/DVLR.
| null | null | null | null |
[] |
[] |
[] |
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] |
119
|
Dual-Rate Dynamic Teacher for Source-Free Domain Adaptive Object Detection
|
[
"Qi He",
"Xiao Wu",
"Jun-Yan He",
"Shuai Li"
] |
https://openaccess.thecvf.com/content/ICCV2025/html/He_Dual-Rate_Dynamic_Teacher_for_Source-Free_Domain_Adaptive_Object_Detection_ICCV_2025_paper.html
|
https://openaccess.thecvf.com/content/ICCV2025/papers/He_Dual-Rate_Dynamic_Teacher_for_Source-Free_Domain_Adaptive_Object_Detection_ICCV_2025_paper.pdf
| null |
@InProceedings{He_2025_ICCV,
author = {He, Qi and Wu, Xiao and He, Jun-Yan and Li, Shuai},
title = {Dual-Rate Dynamic Teacher for Source-Free Domain Adaptive Object Detection},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2025},
pages = {2067-2076}
}
|
Source-Free Domain Adaptive Object Detection transfers knowledge from a labeled source domain to an unlabeled target domain while preserving data privacy by restricting access to source data during adaptation. Existing approaches predominantly leverage the Mean Teacher framework for self-training in the target domain. The exponential moving average (EMA) mechanism in the Mean Teacher stabilizes the training by averaging the student weights over training steps. However, in domain adaptation, its inherent lag in responding to emerging knowledge can hinder the rapid adaptation of the student to target-domain shifts. To address this challenge, Dual-rate Dynamic Teacher (DDT) with Asynchronous EMA (AEMA) is proposed, which implements group-wise parameter updates. In contrast to traditional EMA, which simultaneously updates all parameters, AEMA dynamically decomposes teacher parameters into two functional groups based on their contributions to capture the domain shift. By applying a distinct smoothing coefficient to two groups, AEMA simultaneously enables fast adaptation and historical knowledge retention. Comprehensive experiments carried out on three widely used traffic benchmarks have demonstrated that the proposed DDT achieves superior performance, outperforming SOTA methods by a clear margin. The codes are available at https://github.com/qih96/DDT.
| null | null | null | null |
[] |
[] |
[] |
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120
|
How Do Multimodal Large Language Models Handle Complex Multimodal Reasoning? Placing Them in An Extensible Escape Game
|
[
"Ziyue Wang",
"Yurui Dong",
"Fuwen Luo",
"Minyuan Ruan",
"Zhili Cheng",
"Chi Chen",
"Peng Li",
"Yang Liu"
] |
https://openaccess.thecvf.com/content/ICCV2025/html/Wang_How_Do_Multimodal_Large_Language_Models_Handle_Complex_Multimodal_Reasoning_ICCV_2025_paper.html
|
https://openaccess.thecvf.com/content/ICCV2025/papers/Wang_How_Do_Multimodal_Large_Language_Models_Handle_Complex_Multimodal_Reasoning_ICCV_2025_paper.pdf
|
https://openaccess.thecvf.com/content/ICCV2025/supplemental/Wang_How_Do_Multimodal_ICCV_2025_supplemental.pdf
|
@InProceedings{Wang_2025_ICCV,
author = {Wang, Ziyue and Dong, Yurui and Luo, Fuwen and Ruan, Minyuan and Cheng, Zhili and Chen, Chi and Li, Peng and Liu, Yang},
title = {How Do Multimodal Large Language Models Handle Complex Multimodal Reasoning? Placing Them in An Extensible Escape Game},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2025},
pages = {4807-4817}
}
|
The rapid advancing of Multimodal Large Language Models (MLLMs) has spurred interest in complex multimodal reasoning tasks in the real-world and virtual environment, which require coordinating multiple abilities, including visual perception, visual reasoning, spatial awareness, and target deduction. However, existing evaluations primarily assess the final task completion, often degrading assessments to isolated abilities such as visual grounding and visual question answering. Less attention is given to comprehensively and quantitatively analyzing reasoning process in multimodal environments, which is crucial for understanding model behaviors and underlying reasoning mechanisms beyond merely task success. To address this, we introduce MM-Escape, an extensible benchmark for investigating multimodal reasoning, inspired by real-world escape games. MM-Escape emphasizes intermediate model behaviors alongside final task completion. To achieve this, we develop EscapeCraft, a customizable and open environment that enables models to engage in free-form exploration for assessing multimodal reasoning. Extensive experiments show that MLLMs, regardless of scale, can successfully complete the simplest room escape tasks, with some exhibiting human-like exploration strategies. Yet, performance dramatically drops as task difficulty increases. Moreover, we observe that models severely suffer from accidental success, and that performance bottlenecks vary across models, revealing distinct failure modes and limitations in their multimodal reasoning abilities, such as repetitive trajectories without adaptive exploration, getting stuck in corners due to poor visual spatial awareness, and ineffective use of acquired props, such as the key. We hope our work sheds light on new challenges in multimodal reasoning, and uncovers potential improvements in MLLMs capabilities.
| null | null | null | null |
[] |
[] |
[] |
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] |
121
|
UST-SSM: Unified Spatio-Temporal State Space Models for Point Cloud Video Modeling
|
[
"Peiming Li",
"Ziyi Wang",
"Yulin Yuan",
"Hong Liu",
"Xiangming Meng",
"Junsong Yuan",
"Mengyuan Liu"
] |
https://openaccess.thecvf.com/content/ICCV2025/html/Li_UST-SSM_Unified_Spatio-Temporal_State_Space_Models_for_Point_Cloud_Video_ICCV_2025_paper.html
|
https://openaccess.thecvf.com/content/ICCV2025/papers/Li_UST-SSM_Unified_Spatio-Temporal_State_Space_Models_for_Point_Cloud_Video_ICCV_2025_paper.pdf
| null |
@InProceedings{Li_2025_ICCV,
author = {Li, Peiming and Wang, Ziyi and Yuan, Yulin and Liu, Hong and Meng, Xiangming and Yuan, Junsong and Liu, Mengyuan},
title = {UST-SSM: Unified Spatio-Temporal State Space Models for Point Cloud Video Modeling},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2025},
pages = {6738-6747}
}
|
Point cloud videos capture dynamic 3D motion while reducing the effects of lighting and viewpoint variations, making them highly effective for recognizing subtle and continuous human actions. Although Selective State Space Models (SSMs) have shown good performance in sequence modeling with linear complexity, the spatio-temporal disorder of point cloud videos hinders their unidirectional modeling when directly unfolding the point cloud video into a 1D sequence through temporally sequential scanning. To address this challenge, we propose the Unified Spatio-Temporal State Space Model (UST-SSM), which extends the latest advancements in SSMs to point cloud videos. Specifically, we introduce Spatial-Temporal Selection Scanning (STSS), which reorganizes unordered points into semantic-aware sequences through prompt-guided clustering, thereby enabling the effective utilization of points that are spatially and temporally distant yet similar within the sequence. For missing 4D geometric and motion details, Spatio-Temporal Structure Aggregation (STSA) aggregates spatio-temporal features and compensates. To improve temporal interaction within the sampled sequence, Temporal Interaction Sampling (TIS) enhances fine-grained temporal dependencies through non-anchor frame utilization and expanded receptive fields. Experimental results on the MSR-Action3D, NTU RGB+D, and Synthia 4D datasets validate the effectiveness of our method. Our code is available at https://github.com/wangzy01/UST-SSM.
| null | null | null | null |
[] |
[] |
[] |
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] |
122
|
Recover Biological Structure from Sparse-View Diffraction Images with Neural Volumetric Prior
|
[
"Renzhi He",
"Haowen Zhou",
"Yubei Chen",
"Yi Xue"
] |
https://openaccess.thecvf.com/content/ICCV2025/html/He_Recover_Biological_Structure_from_Sparse-View_Diffraction_Images_with_Neural_Volumetric_ICCV_2025_paper.html
|
https://openaccess.thecvf.com/content/ICCV2025/papers/He_Recover_Biological_Structure_from_Sparse-View_Diffraction_Images_with_Neural_Volumetric_ICCV_2025_paper.pdf
|
https://openaccess.thecvf.com/content/ICCV2025/supplemental/He_Recover_Biological_Structure_ICCV_2025_supplemental.pdf
|
@InProceedings{He_2025_ICCV,
author = {He, Renzhi and Zhou, Haowen and Chen, Yubei and Xue, Yi},
title = {Recover Biological Structure from Sparse-View Diffraction Images with Neural Volumetric Prior},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2025},
pages = {27771-27782}
}
|
Volumetric reconstruction of label-free living cells from non-destructive optical microscopic images reveals cellular metabolism in native environments. However, current optical tomography techniques require hundreds of 2D images to reconstruct a 3D volume, hindering them from intravital imaging of biological samples undergoing rapid dynamics. This poses a challenge of reconstructing the entire volume of semi-transparent biological samples from sparse views due to the restricted viewing angles of microscopes and the limited number of measurements. In this work, we develop Neural Volumetric Prior (NVP) for high-fidelity volumetric reconstruction of semi-transparent biological samples from sparse-view microscopic images. NVP integrates explicit and implicit neural representations and incorporates the physical prior of diffractive optics. We validate NVP on both simulated data and experimentally captured microscopic images. Compared to previous methods, NVP significantly reduces the required number of images by nearly 50-fold and processing time by 3-fold while maintaining state-of-the-art performance.NVP is the first technique to enable volumetric reconstruction of label-free biological samples from sparse-view microscopic images, paving the way for real-time 3D imaging of dynamically changing biological samples.
| null | null | null | null |
[] |
[] |
[] |
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] |
123
|
PropVG: End-to-End Proposal-Driven Visual Grounding with Multi-Granularity Discrimination
|
[
"Ming Dai",
"Wenxuan Cheng",
"Jiedong Zhuang",
"Jiang-jiang Liu",
"Hongshen Zhao",
"Zhenhua Feng",
"Wankou Yang"
] |
https://openaccess.thecvf.com/content/ICCV2025/html/Dai_PropVG_End-to-End_Proposal-Driven_Visual_Grounding_with_Multi-Granularity_Discrimination_ICCV_2025_paper.html
|
https://openaccess.thecvf.com/content/ICCV2025/papers/Dai_PropVG_End-to-End_Proposal-Driven_Visual_Grounding_with_Multi-Granularity_Discrimination_ICCV_2025_paper.pdf
|
https://openaccess.thecvf.com/content/ICCV2025/supplemental/Dai_PropVG_End-to-End_Proposal-Driven_ICCV_2025_supplemental.pdf
|
@InProceedings{Dai_2025_ICCV,
author = {Dai, Ming and Cheng, Wenxuan and Zhuang, Jiedong and Liu, Jiang-jiang and Zhao, Hongshen and Feng, Zhenhua and Yang, Wankou},
title = {PropVG: End-to-End Proposal-Driven Visual Grounding with Multi-Granularity Discrimination},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2025},
pages = {7058-7068}
}
|
Recent advances in visual grounding have largely shifted away from traditional proposal-based two-stage frameworks due to their inefficiency and high computational complexity, favoring end-to-end direct reference paradigms. However, these methods rely exclusively on the referred target for supervision, overlooking the potential benefits of prominent prospective targets. Moreover, existing approaches often fail to incorporate multi-granularity discrimination, which is crucial for robust object identification in complex scenarios. To address these limitations, we propose PropVG, an end-to-end proposal-based framework that, to the best of our knowledge, is the first to seamlessly integrate foreground object proposal generation with referential object comprehension without requiring additional detectors. Furthermore, we introduce a Contrastive-based Refer Scoring (CRS) module, which employs contrastive learning at both sentence and word levels to enhance the model's capability in understanding and distinguishing referred objects. Additionally, we design a Multi-granularity Target Discrimination (MTD) module that fuses object- and semantic-level information to improve the recognition of absent targets. Extensive experiments on gRefCOCO (GREC/GRES), Ref-ZOM, R-RefCOCO/+/g, and RefCOCO/+/g (REC/RES) benchmarks demonstrate the effectiveness of PropVG.
|
2509.04833
|
ICCV2025
|
https://github.com/Dmmm1997/PropVG
| null |
[] |
[] |
[
"Dmmm997/PropVG-Data"
] |
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] |
124
|
StyleSRN: Scene Text Image Super-Resolution with Text Style Embedding
|
[
"Shengrong Yuan",
"Runmin Wang",
"Ke Hao",
"Xuqi Ma",
"Changxin Gao",
"Li Liu",
"Nong Sang"
] |
https://openaccess.thecvf.com/content/ICCV2025/html/Yuan_StyleSRN_Scene_Text_Image_Super-Resolution_with_Text_Style_Embedding_ICCV_2025_paper.html
|
https://openaccess.thecvf.com/content/ICCV2025/papers/Yuan_StyleSRN_Scene_Text_Image_Super-Resolution_with_Text_Style_Embedding_ICCV_2025_paper.pdf
|
https://openaccess.thecvf.com/content/ICCV2025/supplemental/Yuan_StyleSRN_Scene_Text_ICCV_2025_supplemental.pdf
|
@InProceedings{Yuan_2025_ICCV,
author = {Yuan, Shengrong and Wang, Runmin and Hao, Ke and Ma, Xuqi and Gao, Changxin and Liu, Li and Sang, Nong},
title = {StyleSRN: Scene Text Image Super-Resolution with Text Style Embedding},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2025},
pages = {18693-18702}
}
|
Scene text image super-resolution (STISR) focuses on enhancing the clarity and readability of low-resolution text images. Existing methods often rely on text probability distribution priors derived from text recognizers to guide the super-resolution process. While effective in capturing general structural information of text, these priors lack the ability to preserve specific text style details, such as font, stereoscopic effect and spatial transformation, leading to a loss of visual quality and stylistic consistency in the super-resolved images. To address these limitations, we propose a Style embedding-based scene text image Super-Resolution Network (StyleSRN), which introduces a text style embedding mechanism to preserve and enhance text style features during the super-resolution process. The proposed architecture includes a Style Enhancement Block for capturing multi-scale cross-channel dependencies, and a Style Content Fusion Block that effectively integrates text content with style information, ensuring that the structure and style of the restored text are not distorted. Furthermore, we introduce a Text Style Loss based on the Gram matrix to supervise the reconstruction process at the style level, thereby maintaining the stylistic consistency of the restored text images. Extensive experiments on the TextZoom dataset and five scene text recognition benchmarks demonstrate the superiority of our method. The code will be released in the future.
| null | null | null | null |
[] |
[] |
[] |
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] |
125
|
SpatialSplat: Efficient Semantic 3D from Sparse Unposed Images
|
[
"Yu Sheng",
"Jiajun Deng",
"Xinran Zhang",
"Yu Zhang",
"Bei Hua",
"Yanyong Zhang",
"Jianmin Ji"
] |
https://openaccess.thecvf.com/content/ICCV2025/html/Sheng_SpatialSplat_Efficient_Semantic_3D_from_Sparse_Unposed_Images_ICCV_2025_paper.html
|
https://openaccess.thecvf.com/content/ICCV2025/papers/Sheng_SpatialSplat_Efficient_Semantic_3D_from_Sparse_Unposed_Images_ICCV_2025_paper.pdf
|
https://openaccess.thecvf.com/content/ICCV2025/supplemental/Sheng_SpatialSplat_Efficient_Semantic_ICCV_2025_supplemental.pdf
|
@InProceedings{Sheng_2025_ICCV,
author = {Sheng, Yu and Deng, Jiajun and Zhang, Xinran and Zhang, Yu and Hua, Bei and Zhang, Yanyong and Ji, Jianmin},
title = {SpatialSplat: Efficient Semantic 3D from Sparse Unposed Images},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2025},
pages = {26404-26414}
}
|
A major breakthrough in 3D reconstruction is the feedforward paradigm to generate pixel-wise 3D points or Gaussian primitives from sparse, unposed images. To further incorporate semantics while avoiding the significant memory and storage costs of high-dimensional semantic features, existing methods extend this paradigm by associating each primitive with a compressed semantic feature vector.However, these methods have two major limitations: (a) the naively compressed feature compromises expressiveness, affecting the model's ability to capture fine-grained semantics, and (b) the pixel-wise primitive prediction introduces redundancy in overlapping areas, causing unnecessary memory overhead. To this end, we introduce SpatialSplat, a feedforward framework that produces redundancy-aware Gaussians and capitalizes on a dual-field semantic representation. Particularly, with the insight that primitives within the same instance exhibit high semantic consistency, we decompose the semantic representation into a coarse feature field that encodes uncompressed semantics with minimal primitives, and a fine-grained yet low-dimensional feature field that captures detailed inter-instance relationships. Moreover, we propose a selective Gaussian mechanism, which retains only essential Gaussians in the scene, effectively eliminating redundant primitives. Our proposed Spatialsplat learns accurate semantic information and detailed instances prior with more compact 3D Gaussians, making semantic 3D reconstruction more applicable. We conduct extensive experiments to evaluate our method, demonstrating a remarkable 60% reduction in scene representation parameters while achieving superior performance over state-of-the-art methods. The code will be made available for future investigation.
|
2505.23044
| null | null | null |
[] |
[] |
[] |
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] |
126
|
Learning Visual Proxy for Compositional Zero-Shot Learning
|
[
"Shiyu Zhang",
"Cheng Yan",
"Yang Liu",
"Chenchen Jing",
"Lei Zhou",
"Wenjun Wang"
] |
https://openaccess.thecvf.com/content/ICCV2025/html/Zhang_Learning_Visual_Proxy_for_Compositional_Zero-Shot_Learning_ICCV_2025_paper.html
|
https://openaccess.thecvf.com/content/ICCV2025/papers/Zhang_Learning_Visual_Proxy_for_Compositional_Zero-Shot_Learning_ICCV_2025_paper.pdf
|
https://openaccess.thecvf.com/content/ICCV2025/supplemental/Zhang_Learning_Visual_Proxy_ICCV_2025_supplemental.pdf
|
@InProceedings{Zhang_2025_ICCV,
author = {Zhang, Shiyu and Yan, Cheng and Liu, Yang and Jing, Chenchen and Zhou, Lei and Wang, Wenjun},
title = {Learning Visual Proxy for Compositional Zero-Shot Learning},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2025},
pages = {2793-2802}
}
|
Compositional Zero-Shot Learning (CZSL) aims to recognize novel attribute-object compositions by leveraging knowledge from seen compositions. Existing methods typically align textual prototypes with visual features using Vision-Language Models (VLMs), but they face two key limitations: (1) modality gaps hinder the ability to distinguish semantically similar attribute-object pairs, and (2) textual prototypes alone lack the fine-grained visual cues needed for accurate recognition. To address these challenges, we propose Visual Proxy Learning, a method that reduces modality gaps and enhances compositional generalization by initializing visual proxies for attributes, objects, and their compositions from text representations and optimizing the visual space to better capture fine-grained visual cues. To further strengthen cross-modal understanding, we introduce Cross-Modal Joint Learning (CMJL), which enforces consistency between text-image embeddings and fine-grained visual representations. This dual strategy improves generalization to unseen compositions and enhances the discrimination of similar pairs. Extensive experiments demonstrate that our method achieves state-of-the-art performance in closed-world settings and competitive results in open-world scenarios across four CZSL benchmarks, validating its effectiveness in improving compositional generalization.
|
2501.13859
| null | null | null |
[] |
[] |
[] |
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] |
127
|
Processing and acquisition traces in visual encoders: What does CLIP know about your camera?
|
[
"Ryan Ramos",
"Vladan Stojnić",
"Giorgos Kordopatis-Zilos",
"Yuta Nakashima",
"Giorgos Tolias",
"Noa Garcia"
] |
https://openaccess.thecvf.com/content/ICCV2025/html/Ramos_Processing_and_acquisition_traces_in_visual_encoders_What_does_CLIP_ICCV_2025_paper.html
|
https://openaccess.thecvf.com/content/ICCV2025/papers/Ramos_Processing_and_acquisition_traces_in_visual_encoders_What_does_CLIP_ICCV_2025_paper.pdf
|
https://openaccess.thecvf.com/content/ICCV2025/supplemental/Ramos_Processing_and_acquisition_ICCV_2025_supplemental.pdf
|
@InProceedings{Ramos_2025_ICCV,
author = {Ramos, Ryan and Stojni\'c, Vladan and Kordopatis-Zilos, Giorgos and Nakashima, Yuta and Tolias, Giorgos and Garcia, Noa},
title = {Processing and acquisition traces in visual encoders: What does CLIP know about your camera?},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2025},
pages = {17056-17066}
}
|
Prior work has analyzed the robustness of visual encoders to image transformations and corruptions, particularly in cases where such alterations are not seen during training. When this occurs, they introduce a form of distribution shift at test time, often leading to performance degradation. The primary focus has been on severe corruptions that, when applied aggressively, distort useful signals necessary for accurate semantic predictions. We take a different perspective by analyzing parameters of the image acquisition process and transformations that may be subtle or even imperceptible to the human eye. We find that such parameters are systematically encoded in the learned visual representations and can be easily recovered. More strikingly, their presence can have a profound impact, either positively or negatively, on semantic predictions. This effect depends on whether there is a strong correlation or anti-correlation between semantic labels and these acquisition-based or processing-based labels. Our code and data are available at: https://github.com/ryan-caesar-ramos/visual-encoder-traces
|
2508.10637
|
8 main pages, supplementary attached, ICCV 2025 highlight
|
https://github.com/ryan-caesar-ramos/visual-encoder-traces
| null |
[] |
[] |
[] |
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] |
128
|
Holistic Tokenizer for Autoregressive Image Generation
|
[
"Anlin Zheng",
"Haochen Wang",
"Yucheng Zhao",
"Weipeng Deng",
"Tiancai Wang",
"Xiangyu Zhang",
"Xiaojuan Qi"
] |
https://openaccess.thecvf.com/content/ICCV2025/html/Zheng_Holistic_Tokenizer_for_Autoregressive_Image_Generation_ICCV_2025_paper.html
|
https://openaccess.thecvf.com/content/ICCV2025/papers/Zheng_Holistic_Tokenizer_for_Autoregressive_Image_Generation_ICCV_2025_paper.pdf
|
https://openaccess.thecvf.com/content/ICCV2025/supplemental/Zheng_Holistic_Tokenizer_for_ICCV_2025_supplemental.pdf
|
@InProceedings{Zheng_2025_ICCV,
author = {Zheng, Anlin and Wang, Haochen and Zhao, Yucheng and Deng, Weipeng and Wang, Tiancai and Zhang, Xiangyu and Qi, Xiaojuan},
title = {Holistic Tokenizer for Autoregressive Image Generation},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2025},
pages = {16916-16926}
}
|
Vanilla autoregressive image generation models generate visual tokens step-by-step, limiting their ability to capture holistic relationships among token sequences. Moreover, because most visual tokenizers map local image patches into latent tokens, global information is limited. To address this, we introduce Hita, a novel image tokenizer for autoregressive (AR) image generation. It introduces a holistic-to-local tokenization scheme with learnable holistic queries and local patch tokens. Hita incorporates two key strategies to better align with the AR generation process: 1) arranging a sequential structure with holistic tokens at the beginning, followed by patch-level tokens, and using causal attention to maintain awareness of previous tokens; and 2) adopting a lightweight fusion module before feeding the de-quantized tokens into the decoder to control information flow and prioritize holistic tokens. Extensive experiments show that Hita accelerates the training speed of AR generators and outperforms those trained with vanilla tokenizers, achieving 2.59 FID and 281.9 IS on the ImageNet benchmark. Detailed analysis of the holistic representation highlights its ability to capture global image properties, such as textures, materials, and shapes. Additionally, Hita also demonstrates effectiveness in zero-shot style transfer and image in-painting. The code is available at https://github.com/CVMI-Lab/Hita.
|
2507.02358
|
17 pages, 10 figures
|
https://github.com/CVMI-Lab/Hita
| null |
[] |
[] |
[] |
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129
|
ConsistentCity: Semantic Flow-guided Occupancy DiT for Temporally Consistent Driving Scene Synthesis
|
[
"Benjin Zhu",
"Xiaogang Wang",
"Hongsheng Li"
] |
https://openaccess.thecvf.com/content/ICCV2025/html/Zhu_ConsistentCity_Semantic_Flow-guided_Occupancy_DiT_for_Temporally_Consistent_Driving_Scene_ICCV_2025_paper.html
|
https://openaccess.thecvf.com/content/ICCV2025/papers/Zhu_ConsistentCity_Semantic_Flow-guided_Occupancy_DiT_for_Temporally_Consistent_Driving_Scene_ICCV_2025_paper.pdf
| null |
@InProceedings{Zhu_2025_ICCV,
author = {Zhu, Benjin and Wang, Xiaogang and Li, Hongsheng},
title = {ConsistentCity: Semantic Flow-guided Occupancy DiT for Temporally Consistent Driving Scene Synthesis},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2025},
pages = {26382-26392}
}
|
Scene synthesis plays a crucial role in autonomous driving by addressing data scarcity and close-loop validation. Current approaches struggle to maintain temporal consistency in synthesized videos while preserving fine-grained details. We introduce ConsistentCity, a two-stage framework with a novel Semantic Flow-guided Diffusion Transformers (SF-DiT) that convert sequential BEV semantic maps into temporally consistent driving videos. Operating in a pretrained occupancy VQ-VAE latent space, our SF-DiT generates temporally consistent 3D occupancy, which provides guidance for controlled image and video diffusion for scene synthesis. To address the temporal consistency, SF-DiT enhances standard DiT blocks with temporal semantic modeling through two designs: (1) A Semantic Flow Estimation module capturing scene motions (flow, uncertainty, and classification) from sequential BEV semantic maps, and (2) A Semantic Flow-Modulated Cross-Attention module that dynamically adapts attention based on semantic flow patterns. This integration of semantic flow modeling in DiT enables consistent scene evolution understanding. Evaluations of image and video synthesis on nuScenes dataset demonstrate state-of-the-art performance with FID 8.3 and FVD 73.6, and superior temporal occupancy generation results on nuCraft and OpenOccupancy benchmarks.
| null | null | null |
https://benjin.me/publication/iccv25_consistentcity/
|
[] |
[] |
[] |
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] |
130
|
G2PDiffusion: Cross-Species Genotype-to-Phenotype Prediction via Evolutionary Diffusion
|
[
"Mengdi Liu",
"Zhangyang Gao",
"Hong Chang",
"Stan Z. Li",
"Shiguang Shan",
"Xilin Chen"
] |
https://openaccess.thecvf.com/content/ICCV2025/html/Liu_G2PDiffusion_Cross-Species_Genotype-to-Phenotype_Prediction_via_Evolutionary_Diffusion_ICCV_2025_paper.html
|
https://openaccess.thecvf.com/content/ICCV2025/papers/Liu_G2PDiffusion_Cross-Species_Genotype-to-Phenotype_Prediction_via_Evolutionary_Diffusion_ICCV_2025_paper.pdf
| null |
@InProceedings{Liu_2025_ICCV,
author = {Liu, Mengdi and Gao, Zhangyang and Chang, Hong and Li, Stan Z. and Shan, Shiguang and Chen, Xilin},
title = {G2PDiffusion: Cross-Species Genotype-to-Phenotype Prediction via Evolutionary Diffusion},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2025},
pages = {20705-20714}
}
|
Understanding how genes influence phenotype across species is a fundamental challenge in genetic engineering, which will facilitate advances in various fields such as crop breeding, conservation biology, and personalized medicine. However, current phenotype prediction models are limited to individual species and expensive phenotype labeling process, making the genotype-to-phenotype prediction a highly domain-dependent and data-scarce problem. To this end, we suggest taking images as morphological proxies, facilitating cross-species generalization through large-scale multimodal pretraining. We propose the first genotype-to-phenotype diffusion model (G2PDiffusion) that generates morphological images from DNA considering two critical evolutionary signals, i.e., multiple sequence alignments (MSA) and environmental contexts. The model contains three novel components: 1) a MSA retrieval engine that identifies conserved and co-evolutionary patterns; 2) an environment-aware MSA conditional encoder that effectively models complex genotype-environment interactions; and 3) an adaptive phenomic alignment module to improve genotype-phenotype consistency. Extensive experiments show that integrating evolutionary signals with environmental context enriches the model's understanding of phenotype variability across species, thereby offering a valuable and promising exploration into advanced AI-assisted genomic analysis.
|
2502.04684
| null | null | null |
[] |
[] |
[] |
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131
|
PathFinder: A Multi-Modal Multi-Agent System for Medical Diagnostic Decision-Making Applied to Histopathology
|
[
"Fatemeh Ghezloo",
"Mehmet Saygin Seyfioglu",
"Rustin Soraki",
"Wisdom O. Ikezogwo",
"Beibin Li",
"Tejoram Vivekanandan",
"Joann G. Elmore",
"Ranjay Krishna",
"Linda Shapiro"
] |
https://openaccess.thecvf.com/content/ICCV2025/html/Ghezloo_PathFinder_A_Multi-Modal_Multi-Agent_System_for_Medical_Diagnostic_Decision-Making_Applied_ICCV_2025_paper.html
|
https://openaccess.thecvf.com/content/ICCV2025/papers/Ghezloo_PathFinder_A_Multi-Modal_Multi-Agent_System_for_Medical_Diagnostic_Decision-Making_Applied_ICCV_2025_paper.pdf
|
https://openaccess.thecvf.com/content/ICCV2025/supplemental/Ghezloo_PathFinder_A_Multi-Modal_ICCV_2025_supplemental.pdf
|
@InProceedings{Ghezloo_2025_ICCV,
author = {Ghezloo, Fatemeh and Seyfioglu, Mehmet Saygin and Soraki, Rustin and Ikezogwo, Wisdom O. and Li, Beibin and Vivekanandan, Tejoram and Elmore, Joann G. and Krishna, Ranjay and Shapiro, Linda},
title = {PathFinder: A Multi-Modal Multi-Agent System for Medical Diagnostic Decision-Making Applied to Histopathology},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2025},
pages = {23431-23441}
}
|
Diagnosing diseases through histopathology whole slide images (WSIs) is fundamental in modern pathology but is challenged by the gigapixel scale and complexity of WSIs. Trained histopathologists overcome this challenge by navigating the WSI, looking for relevant patches, taking notes, and compiling them to produce a final holistic diagnostic. Traditional AI approaches, such as multiple instance learning and transformer-based models, fail short of such a holistic, iterative, multi-scale diagnostic procedure, limiting their adoption in the real-world. We introduce PathFinder, a multi-modal, multi-agent framework that emulates the decision-making process of expert pathologists. PathFinder integrates four AI agents, the Triage Agent, Navigation Agent, Description Agent, and Diagnosis Agent, that collaboratively navigate WSIs, gather evidence, and provide comprehensive diagnoses with natural language explanations. The Triage Agent classifies the WSI as benign or risky; if risky, the Navigation and Description Agents iteratively focus on significant regions, generating importance maps and descriptive insights of sampled patches. Finally, the Diagnosis Agent synthesizes the findings to determine the patient's diagnostic classification. Our Experiments show that PathFinder outperforms state-of-the-art methods in skin melanoma diagnosis by 8% while offering inherent explainability through natural language descriptions of diagnostically relevant patches. Qualitative analysis by pathologists shows that the Description Agent's outputs are of high quality and comparable to GPT-4o. PathFinder is also the first AI-based system to surpass the average performance of pathologists in this challenging melanoma classification task by 9%, setting a new record for efficient, accurate, and interpretable AI-assisted diagnostics in pathology. Our Data, code and models will be made available.
|
2502.08916
| null | null | null |
[] |
[] |
[] |
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] |
132
|
VLABench: A Large-Scale Benchmark for Language-Conditioned Robotics Manipulation with Long-Horizon Reasoning Tasks
|
[
"Shiduo Zhang",
"Zhe Xu",
"Peiju Liu",
"Xiaopeng Yu",
"Yuan Li",
"Qinghui Gao",
"Zhaoye Fei",
"Zhangyue Yin",
"Zuxuan Wu",
"Yu-Gang Jiang",
"Xipeng Qiu"
] |
https://openaccess.thecvf.com/content/ICCV2025/html/Zhang_VLABench_A_Large-Scale_Benchmark_for_Language-Conditioned_Robotics_Manipulation_with_Long-Horizon_ICCV_2025_paper.html
|
https://openaccess.thecvf.com/content/ICCV2025/papers/Zhang_VLABench_A_Large-Scale_Benchmark_for_Language-Conditioned_Robotics_Manipulation_with_Long-Horizon_ICCV_2025_paper.pdf
|
https://openaccess.thecvf.com/content/ICCV2025/supplemental/Zhang_VLABench_A_Large-Scale_ICCV_2025_supplemental.pdf
|
@InProceedings{Zhang_2025_ICCV,
author = {Zhang, Shiduo and Xu, Zhe and Liu, Peiju and Yu, Xiaopeng and Li, Yuan and Gao, Qinghui and Fei, Zhaoye and Yin, Zhangyue and Wu, Zuxuan and Jiang, Yu-Gang and Qiu, Xipeng},
title = {VLABench: A Large-Scale Benchmark for Language-Conditioned Robotics Manipulation with Long-Horizon Reasoning Tasks},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2025},
pages = {11142-11152}
}
|
General-purposed embodied agents are designed to understand the users' natural instructions or intentions and act precisely to complete universal tasks. Recently, methods based on foundation models especially Vision-Language-Action models (VLAs) have shown a substantial potential to solve language-conditioned manipulation (LCM) tasks well. However, existing benchmarks do not adequately meet the needs of VLAs and relative algorithms. To better define such general-purpose tasks in the context of LLMs and advance the research in VLAs, we present VLABench, an open-source benchmark for evaluating universal LCM task learning. VLABench provides 100 carefully designed categories of tasks, with strong randomization in each category of task and a total of 2000+ objects. VLABench stands out from previous benchmarks in four key aspects: 1) tasks requiring world knowledge and common sense transfer, 2) natural language instructions with implicit human intentions rather than templates, 3) long-horizon tasks demanding multi-step reasoning, and 4) evaluation of both action policies and language model capabilities. The benchmark assesses multiple competencies including understanding of mesh&texture, spatial relationship, semantic instruction, physical laws, knowledge transfer and reasoning, etc. To support the downstream finetuning, we provide high-quality training data collected via an automated framework incorporating heuristic skills and prior information. The experimental results indicate that both the current state-of-the-art pretrained VLAs and the workflow based on VLMs face challenges in our tasks.
|
2412.18194
| null | null | null |
[] |
[] |
[
"VLABench/vlabench_primitive_ft_dataset"
] |
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] |
133
|
MolParser: End-to-end Visual Recognition of Molecule Structures in the Wild
|
[
"Xi Fang",
"Jiankun Wang",
"Xiaochen Cai",
"Shangqian Chen",
"Shuwen Yang",
"Haoyi Tao",
"Nan Wang",
"Lin Yao",
"Linfeng Zhang",
"Guolin Ke"
] |
https://openaccess.thecvf.com/content/ICCV2025/html/Fang_MolParser_End-to-end_Visual_Recognition_of_Molecule_Structures_in_the_Wild_ICCV_2025_paper.html
|
https://openaccess.thecvf.com/content/ICCV2025/papers/Fang_MolParser_End-to-end_Visual_Recognition_of_Molecule_Structures_in_the_Wild_ICCV_2025_paper.pdf
|
https://openaccess.thecvf.com/content/ICCV2025/supplemental/Fang_MolParser_End-to-end_Visual_ICCV_2025_supplemental.pdf
|
@InProceedings{Fang_2025_ICCV,
author = {Fang, Xi and Wang, Jiankun and Cai, Xiaochen and Chen, Shangqian and Yang, Shuwen and Tao, Haoyi and Wang, Nan and Yao, Lin and Zhang, Linfeng and Ke, Guolin},
title = {MolParser: End-to-end Visual Recognition of Molecule Structures in the Wild},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2025},
pages = {24528-24538}
}
|
In recent decades, chemistry publications and patents have increased rapidly. A significant portion of key information is embedded in molecular structure figures, complicating large-scale literature searches and limiting the application of large language models in fields such as biology, chemistry, and pharmaceuticals. The automatic extraction of precise chemical structures is of critical importance. However, the presence of numerous Markush structures in real-world documents, along with variations in molecular image quality, drawing styles, and noise, significantly limits the performance of existing optical chemical structure recognition (OCSR) methods. We present MolParser, a novel end-to-end OCSR method that efficiently and accurately recognizes chemical structures from real-world documents, including difficult Markush structure. We use a extended SMILES encoding rule to annotate our training dataset. Under this rule, we build MolParser-7M, the largest annotated molecular image dataset to our knowledge. While utilizing a large amount of synthetic data, we employed active learning methods to incorporate substantial in-the-wild data, specifically samples cropped from real patents and scientific literature, into the training process. We trained an end-to-end molecular image captioning model, MolParser, using a curriculum learning approach. MolParser significantly outperforms classical and learning-based methods across most scenarios, with potential for broader downstream applications. The dataset is publicly available in huggingface.
|
2411.11098
| null | null | null |
[] |
[
"UniParser/MolDet"
] |
[
"UniParser/MolParser-7M"
] |
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] |
134
|
UPRE: Zero-Shot Domain Adaptation for Object Detection via Unified Prompt and Representation Enhancement
|
[
"Xiao Zhang",
"Fei Wei",
"Yong Wang",
"Wenda Zhao",
"Feiyi Li",
"Xiangxiang Chu"
] |
https://openaccess.thecvf.com/content/ICCV2025/html/Zhang_UPRE_Zero-Shot_Domain_Adaptation_for_Object_Detection_via_Unified_Prompt_ICCV_2025_paper.html
|
https://openaccess.thecvf.com/content/ICCV2025/papers/Zhang_UPRE_Zero-Shot_Domain_Adaptation_for_Object_Detection_via_Unified_Prompt_ICCV_2025_paper.pdf
|
https://openaccess.thecvf.com/content/ICCV2025/supplemental/Zhang_UPRE_Zero-Shot_Domain_ICCV_2025_supplemental.pdf
|
@InProceedings{Zhang_2025_ICCV,
author = {Zhang, Xiao and Wei, Fei and Wang, Yong and Zhao, Wenda and Li, Feiyi and Chu, Xiangxiang},
title = {UPRE: Zero-Shot Domain Adaptation for Object Detection via Unified Prompt and Representation Enhancement},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2025},
pages = {508-518}
}
|
Zero-shot domain adaptation (ZSDA) presents substantial challenges due to the lack of images in the target domain. Previous approaches leverage Vision-Language Models (VLMs) to tackle this challenge, exploiting their zero-shot learning capabilities. However, these methods primarily address domain distribution shifts and overlook the misalignment between the detection task and VLMs, which rely on manually crafted prompts. To overcome these limitations, we propose the unified prompt and representation enhancement (UPRE) framework, which jointly optimizes both textual prompts and visual representations. Specifically, our approach introduces a multi-view domain prompt that combines linguistic domain priors with detection-specific knowledge, and a visual representation enhancement module that produces domain style variations. Furthermore, we introduce multi-level enhancement strategies, including relative domain distance and positive-negative separation, which align multi-modal representations at the image level and capture diverse visual representations at the instance level, respectively. Extensive experiments conducted on nine benchmark datasets demonstrate the superior performance of our framework in ZSDA detection scenarios.
|
2507.00721
|
ICCV2025
|
https://github.com/AMAP-ML/UPRE
| null |
[] |
[] |
[] |
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] |
135
|
Heavy Labels Out! Dataset Distillation with Label Space Lightening
|
[
"Ruonan Yu",
"Songhua Liu",
"Zigeng Chen",
"Jingwen Ye",
"Xinchao Wang"
] |
https://openaccess.thecvf.com/content/ICCV2025/html/Yu_Heavy_Labels_Out_Dataset_Distillation_with_Label_Space_Lightening_ICCV_2025_paper.html
|
https://openaccess.thecvf.com/content/ICCV2025/papers/Yu_Heavy_Labels_Out_Dataset_Distillation_with_Label_Space_Lightening_ICCV_2025_paper.pdf
|
https://openaccess.thecvf.com/content/ICCV2025/supplemental/Yu_Heavy_Labels_Out_ICCV_2025_supplemental.zip
|
@InProceedings{Yu_2025_ICCV,
author = {Yu, Ruonan and Liu, Songhua and Chen, Zigeng and Ye, Jingwen and Wang, Xinchao},
title = {Heavy Labels Out! Dataset Distillation with Label Space Lightening},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2025},
pages = {5017-5026}
}
|
Dataset distillation or condensation aims to condense a large-scale training dataset into a much smaller synthetic one such that the training performance of distilled and original sets on neural networks are similar. Although the number of training samples can be reduced substantially, current state-of-the-art methods heavily rely on enormous soft labels to achieve satisfactory performance. As a result, the required storage can be comparable even to original datasets, especially for large-scale ones. To solve this problem, instead of storing these heavy labels, we propose a novel label-lightening framework termed HeLlO aiming at effective image-to-label projectors, with which synthetic labels can be directly generated online from synthetic images. Specifically, to construct such projectors, we leverage prior knowledge in open-source foundation models, e.g., CLIP, and introduce a LoRA-like fine-tuning strategy to mitigate the gap between pre-trained and target distributions, so that original models for soft-label generation can be distilled into a group of low-rank matrices. Moreover, an effective image optimization method is proposed to further mitigate the potential error between the original and distilled label generators. Extensive experiments show that our method significantly reduces the storage cost to merely 0.001% compared to full soft-label storage methods while achieving comparable performance to state-of-the-art dataset distillation methods on large-scale datasets. Our codes are available at https://github.com/Lexie-YU/HeLlO.
|
2408.08201
| null | null | null |
[] |
[] |
[] |
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] |
136
|
VideoMiner: Iteratively Grounding Key Frames of Hour-Long Videos via Tree-based Group Relative Policy Optimization
|
[
"Xinye Cao",
"Hongcan Guo",
"Jiawen Qian",
"Guoshun Nan",
"Chao Wang",
"Yuqi Pan",
"Tianhao Hou",
"Xiaojuan Wang",
"Yutong Gao"
] |
https://openaccess.thecvf.com/content/ICCV2025/html/Cao_VideoMiner_Iteratively_Grounding_Key_Frames_of_Hour-Long_Videos_via_Tree-based_ICCV_2025_paper.html
|
https://openaccess.thecvf.com/content/ICCV2025/papers/Cao_VideoMiner_Iteratively_Grounding_Key_Frames_of_Hour-Long_Videos_via_Tree-based_ICCV_2025_paper.pdf
|
https://openaccess.thecvf.com/content/ICCV2025/supplemental/Cao_VideoMiner_Iteratively_Grounding_ICCV_2025_supplemental.pdf
|
@InProceedings{Cao_2025_ICCV,
author = {Cao, Xinye and Guo, Hongcan and Qian, Jiawen and Nan, Guoshun and Wang, Chao and Pan, Yuqi and Hou, Tianhao and Wang, Xiaojuan and Gao, Yutong},
title = {VideoMiner: Iteratively Grounding Key Frames of Hour-Long Videos via Tree-based Group Relative Policy Optimization},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2025},
pages = {23773-23783}
}
|
Understanding hour-long videos with multi-modal large language models (MM-LLMs) enriches the landscape of human-centered AI applications. However, for end-to-end video understanding with LLMs, uniformly sampling video frames results in LLMs being overwhelmed by a vast amount of irrelevant information as video length increases. Existing hierarchical key frame extraction methods improve the accuracy of video understanding but still face two critical challenges. 1) How can the interference of extensive redundant information in long videos be mitigated? 2) How can a model dynamically adapt to complex hierarchical structures while accurately identifying key frames? To address these issues, we propose VideoMiner, which iteratively segments, captions, and clusters long videos, forming a hierarchical tree structure. The proposed VideoMiner progresses from long videos to events to frames while preserving temporal coherence, effectively addressing the first challenge. To precisely locate key frames, we introduce T-GRPO, a tree-based group relative policy optimization in reinforcement learning method that guides the exploration of the VideoMiner. The proposed T-GRPO is specifically designed for tree structures, integrating spatiotemporal information at the event level while being guided by the question, thus solving the second challenge. We achieve superior performance in all long-video understanding tasks and uncover several interesting insights. Our proposed T-GRPO surprisingly incentivizes the model to spontaneously generate a reasoning chain. Additionally, the designed tree growth auxin dynamically adjusts the expansion depth, obtaining accuracy and efficiency gains. The code is publicly available at https://github.com/caoxinye/VideoMiner.
|
2510.06040
| null | null | null |
[] |
[] |
[] |
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] |
137
|
Hierarchical Visual Prompt Learning for Continual Video Instance Segmentation
|
[
"Jiahua Dong",
"Hui Yin",
"Wenqi Liang",
"Hanbin Zhao",
"Henghui Ding",
"Nicu Sebe",
"Salman Khan",
"Fahad Shahbaz Khan"
] |
https://openaccess.thecvf.com/content/ICCV2025/html/Dong_Hierarchical_Visual_Prompt_Learning_for_Continual_Video_Instance_Segmentation_ICCV_2025_paper.html
|
https://openaccess.thecvf.com/content/ICCV2025/papers/Dong_Hierarchical_Visual_Prompt_Learning_for_Continual_Video_Instance_Segmentation_ICCV_2025_paper.pdf
|
https://openaccess.thecvf.com/content/ICCV2025/supplemental/Dong_Hierarchical_Visual_Prompt_ICCV_2025_supplemental.pdf
|
@InProceedings{Dong_2025_ICCV,
author = {Dong, Jiahua and Yin, Hui and Liang, Wenqi and Zhao, Hanbin and Ding, Henghui and Sebe, Nicu and Khan, Salman and Khan, Fahad Shahbaz},
title = {Hierarchical Visual Prompt Learning for Continual Video Instance Segmentation},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2025},
pages = {11829-11839}
}
|
Video instance segmentation (VIS) has gained significant attention for its capability in tracking and segmenting object instances across video frames. However, most of the existing VIS approaches unrealistically assume that the categories of object instances remain fixed over time. Moreover, they experience catastrophic forgetting of old classes when required to continuously learn object instances belonging to new categories. To resolve these challenges, we develop a novel Hierarchical Visual Prompt Learning (HVPL) model that overcomes catastrophic forgetting of previous categories from both frame-level and video-level perspectives. Specifically, to mitigate forgetting at the frame level, we devise a task-specific frame prompt and an orthogonal gradient correction (OGC) module. The OGC module helps the frame prompt encode task-specific global instance information for new classes in each individual frame by projecting its gradients onto the orthogonal feature space of old classes. Furthermore, to address forgetting at the video level, we design a task-specific video prompt and a video context decoder. This decoder first embeds structural inter-class relationships across frames into the frame prompt features, and then propagates task-specific global video contexts from the frame prompt features to the video prompt. Through rigorous comparisons, our HVPL model proves to be more effective than baseline approaches.
|
2508.08612
|
Accepted to ICCV2025
|
https://github.com/JiahuaDong/HVPL
| null |
[] |
[] |
[] |
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] |
138
|
FROSS: Faster-Than-Real-Time Online 3D Semantic Scene Graph Generation from RGB-D Images
|
[
"Hao-Yu Hou",
"Chun-Yi Lee",
"Motoharu Sonogashira",
"Yasutomo Kawanishi"
] |
https://openaccess.thecvf.com/content/ICCV2025/html/Hou_FROSS_Faster-Than-Real-Time_Online_3D_Semantic_Scene_Graph_Generation_from_RGB-D_ICCV_2025_paper.html
|
https://openaccess.thecvf.com/content/ICCV2025/papers/Hou_FROSS_Faster-Than-Real-Time_Online_3D_Semantic_Scene_Graph_Generation_from_RGB-D_ICCV_2025_paper.pdf
|
https://openaccess.thecvf.com/content/ICCV2025/supplemental/Hou_FROSS_Faster-Than-Real-Time_Online_ICCV_2025_supplemental.pdf
|
@InProceedings{Hou_2025_ICCV,
author = {Hou, Hao-Yu and Lee, Chun-Yi and Sonogashira, Motoharu and Kawanishi, Yasutomo},
title = {FROSS: Faster-Than-Real-Time Online 3D Semantic Scene Graph Generation from RGB-D Images},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2025},
pages = {28818-28827}
}
|
The ability to abstract complex 3D environments into simplified and structured representations is crucial across various domains. 3D semantic scene graphs (SSGs) achieve this by representing objects as nodes and their interrelationships as edges, facilitating high-level scene understanding. Existing methods for 3D SSG generation, however, face significant challenges, including high computational demands and non-incremental processing that hinder their suitability for real-time open-world applications. To address this issue, we propose FROSS (Faster-than-Real-Time Online 3D Semantic Scene Graph Generation), an innovative approach for online and faster-than-real-time 3D SSG generation that leverages the direct lifting of 2D scene graphs to 3D space and represents objects as 3D Gaussian distributions. This framework eliminates the dependency on precise and computationally-intensive point cloud processing. Furthermore, we extend the Replica dataset with inter-object relationship annotations, creating the ReplicaSSG dataset for comprehensive evaluation of FROSS. The experimental results from evaluations on ReplicaSSG and 3DSSG datasets show that FROSS can achieve superior performance while operating significantly faster than prior 3D SSG generation methods. Our implementation and dataset are publicly available at https://github.com/Howardkhh/FROSS.
| null | null | null | null |
[] |
[] |
[] |
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] |
139
|
HVPUNet: Hybrid-Voxel Point-cloud Upsampling Network
|
[
"Juhyung Ha",
"Vibhas Kumar Vats",
"Soon-heung Jung",
"Alimoor Reza",
"David J. Crandall"
] |
https://openaccess.thecvf.com/content/ICCV2025/html/Ha_HVPUNet_Hybrid-Voxel_Point-cloud_Upsampling_Network_ICCV_2025_paper.html
|
https://openaccess.thecvf.com/content/ICCV2025/papers/Ha_HVPUNet_Hybrid-Voxel_Point-cloud_Upsampling_Network_ICCV_2025_paper.pdf
| null |
@InProceedings{Ha_2025_ICCV,
author = {Ha, Juhyung and Vats, Vibhas Kumar and Jung, Soon-heung and Reza, Alimoor and Crandall, David J.},
title = {HVPUNet: Hybrid-Voxel Point-cloud Upsampling Network},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2025},
pages = {29153-29162}
}
|
Point-cloud upsampling aims to generate dense point sets from sparse or incomplete 3D data. Most existing work uses a point-to-point framework. While this method achieves high geometric precision, it is slow because of irregular memory accesses to process unstructured point data. Alternatively, voxel-based methods offer computational efficiency by using regular grids, but struggle to preserve precise point locations due to discretization. To resolve this efficiency-precision trade-off, we introduce Hybrid Voxels, a representation that combines both voxel occupancy and a continuous point offset. We then present the Hybrid-Voxel Point-cloud Upsampling Network (HVPUNet), an efficient framework built upon this representation. HVPUNet integrates two key modules: (1) Shape Completion to restore missing geometry by filling empty voxels, and (2) Super-Resolution to enhance spatial resolution and capture finer surface details. We also use progressive refinement, operational voxel expansion, and implicit geometric learning. Experimental results demonstrate that HVPUNet can upsample point clouds at significantly lower computational cost than the state-of-the-art, but with comparable model accuracy.
| null | null | null | null |
[] |
[] |
[] |
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] |
140
|
MorphoGen: Efficient Unconditional Generation of Long-Range Projection Neuronal Morphology via a Global-to-Local Framework
|
[
"Tianfang Zhu",
"Hongyang Zhou",
"Anan Li"
] |
https://openaccess.thecvf.com/content/ICCV2025/html/Zhu_MorphoGen_Efficient_Unconditional_Generation_of_Long-Range_Projection_Neuronal_Morphology_via_ICCV_2025_paper.html
|
https://openaccess.thecvf.com/content/ICCV2025/papers/Zhu_MorphoGen_Efficient_Unconditional_Generation_of_Long-Range_Projection_Neuronal_Morphology_via_ICCV_2025_paper.pdf
| null |
@InProceedings{Zhu_2025_ICCV,
author = {Zhu, Tianfang and Zhou, Hongyang and Li, Anan},
title = {MorphoGen: Efficient Unconditional Generation of Long-Range Projection Neuronal Morphology via a Global-to-Local Framework},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2025},
pages = {13021-13031}
}
|
Capturing the spatial patterns of neurons and generating high-fidelity morphological data remain critical challenges in developing biologically realistic large-scale brain network models. Existing methods fail to reconcile anatomical complexity with diversity and computational scalability. We propose MorphoGen, a hierarchical framework integrating global structure prediction through denoising diffusion probabilistic models (DDPMs) with local neurites optimization. The pipeline initiates with DDPM-generated coarse-grained neuronal point clouds, followed by skeletonization and growth-guided linking to derive plausible tree-like structures, and culminates in natural neural fibers refinement via a pragmatic smoothing network. Comprehensive evaluations across three distinct long-range projection neuron datasets demonstrate that the proposed method improves 1-Nearest Neighbor Accuracy by approximately 12% on average compared to state-of-the-art baseline, reduces average training time by around 55%, and aligns the distributions of several morphometrics with real data. This work establishes a novel global-to-local paradigm for neuronal morphology generation, offering a more direct and efficient approach compared to current branch-sequential modeling methods. Code is available at https://github.com/Brainsmatics/MorphoGen.
| null | null | null | null |
[] |
[] |
[] |
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] |
141
|
REDUCIO! Generating 1K Video within 16 Seconds using Extremely Compressed Motion Latents
|
[
"Rui Tian",
"Qi Dai",
"Jianmin Bao",
"Kai Qiu",
"Yifan Yang",
"Chong Luo",
"Zuxuan Wu",
"Yu-Gang Jiang"
] |
https://openaccess.thecvf.com/content/ICCV2025/html/Tian_REDUCIO_Generating_1K_Video_within_16_Seconds_using_Extremely_Compressed_ICCV_2025_paper.html
|
https://openaccess.thecvf.com/content/ICCV2025/papers/Tian_REDUCIO_Generating_1K_Video_within_16_Seconds_using_Extremely_Compressed_ICCV_2025_paper.pdf
|
https://openaccess.thecvf.com/content/ICCV2025/supplemental/Tian_REDUCIO_Generating_1K_ICCV_2025_supplemental.pdf
|
@InProceedings{Tian_2025_ICCV,
author = {Tian, Rui and Dai, Qi and Bao, Jianmin and Qiu, Kai and Yang, Yifan and Luo, Chong and Wu, Zuxuan and Jiang, Yu-Gang},
title = {REDUCIO! Generating 1K Video within 16 Seconds using Extremely Compressed Motion Latents},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2025},
pages = {19237-19247}
}
|
Commercial video generation models have exhibited realistic, high-fidelity results but are still restricted to limited access.One crucial obstacle for large-scale applications is the expensive training and inference cost.In this paper, we argue that videos contain significantly more redundant information than images, allowing them to be encoded with very few motion latents.Towards this goal, we design an image-conditioned VAE that projects videos into extremely compressed latent space and decode them based on content images. This magic Reducio charm enables 64xreduction of latents compared to a common 2D VAE, without sacrificing the quality.Building upon Reducio-VAE, we can train diffusion models for high-resolution video generation efficiently. Specifically, we adopt a two-stage generation paradigm, first generating a condition image via text-to-image generation, followed by text-image-to-video generation with the proposed Reducio-DiT. Extensive experiments show that our model achieves strong performance in evaluation.More importantly, our method significantly boosts the training and inference efficiency of video LDMs. Reducio-DiT is trained in just 3.2K A100 GPU hours in total and can generate a 16-frame 1024x1024 video clip within 15.5 seconds on a single A100 GPU. Code is available at https://github.com/microsoft/Reducio-VAE.
|
2411.13552
|
Accepted to ICCV2025. Code available at
https://github.com/microsoft/Reducio-VAE
|
https://github.com/microsoft/Reducio-VAE
| null |
[] |
[
"microsoft/Reducio-VAE"
] |
[] |
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] |
142
|
EgoAgent: A Joint Predictive Agent Model in Egocentric Worlds
|
[
"Lu Chen",
"Yizhou Wang",
"Shixiang Tang",
"Qianhong Ma",
"Tong He",
"Wanli Ouyang",
"Xiaowei Zhou",
"Hujun Bao",
"Sida Peng"
] |
https://openaccess.thecvf.com/content/ICCV2025/html/Chen_EgoAgent_A_Joint_Predictive_Agent_Model_in_Egocentric_Worlds_ICCV_2025_paper.html
|
https://openaccess.thecvf.com/content/ICCV2025/papers/Chen_EgoAgent_A_Joint_Predictive_Agent_Model_in_Egocentric_Worlds_ICCV_2025_paper.pdf
|
https://openaccess.thecvf.com/content/ICCV2025/supplemental/Chen_EgoAgent_A_Joint_ICCV_2025_supplemental.pdf
|
@InProceedings{Chen_2025_ICCV,
author = {Chen, Lu and Wang, Yizhou and Tang, Shixiang and Ma, Qianhong and He, Tong and Ouyang, Wanli and Zhou, Xiaowei and Bao, Hujun and Peng, Sida},
title = {EgoAgent: A Joint Predictive Agent Model in Egocentric Worlds},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2025},
pages = {6970-6980}
}
|
Learning an agent model that behaves like humans--capable of jointly perceiving the environment, predicting the future, and taking actions from a first-person perspective--is a fundamental challenge in computer vision. Existing methods typically train separate models for these abilities, which fail to capture their intrinsic relationships and prevent them from learning from each other. Inspired by how humans learn through the perception-action loop, we propose EgoAgent, a unified agent model that simultaneously learns to represent, predict, and act within a single transformer. EgoAgent explicitly models the causal and temporal dependencies among these abilities by formulating the task as an interleaved sequence of states and actions. It further introduces a joint embedding-action-prediction architecture with temporally asymmetric predictor and observer branches, enabling synergistic optimization across all three capabilities. Comprehensive evaluations of EgoAgent on representative tasks such as image classification, egocentric future state prediction, and 3D human motion prediction demonstrate the superiority of our method. The code and trained models will be publicly available at https://github.com/zju3dv/EgoAgent.
|
2502.05857
| null | null | null |
[] |
[] |
[] |
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] |
143
|
Growing a Twig to Accelerate Large Vision-Language Models
|
[
"Zhenwei Shao",
"Mingyang Wang",
"Zhou Yu",
"Wenwen Pan",
"Yan Yang",
"Tao Wei",
"Hongyuan Zhang",
"Ning Mao",
"Wei Chen",
"Jun Yu"
] |
https://openaccess.thecvf.com/content/ICCV2025/html/Shao_Growing_a_Twig_to_Accelerate_Large_Vision-Language_Models_ICCV_2025_paper.html
|
https://openaccess.thecvf.com/content/ICCV2025/papers/Shao_Growing_a_Twig_to_Accelerate_Large_Vision-Language_Models_ICCV_2025_paper.pdf
|
https://openaccess.thecvf.com/content/ICCV2025/supplemental/Shao_Growing_a_Twig_ICCV_2025_supplemental.pdf
|
@InProceedings{Shao_2025_ICCV,
author = {Shao, Zhenwei and Wang, Mingyang and Yu, Zhou and Pan, Wenwen and Yang, Yan and Wei, Tao and Zhang, Hongyuan and Mao, Ning and Chen, Wei and Yu, Jun},
title = {Growing a Twig to Accelerate Large Vision-Language Models},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2025},
pages = {20064-20074}
}
|
Large vision-language models (VLMs) have demonstrated remarkable capabilities in open-world multimodal understanding, yet their high computational overheads pose great challenges for practical deployment. Some recent works have proposed methods to accelerate VLMs by pruning redundant visual tokens guided by the attention maps of VLM's early layers. Despite the success of these token pruning methods, they still suffer from two major shortcomings: (i) considerable accuracy drop due to insensitive attention signals in early layers, and (ii) limited speedup when generating long responses (e.g., 30 tokens). To address the limitations above, we present TwigVLM---a simple and general architecture by "growing" a lightweight twig upon an early layer of the base VLM. Compared with most existing VLM acceleration methods purely based on visual token pruning, our TwigVLM not only achieves better accuracy retention by employing a twig-guided token pruning (TTP) strategy, but also yields higher generation speed by utilizing a self-speculative decoding (SSD) strategy. Taking LLaVA-1.5-7B as the base VLM, experimental results show that TwigVLM preserves 96% of the original performance after pruning 88.9% of visual tokens and achieves 154% speedup in generating long responses, delivering significantly better performance in terms of both accuracy and speed over the state-of-the-art VLM acceleration methods.
|
2503.14075
|
accepted at ICCV 2025
| null | null |
[] |
[] |
[] |
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] |
144
|
Is Less More? Exploring Token Condensation as Training-free Test-time Adaptation
|
[
"Zixin Wang",
"Dong Gong",
"Sen Wang",
"Zi Huang",
"Yadan Luo"
] |
https://openaccess.thecvf.com/content/ICCV2025/html/Wang_Is_Less_More_Exploring_Token_Condensation_as_Training-free_Test-time_Adaptation_ICCV_2025_paper.html
|
https://openaccess.thecvf.com/content/ICCV2025/papers/Wang_Is_Less_More_Exploring_Token_Condensation_as_Training-free_Test-time_Adaptation_ICCV_2025_paper.pdf
|
https://openaccess.thecvf.com/content/ICCV2025/supplemental/Wang_Is_Less_More_ICCV_2025_supplemental.pdf
|
@InProceedings{Wang_2025_ICCV,
author = {Wang, Zixin and Gong, Dong and Wang, Sen and Huang, Zi and Luo, Yadan},
title = {Is Less More? Exploring Token Condensation as Training-free Test-time Adaptation},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2025},
pages = {144-154}
}
|
Contrastive Language-Image Pretraining (CLIP) excels at learning generalizable image representations but often falls short in zero-shot inference on certain downstream datasets. Test-time adaptation (TTA) mitigates this issue by adjusting components like normalization layers or context prompts, yet it typically requires large batch sizes and extensive augmentations, leading to high computational costs. This raises a key question: Can VLMs' performance drop in specific test cases be mitigated through efficient, training-free approaches? To explore the solution, we investigate token condensation (TC) techniques, originally designed to enhance vision transformer efficiency by refining token usage during inference. We observe that informative tokens improve visual-text alignment in VLMs like CLIP on unseen datasets. However, existing TC methods often fail to maintain in-distribution performance when reducing tokens, prompting us to ask: How can we transform TC into an effective "free-lunch" adaptation strategy for VLMs? To address this, we propose Token Condensation as Adaptation (TCA), a training-free adaptation method that takes a step beyond standard TC. Rather than passively discarding tokens, TCA condenses token representation by introducing reservoir-based domain anchor tokens for information-preserving token reduction and logit correction. TCA achieves up to a 21.4% performance improvement over the strongest baseline on cross-dataset benchmark and the CIFAR-100-Corrupted dataset while reducing GFLOPs by 12.2% to 48.9%, with minimal hyperparameter dependency on both CLIP and SigLIP series. Code is available at https://github.com/Jo-wang/TCA.
|
2410.14729
|
16 pages, 8 figures
| null | null |
[] |
[] |
[] |
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] |
145
|
Structure-Guided Diffusion Models for High-Fidelity Portrait Shadow Removal
|
[
"Wanchang Yu",
"Qing Zhang",
"Rongjia Zheng",
"Wei-Shi Zheng"
] |
https://openaccess.thecvf.com/content/ICCV2025/html/Yu_Structure-Guided_Diffusion_Models_for_High-Fidelity_Portrait_Shadow_Removal_ICCV_2025_paper.html
|
https://openaccess.thecvf.com/content/ICCV2025/papers/Yu_Structure-Guided_Diffusion_Models_for_High-Fidelity_Portrait_Shadow_Removal_ICCV_2025_paper.pdf
|
https://openaccess.thecvf.com/content/ICCV2025/supplemental/Yu_Structure-Guided_Diffusion_Models_ICCV_2025_supplemental.pdf
|
@InProceedings{Yu_2025_ICCV,
author = {Yu, Wanchang and Zhang, Qing and Zheng, Rongjia and Zheng, Wei-Shi},
title = {Structure-Guided Diffusion Models for High-Fidelity Portrait Shadow Removal},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2025},
pages = {11675-11684}
}
|
We present a diffusion-based portrait shadow removal approach that can robustly produce high-fidelity results. Unlike previous methods, we cast shadow removal as diffusion-based inpainting. To this end, we first train a shadow-independent structure extraction network on a real-world portrait dataset with various synthetic lighting conditions, which allows to generate a shadow-independent structure map including facial details while excluding the unwanted shadow boundaries. The structure map is then used as condition to train a structure-guided inpainting diffusion model for removing shadows in a generative manner. Finally, to restore the fine-scale details (e.g., eyelashes, moles and spots) that may not be captured by the structure map, we take the gradients inside the shadow regions as guidance and train a detail restoration diffusion model to refine the shadow removal result. Extensive experiments on the benchmark datasets show that our method clearly outperforms existing methods, and is effective to avoid previously common issues such as facial identity tampering, shadow residual, color distortion, structure blurring, and loss of details. Our code is available at https://github.com/wanchang-yu/Structure-Guided-Diffusion-for-Portrait-Shadow-Removal.
|
2507.04692
|
Accepted by ICCV2025
|
https://github.com/wanchang-yu/Structure-Guided-Diffusion-for-Portrait-Shadow-Removal
| null |
[] |
[] |
[] |
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] |
146
|
Controllable Latent Space Augmentation for Digital Pathology
|
[
"Sofiène Boutaj",
"Marin Scalbert",
"Pierre Marza",
"Florent Couzinie-Devy",
"Maria Vakalopoulou",
"Stergios Christodoulidis"
] |
https://openaccess.thecvf.com/content/ICCV2025/html/Boutaj_Controllable_Latent_Space_Augmentation_for_Digital_Pathology_ICCV_2025_paper.html
|
https://openaccess.thecvf.com/content/ICCV2025/papers/Boutaj_Controllable_Latent_Space_Augmentation_for_Digital_Pathology_ICCV_2025_paper.pdf
|
https://openaccess.thecvf.com/content/ICCV2025/supplemental/Boutaj_Controllable_Latent_Space_ICCV_2025_supplemental.pdf
|
@InProceedings{Boutaj_2025_ICCV,
author = {Boutaj, Sofi\`ene and Scalbert, Marin and Marza, Pierre and Couzinie-Devy, Florent and Vakalopoulou, Maria and Christodoulidis, Stergios},
title = {Controllable Latent Space Augmentation for Digital Pathology},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2025},
pages = {22165-22174}
}
|
Whole slide image (WSI) analysis in digital pathology presents unique challenges due to the gigapixel resolution of WSIs and the scarcity of dense supervision signals. While Multiple Instance Learning (MIL) is a natural fit for slide-level tasks, training robust models requires large and diverse datasets. Even though image augmentation techniques could be utilized to increase data variability and reduce overfitting, implementing them effectively is not a trivial task. Traditional patch-level augmentation is prohibitively expensive due to the large number of patches extracted from each WSI, and existing feature-level augmentation methods lack control over transformation semantics. We introduce HistAug, a fast and efficient generative model for controllable augmentations in the latent space for digital pathology. By conditioning on explicit patch-level transformations (e.g., hue, erosion), HistAug generates realistic augmented embeddings while preserving initial semantic information. Our method allows the processing of a large number of patches in a single forward pass efficiently, while at the same time consistently improving MIL model performance. Experiments across multiple slide-level tasks and diverse organs show that HistAug outperforms existing methods, particularly in low-data regimes. Ablation studies confirm the benefits of learned transformations over noise-based perturbations and highlight the importance of uniform WSI-wise augmentation.
|
2508.14588
|
Accepted at ICCV 2025
|
https://github.com/MICS-Lab/HistAug
| null |
[] |
[
"sofieneb/histaug-conch"
] |
[] |
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] |
147
|
Global Motion Corresponder for 3D Point-Based Scene Interpolation under Large Motion
|
[
"Junru Lin",
"Chirag Vashist",
"Mikaela Angelina Uy",
"Colton Stearns",
"Xuan Luo",
"Leonidas Guibas",
"Ke Li"
] |
https://openaccess.thecvf.com/content/ICCV2025/html/Lin_Global_Motion_Corresponder_for_3D_Point-Based_Scene_Interpolation_under_Large_ICCV_2025_paper.html
|
https://openaccess.thecvf.com/content/ICCV2025/papers/Lin_Global_Motion_Corresponder_for_3D_Point-Based_Scene_Interpolation_under_Large_ICCV_2025_paper.pdf
|
https://openaccess.thecvf.com/content/ICCV2025/supplemental/Lin_Global_Motion_Corresponder_ICCV_2025_supplemental.pdf
|
@InProceedings{Lin_2025_ICCV,
author = {Lin, Junru and Vashist, Chirag and Uy, Mikaela Angelina and Stearns, Colton and Luo, Xuan and Guibas, Leonidas and Li, Ke},
title = {Global Motion Corresponder for 3D Point-Based Scene Interpolation under Large Motion},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2025},
pages = {7884-7893}
}
|
Existing dynamic scene interpolation methods typically assume that the motion between consecutive timesteps is small enough so that displacements can be locally approximated by linear models. In practice, even slight deviations from this small-motion assumption can cause conventional techniques to fail. In this paper, we introduce Global Motion Corresponder (GMC), a novel approach that robustly handles large motion and achieves smooth transitions. GMC learns unary potential fields that predict SE(3) mappings into a shared canonical space, balancing correspondence, spatial and semantic smoothness, and local rigidity. We demonstrate that our method significantly outperforms existing baselines on 3D scene interpolation when the two states undergo large global motions. Furthermore, our method enables extrapolation capabilities where other baseline methods cannot.
|
2508.20136
|
https://junrul.github.io/gmc/
| null | null |
[] |
[] |
[] |
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] |
148
|
WINS: Winograd Structured Pruning for Fast Winograd Convolution
|
[
"Cheonjun Park",
"Hyun Jae Oh",
"Mincheol Park",
"Hyunchan Moon",
"Minsik Kim",
"Suhyun Kim",
"Myung Kuk Yoon",
"Won Woo Ro"
] |
https://openaccess.thecvf.com/content/ICCV2025/html/Park_WINS_Winograd_Structured_Pruning_for_Fast_Winograd_Convolution_ICCV_2025_paper.html
|
https://openaccess.thecvf.com/content/ICCV2025/papers/Park_WINS_Winograd_Structured_Pruning_for_Fast_Winograd_Convolution_ICCV_2025_paper.pdf
|
https://openaccess.thecvf.com/content/ICCV2025/supplemental/Park_WINS_Winograd_Structured_ICCV_2025_supplemental.pdf
|
@InProceedings{Park_2025_ICCV,
author = {Park, Cheonjun and Oh, Hyun Jae and Park, Mincheol and Moon, Hyunchan and Kim, Minsik and Kim, Suhyun and Yoon, Myung Kuk and Ro, Won Woo},
title = {WINS: Winograd Structured Pruning for Fast Winograd Convolution},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2025},
pages = {22477-22487}
}
|
Recent GPUs leverage Winograd convolution and structured pruning to significantly accelerate inference. First, Winograd convolution is theoretically 2.25x faster than standard convolution. Second, structured pruning reduces inference time without additional overhead as the pruning ratio increases. However, applying conventional structured pruning alongside Winograd convolution is inefficient. Existing structured pruning methods, which do not account for how GPUs process Winograd convolution, require large pruning unit sizes, leading to significant information loss. In this paper, we propose Winograd Structured Pruning (WINS), the first approach to employ optimized structured pruning for Winograd convolution. WINS is designed based on an in-depth analysis of Winograd convolution's computational characteristics on GPUs. Additionally, we introduce two variants, WINS-B and WINS-AB, which further enhance performance. Experimental results show that WINS-AB achieves up to 2.8x practical speedup in baseline inference on GPUs while preserving the accuracy of ResNet-18 on ImageNet.
| null | null | null | null |
[] |
[] |
[] |
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] |
149
|
Revelio: Interpreting and leveraging semantic information in diffusion models
|
[
"Dahye Kim",
"Xavier Thomas",
"Deepti Ghadiyaram"
] |
https://openaccess.thecvf.com/content/ICCV2025/html/Kim_Revelio_Interpreting_and_leveraging_semantic_information_in_diffusion_models_ICCV_2025_paper.html
|
https://openaccess.thecvf.com/content/ICCV2025/papers/Kim_Revelio_Interpreting_and_leveraging_semantic_information_in_diffusion_models_ICCV_2025_paper.pdf
|
https://openaccess.thecvf.com/content/ICCV2025/supplemental/Kim_Revelio_Interpreting_and_ICCV_2025_supplemental.pdf
|
@InProceedings{Kim_2025_ICCV,
author = {Kim, Dahye and Thomas, Xavier and Ghadiyaram, Deepti},
title = {Revelio: Interpreting and leveraging semantic information in diffusion models},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2025},
pages = {4659-4669}
}
|
We study how rich visual semantic information is represented within various layers and denoising timesteps of different diffusion architectures. We uncover monosemantic interpretable features by leveraging k-sparse autoencoders (k-SAE). We substantiate our mechanistic interpretations via transfer learning using light-weight classifiers on off-the-shelf diffusion models' features. On 4 datasets, we demonstrate the effectiveness of diffusion features for representation learning. We provide an in-depth analysis of how different diffusion architectures, pre-training datasets, and language model conditioning impacts visual representation granularity, inductive biases, and transfer learning capabilities. Our work is a critical step towards deepening interpretability of black-box diffusion models. Code and visualizations available at: https://github.com/revelio-diffusion/revelio
| null | null | null |
https://revelio-diffusion.github.io/revelio/
|
[] |
[] |
[] |
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] |
150
|
UniRes: Universal Image Restoration for Complex Degradations
|
[
"Mo Zhou",
"Keren Ye",
"Mauricio Delbracio",
"Peyman Milanfar",
"Vishal M. Patel",
"Hossein Talebi"
] |
https://openaccess.thecvf.com/content/ICCV2025/html/Zhou_UniRes_Universal_Image_Restoration_for_Complex_Degradations_ICCV_2025_paper.html
|
https://openaccess.thecvf.com/content/ICCV2025/papers/Zhou_UniRes_Universal_Image_Restoration_for_Complex_Degradations_ICCV_2025_paper.pdf
|
https://openaccess.thecvf.com/content/ICCV2025/supplemental/Zhou_UniRes_Universal_Image_ICCV_2025_supplemental.pdf
|
@InProceedings{Zhou_2025_ICCV,
author = {Zhou, Mo and Ye, Keren and Delbracio, Mauricio and Milanfar, Peyman and Patel, Vishal M. and Talebi, Hossein},
title = {UniRes: Universal Image Restoration for Complex Degradations},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2025},
pages = {13237-13247}
}
|
Real-world image restoration is hampered by diverse degradations stemming from varying capture conditions, capture devices and post-processing pipelines. Existing works make improvements through simulating those degradations and leveraging image generative priors, however generalization to in-the-wild data remains an unresolved problem. In this paper, we focus on complex degradations, i.e., arbitrary mixtures of multiple types of known degradations, which is frequently seen in the wild. A simple yet flexible diffusion-based framework, named UniRes, is proposed to address such degradations in an end-to-end manner. It combines several specialized models during the diffusion sampling steps, hence transferring the knowledge from several well-isolated restoration tasks to the restoration of complex in-the-wild degradations. This only requires well-isolated training data for several degradation types. The framework is flexible as extensions can be added through a unified formulation, and the fidelity-quality trade-off can be adjusted through a new paradigm. Our proposed method is evaluated on both complex-degradation and single-degradation image restoration datasets. Extensive qualitative and quantitative experimental results show consistent performance gain especially for images with complex degradations.
|
2506.05599
| null | null | null |
[] |
[] |
[] |
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] |
151
|
RayGaussX: Accelerating Gaussian-Based Ray Marching for Real-Time and High-Quality Novel View Synthesis
|
[
"Hugo Blanc",
"Jean-Emmanuel Deschaud",
"Alexis Paljic"
] |
https://openaccess.thecvf.com/content/ICCV2025/html/Blanc_RayGaussX_Accelerating_Gaussian-Based_Ray_Marching_for_Real-Time_and_High-Quality_Novel_ICCV_2025_paper.html
|
https://openaccess.thecvf.com/content/ICCV2025/papers/Blanc_RayGaussX_Accelerating_Gaussian-Based_Ray_Marching_for_Real-Time_and_High-Quality_Novel_ICCV_2025_paper.pdf
|
https://openaccess.thecvf.com/content/ICCV2025/supplemental/Blanc_RayGaussX_Accelerating_Gaussian-Based_ICCV_2025_supplemental.pdf
|
@InProceedings{Blanc_2025_ICCV,
author = {Blanc, Hugo and Deschaud, Jean-Emmanuel and Paljic, Alexis},
title = {RayGaussX: Accelerating Gaussian-Based Ray Marching for Real-Time and High-Quality Novel View Synthesis},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2025},
pages = {27575-27584}
}
|
RayGauss has recently achieved state-of-the-art results on synthetic and indoor scenes, representing radiance and density fields with irregularly distributed elliptical basis functions rendered via volume ray casting using a Bounding Volume Hierarchy (BVH). However, its computational cost prevents real-time rendering on real-world scenes. Our approach, RayGaussX, builds on RayGauss by introducing key contributions that significantly accelerate both training and inference. Specifically, we incorporate volumetric rendering acceleration strategies such as empty-space skipping and adaptive sampling, enhance ray coherence, and introduce scale regularization to reduce false-positive intersections. Additionally, we propose a new densification criterion that improves density distribution in distant regions, leading to enhanced graphical quality on larger scenes. As a result, RayGaussX achieves 5x to 12x faster training and 50x to 80x higher rendering speeds (FPS) on real-world datasets while improving visual quality by up to +0.56 dB in PSNR. The code will soon be publicly available on GitHub.
|
2509.07782
| null | null | null |
[] |
[] |
[] |
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] |
152
|
CoopTrack: Exploring End-to-End Learning for Efficient Cooperative Sequential Perception
|
[
"Jiaru Zhong",
"Jiahao Wang",
"Jiahui Xu",
"Xiaofan Li",
"Zaiqing Nie",
"Haibao Yu"
] |
https://openaccess.thecvf.com/content/ICCV2025/html/Zhong_CoopTrack_Exploring_End-to-End_Learning_for_Efficient_Cooperative_Sequential_Perception_ICCV_2025_paper.html
|
https://openaccess.thecvf.com/content/ICCV2025/papers/Zhong_CoopTrack_Exploring_End-to-End_Learning_for_Efficient_Cooperative_Sequential_Perception_ICCV_2025_paper.pdf
|
https://openaccess.thecvf.com/content/ICCV2025/supplemental/Zhong_CoopTrack_Exploring_End-to-End_ICCV_2025_supplemental.pdf
|
@InProceedings{Zhong_2025_ICCV,
author = {Zhong, Jiaru and Wang, Jiahao and Xu, Jiahui and Li, Xiaofan and Nie, Zaiqing and Yu, Haibao},
title = {CoopTrack: Exploring End-to-End Learning for Efficient Cooperative Sequential Perception},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2025},
pages = {26954-26965}
}
|
Cooperative perception aims to address the inherent limitations of single-vehicle autonomous driving systems through information exchange among multiple agents. Previous research has primarily focused on single-frame perception tasks. However, the more challenging cooperative sequential perception tasks, such as cooperative 3D multi-object tracking, have not been thoroughly investigated. Therefore, we propose CoopTrack, a fully instance-level end-to-end framework for cooperative tracking, featuring learnable instance association, which fundamentally differs from existing approaches. CoopTrack transmits sparse instance-level features that significantly enhance perception capabilities while maintaining low transmission costs. Furthermore, the framework comprises two key components: Multi-Dimensional Feature Extraction, and Cross-Agent Association and Aggregation, which collectively enable comprehensive instance representation with semantic and motion features, and adaptive cross-agent association and fusion based on a feature graph. Experiments on both the V2X-Seq and Griffin datasets demonstrate that CoopTrack achieves excellent performance. Specifically, it attains state-of-the-art results on V2X-Seq, with 39.0% mAP and 32.8% AMOTA. The project is available at https://github.com/zhongjiaru/CoopTrack.
|
2507.19239
|
Accepted by ICCV 2025 (Highlight)
|
https://github.com/zhongjiaru/CoopTrack
| null |
[] |
[] |
[] |
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] |
153
|
ContraGS: Codebook-Condensed and Trainable Gaussian Splatting for Fast, Memory-Efficient Reconstruction
|
[
"Sankeerth Durvasula",
"Sharanshangar Muhunthan",
"Zain Moustafa",
"Richard Chen",
"Ruofan Liang",
"Yushi Guan",
"Nilesh Ahuja",
"Nilesh Jain",
"Selvakumar Panneer",
"Nandita Vijaykumar"
] |
https://openaccess.thecvf.com/content/ICCV2025/html/Durvasula_ContraGS_Codebook-Condensed_and_Trainable_Gaussian_Splatting_for_Fast_Memory-Efficient_Reconstruction_ICCV_2025_paper.html
|
https://openaccess.thecvf.com/content/ICCV2025/papers/Durvasula_ContraGS_Codebook-Condensed_and_Trainable_Gaussian_Splatting_for_Fast_Memory-Efficient_Reconstruction_ICCV_2025_paper.pdf
|
https://openaccess.thecvf.com/content/ICCV2025/supplemental/Durvasula_ContraGS_Codebook-Condensed_and_ICCV_2025_supplemental.pdf
|
@InProceedings{Durvasula_2025_ICCV,
author = {Durvasula, Sankeerth and Muhunthan, Sharanshangar and Moustafa, Zain and Chen, Richard and Liang, Ruofan and Guan, Yushi and Ahuja, Nilesh and Jain, Nilesh and Panneer, Selvakumar and Vijaykumar, Nandita},
title = {ContraGS: Codebook-Condensed and Trainable Gaussian Splatting for Fast, Memory-Efficient Reconstruction},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2025},
pages = {28935-28945}
}
|
3D Gaussian Splatting (3DGS) is a state-of-art technique to model real-world scenes with high quality and real-time rendering.Typically, a higher quality representation can be achieved by using a large number of 3D Gaussians. However, using large 3D Gaussian counts significantly increases the GPU device memory for storing model parameters. A large model thus requires powerful GPUs with high memory capacities for training and has slower training/rendering latencies due to the inefficiencies of memory access and data movement. In this work, we introduce ContraGS, a method to enable training directly on compressed 3DGS representations without reducing the Gaussian Counts, and thus with a little loss in model quality. ContraGS leverages codebooks to compactly store a set of Gaussian parameter vectors throughout the training process, thereby significantly reducing memory consumption. While codebooks have been demonstrated to be highly effective at compressing fully trained 3DGS models, directly training using codebook representations is an unsolved challenge. ContraGS solves the problem of learning non-differentiable parameters in codebook-compressed representations by posing parameter estimation as a Bayesian inference problem. To this end, ContraGS provides a framework that effectively uses MCMC sampling to sample over a posterior distribution of these compressed representations. With ContraGS, we demonstrate that ContraGS significantly reduces the peak memory during training (on average 3.49X) and accelerated training and rendering 1.36Xand 1.88X on average, respectively), while retraining close to state-of-art quality.
|
2509.03775
| null | null | null |
[] |
[] |
[] |
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] |
154
|
NATRA: Noise-Agnostic Framework for Trajectory Prediction with Noisy Observations
|
[
"Rongqing Li",
"Changsheng Li",
"Ruilin Lv",
"Yuhang Li",
"Yang Gao",
"Xiaolu Zhang",
"Jun Zhou"
] |
https://openaccess.thecvf.com/content/ICCV2025/html/Li_NATRA_Noise-Agnostic_Framework_for_Trajectory_Prediction_with_Noisy_Observations_ICCV_2025_paper.html
|
https://openaccess.thecvf.com/content/ICCV2025/papers/Li_NATRA_Noise-Agnostic_Framework_for_Trajectory_Prediction_with_Noisy_Observations_ICCV_2025_paper.pdf
|
https://openaccess.thecvf.com/content/ICCV2025/supplemental/Li_NATRA_Noise-Agnostic_Framework_ICCV_2025_supplemental.pdf
|
@InProceedings{Li_2025_ICCV,
author = {Li, Rongqing and Li, Changsheng and Lv, Ruilin and Li, Yuhang and Gao, Yang and Zhang, Xiaolu and Zhou, Jun},
title = {NATRA: Noise-Agnostic Framework for Trajectory Prediction with Noisy Observations},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2025},
pages = {27872-27884}
}
|
Trajectory prediction aims to forecast an agent's future trajectories based on its historical observed trajectories, which is a critical task for various applications such as autonomous driving, robotics, and surveillance systems. Most existing trajectory prediction methods assume that the observed trajectories collected for forecasting are clean. However, in real-world scenarios, noise is inevitably introduced into the observations, resulting in the collapse of the existing approaches. Therefore, it is essential to perform robust trajectory prediction based on noisy observations, which is a more practical scenario. In this paper, we propose **NATRA**, a **N**oise-**A**gnostic framework capable of tackling the problem of **TRA**jectory prediction with arbitrary types of noisy observations. Specifically, we put forward a mutual information-based mechanism to denoise the original noisy observations. It optimizes the produced trajectories to exhibit a pattern that closely resembles the clean trajectory pattern while deviating from the noisy one.Considering that the trajectory structure may be destroyed through the only optimization of mutual information, we introduce an additional reconstruction loss to preserve the structure information of the produced observed trajectories. Moreover, we further propose a ranking loss to further enhance the performance. Because NATRA does not rely on any specific module tailored to particular noise distributions, it can handle arbitrary types of noise in principle. Additionally, our proposed NATRA can be easily integrated into existing trajectory prediction models. Extensive experiments on both synthetic and real-world noisy datasets demonstrate the effectiveness of our method.
| null | null | null | null |
[] |
[] |
[] |
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] |
155
|
Your Text Encoder Can Be An Object-Level Watermarking Controller
|
[
"Naresh Kumar Devulapally",
"Mingzhen Huang",
"Vishal Asnani",
"Shruti Agarwal",
"Siwei Lyu",
"Vishnu Suresh Lokhande"
] |
https://openaccess.thecvf.com/content/ICCV2025/html/Devulapally_Your_Text_Encoder_Can_Be_An_Object-Level_Watermarking_Controller_ICCV_2025_paper.html
|
https://openaccess.thecvf.com/content/ICCV2025/papers/Devulapally_Your_Text_Encoder_Can_Be_An_Object-Level_Watermarking_Controller_ICCV_2025_paper.pdf
|
https://openaccess.thecvf.com/content/ICCV2025/supplemental/Devulapally_Your_Text_Encoder_ICCV_2025_supplemental.pdf
|
@InProceedings{Devulapally_2025_ICCV,
author = {Devulapally, Naresh Kumar and Huang, Mingzhen and Asnani, Vishal and Agarwal, Shruti and Lyu, Siwei and Lokhande, Vishnu Suresh},
title = {Your Text Encoder Can Be An Object-Level Watermarking Controller},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2025},
pages = {16576-16585}
}
|
Invisible watermarking of AI-generated images can help with copyright protection, enabling detection and identification of AI-generated media. In this work, we present a novel approach to watermark images of T2I Latent Diffusion Models (LDMs). By only fine-tuning text token embeddings \mathcal W _*, we enable watermarking in selected objects or parts of the image, offering greater flexibility compared to traditional full-image watermarking. Our method leverages the text encoder's compatibility across various LDMs, allowing plug-and-play integration for different LDMs. Moreover, introducing the watermark early in the encoding stage improves robustness to adversarial perturbations in later stages of the pipeline. Our approach achieves 99% bit accuracy (48 bits) with a 10^5 xreduction in model parameters, enabling efficient watermarking.
|
2503.11945
| null | null | null |
[] |
[] |
[] |
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] |
156
|
Spatial-Temporal Aware Visuomotor Diffusion Policy Learning
|
[
"Zhenyang Liu",
"Yikai Wang",
"Kuanning Wang",
"Longfei Liang",
"Xiangyang Xue",
"Yanwei Fu"
] |
https://openaccess.thecvf.com/content/ICCV2025/html/Liu_Spatial-Temporal_Aware_Visuomotor_Diffusion_Policy_Learning_ICCV_2025_paper.html
|
https://openaccess.thecvf.com/content/ICCV2025/papers/Liu_Spatial-Temporal_Aware_Visuomotor_Diffusion_Policy_Learning_ICCV_2025_paper.pdf
|
https://openaccess.thecvf.com/content/ICCV2025/supplemental/Liu_Spatial-Temporal_Aware_Visuomotor_ICCV_2025_supplemental.zip
|
@InProceedings{Liu_2025_ICCV,
author = {Liu, Zhenyang and Wang, Yikai and Wang, Kuanning and Liang, Longfei and Xue, Xiangyang and Fu, Yanwei},
title = {Spatial-Temporal Aware Visuomotor Diffusion Policy Learning},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2025},
pages = {7122-7131}
}
|
Visual imitation learning is effective for robots to learn versatile tasks. However, many existing methods rely on behavior cloning with supervised historical trajectories, limiting their 3D spatial and 4D spatiotemporal awareness. Consequently, these methods struggle to capture the 3D structures and 4D spatiotemporal relationships necessary for real-world deployment. In this work, we propose 4D Diffusion Policy (DP4), a novel visual imitation learning method that incorporates spatiotemporal awareness into diffusion-based policies. Unlike traditional approaches that rely on trajectory cloning, DP4 leverages a dynamic Gaussian world model to guide the learning of 3D spatial and 4D spatiotemporal perceptions from interactive environments. Our method constructs the current 3D scene from a single-view RGB-D observation and predicts the future 3D scene, optimizing trajectory generation by explicitly modeling both spatial and temporal dependencies. Extensive experiments across 17 simulation tasks with 173 variants and 3 real-world robotic tasks demonstrate that the 4D Diffusion Policy (DP4) outperforms baseline methods, improving the average simulation task success rate by 16.4% (Adroit), 14% (DexArt), and 6.45% (RLBench), and the average real-world robotic task success rate by 8.6%.
|
2507.06710
| null | null | null |
[] |
[] |
[] |
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] |
157
|
ModalTune: Fine-Tuning Slide-Level Foundation Models with Multi-Modal Information for Multi-task Learning in Digital Pathology
|
[
"Vishwesh Ramanathan",
"Tony Xu",
"Pushpak Pati",
"Faruk Ahmed",
"Maged Goubran",
"Anne L. Martel"
] |
https://openaccess.thecvf.com/content/ICCV2025/html/Ramanathan_ModalTune_Fine-Tuning_Slide-Level_Foundation_Models_with_Multi-Modal_Information_for_Multi-task_ICCV_2025_paper.html
|
https://openaccess.thecvf.com/content/ICCV2025/papers/Ramanathan_ModalTune_Fine-Tuning_Slide-Level_Foundation_Models_with_Multi-Modal_Information_for_Multi-task_ICCV_2025_paper.pdf
|
https://openaccess.thecvf.com/content/ICCV2025/supplemental/Ramanathan_ModalTune_Fine-Tuning_Slide-Level_ICCV_2025_supplemental.pdf
|
@InProceedings{Ramanathan_2025_ICCV,
author = {Ramanathan, Vishwesh and Xu, Tony and Pati, Pushpak and Ahmed, Faruk and Goubran, Maged and Martel, Anne L.},
title = {ModalTune: Fine-Tuning Slide-Level Foundation Models with Multi-Modal Information for Multi-task Learning in Digital Pathology},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2025},
pages = {23912-23923}
}
|
Prediction tasks in digital pathology are challenging due to the massive size of whole-slide images (WSIs) and the weak nature of training signals. Advances in computing, data availability, and self-supervised learning (SSL) have paved the way for slide-level foundation models (SLFMs) that can improve prediction tasks in low-data regimes. However, current methods under-utilize shared information between tasks and modalities. To overcome this challenge, we propose ModalTune, a novel fine-tuning framework which introduces the Modal Adapter to integrate new modalities without modifying SLFM weights. Additionally, we use large-language models (LLMs) to encode labels as text, capturing semantic relationships across multiple tasks and cancer types in a single training recipe. ModalTune achieves state-of-the-art (SOTA) results against both uni-modal and multi-modal models across four cancer types, jointly improving survival and cancer subtype prediction while remaining competitive in pan-cancer settings. Additionally, we show ModalTune is generalizable to two out-of-distribution (OOD) datasets. To our knowledge, this is the first unified fine-tuning framework for multi-modal, multi-task, and pan-cancer modeling in digital pathology.
|
2503.17564
| null | null | null |
[] |
[] |
[] |
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] |
158
|
Harnessing Vision Foundation Models for High-Performance, Training-Free Open Vocabulary Segmentation
|
[
"Yuheng Shi",
"Minjing Dong",
"Chang Xu"
] |
https://openaccess.thecvf.com/content/ICCV2025/html/Shi_Harnessing_Vision_Foundation_Models_for_High-Performance_Training-Free_Open_Vocabulary_Segmentation_ICCV_2025_paper.html
|
https://openaccess.thecvf.com/content/ICCV2025/papers/Shi_Harnessing_Vision_Foundation_Models_for_High-Performance_Training-Free_Open_Vocabulary_Segmentation_ICCV_2025_paper.pdf
|
https://openaccess.thecvf.com/content/ICCV2025/supplemental/Shi_Harnessing_Vision_Foundation_ICCV_2025_supplemental.pdf
|
@InProceedings{Shi_2025_ICCV,
author = {Shi, Yuheng and Dong, Minjing and Xu, Chang},
title = {Harnessing Vision Foundation Models for High-Performance, Training-Free Open Vocabulary Segmentation},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2025},
pages = {23487-23497}
}
|
While CLIP has advanced open-vocabulary predictions, its performance on semantic segmentation remains suboptimal. This shortfall primarily stems from its spatial-invariant semantic features and constrained resolution. While previous adaptations addressed spatial invariance semantic by modifying the self-attention in CLIP's image encoder, the issue of limited resolution remains unexplored. Different from previous segment-then-splice methods that segment sub-images via a sliding window and splice the results, we introduce a splice-then-segment paradigm that incorporates Segment-Anything Model (SAM) to tackle the resolution issue since SAM excels at extracting fine-grained semantic correlations from high-resolution images. Specifically, we introduce Trident, a training-free framework that first splices features extracted by CLIP and DINO from sub-images, then leverages SAM's encoder to create a correlation matrix for global aggregation, enabling a broadened receptive field. Besides, we propose a refinement strategy for CLIP's coarse segmentation outputs by transforming them into prompts for SAM. Trident achieves a significant improvement in the mIoU across eight popular benchmarks compared with the current SOTA. Furthermore, it can also be utilized to generate visual prompts that enhance the performance of Large Vision-Language Models (LVLMs). Code is available at https://github.com/YuHengsss/Trident.
|
2411.09219
|
12 pages, 5 figures
|
https://github.com/YuHengsss/Trident
| null |
[] |
[] |
[] |
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] |
159
|
FixTalk: Taming Identity Leakage for High-Quality Talking Head Generation in Extreme Cases
|
[
"Shuai Tan",
"Bill Gong",
"Bin Ji",
"Ye Pan"
] |
https://openaccess.thecvf.com/content/ICCV2025/html/Tan_FixTalk_Taming_Identity_Leakage_for_High-Quality_Talking_Head_Generation_in_ICCV_2025_paper.html
|
https://openaccess.thecvf.com/content/ICCV2025/papers/Tan_FixTalk_Taming_Identity_Leakage_for_High-Quality_Talking_Head_Generation_in_ICCV_2025_paper.pdf
|
https://openaccess.thecvf.com/content/ICCV2025/supplemental/Tan_FixTalk_Taming_Identity_ICCV_2025_supplemental.zip
|
@InProceedings{Tan_2025_ICCV,
author = {Tan, Shuai and Gong, Bill and Ji, Bin and Pan, Ye},
title = {FixTalk: Taming Identity Leakage for High-Quality Talking Head Generation in Extreme Cases},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2025},
pages = {24-36}
}
|
Talking head generation is gaining significant importance across various domains, with a growing demand for high-quality rendering. However, existing methods often suffer from identity leakage (IL) and rendering artifacts (RA), particularly in extreme cases. Through an in-depth analysis of previous approaches, we identify two key insights: (1) IL arises from identity information embedded within motion features, and (2) this identity information can be leveraged to address RA. Building on these findings, this paper introduces FixTalk, a novel framework designed to simultaneously resolve both issues for high-quality talking head generation. Firstly, we propose an Enhanced Motion Indicator (EMI) to effectively decouple identity information from motion features, mitigating the impact of IL on generated talking heads. To address RA, we introduce an Enhanced Detail Indicator (EDI), which utilizes the leaked identity information to supplement missing details, thus fixing the artifacts. Extensive experiments demonstrate that FixTalk effectively mitigates IL and RA, achieving superior performance compared to state-of-the-art methods.
|
2507.01390
| null | null | null |
[] |
[] |
[] |
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] |
160
|
MinCD-PnP: Learning 2D-3D Correspondences with Approximate Blind PnP
|
[
"Pei An",
"Jiaqi Yang",
"Muyao Peng",
"You Yang",
"Qiong Liu",
"Xiaolin Wu",
"Liangliang Nan"
] |
https://openaccess.thecvf.com/content/ICCV2025/html/An_MinCD-PnP_Learning_2D-3D_Correspondences_with_Approximate_Blind_PnP_ICCV_2025_paper.html
|
https://openaccess.thecvf.com/content/ICCV2025/papers/An_MinCD-PnP_Learning_2D-3D_Correspondences_with_Approximate_Blind_PnP_ICCV_2025_paper.pdf
|
https://openaccess.thecvf.com/content/ICCV2025/supplemental/An_MinCD-PnP_Learning_2D-3D_ICCV_2025_supplemental.pdf
|
@InProceedings{An_2025_ICCV,
author = {An, Pei and Yang, Jiaqi and Peng, Muyao and Yang, You and Liu, Qiong and Wu, Xiaolin and Nan, Liangliang},
title = {MinCD-PnP: Learning 2D-3D Correspondences with Approximate Blind PnP},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2025},
pages = {26519-26528}
}
|
Image-to-point-cloud (I2P) registration is a fundamental problem in computer vision, focusing on establishing 2D-3D correspondences between an image and a point cloud. Recently, the differentiable perspective-n-point (PnP) has been widely used to supervise I2P registration networks by enforcing projective constraints on 2D-3D correspondences. However, differentiable PnP is highly sensitive to noise and outliers in the predicted correspondences, which hinders the effectiveness of correspondence learning. Inspired by the robustness of blind PnP to noise and outliers in correspondences, we propose an approximate blind PnP-based correspondence learning approach. To mitigate the high computational cost of blind PnP, we reformulate it as a more tractable problem: minimizing the Chamfer distance between learned 2D and 3D keypoints, referred to as MinCD-PnP. To effectively solve MinCD-PnP, we introduce a lightweight multi-task learning module, MinCD-Net, which can be easily integrated into the existing I2P registration architectures. Extensive experiments on 7-Scenes, RGBD-V2, ScanNet, self-collected, and KITTI datasets demonstrate that MinCD-Net outperforms state-of-the-art methods and achieves higher inlier ratio and registration recall in both cross-scene and cross-dataset settings. The source code: https://github.com/anpei96/mincd-pnp-demo.
| null | null | null | null |
[] |
[] |
[] |
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] |
161
|
Attention to Trajectory: Trajectory-Aware Open-Vocabulary Tracking
|
[
"Yunhao Li",
"Yifan Jiao",
"Dan Meng",
"Heng Fan",
"Libo Zhang"
] |
https://openaccess.thecvf.com/content/ICCV2025/html/Li_Attention_to_Trajectory_Trajectory-Aware_Open-Vocabulary_Tracking_ICCV_2025_paper.html
|
https://openaccess.thecvf.com/content/ICCV2025/papers/Li_Attention_to_Trajectory_Trajectory-Aware_Open-Vocabulary_Tracking_ICCV_2025_paper.pdf
|
https://openaccess.thecvf.com/content/ICCV2025/supplemental/Li_Attention_to_Trajectory_ICCV_2025_supplemental.pdf
|
@InProceedings{Li_2025_ICCV,
author = {Li, Yunhao and Jiao, Yifan and Meng, Dan and Fan, Heng and Zhang, Libo},
title = {Attention to Trajectory: Trajectory-Aware Open-Vocabulary Tracking},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2025},
pages = {14390-14398}
}
|
Open-Vocabulary Multi-Object Tracking (OV-MOT) aims to enable approaches to track objects without being limited to a predefined set of categories. Current OV-MOT methods typically rely primarily on instance-level detection and association, often overlooking trajectory information that is unique and essential for object tracking tasks. Utilizing trajectory information can enhance association stability and classification accuracy, especially in cases of occlusion and category ambiguity, thereby improving adaptability to novel classes. Thus motivated, in this paper we propose TRACT, an open-vocabulary tracker that leverages trajectory information to improve both object association and classification in OV-MOT. Specifically, we introduce a Trajectory Consistency Reinforcement (TCR) strategy, that benefits tracking performance by improving target identity and category consistency. In addition, we present TraCLIP, a plug-and-play trajectory classification module. It integrates Trajectory Feature Aggregation (TFA) and Trajectory Semantic Enrichment (TSE) strategies to fully leverage trajectory information from visual and language perspectives for enhancing the classification results. Extensive experiments on OV-TAO show that our TRACT significantly improves tracking performance, highlighting trajectory information as a valuable asset for OV-MOT. We will release TRACT at https://github.com/Nathan-Li123/TRACT.
|
2503.08145
| null | null | null |
[] |
[] |
[] |
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] |
162
|
Hallucinatory Image Tokens: A Training-free EAZY Approach to Detecting and Mitigating Object Hallucinations in LVLMs
|
[
"Liwei Che",
"Tony Qingze Liu",
"Jing Jia",
"Weiyi Qin",
"Ruixiang Tang",
"Vladimir Pavlovic"
] |
https://openaccess.thecvf.com/content/ICCV2025/html/Che_Hallucinatory_Image_Tokens_A_Training-free_EAZY_Approach_to_Detecting_and_ICCV_2025_paper.html
|
https://openaccess.thecvf.com/content/ICCV2025/papers/Che_Hallucinatory_Image_Tokens_A_Training-free_EAZY_Approach_to_Detecting_and_ICCV_2025_paper.pdf
|
https://openaccess.thecvf.com/content/ICCV2025/supplemental/Che_Hallucinatory_Image_Tokens_ICCV_2025_supplemental.pdf
|
@InProceedings{Che_2025_ICCV,
author = {Che, Liwei and Liu, Tony Qingze and Jia, Jing and Qin, Weiyi and Tang, Ruixiang and Pavlovic, Vladimir},
title = {Hallucinatory Image Tokens: A Training-free EAZY Approach to Detecting and Mitigating Object Hallucinations in LVLMs},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2025},
pages = {21635-21644}
}
|
Despite their remarkable potential, Large Vision-Language Models (LVLMs) still face challenges with object hallucination, a problem where their generated outputs mistakenly incorporate objects that do not actually exist. Although most works focus on addressing this issue within the language-model backbone, our work shifts the focus to the image input source, investigating how specific image tokens contribute to hallucinations. Our analysis reveals that a small subset of image tokens with high attention scores are the main drivers of object hallucination. By removing these hallucinatory image tokens (only 1.5% of all image tokens), the issue can be effectively mitigated. This finding holds consistently across different models. Building on this insight, we introduce \eazy, a novel, training-free method that automatically identifies and Eliminates hAllucinations by Zeroing out hallucinator Y image tokens. We utilize EAZY for unsupervised object hallucination detection, achieving a 15% improvement compared to previous methods. Additionally, EAZY demonstrates remarkable effectiveness in mitigating hallucinations while preserving model utility and seamlessly adapting to various LVLM architectures.
| null | null | null | null |
[] |
[] |
[] |
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] |
163
|
CVPT: Cross Visual Prompt Tuning
|
[
"Lingyun Huang",
"Jianxu Mao",
"Junfei Yi",
"Ziming Tao",
"Yaonan Wang"
] |
https://openaccess.thecvf.com/content/ICCV2025/html/Huang_CVPT_Cross_Visual_Prompt_Tuning_ICCV_2025_paper.html
|
https://openaccess.thecvf.com/content/ICCV2025/papers/Huang_CVPT_Cross_Visual_Prompt_Tuning_ICCV_2025_paper.pdf
| null |
@InProceedings{Huang_2025_ICCV,
author = {Huang, Lingyun and Mao, Jianxu and Yi, Junfei and Tao, Ziming and Wang, Yaonan},
title = {CVPT: Cross Visual Prompt Tuning},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2025},
pages = {848-858}
}
|
Parameter-Efficient Fine-Tuning (PEFT) has emerged to mitigate the computational demands of large-scale models. Within computer vision, adapter-based PEFT methods are often favored over prompt-based approaches like Visual Prompt Tuning (VPT) due to the latter's performance and efficiency limitations. Our analysis reveals that VPT's shortcomings stem from its prompt deployment strategy, which can distort the model's inherent self-attention mechanism. To address this, we propose Cross Visual Prompt Tuning (CVPT). CVPT introduces a cross-attention module to directly model interactions between prompts and image tokens. This design decouples the prompts from the input sequence, preserving the original self-attention integrity while enabling efficient feature integration. Furthermore, we employ a weight-sharing mechanism for cross-attention initialization, which enhances representative capability without a large parameter overhead. Extensive experiments across 25 datasets show that CVPT significantly outperforms VPT. For instance, on the VTAB-1K benchmark, CVPT achieves over 4% higher average accuracy, rivaling leading adapter-based methods in both performance and efficiency. Our work confirms that prompt-based methods can achieve exceptional results in visual fine-tuning. The code is available at https://github.com/Lingyun0419/CVPT
|
2408.14961
| null |
https://github.com/Lingyun0419/CVPT
| null |
[] |
[] |
[] |
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] |
164
|
On the Robustness Tradeoff in Fine-Tuning
|
[
"Kunyang Li",
"Jean-Charles Noirot Ferrand",
"Ryan Sheatsley",
"Blaine Hoak",
"Yohan Beugin",
"Eric Pauley",
"Patrick McDaniel"
] |
https://openaccess.thecvf.com/content/ICCV2025/html/Li_On_the_Robustness_Tradeoff_in_Fine-Tuning_ICCV_2025_paper.html
|
https://openaccess.thecvf.com/content/ICCV2025/papers/Li_On_the_Robustness_Tradeoff_in_Fine-Tuning_ICCV_2025_paper.pdf
|
https://openaccess.thecvf.com/content/ICCV2025/supplemental/Li_On_the_Robustness_ICCV_2025_supplemental.pdf
|
@InProceedings{Li_2025_ICCV,
author = {Li, Kunyang and Ferrand, Jean-Charles Noirot and Sheatsley, Ryan and Hoak, Blaine and Beugin, Yohan and Pauley, Eric and McDaniel, Patrick},
title = {On the Robustness Tradeoff in Fine-Tuning},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2025},
pages = {4898-4907}
}
|
Fine-tuning has become the standard practice for adapting pre-trained models to downstream tasks. However, the impact on model robustness is not well understood. In this work, we characterize the robustness-accuracy trade-off in fine-tuning. We evaluate the robustness and accuracy of fine-tuned models over 6 benchmark datasets and 7 different fine-tuning strategies. We observe a consistent trade-off between adversarial robustness and accuracy. Peripheral updates such as BitFit are more effective for simple tasks---over 75% above the average measured by the area under the Pareto frontiers on CIFAR-10 and CIFAR-100. In contrast, fine-tuning information-heavy layers, such as attention layers via Compacter, achieves a better Pareto frontier on more complex tasks---57.5% and 34.6% above the average on Caltech-256 and CUB-200, respectively. Lastly, we observe that the robustness of fine-tuning against out-of-distribution data closely tracks accuracy. These insights emphasize the need for robustness-aware fine-tuning to ensure reliable real-world deployments.
|
2503.14836
|
Accepted to International Conference on Computer Vision, ICCV 2025
| null | null |
[] |
[] |
[] |
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] |
165
|
Lay2Story: Extending Diffusion Transformers for Layout-Togglable Story Generation
|
[
"Ao Ma",
"Jiasong Feng",
"Ke Cao",
"Jing Wang",
"Yun Wang",
"Quanwei Zhang",
"Zhanjie Zhang"
] |
https://openaccess.thecvf.com/content/ICCV2025/html/Ma_Lay2Story_Extending_Diffusion_Transformers_for_Layout-Togglable_Story_Generation_ICCV_2025_paper.html
|
https://openaccess.thecvf.com/content/ICCV2025/papers/Ma_Lay2Story_Extending_Diffusion_Transformers_for_Layout-Togglable_Story_Generation_ICCV_2025_paper.pdf
|
https://openaccess.thecvf.com/content/ICCV2025/supplemental/Ma_Lay2Story_Extending_Diffusion_ICCV_2025_supplemental.pdf
|
@InProceedings{Ma_2025_ICCV,
author = {Ma, Ao and Feng, Jiasong and Cao, Ke and Wang, Jing and Wang, Yun and Zhang, Quanwei and Zhang, Zhanjie},
title = {Lay2Story: Extending Diffusion Transformers for Layout-Togglable Story Generation},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2025},
pages = {16102-16111}
}
|
Storytelling tasks involving generating consistent subjects have gained significant attention recently. However, existing methods, whether training-free or training-based, continue to face challenges in maintaining subject consistency due to the lack of fine-grained guidance and inter-frame interaction. Additionally, the scarcity of high-quality data in this field makes it difficult to precisely control storytelling tasks, including the subject's position, appearance, clothing, expression, and posture, thereby hindering further advancements. In this paper, we demonstrate that layout conditions, such as the subject's position and detailed attributes, effectively facilitate fine-grained interactions between frames. This not only strengthens the consistency of the generated frame sequence but also allows for precise control over the subject's position, appearance, and other key details. Building on this, we introduce an advanced storytelling task: Layout-Toggable Storytelling, which enables precise subject control by incorporating layout conditions. To address the lack of high-quality datasets with layout annotations for this task, we develop Lay2Story-1M, which contains over 1 million 720p and higher-resolution images, processed from approximately 11,300 hours of cartoon videos. Building on Lay2Story-1M, we create Lay2Story-Bench, a benchmark with 3,000 prompts designed to evaluate the performance of different methods on this task. Furthermore, we propose Lay2Story, a robust framework based on the Diffusion Transformers (DiTs) architecture for Layout-Togglable Storytelling tasks. Through both qualitative and quantitative experiments, we find that our method outperforms the previous state-of-the-art (SOTA) techniques, achieving the best results in terms of consistency, semantic correlation, and aesthetic quality.
|
2508.08949
|
Accepted by ICCV 2025
| null | null |
[] |
[] |
[] |
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] |
166
|
Unfolding-Associative Encoder-Decoder Network with Progressive Alignment for Pansharpening
|
[
"Shijie Fang",
"Hongping Gan"
] |
https://openaccess.thecvf.com/content/ICCV2025/html/Fang_Unfolding-Associative_Encoder-Decoder_Network_with_Progressive_Alignment_for_Pansharpening_ICCV_2025_paper.html
|
https://openaccess.thecvf.com/content/ICCV2025/papers/Fang_Unfolding-Associative_Encoder-Decoder_Network_with_Progressive_Alignment_for_Pansharpening_ICCV_2025_paper.pdf
|
https://openaccess.thecvf.com/content/ICCV2025/supplemental/Fang_Unfolding-Associative_Encoder-Decoder_Network_ICCV_2025_supplemental.pdf
|
@InProceedings{Fang_2025_ICCV,
author = {Fang, Shijie and Gan, Hongping},
title = {Unfolding-Associative Encoder-Decoder Network with Progressive Alignment for Pansharpening},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2025},
pages = {13651-13661}
}
|
Deep Unfolding Networks (DUNs) have emerged as a powerful framework for pansharpening due to their interpretable fusion strategies. However, existing DUNs are limited by their serial iterative architectures, which hinder cross-stage and cross-modal feature interactions at different abstraction levels. This limitation results in insufficient integration of multi-level multimodal features and compromised reconstruction accuracy. To address these challenges, we propose the Unfolding-Associative Encoder-Decoder Network (UED-Net), an innovative framework that iteratively extracts multi-level cross-modal degradation encodings and recursively refines features for cross-stage adaptive aggregation decoding through lightweight processes. Specifically, we first introduce the spatial-spectral encoding module, which progressively and interpretably perceives the hierarchical degradation encoding features of both space and spectrum. Moreover, we develop the unfolding-associative attention module to capture pixel-level attention across stages, thereby leveraging the causal relationships of multi-level features for aggregation during decoding. Meanwhile, we implement a progressive alignment mechanism, which coordinates both feature distribution and alignment of spatial and spectral modalities between iterative stages to facilitate adaptive fusion. These modules enable UED-Net to achieve efficient pansharpening by aggregating multi-level features. Extensive qualitative and quantitative experiments confirm the superiority of UED-Net.
| null | null | null | null |
[] |
[] |
[] |
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] |
167
|
DeepMesh: Auto-Regressive Artist-mesh Creation with Reinforcement Learning
|
[
"Ruowen Zhao",
"Junliang Ye",
"Zhengyi Wang",
"Guangce Liu",
"Yiwen Chen",
"Yikai Wang",
"Jun Zhu"
] |
https://openaccess.thecvf.com/content/ICCV2025/html/Zhao_DeepMesh_Auto-Regressive_Artist-mesh_Creation_with_Reinforcement_Learning_ICCV_2025_paper.html
|
https://openaccess.thecvf.com/content/ICCV2025/papers/Zhao_DeepMesh_Auto-Regressive_Artist-mesh_Creation_with_Reinforcement_Learning_ICCV_2025_paper.pdf
|
https://openaccess.thecvf.com/content/ICCV2025/supplemental/Zhao_DeepMesh_Auto-Regressive_Artist-mesh_ICCV_2025_supplemental.pdf
|
@InProceedings{Zhao_2025_ICCV,
author = {Zhao, Ruowen and Ye, Junliang and Wang, Zhengyi and Liu, Guangce and Chen, Yiwen and Wang, Yikai and Zhu, Jun},
title = {DeepMesh: Auto-Regressive Artist-mesh Creation with Reinforcement Learning},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2025},
pages = {10612-10623}
}
|
Triangle meshes play a crucial role in 3D applications for efficient manipulation and rendering. While auto-regressive methods generate structured meshes by predicting discrete vertex tokens, they are often constrained by limited face counts and mesh incompleteness. To address these challenges, we propose DeepMesh, a framework that optimizes mesh generation through two key innovations: (1) an efficient pre-training strategy incorporating a novel tokenization algorithm, along with improvements in data curation and processing, and (2) the introduction of Reinforcement Learning (RL) into 3D mesh generation to achieve human preference alignment via Direct Preference Optimization (DPO). We design a scoring standard that combines human evaluation with 3D metrics to collect preference pairs for DPO, ensuring both visual appeal and geometric accuracy. Conditioned on point clouds and images, DeepMesh generates meshes with intricate details and precise topology, outperforming state-of-the-art methods in both precision and quality.
|
2503.15265
|
Project page: https://zhaorw02.github.io/DeepMesh/
| null | null |
[] |
[
"zzzrw/DeepMesh"
] |
[] |
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] |
168
|
VisRL: Intention-Driven Visual Perception via Reinforced Reasoning
|
[
"Zhangquan Chen",
"Xufang Luo",
"Dongsheng Li"
] |
https://openaccess.thecvf.com/content/ICCV2025/html/Chen_VisRL_Intention-Driven_Visual_Perception_via_Reinforced_Reasoning_ICCV_2025_paper.html
|
https://openaccess.thecvf.com/content/ICCV2025/papers/Chen_VisRL_Intention-Driven_Visual_Perception_via_Reinforced_Reasoning_ICCV_2025_paper.pdf
|
https://openaccess.thecvf.com/content/ICCV2025/supplemental/Chen_VisRL_Intention-Driven_Visual_ICCV_2025_supplemental.pdf
|
@InProceedings{Chen_2025_ICCV,
author = {Chen, Zhangquan and Luo, Xufang and Li, Dongsheng},
title = {VisRL: Intention-Driven Visual Perception via Reinforced Reasoning},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2025},
pages = {2545-2555}
}
|
Visual understanding is inherently intention-driven--humans selectively focus on different regions of a scene based on their goals. Recent advances in large multimodal models (LMMs) enable flexible expression of such intentions through natural language, allowing queries to guide visual reasoning processes. Frameworks like Visual Chain-of-Thought have demonstrated the benefit of incorporating explicit reasoning steps, where the model predicts a focus region before answering a query. However, existing approaches rely heavily on supervised training with annotated intermediate bounding boxes, which severely limits scalability due to the combinatorial explosion of intention-region pairs. To overcome this limitation, we propose VisRL, the first framework that applies reinforcement learning (RL) to the problem of intention-driven visual perception. VisRL optimizes the entire visual reasoning process using only reward signals. By treating intermediate focus selection as an internal decision optimized through trial-and-error, our method eliminates the need for costly region annotations while aligning more closely with how humans learn to perceive the world. Extensive experiments across multiple benchmarks show that VisRL consistently outperforms strong baselines, demonstrating both its effectiveness and its strong generalization across different LMMs.
|
2503.07523
|
18pages,11 figures
|
https://github.com/zhangquanchen/VisRL
| null |
[] |
[] |
[] |
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] |
169
|
PINO: Person-Interaction Noise Optimization for Long-Duration and Customizable Motion Generation of Arbitrary-Sized Groups
|
[
"Sakuya Ota",
"Qing Yu",
"Kent Fujiwara",
"Satoshi Ikehata",
"Ikuro Sato"
] |
https://openaccess.thecvf.com/content/ICCV2025/html/Ota_PINO_Person-Interaction_Noise_Optimization_for_Long-Duration_and_Customizable_Motion_Generation_ICCV_2025_paper.html
|
https://openaccess.thecvf.com/content/ICCV2025/papers/Ota_PINO_Person-Interaction_Noise_Optimization_for_Long-Duration_and_Customizable_Motion_Generation_ICCV_2025_paper.pdf
|
https://openaccess.thecvf.com/content/ICCV2025/supplemental/Ota_PINO_Person-Interaction_Noise_ICCV_2025_supplemental.zip
|
@InProceedings{Ota_2025_ICCV,
author = {Ota, Sakuya and Yu, Qing and Fujiwara, Kent and Ikehata, Satoshi and Sato, Ikuro},
title = {PINO: Person-Interaction Noise Optimization for Long-Duration and Customizable Motion Generation of Arbitrary-Sized Groups},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2025},
pages = {10676-10685}
}
|
Generating realistic group interactions involving multiple characters remains challenging due to increasing complexity as group size expands. While existing conditional diffusion models incrementally generate motions by conditioning on previously generated characters, they rely on single shared prompts, limiting nuanced control and leading to overly simplified interactions. In this paper, we introduce Person-Interaction Noise Optimization (PINO), a novel, training-free framework designed for generating realistic and customizable interactions among groups of arbitrary size. PINO decomposes complex group interactions into semantically relevant pairwise interactions, and leverages pretrained two-person interaction diffusion models to incrementally compose group interactions. To ensure physical plausibility and avoid common artifacts such as overlapping or penetration between characters, PINO employs physics-based penalties during noise optimization. This approach allows precise user control over character orientation, speed, and spatial relationships without additional training. Comprehensive evaluations demonstrate that PINO generates visually realistic, physically coherent, and adaptable multi-person interactions suitable for diverse animation, gaming, and robotics applications.
|
2507.19292
|
Accepted to ICCV 2025, Project page: https://sinc865.github.io/pino/
| null | null |
[] |
[] |
[] |
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170
|
MagicDrive-V2: High-Resolution Long Video Generation for Autonomous Driving with Adaptive Control
|
[
"Ruiyuan Gao",
"Kai Chen",
"Bo Xiao",
"Lanqing Hong",
"Zhenguo Li",
"Qiang Xu"
] |
https://openaccess.thecvf.com/content/ICCV2025/html/Gao_MagicDrive-V2_High-Resolution_Long_Video_Generation_for_Autonomous_Driving_with_Adaptive_ICCV_2025_paper.html
|
https://openaccess.thecvf.com/content/ICCV2025/papers/Gao_MagicDrive-V2_High-Resolution_Long_Video_Generation_for_Autonomous_Driving_with_Adaptive_ICCV_2025_paper.pdf
|
https://openaccess.thecvf.com/content/ICCV2025/supplemental/Gao_MagicDrive-V2_High-Resolution_Long_ICCV_2025_supplemental.pdf
|
@InProceedings{Gao_2025_ICCV,
author = {Gao, Ruiyuan and Chen, Kai and Xiao, Bo and Hong, Lanqing and Li, Zhenguo and Xu, Qiang},
title = {MagicDrive-V2: High-Resolution Long Video Generation for Autonomous Driving with Adaptive Control},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2025},
pages = {28135-28144}
}
|
The rapid advancement of diffusion models has greatly improved video synthesis, especially in controllable video generation, which is vital for applications like autonomous driving. Although DiT with 3D VAE has become a standard framework for video generation, it introduces challenges in controllable driving video generation, especially for frame-wise geometric control, rendering existing methods ineffective. To address these issues, we propose MagicDrive-V2, a novel approach that integrates the MVDiT block and spatial-temporal conditional encoding to enable multi-view video generation and precise geometric control. Additionally, we introduce an efficient method for obtaining contextual descriptions for videos to support diverse textual control, along with a progressive training strategy using mixed video data to enhance training efficiency and generalizability. Consequently, MagicDrive-V2 enables multi-view driving video synthesis with 3.3x resolution and 4x frame count (compared to current SOTA), rich contextual control, and geometric controls. Extensive experiments demonstrate MagicDrive-V2's ability, unlocking broader applications in autonomous driving. Project page: https://flymin.github.io/magicdrive-v2/
| null | null | null | null |
[] |
[] |
[] |
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] |
171
|
Communication-Efficient Multi-Vehicle Collaborative Semantic Segmentation via Sparse 3D Gaussian Sharing
|
[
"Tianyu Hong",
"Xiaobo Zhou",
"Wenkai Hu",
"Qi Xie",
"Zhihui Ke",
"Tie Qiu"
] |
https://openaccess.thecvf.com/content/ICCV2025/html/Hong_Communication-Efficient_Multi-Vehicle_Collaborative_Semantic_Segmentation_via_Sparse_3D_Gaussian_Sharing_ICCV_2025_paper.html
|
https://openaccess.thecvf.com/content/ICCV2025/papers/Hong_Communication-Efficient_Multi-Vehicle_Collaborative_Semantic_Segmentation_via_Sparse_3D_Gaussian_Sharing_ICCV_2025_paper.pdf
|
https://openaccess.thecvf.com/content/ICCV2025/supplemental/Hong_Communication-Efficient_Multi-Vehicle_Collaborative_ICCV_2025_supplemental.zip
|
@InProceedings{Hong_2025_ICCV,
author = {Hong, Tianyu and Zhou, Xiaobo and Hu, Wenkai and Xie, Qi and Ke, Zhihui and Qiu, Tie},
title = {Communication-Efficient Multi-Vehicle Collaborative Semantic Segmentation via Sparse 3D Gaussian Sharing},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2025},
pages = {28622-28631}
}
|
Collaborative perception is considered a promising approach to address the inherent limitations of single-vehicle systems by sharing data among vehicles, thereby enhancing performance in perception tasks such as bird's-eye view (BEV) semantic segmentation. However, existing methods share the entire dense, scene-level BEV feature, which contains significant redundancy and lacks height information, ultimately leading to unavoidable bandwidth waste and performance degradation. To address these challenges, we present GSCOOP, the first collaborative semantic segmentation framework that leverages sparse, object-centric 3D Gaussians to fundamentally overcome communication bottlenecks. By representing scenes with compact Gaussians that preserve complete spatial information, GSCOOP achieves both high perception accuracy and communication efficiency. To further optimize transmission, we introduce the Priority-Based Gaussian Selection (PGS) module to adaptively select critical Gaussians and a Semantic Gaussian Compression (SGC) module to compress Gaussian attributes with minimal overhead. Extensive experiments on OPV2V and V2X-Seq demonstrate that GSCOOP achieves state-of-the-art performance, even with more than 500X lower communication volume.
| null | null | null | null |
[] |
[] |
[] |
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] |
172
|
Category-Specific Selective Feature Enhancement for Long-Tailed Multi-Label Image Classification
|
[
"Ruiqi Du",
"Xu Tang",
"Xiangrong Zhang",
"Jingjing Ma"
] |
https://openaccess.thecvf.com/content/ICCV2025/html/Du_Category-Specific_Selective_Feature_Enhancement_for_Long-Tailed_Multi-Label_Image_Classification_ICCV_2025_paper.html
|
https://openaccess.thecvf.com/content/ICCV2025/papers/Du_Category-Specific_Selective_Feature_Enhancement_for_Long-Tailed_Multi-Label_Image_Classification_ICCV_2025_paper.pdf
|
https://openaccess.thecvf.com/content/ICCV2025/supplemental/Du_Category-Specific_Selective_Feature_ICCV_2025_supplemental.pdf
|
@InProceedings{Du_2025_ICCV,
author = {Du, Ruiqi and Tang, Xu and Zhang, Xiangrong and Ma, Jingjing},
title = {Category-Specific Selective Feature Enhancement for Long-Tailed Multi-Label Image Classification},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2025},
pages = {3757-3766}
}
|
Since real-world multi-label data often exhibit significant label imbalance, long-tailed multi-label image classification has emerged as a prominent research area in computer vision. Traditionally, it is considered that deep neural networks' classifiers are vulnerable to long-tailed distributions, whereas the feature extraction backbone remains relatively robust. However, our analysis from the feature learning perspective reveals that the backbone struggles to maintain high sensitivity to sample-scarce categories but retains the ability to localize specific areas effectively. Based on this observation, we propose a new model for long-tailed multi-label image classification named category-specific selective feature enhancement (CSSFE). First, it utilizes the retained localization capability of the backbone to capture label-dependent class activation maps. Then, a progressive attention enhancement mechanism, updating from head to medium to tail categories, is introduced to address the low-confidence issue in medium and tail categories. Finally, visual features are extracted according to the optimized class activation maps and combined with semantic information to perform the classification task. Extensive experiments on two benchmark datasets highlight our findings' generalizability and the proposed CSSFE's superior performance.
| null | null | null | null |
[] |
[] |
[] |
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] |
173
|
Sparse-Dense Side-Tuner for efficient Video Temporal Grounding
|
[
"David Pujol-Perich",
"Sergio Escalera",
"Albert Clapés"
] |
https://openaccess.thecvf.com/content/ICCV2025/html/Pujol-Perich_Sparse-Dense_Side-Tuner_for_efficient_Video_Temporal_Grounding_ICCV_2025_paper.html
|
https://openaccess.thecvf.com/content/ICCV2025/papers/Pujol-Perich_Sparse-Dense_Side-Tuner_for_efficient_Video_Temporal_Grounding_ICCV_2025_paper.pdf
|
https://openaccess.thecvf.com/content/ICCV2025/supplemental/Pujol-Perich_Sparse-Dense_Side-Tuner_for_ICCV_2025_supplemental.pdf
|
@InProceedings{Pujol-Perich_2025_ICCV,
author = {Pujol-Perich, David and Escalera, Sergio and Clap\'es, Albert},
title = {Sparse-Dense Side-Tuner for efficient Video Temporal Grounding},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2025},
pages = {21515-21524}
}
|
Video Temporal Grounding (VTG) involves Moment Retrieval (MR) and Highlight Detection (HD) based on textual queries. For this, most methods rely solely on final-layer features of frozen large pre-trained backbones, limiting their adaptability to new domains. While full fine-tuning is often impractical, parameter-efficient fine-tuning --and particularly side-tuning (ST)-- has emerged as an effective alternative. However, prior ST approaches this problem from a frame-level refinement perspective, overlooking the inherent sparse nature of MR. To address this, we propose the Sparse-Dense Side-Tuner (SDST), the first anchor-free ST architecture for VTG. We also introduce the Reference-based Deformable Self-Attention, a novel mechanism that enhances the context modeling of the deformable attention --a key limitation of existing anchor-free methods. Additionally, we present the first effective integration of InternVideo2 backbone into an ST framework, showing its profound implications in performance. Overall, our method significantly improves existing ST methods, achieving highly competitive or SOTA results on QVHighlights, TACoS, and Charades-STA, while reducing up to a 73% the parameter count w.r.t. the existing SOTA methods. The code is publicly accessible at https://github.com/davidpujol/SDST.
|
2507.07744
| null |
https://github.com/davidpujol/SDST
| null |
[] |
[] |
[] |
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] |
174
|
Corvid: Improving Multimodal Large Language Models Towards Chain-of-Thought Reasoning
|
[
"Jingjing Jiang",
"Chao Ma",
"Xurui Song",
"Hanwang Zhang",
"Jun Luo"
] |
https://openaccess.thecvf.com/content/ICCV2025/html/Jiang_Corvid_Improving_Multimodal_Large_Language_Models_Towards_Chain-of-Thought_Reasoning_ICCV_2025_paper.html
|
https://openaccess.thecvf.com/content/ICCV2025/papers/Jiang_Corvid_Improving_Multimodal_Large_Language_Models_Towards_Chain-of-Thought_Reasoning_ICCV_2025_paper.pdf
|
https://openaccess.thecvf.com/content/ICCV2025/supplemental/Jiang_Corvid_Improving_Multimodal_ICCV_2025_supplemental.pdf
|
@InProceedings{Jiang_2025_ICCV,
author = {Jiang, Jingjing and Ma, Chao and Song, Xurui and Zhang, Hanwang and Luo, Jun},
title = {Corvid: Improving Multimodal Large Language Models Towards Chain-of-Thought Reasoning},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2025},
pages = {3034-3046}
}
|
Recent advancements in multimodal large language models (MLLMs) have demonstrated exceptional performance in multimodal perception and understanding. However, leading open-source MLLMs exhibit significant limitations in complex and structured reasoning, particularly in tasks requiring deep reasoning for decision-making and problem-solving. In this work, we present Corvid, an MLLM with enhanced chain-of-thought (CoT) reasoning capabilities. Architecturally, Corvid incorporates a hybrid vision encoder for informative visual representation and a meticulously designed connector (GateMixer) to facilitate cross-modal alignment. To enhance Corvid's CoT reasoning capabilities, we introduce MCoT-Instruct-287K, a high-quality multimodal CoT instruction-following dataset, refined and standardized from diverse public reasoning sources. Leveraging this dataset, we fine-tune Corvid with a two-stage CoT-formatted training approach to progressively enhance its step-by-step reasoning abilities. Furthermore, we propose an effective inference-time scaling strategy that enables Corvid to mitigate over-reasoning and under-reasoning through self-verification. Extensive experiments demonstrate that Corvid outperforms existing o1-like MLLMs and state-of-the-art MLLMs with similar parameter scales, with notable strengths in mathematical reasoning and science problem-solving. Project page: https://mm-vl.github.io/corvid.
|
2507.07424
|
ICCV 2025
| null | null |
[] |
[] |
[] |
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] |
175
|
CompCap: Improving Multimodal Large Language Models with Composite Captions
|
[
"Xiaohui Chen",
"Satya Narayan Shukla",
"Mahmoud Azab",
"Aashu Singh",
"Qifan Wang",
"David Yang",
"ShengYun Peng",
"Hanchao Yu",
"Shen Yan",
"Xuewen Zhang",
"Baosheng He"
] |
https://openaccess.thecvf.com/content/ICCV2025/html/Chen_CompCap_Improving_Multimodal_Large_Language_Models_with_Composite_Captions_ICCV_2025_paper.html
|
https://openaccess.thecvf.com/content/ICCV2025/papers/Chen_CompCap_Improving_Multimodal_Large_Language_Models_with_Composite_Captions_ICCV_2025_paper.pdf
|
https://openaccess.thecvf.com/content/ICCV2025/supplemental/Chen_CompCap_Improving_Multimodal_ICCV_2025_supplemental.pdf
|
@InProceedings{Chen_2025_ICCV,
author = {Chen, Xiaohui and Shukla, Satya Narayan and Azab, Mahmoud and Singh, Aashu and Wang, Qifan and Yang, David and Peng, ShengYun and Yu, Hanchao and Yan, Shen and Zhang, Xuewen and He, Baosheng},
title = {CompCap: Improving Multimodal Large Language Models with Composite Captions},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2025},
pages = {23582-23592}
}
|
How well can Multimodal Large Language Models (MLLMs) understand composite images? Composite images (CIs) are synthetic visuals created by merging multiple visual elements, such as charts, posters, or screenshots, rather than being captured directly by a camera. While CIs are prevalent in real-world applications, recent MLLM developments have primarily focused on interpreting natural images (NIs). Our research reveals that current MLLMs face significant challenges in accurately understanding CIs, often struggling to extract information or perform complex reasoning based on these images. We find that existing training data for CIs are mostly formatted for question-answer tasks (e.g., in datasets like ChartQA and ScienceQA), while high-quality image-caption datasets, critical for robust vision-language alignment, are only available for NIs. To bridge this gap, we introduce Composite Captions (CompCap), a flexible framework that leverages Large Language Models (LLMs) and automation tools to synthesize CIs with accurate and detailed captions. Using CompCap, we curate CompCap-118K, a dataset containing 118K image-caption pairs across six CI types. We validate the effectiveness of CompCap-118K by supervised fine-tuning MLLMs of three sizes: xGen-MM-inst.-4B and LLaVA-NeXT-Vicuna-7B/13B. Empirical results show that CompCap-118K significantly enhances MLLMs' understanding of CIs, yielding average gains of 1.7%, 2.0%, and 2.9% across eleven benchmarks, respectively.
|
2412.05243
| null | null | null |
[] |
[] |
[
"xchen16/CompCap-gpt4"
] |
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] |
176
|
COSMO: Combination of Selective Memorization for Low-cost Vision-and-Language Navigation
|
[
"Siqi Zhang",
"Yanyuan Qiao",
"Qunbo Wang",
"Zike Yan",
"Qi Wu",
"Zhihua Wei",
"Jing Liu"
] |
https://openaccess.thecvf.com/content/ICCV2025/html/Zhang_COSMO_Combination_of_Selective_Memorization_for_Low-cost_Vision-and-Language_Navigation_ICCV_2025_paper.html
|
https://openaccess.thecvf.com/content/ICCV2025/papers/Zhang_COSMO_Combination_of_Selective_Memorization_for_Low-cost_Vision-and-Language_Navigation_ICCV_2025_paper.pdf
|
https://openaccess.thecvf.com/content/ICCV2025/supplemental/Zhang_COSMO_Combination_of_ICCV_2025_supplemental.pdf
|
@InProceedings{Zhang_2025_ICCV,
author = {Zhang, Siqi and Qiao, Yanyuan and Wang, Qunbo and Yan, Zike and Wu, Qi and Wei, Zhihua and Liu, Jing},
title = {COSMO: Combination of Selective Memorization for Low-cost Vision-and-Language Navigation},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2025},
pages = {5511-5522}
}
|
Vision-and-Language Navigation (VLN) tasks have gained prominence within artificial intelligence research due to their potential application in fields like home assistants. Many contemporary VLN approaches, while based on transformer architectures, have increasingly incorporated additional components such as external knowledge bases or map information to enhance performance. These additions, while boosting performance, also lead to larger models and increased computational costs. In this paper, to achieve both high performance and low computational costs, we propose a novel architecture with the **co**mbination of **s**elective **m**em**o**rization (COSMO), which integrates state-space modules (SSMs) and transformer modules. However, direct application of SSMs in VLN results in significant performance degradation. Therefore, we propose two VLN-customized selective state space modules: the Round Selective Scan (RSS) and the Cross-modal Selective State Space Module (CS3). RSS facilitates comprehensive inter-modal interactions within a single scan, while the CS3 module adapts the selective state space module into a dual-stream architecture, thereby enhancing the acquisition of cross-modal interactions. Experimental validations on three mainstream VLN benchmarks, REVERIE, R2R, and R2R-CE, not only demonstrate competitive navigation performance of our model but also show a significant reduction in computational costs. Code is available at \href https://github.com/siqiZ805/VLN-COSMO.git VLN-COSMO .
|
2503.24065
| null | null | null |
[] |
[] |
[] |
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] |
177
|
STI-Bench: Are MLLMs Ready for Precise Spatial-Temporal World Understanding?
|
[
"Yun Li",
"Yiming Zhang",
"Tao Lin",
"Xiangrui Liu",
"Wenxiao Cai",
"Zheng Liu",
"Bo Zhao"
] |
https://openaccess.thecvf.com/content/ICCV2025/html/Li_STI-Bench_Are_MLLMs_Ready_for_Precise_Spatial-Temporal_World_Understanding_ICCV_2025_paper.html
|
https://openaccess.thecvf.com/content/ICCV2025/papers/Li_STI-Bench_Are_MLLMs_Ready_for_Precise_Spatial-Temporal_World_Understanding_ICCV_2025_paper.pdf
| null |
@InProceedings{Li_2025_ICCV,
author = {Li, Yun and Zhang, Yiming and Lin, Tao and Liu, Xiangrui and Cai, Wenxiao and Liu, Zheng and Zhao, Bo},
title = {STI-Bench: Are MLLMs Ready for Precise Spatial-Temporal World Understanding?},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2025},
pages = {5622-5632}
}
|
The use of Multimodal Large Language Models (MLLMs) as an end-to-end solution for Embodied AI and Autonomous Driving has become a prevailing trend. While MLLMs have been extensively studied for visual semantic understanding tasks, their ability to perform precise and quantitative spatial-temporal understanding in real-world applications remains largely unexamined, leading to uncertain prospects. To address this gap, we introduce ST-Bench, a benchmark designed to evaluate MLLMs' spatial-temporal understanding through challenging tasks such as estimating and predicting the appearance, pose, displacement, and motion of objects. Our benchmark encompasses a wide range of robot and vehicle operations across desktop, indoor, and outdoor scenarios. The extensive experiments reveals that the state-of-the-art MLLMs still struggle in real-world spatial-temporal understanding, especially in tasks requiring precise distance estimation and motion analysis.
| null | null | null |
https://mint-sjtu.github.io/STI-Bench.io/
|
[] |
[] |
[] |
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] |
178
|
Describe, Adapt and Combine: Empowering CLIP Encoders for Open-set 3D Object Retrieval
|
[
"Zhichuan Wang",
"Yang Zhou",
"Zhe Liu",
"Rui Yu",
"Song Bai",
"Yulong Wang",
"Xinwei He",
"Xiang Bai"
] |
https://openaccess.thecvf.com/content/ICCV2025/html/Wang_Describe_Adapt_and_Combine_Empowering_CLIP_Encoders_for_Open-set_3D_ICCV_2025_paper.html
|
https://openaccess.thecvf.com/content/ICCV2025/papers/Wang_Describe_Adapt_and_Combine_Empowering_CLIP_Encoders_for_Open-set_3D_ICCV_2025_paper.pdf
|
https://openaccess.thecvf.com/content/ICCV2025/supplemental/Wang_Describe_Adapt_and_ICCV_2025_supplemental.pdf
|
@InProceedings{Wang_2025_ICCV,
author = {Wang, Zhichuan and Zhou, Yang and Liu, Zhe and Yu, Rui and Bai, Song and Wang, Yulong and He, Xinwei and Bai, Xiang},
title = {Describe, Adapt and Combine: Empowering CLIP Encoders for Open-set 3D Object Retrieval},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2025},
pages = {21026-21036}
}
|
Open-set 3D object retrieval (3DOR) is an emerging task aiming to retrieve 3D objects of unseen categories beyond the training set. Existing methods typically utilize all modalities (i.e., voxels, point clouds, multi-view images) and train specific backbones before fusion. However, they still struggle to produce generalized representations due to insufficient 3D training data. Being contrastively pre-trained on web-scale image-text pairs, CLIP inherently produces generalized representations for a wide range of downstream tasks. Building upon it, we present a simple yet effective framework named Describe, Adapt and Combine (DAC) by taking only multi-view images for open-set 3DOR. DAC innovatively synergizes a CLIP model with a multi-modal large language model (MLLM) to learn generalized 3D representations, where the MLLM is used for dual purposes. First, it describes the seen category information to align with CLIP's training objective for adaptation during training. Second, it provides external hints about unknown objects complementary to visual cues during inference. To improve the synergy, we introduce an Additive-Bias Low-Rank adaptation (AB-LoRA), which alleviates overfitting and further enhances the generalization to unseen categories. With only multi-view images, DAC significantly surpasses prior arts by an average of +10.01% mAP on four open-set 3DOR datasets. Moreover, its generalization is also validated on image-based and cross-dataset setups. Code is available at https://github.com/wangzhichuan123/DAC.
|
2507.21489
|
Accepted to ICCV 2025
|
https://github.com/wangzhichuan123/DAC
| null |
[] |
[] |
[] |
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] |
179
|
Embodied Image Captioning: Self-supervised Learning Agents for Spatially Coherent Image Descriptions
|
[
"Tommaso Galliena",
"Tommaso Apicella",
"Stefano Rosa",
"Pietro Morerio",
"Alessio Del Bue",
"Lorenzo Natale"
] |
https://openaccess.thecvf.com/content/ICCV2025/html/Galliena_Embodied_Image_Captioning_Self-supervised_Learning_Agents_for_Spatially_Coherent_Image_ICCV_2025_paper.html
|
https://openaccess.thecvf.com/content/ICCV2025/papers/Galliena_Embodied_Image_Captioning_Self-supervised_Learning_Agents_for_Spatially_Coherent_Image_ICCV_2025_paper.pdf
|
https://openaccess.thecvf.com/content/ICCV2025/supplemental/Galliena_Embodied_Image_Captioning_ICCV_2025_supplemental.pdf
|
@InProceedings{Galliena_2025_ICCV,
author = {Galliena, Tommaso and Apicella, Tommaso and Rosa, Stefano and Morerio, Pietro and Del Bue, Alessio and Natale, Lorenzo},
title = {Embodied Image Captioning: Self-supervised Learning Agents for Spatially Coherent Image Descriptions},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2025},
pages = {24370-24379}
}
|
We present a self-supervised method to improve an agent's abilities in describing arbitrary objects while actively exploring a generic environment. This is a challenging problem, as current models struggle to obtain coherent image captions due to different camera viewpoints and clutter. We propose a three-phase framework to fine-tune existing captioning models that enhances caption accuracy and consistency across views via a consensus mechanism. First, an agent explores the environment, collecting noisy image-caption pairs. Then, a consistent pseudo-caption for each object instance is distilled via consensus using a large language model. Finally, these pseudo-captions are used to fine-tune an off-the-shelf captioning model, with the addition of contrastive learning. We analyse the performance of the combination of captioning models, exploration policies, pseudo-labeling methods, and fine-tuning strategies, on our manually labeled test set. Results show that a policy can be trained to mine samples with higher disagreement compared to classical baselines. Our pseudo-captioning method, in combination with all policies, has a higher semantic similarity compared to other existing methods, and fine-tuning improves caption accuracy and consistency by a significant margin. Code and test set annotations available at https://hsp-iit.github.io/embodied-captioning.
|
2504.08531
|
11 pages, 8 figures, 5 tables, code and test set annotations
available at https://hsp-iit.github.io/embodied-captioning/
| null |
https://hsp-iit.github.io/embodied-captioning/
|
[] |
[] |
[
"TommyBsk/Embodied-Captioning"
] |
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] |
180
|
CAD-Assistant: Tool-Augmented VLLMs as Generic CAD Task Solvers
|
[
"Dimitrios Mallis",
"Ahmet Serda Karadeniz",
"Sebastian Cavada",
"Danila Rukhovich",
"Niki Foteinopoulou",
"Kseniya Cherenkova",
"Anis Kacem",
"Djamila Aouada"
] |
https://openaccess.thecvf.com/content/ICCV2025/html/Mallis_CAD-Assistant_Tool-Augmented_VLLMs_as_Generic_CAD_Task_Solvers_ICCV_2025_paper.html
|
https://openaccess.thecvf.com/content/ICCV2025/papers/Mallis_CAD-Assistant_Tool-Augmented_VLLMs_as_Generic_CAD_Task_Solvers_ICCV_2025_paper.pdf
|
https://openaccess.thecvf.com/content/ICCV2025/supplemental/Mallis_CAD-Assistant_Tool-Augmented_VLLMs_ICCV_2025_supplemental.pdf
|
@InProceedings{Mallis_2025_ICCV,
author = {Mallis, Dimitrios and Karadeniz, Ahmet Serda and Cavada, Sebastian and Rukhovich, Danila and Foteinopoulou, Niki and Cherenkova, Kseniya and Kacem, Anis and Aouada, Djamila},
title = {CAD-Assistant: Tool-Augmented VLLMs as Generic CAD Task Solvers},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2025},
pages = {7284-7294}
}
|
We propose CAD-Assistant, a general-purpose CAD agent for AI-assisted design. Our approach is based on a powerful Vision and Large Language Model (VLLM) as a planner and a tool-augmentation paradigm using CAD-specific tools. CAD-Assistant addresses multimodal user queries by generating actions that are iteratively executed on a Python interpreter equipped with the FreeCAD software, accessed via its Python API. Our framework is able to assess the impact of generated CAD commands on geometry and adapts subsequent actions based on the evolving state of the CAD design. We consider a wide range of CAD-specific tools including a sketch image parameterizer, rendering modules, a 2D cross-section generator, and other specialized routines. CAD-Assistant is evaluated on multiple CAD benchmarks, where it outperforms VLLM baselines and supervised task-specific methods. Beyond existing benchmarks, we qualitatively demonstrate the potential of tool-augmented VLLMs as general-purpose CAD solvers across diverse workflows.
| null | null | null | null |
[] |
[] |
[] |
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] |
181
|
Fast Image Super-Resolution via Consistency Rectified Flow
|
[
"Jiaqi Xu",
"Wenbo Li",
"Haoze Sun",
"Fan Li",
"Zhixin Wang",
"Long Peng",
"Jingjing Ren",
"Haoran Yang",
"Xiaowei Hu",
"Renjing Pei",
"Pheng-Ann Heng"
] |
https://openaccess.thecvf.com/content/ICCV2025/html/Xu_Fast_Image_Super-Resolution_via_Consistency_Rectified_Flow_ICCV_2025_paper.html
|
https://openaccess.thecvf.com/content/ICCV2025/papers/Xu_Fast_Image_Super-Resolution_via_Consistency_Rectified_Flow_ICCV_2025_paper.pdf
|
https://openaccess.thecvf.com/content/ICCV2025/supplemental/Xu_Fast_Image_Super-Resolution_ICCV_2025_supplemental.pdf
|
@InProceedings{Xu_2025_ICCV,
author = {Xu, Jiaqi and Li, Wenbo and Sun, Haoze and Li, Fan and Wang, Zhixin and Peng, Long and Ren, Jingjing and Yang, Haoran and Hu, Xiaowei and Pei, Renjing and Heng, Pheng-Ann},
title = {Fast Image Super-Resolution via Consistency Rectified Flow},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2025},
pages = {11755-11765}
}
|
Diffusion models (DMs) have demonstrated remarkable success in real-world image super-resolution (SR), yet their reliance on time-consuming multi-step sampling largely hinders their practical applications. While recent efforts have introduced few- or single-step solutions, existing methods either inefficiently model the process from noisy input or fail to fully exploit iterative generative priors, compromising the fidelity and quality of the reconstructed images. To address this issue, we propose FlowSR, a novel approach that reformulates the SR problem as a rectified flow from low-resolution (LR) to high-resolution (HR) images. Our method leverages an improved consistency learning strategy to enable high-quality SR in a single step. Specifically, we refine the original consistency distillation process by incorporating HR regularization, ensuring that the learned SR flow not only enforces self-consistency but also converges precisely to the ground-truth HR target. Furthermore, we introduce a fast-slow scheduling strategy, where adjacent timesteps for consistency learning are sampled from two distinct schedulers: a fast scheduler with fewer timesteps to improve efficiency, and a slow scheduler with more timesteps to capture fine-grained texture details. Extensive experiments demonstrate that FlowSR achieves outstanding performance in both efficiency and image quality.
| null | null | null | null |
[] |
[] |
[] |
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] |
182
|
Adapt Foundational Segmentation Models with Heterogeneous Searching Space
|
[
"Li Yi",
"Jie Hu",
"Songan Zhang",
"Guannan Jiang"
] |
https://openaccess.thecvf.com/content/ICCV2025/html/Yi_Adapt_Foundational_Segmentation_Models_with_Heterogeneous_Searching_Space_ICCV_2025_paper.html
|
https://openaccess.thecvf.com/content/ICCV2025/papers/Yi_Adapt_Foundational_Segmentation_Models_with_Heterogeneous_Searching_Space_ICCV_2025_paper.pdf
|
https://openaccess.thecvf.com/content/ICCV2025/supplemental/Yi_Adapt_Foundational_Segmentation_ICCV_2025_supplemental.zip
|
@InProceedings{Yi_2025_ICCV,
author = {Yi, Li and Hu, Jie and Zhang, Songan and Jiang, Guannan},
title = {Adapt Foundational Segmentation Models with Heterogeneous Searching Space},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2025},
pages = {23364-23373}
}
|
Foundation Segmentation Models (FSMs) show suboptimal performance on unconventional image domains like camouflage objects. Fine-tuning is often impractical due to data preparation challenges, time limits, and optimization issues. To boost segmentation performance while keeping zero-shot features, one approach is pre-augmenting images for the segmentation model. However, existing image augmentations mainly depend on rule-based methods, restricting augmentation effectiveness. Though learning-based methods can diversify augmentation, rule-based ones are degree-describable (e.g., slight/intense brightening), while learning-based methods usually predict non-degree-describable ground truths (e.g., depth estimation), creating a heterogeneous search space when combined. To this end, we propose an "Augmenting-to-Adapt" paradigm, replacing traditional rule-based augmentation with an optimal heterogeneous augmentation policy to enhance segmentation. Our method uses 32 augmentation techniques (22 rule-based, 10 learning-based) to ease parameter misalignment, forming a robust, multi-discrete heterogeneous search space.To apply the optimal policy in real-world scenarios, we distill the augmentation process to speed up the preprocess. Extensive evaluations across diverse datasets and domains show our method significantly improves model adaptation with a domain-specific augmentation strategy. We will release our code to support further research.
| null | null | null | null |
[] |
[] |
[] |
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Adversarial Exploitation of Data Diversity Improves Visual Localization
|
[
"Sihang Li",
"Siqi Tan",
"Bowen Chang",
"Jing Zhang",
"Chen Feng",
"Yiming Li"
] |
https://openaccess.thecvf.com/content/ICCV2025/html/Li_Adversarial_Exploitation_of_Data_Diversity_Improves_Visual_Localization_ICCV_2025_paper.html
|
https://openaccess.thecvf.com/content/ICCV2025/papers/Li_Adversarial_Exploitation_of_Data_Diversity_Improves_Visual_Localization_ICCV_2025_paper.pdf
|
https://openaccess.thecvf.com/content/ICCV2025/supplemental/Li_Adversarial_Exploitation_of_ICCV_2025_supplemental.pdf
|
@InProceedings{Li_2025_ICCV,
author = {Li, Sihang and Tan, Siqi and Chang, Bowen and Zhang, Jing and Feng, Chen and Li, Yiming},
title = {Adversarial Exploitation of Data Diversity Improves Visual Localization},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2025},
pages = {26848-26858}
}
|
Visual localization, which estimates a camera's pose within a known scene, is a fundamental capability for autonomous systems. While absolute pose regression (APR) methods have shown promise for efficient inference, they often struggle with generalization. Recent approaches attempt to address this through data augmentation with varied viewpoints, yet they overlook a critical factor: appearance diversity.In this work, we identify appearance variation as the key to robust localization. Specifically, we first lift real 2D images into 3D Gaussian Splats with varying appearance and deblurring capabilities, enabling the synthesis of diverse training data that varies not just in poses but also in environmental conditions such as lighting and weather. To fully unleash the potential of the appearance-diverse data, we build a two-branch joint training pipeline with an adversarial discriminator to bridge the syn-to-real gap.Extensive experiments demonstrate that our approach significantly outperforms state-of-the-art methods, reducing translation and rotation errors by 50% and 22% on indoor datasets, and 37% and 42% on outdoor datasets. Most notably, our method shows remarkable robustness in dynamic driving scenarios under varying weather conditions and in day-to-night scenarios, where previous APR methods fail.
| null | null | null | null |
[] |
[] |
[] |
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] |
184
|
Learning Implicit Features with Flow-Infused Transformations for Realistic Virtual Try-On
|
[
"Delong Zhang",
"Qiwei Huang",
"Yang Sun",
"Yuanliu Liu",
"Wei-Shi Zheng",
"Pengfei Xiong",
"Wei Zhang"
] |
https://openaccess.thecvf.com/content/ICCV2025/html/Zhang_Learning_Implicit_Features_with_Flow-Infused_Transformations_for_Realistic_Virtual_Try-On_ICCV_2025_paper.html
|
https://openaccess.thecvf.com/content/ICCV2025/papers/Zhang_Learning_Implicit_Features_with_Flow-Infused_Transformations_for_Realistic_Virtual_Try-On_ICCV_2025_paper.pdf
|
https://openaccess.thecvf.com/content/ICCV2025/supplemental/Zhang_Learning_Implicit_Features_ICCV_2025_supplemental.pdf
|
@InProceedings{Zhang_2025_ICCV,
author = {Zhang, Delong and Huang, Qiwei and Sun, Yang and Liu, Yuanliu and Zheng, Wei-Shi and Xiong, Pengfei and Zhang, Wei},
title = {Learning Implicit Features with Flow-Infused Transformations for Realistic Virtual Try-On},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2025},
pages = {18736-18745}
}
|
Diffusion-based virtual try-on aims to synthesize a realistic image that seamlessly integrating the specific garment into a target model. The primary challenge lies in effectively guiding the warping process of the latent diffusion model. However, previous methods either lack direct guidance or explicitly warp the garment image, which highly depends on the performance of the warping module. In this paper, we propose FIA-VTON, which leverages the implicit flow feature as guidance by adopting a Flow Infused Attention module on virtual try-on. The dense warp flow map is projected as indirect guidance to enhance the feature map warping in the generation process implicitly, which is less sensitive to the warping estimation accuracy than an explicit warp of the garment image. To further enhance implicit warp guidance, we incorporate high-level spatial attention to complement the dense warp. Experimental results on the VTON-HD and DressCode dataset significantly outperform state-of-the-art methods, demonstrating that FIA-VTON is effective and robust for virtual try-on.
| null | null | null | null |
[] |
[] |
[] |
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] |
185
|
BoxDreamer: Dreaming Box Corners for Generalizable Object Pose Estimation
|
[
"Yuanhong Yu",
"Xingyi He",
"Chen Zhao",
"Junhao Yu",
"Jiaqi Yang",
"Ruizhen Hu",
"Yujun Shen",
"Xing Zhu",
"Xiaowei Zhou",
"Sida Peng"
] |
https://openaccess.thecvf.com/content/ICCV2025/html/Yu_BoxDreamer_Dreaming_Box_Corners_for_Generalizable_Object_Pose_Estimation_ICCV_2025_paper.html
|
https://openaccess.thecvf.com/content/ICCV2025/papers/Yu_BoxDreamer_Dreaming_Box_Corners_for_Generalizable_Object_Pose_Estimation_ICCV_2025_paper.pdf
|
https://openaccess.thecvf.com/content/ICCV2025/supplemental/Yu_BoxDreamer_Dreaming_Box_ICCV_2025_supplemental.zip
|
@InProceedings{Yu_2025_ICCV,
author = {Yu, Yuanhong and He, Xingyi and Zhao, Chen and Yu, Junhao and Yang, Jiaqi and Hu, Ruizhen and Shen, Yujun and Zhu, Xing and Zhou, Xiaowei and Peng, Sida},
title = {BoxDreamer: Dreaming Box Corners for Generalizable Object Pose Estimation},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2025},
pages = {9374-9384}
}
|
This paper presents a generalizable RGB-based approach for object pose estimation, specifically designed to address challenges in sparse-view settings. While existing methods can estimate the poses of unseen objects, their generalization ability remains limited in scenarios involving occlusions and sparse reference views, restricting their real-world applicability. To overcome these limitations, we introduce corner points of the object bounding box as an intermediate representation of the object pose. The 3D object corners can be reliably recovered from sparse input views, while the 2D corner points in the target view are estimated through a novel reference-based point synthesizer, which works well even in scenarios involving occlusions. As object semantic points, object corners naturally establish 2D-3D correspondences for object pose estimation with a PnP algorithm. Extensive experiments on the YCB-Video and Occluded-LINEMOD datasets show that our approach outperforms state-of-the-art methods, highlighting the effectiveness of the proposed representation and significantly enhancing the generalization capabilities of object pose estimation, which is crucial for real-world applications.
|
2504.07955
|
Project page: https://zju3dv.github.io/boxdreamer
| null | null |
[] |
[] |
[] |
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] |
186
|
LawDIS: Language-Window-based Controllable Dichotomous Image Segmentation
|
[
"Xinyu Yan",
"Meijun Sun",
"Ge-Peng Ji",
"Fahad Shahbaz Khan",
"Salman Khan",
"Deng-Ping Fan"
] |
https://openaccess.thecvf.com/content/ICCV2025/html/Yan_LawDIS_Language-Window-based_Controllable_Dichotomous_Image_Segmentation_ICCV_2025_paper.html
|
https://openaccess.thecvf.com/content/ICCV2025/papers/Yan_LawDIS_Language-Window-based_Controllable_Dichotomous_Image_Segmentation_ICCV_2025_paper.pdf
|
https://openaccess.thecvf.com/content/ICCV2025/supplemental/Yan_LawDIS_Language-Window-based_Controllable_ICCV_2025_supplemental.zip
|
@InProceedings{Yan_2025_ICCV,
author = {Yan, Xinyu and Sun, Meijun and Ji, Ge-Peng and Khan, Fahad Shahbaz and Khan, Salman and Fan, Deng-Ping},
title = {LawDIS: Language-Window-based Controllable Dichotomous Image Segmentation},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2025},
pages = {23902-23911}
}
|
We present LawDIS, a language-window-based controllable dichotomous image segmentation (DIS) framework that produces high-quality object masks. Our framework recasts DIS as an image-conditioned mask generation task within a latent diffusion model, enabling seamless integration of user controls. LawDIS is enhanced with macro-to-micro control modes. Specifically, in macro mode, we introduce a language-controlled segmentation strategy (LS) to generate an initial mask based on user-provided language prompts. In micro mode, a window-controlled refinement strategy (WR) allows flexible refinement of user-defined regions (i.e., size-adjustable windows) within the initial mask. Coordinated by a mode switcher, these modes can operate independently or jointly, making the framework well-suited for high-accuracy, personalised applications. Extensive experiments on the DIS5K benchmark reveal that our LawDIS significantly outperforms 11 cutting-edge methods across all metrics. Notably, compared to the second-best model MVANet, we achieve weighted F-measure gains of 4.6% with both the LS and WR strategies and 3.6% gains with only the LS strategy on DIS-TE. Codes will be made available at https://github.com/XinyuYanTJU/LawDIS.
|
2508.01152
|
17 pages, 10 figures, ICCV 2025
|
https://github.com/XinyuYanTJU/LawDIS
| null |
[] |
[] |
[] |
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] |
187
|
3DGraphLLM: Combining Semantic Graphs and Large Language Models for 3D Scene Understanding
|
[
"Tatiana Zemskova",
"Dmitry Yudin"
] |
https://openaccess.thecvf.com/content/ICCV2025/html/Zemskova_3DGraphLLM_Combining_Semantic_Graphs_and_Large_Language_Models_for_3D_ICCV_2025_paper.html
|
https://openaccess.thecvf.com/content/ICCV2025/papers/Zemskova_3DGraphLLM_Combining_Semantic_Graphs_and_Large_Language_Models_for_3D_ICCV_2025_paper.pdf
|
https://openaccess.thecvf.com/content/ICCV2025/supplemental/Zemskova_3DGraphLLM_Combining_Semantic_ICCV_2025_supplemental.pdf
|
@InProceedings{Zemskova_2025_ICCV,
author = {Zemskova, Tatiana and Yudin, Dmitry},
title = {3DGraphLLM: Combining Semantic Graphs and Large Language Models for 3D Scene Understanding},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2025},
pages = {8885-8895}
}
|
A 3D scene graph represents a compact scene model by capturing both the objects present and the semantic relationships between them, making it a promising structure for robotic applications. To effectively interact with users, an embodied intelligent agent should be able to answer a wide range of natural language queries about the surrounding 3D environment. Large Language Models (LLMs) are beneficial solutions for user-robot interaction due to their natural language understanding and reasoning abilities. Recent methods for learning scene representations have shown that adapting these representations to the 3D world can significantly improve the quality of LLM responses. However, existing methods typically rely only on geometric information, such as object coordinates, and overlook the rich semantic relationships between objects. In this work, we propose 3DGraphLLM, a method for constructing a learnable representation of a 3D scene graph that explicitly incorporates semantic relationships. This representation is used as input to LLMs for performing 3D vision-language tasks. In our experiments on popular ScanRefer, Multi3DRefer, ScanQA, Sqa3D, and Scan2cap datasets, we demonstrate that our approach outperforms baselines that do not leverage semantic relationships between objects. The code is publicly available at https://github.com/CognitiveAISystems/3DGraphLLM.
|
2412.18450
| null |
https://github.com/CognitiveAISystems/3DGraphLLM
| null |
[] |
[
"wingrune/3DGraphLLM"
] |
[] |
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] |
188
|
FineMotion: A Dataset and Benchmark with both Spatial and Temporal Annotation for Fine-grained Motion Generation and Editing
|
[
"Bizhu Wu",
"Jinheng Xie",
"Meidan Ding",
"Zhe Kong",
"Jianfeng Ren",
"Ruibin Bai",
"Rong Qu",
"Linlin Shen"
] |
https://openaccess.thecvf.com/content/ICCV2025/html/Wu_FineMotion_A_Dataset_and_Benchmark_with_both_Spatial_and_Temporal_ICCV_2025_paper.html
|
https://openaccess.thecvf.com/content/ICCV2025/papers/Wu_FineMotion_A_Dataset_and_Benchmark_with_both_Spatial_and_Temporal_ICCV_2025_paper.pdf
|
https://openaccess.thecvf.com/content/ICCV2025/supplemental/Wu_FineMotion_A_Dataset_ICCV_2025_supplemental.zip
|
@InProceedings{Wu_2025_ICCV,
author = {Wu, Bizhu and Xie, Jinheng and Ding, Meidan and Kong, Zhe and Ren, Jianfeng and Bai, Ruibin and Qu, Rong and Shen, Linlin},
title = {FineMotion: A Dataset and Benchmark with both Spatial and Temporal Annotation for Fine-grained Motion Generation and Editing},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2025},
pages = {13837-13846}
}
|
Generating realistic human motions from textual descriptions has undergone significant advancements. However, existing methods often overlook specific body part movements and their timing. In this paper, we address this issue by enriching the textual description with more details. Specifically, we propose the FineMotion dataset, which contains over 442,000 human motion snippets -- short segments of human motion sequences -- and their corresponding detailed descriptions of human body part movements. Additionally, the dataset includes about 95k detailed paragraphs describing the movements of human body parts in entire motion sequences. Experimental results demonstrate the significance of our dataset on the text-driven fine-grained human motion generation task, especially with a remarkable +15.3% improvement in Top-3 accuracy for the MDM model. Notably, we further support a zero-shot pipeline of fine-grained motion editing, which focuses on detailed editing in both spatial and temporal dimensions via text. Dataset and code available at: CVI-SZU/FineMotion
|
2507.19850
| null | null | null |
[] |
[] |
[] |
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] |
189
|
MC-Bench: A Benchmark for Multi-Context Visual Grounding in the Era of MLLMs
|
[
"Yunqiu Xu",
"Linchao Zhu",
"Yi Yang"
] |
https://openaccess.thecvf.com/content/ICCV2025/html/Xu_MC-Bench_A_Benchmark_for_Multi-Context_Visual_Grounding_in_the_Era_ICCV_2025_paper.html
|
https://openaccess.thecvf.com/content/ICCV2025/papers/Xu_MC-Bench_A_Benchmark_for_Multi-Context_Visual_Grounding_in_the_Era_ICCV_2025_paper.pdf
|
https://openaccess.thecvf.com/content/ICCV2025/supplemental/Xu_MC-Bench_A_Benchmark_ICCV_2025_supplemental.pdf
|
@InProceedings{Xu_2025_ICCV,
author = {Xu, Yunqiu and Zhu, Linchao and Yang, Yi},
title = {MC-Bench: A Benchmark for Multi-Context Visual Grounding in the Era of MLLMs},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2025},
pages = {17675-17687}
}
|
While multimodal large language models (MLLMs) have demonstrated extraordinary vision-language understanding capabilities, their abilities to solve instance-level visual-language problems beyond a single image warrant further exploration. To assess these unproven abilities of MLLMs, this paper proposes a new visual grounding task called multi-context visual grounding, which aims to localize instances of interest across multiple images based on open-ended text prompts. In order to facilitate this research, we construct a new dataset MC-Bench that features 2K high-quality and manually annotated samples. Each sample consists of an instance-level labeled image pair and a corresponding text prompt that indicates the target instances in the images. These text prompts are highly open-ended and follow three distinct styles, covering 20 practical skills. We benchmark over 20 state-of-the-art MLLMs and foundation models with potential multi-context visual grounding capabilities, along with our developed simple yet effective agentic baseline and a finetuned baseline by multi-context instruction tuning. Our evaluation reveals a non-trivial performance gap between existing MLLMs and humans, along with some insightful observations that suggest potential future directions. We hope that MC-Bench and our empirical findings encourage the research community to further advance the untapped potentials of MLLMs in instance-level tasks, particularly in multi-image contexts. Project page: https://xuyunqiu.github.io/MC-Bench.
| null | null | null | null |
[] |
[] |
[] |
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] |
190
|
What Makes for Text to 360-degree Panorama Generation with Stable Diffusion?
|
[
"Jinhong Ni",
"Chang-Bin Zhang",
"Qiang Zhang",
"Jing Zhang"
] |
https://openaccess.thecvf.com/content/ICCV2025/html/Ni_What_Makes_for_Text_to_360-degree_Panorama_Generation_with_Stable_ICCV_2025_paper.html
|
https://openaccess.thecvf.com/content/ICCV2025/papers/Ni_What_Makes_for_Text_to_360-degree_Panorama_Generation_with_Stable_ICCV_2025_paper.pdf
|
https://openaccess.thecvf.com/content/ICCV2025/supplemental/Ni_What_Makes_for_ICCV_2025_supplemental.pdf
|
@InProceedings{Ni_2025_ICCV,
author = {Ni, Jinhong and Zhang, Chang-Bin and Zhang, Qiang and Zhang, Jing},
title = {What Makes for Text to 360-degree Panorama Generation with Stable Diffusion?},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2025},
pages = {16555-16564}
}
|
Recent prosperity of text-to-image diffusion models, e.g. Stable Diffusion, has stimulated research to adapt them to 360-degree panorama generation. Prior work has demonstrated the feasibility of using conventional low-rank adaptation techniques on pre-trained diffusion models to generate panoramic images. However, the substantial domain gap between perspective and panoramic images raises questions about the underlying mechanisms enabling this empirical success. We hypothesize and examine that the trainable counterparts exhibit distinct behaviors when fine-tuned on panoramic data, and such an adaptation conceals some intrinsic mechanism to leverage the prior knowledge within the pre-trained diffusion models. Our analysis reveals the following: 1) the query and key matrices in the attention modules are responsible for common information that can be shared between the panoramic and perspective domains, thus are less relevant to panorama generation; and 2) the value and output weight matrices specialize in adapting pre-trained knowledge to the panoramic domain, playing a more critical role during fine-tuning for panorama generation. We empirically verify these insights by introducing a simple framework called UniPano, with the objective of establishing an elegant baseline for future research. UniPano not only outperforms existing methods but also significantly reduces memory usage and training time compared to prior dual-branch approaches, making it scalable for end-to-end panorama generation with higher resolution. The code is available.
|
2505.22129
| null | null | null |
[] |
[] |
[] |
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] |
191
|
CHORDS: Diffusion Sampling Accelerator with Multi-core Hierarchical ODE Solvers
|
[
"Jiaqi Han",
"Haotian Ye",
"Puheng Li",
"Minkai Xu",
"James Zou",
"Stefano Ermon"
] |
https://openaccess.thecvf.com/content/ICCV2025/html/Han_CHORDS_Diffusion_Sampling_Accelerator_with_Multi-core_Hierarchical_ODE_Solvers_ICCV_2025_paper.html
|
https://openaccess.thecvf.com/content/ICCV2025/papers/Han_CHORDS_Diffusion_Sampling_Accelerator_with_Multi-core_Hierarchical_ODE_Solvers_ICCV_2025_paper.pdf
|
https://openaccess.thecvf.com/content/ICCV2025/supplemental/Han_CHORDS_Diffusion_Sampling_ICCV_2025_supplemental.pdf
|
@InProceedings{Han_2025_ICCV,
author = {Han, Jiaqi and Ye, Haotian and Li, Puheng and Xu, Minkai and Zou, James and Ermon, Stefano},
title = {CHORDS: Diffusion Sampling Accelerator with Multi-core Hierarchical ODE Solvers},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2025},
pages = {19386-19395}
}
|
Diffusion-based generative models have become dominant generators of high-fidelity images and videos but remain limited by their computationally expensive inference procedures. Existing acceleration techniques either require extensive model retraining or compromise significantly on sample quality. This paper explores a general, training-free, and model-agnostic acceleration strategy via multi-core parallelism. Our framework views multi-core diffusion sampling as an ODE solver pipeline, where slower yet accurate solvers progressively rectify faster solvers through a theoretically justified inter-core communication mechanism. This motivates our multi-core training-free diffusion sampling accelerator, CHORDS, which is compatible with various diffusion samplers, model architectures, and modalities. Through extensive experiments, CHORDS significantly accelerates sampling across diverse large-scale image and video diffusion models, yielding up to 2.1x speedup with four cores, improving by 50% over baselines, and 2.9x speedup with eight cores, all without quality degradation. This advancement enables CHORDS to establish a solid foundation for real-time, high-fidelity diffusion generation.
|
2507.15260
|
ICCV 2025
| null | null |
[] |
[] |
[] |
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] |
192
|
HERMES: temporal-coHERent long-forM understanding with Episodes and Semantics
|
[
"Gueter Josmy Faure",
"Jia-Fong Yeh",
"Min-Hung Chen",
"Hung-Ting Su",
"Shang-Hong Lai",
"Winston H. Hsu"
] |
https://openaccess.thecvf.com/content/ICCV2025/html/Faure_HERMES_temporal-coHERent_long-forM_understanding_with_Episodes_and_Semantics_ICCV_2025_paper.html
|
https://openaccess.thecvf.com/content/ICCV2025/papers/Faure_HERMES_temporal-coHERent_long-forM_understanding_with_Episodes_and_Semantics_ICCV_2025_paper.pdf
|
https://openaccess.thecvf.com/content/ICCV2025/supplemental/Faure_HERMES_temporal-coHERent_long-forM_ICCV_2025_supplemental.pdf
|
@InProceedings{Faure_2025_ICCV,
author = {Faure, Gueter Josmy and Yeh, Jia-Fong and Chen, Min-Hung and Su, Hung-Ting and Lai, Shang-Hong and Hsu, Winston H.},
title = {HERMES: temporal-coHERent long-forM understanding with Episodes and Semantics},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2025},
pages = {22911-22921}
}
|
Long-form video understanding presents unique challenges that extend beyond traditional short-video analysis approaches, particularly in capturing long-range dependencies, processing redundant information efficiently, and extracting high-level semantic concepts. To address these challenges, we propose a novel approach that more accurately reflects human cognition. This paper introduces HERMES: temporal-coHERent long-forM understanding with Episodes and Semantics, featuring two versatile modules that can enhance existing video-language models or operate as a standalone system. Our Episodic COmpressor (ECO) efficiently aggregates representations from micro to semi-macro levels, reducing computational overhead while preserving temporal dependencies. Our Semantics ReTRiever (SeTR) enriches these representations with semantic information by focusing on broader context, dramatically reducing feature dimensionality while preserving relevant macro-level information. We demonstrate that these modules can be seamlessly integrated into existing SOTA models, consistently improving their performance while reducing inference latency by up to 43% and memory usage by 46%. As a standalone system, HERMES achieves state-of-the-art performance across multiple long-video understanding benchmarks in both zero-shot and fully-supervised settings. Our project page and code can be found at https://joslefaure.github.io/assets/html/hermes.html.
|
2408.17443
|
Accepted for ICCV 2025. Project page:
https://joslefaure.github.io/assets/html/hermes.html
| null |
https://joslefaure.github.io/assets/html/hermes.html
|
[] |
[] |
[] |
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] |
193
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CoralSRT: Revisiting Coral Reef Semantic Segmentation by Feature Rectification via Self-supervised Guidance
|
[
"Ziqiang Zheng",
"Yuk-Kwan Wong",
"Binh-Son Hua",
"Jianbo Shi",
"Sai-Kit Yeung"
] |
https://openaccess.thecvf.com/content/ICCV2025/html/Zheng_CoralSRT_Revisiting_Coral_Reef_Semantic_Segmentation_by_Feature_Rectification_via_ICCV_2025_paper.html
|
https://openaccess.thecvf.com/content/ICCV2025/papers/Zheng_CoralSRT_Revisiting_Coral_Reef_Semantic_Segmentation_by_Feature_Rectification_via_ICCV_2025_paper.pdf
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https://openaccess.thecvf.com/content/ICCV2025/supplemental/Zheng_CoralSRT_Revisiting_Coral_ICCV_2025_supplemental.zip
|
@InProceedings{Zheng_2025_ICCV,
author = {Zheng, Ziqiang and Wong, Yuk-Kwan and Hua, Binh-Son and Shi, Jianbo and Yeung, Sai-Kit},
title = {CoralSRT: Revisiting Coral Reef Semantic Segmentation by Feature Rectification via Self-supervised Guidance},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2025},
pages = {19967-19977}
}
|
We investigate coral reef semantic segmentation, in which coral reefs are governed by multifaceted factors, like genes, environmental changes, and internal interactions. Unlike segmenting structural units/instances, which are predictable and follow a set pattern, also referred to as commonsense or prior, segmenting coral reefs involves modeling self-repeated, asymmetric, and amorphous distribution of elements, e.g., corals can grow in almost any shape and appearance. We revisited existing segmentation approaches and found that both computer vision and coral reef communities failed to incorporate the intrinsic properties of corals into model design. In this work, we propose a simple formulation for coral reef semantic segmentation: we regard the segment as the basis to model both within-segment and cross-segment affinities. We propose CoralSRT, a feature rectification module via self-supervised guidance, to reduce the stochasticity of coral features extracted by powerful foundation models (FMs), as demonstrated in Fig. 1. We incorporate intrinsic properties of corals to strengthen within-segment affinity by guiding the features within generated segments to align with the centrality. We investigate features from FMs that were optimized by various pretext tasks on significantly large-scale unlabeled or labeled data, which already contain rich information for modeling both within-segment and cross-segment affinities, enabling the adaptation of FMs for coral segmentation. CoralSRT can rectify features from FMs to more efficient features for label propagation and lead to further significant semantic segmentation performance gains, all without requiring additional human supervision, retraining/finetuning FMs or even domain-specific data. These advantages help reduce human effort and the need for domain expertise in data collection and labeling. Our method is easy to implement, and also task- and model-agnostic. CoralSRT bridges the self-supervised pre-training and supervised training in the feature space, also offering insights for segmenting elements/stuffs (e.g., grass, plants, cells, and biofoulings).
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] |
194
|
VGMamba: Attribute-to-Location Clue Reasoning for Quantity-Agnostic 3D Visual Grounding
|
[
"Yihang Zhu",
"Jinhao Zhang",
"Yuxuan Wang",
"Aming Wu",
"Cheng Deng"
] |
https://openaccess.thecvf.com/content/ICCV2025/html/Zhu_VGMamba_Attribute-to-Location_Clue_Reasoning_for_Quantity-Agnostic_3D_Visual_Grounding_ICCV_2025_paper.html
|
https://openaccess.thecvf.com/content/ICCV2025/papers/Zhu_VGMamba_Attribute-to-Location_Clue_Reasoning_for_Quantity-Agnostic_3D_Visual_Grounding_ICCV_2025_paper.pdf
| null |
@InProceedings{Zhu_2025_ICCV,
author = {Zhu, Yihang and Zhang, Jinhao and Wang, Yuxuan and Wu, Aming and Deng, Cheng},
title = {VGMamba: Attribute-to-Location Clue Reasoning for Quantity-Agnostic 3D Visual Grounding},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2025},
pages = {5295-5304}
}
|
As an important direction of embodied intelligence, 3D Visual Grounding has attracted much attention, aiming to identify 3D objects matching the given language description. Most existing methods often follow a two-stage process, i.e., first detecting proposal objects and identifying the right objects based on the relevance to the given query. However, when the query is complex, it is difficult to leverage an abstract language representation to lock the corresponding objects accurately, affecting the grounding performance. In general, given a specific object, humans usually follow two clues to finish the corresponding grounding, i.e., attribute and location clues. To this end, we explore a new mechanism, attribute-to-location clue reasoning, to conduct accurate grounding. Particularly, we propose a VGMamba network that consists of an SVD-based attribute mamba, location mamba, and multi-modal fusion mamba. Taking a 3D point cloud scene and language query as the input, we first exploit SVD to make a decomposition of the extracted features. Then, a slidingwindow operation is conducted to capture attribute characteristics. Next, a location mamba is presented to obtain the corresponding location information. Finally, by means of multi-modal mamba fusion, the model could effectively localize the object that matches the given query. In the experiment, our method is verified on four datasets. Extensive experimental results demonstrate the superiority of our method.
| null | null | null | null |
[] |
[] |
[] |
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] |
195
|
Decouple and Track: Benchmarking and Improving Video Diffusion Transformers For Motion Transfer
|
[
"Qingyu Shi",
"Jianzong Wu",
"Jinbin Bai",
"Jiangning Zhang",
"Lu Qi",
"Yunhai Tong",
"Xiangtai Li"
] |
https://openaccess.thecvf.com/content/ICCV2025/html/Shi_Decouple_and_Track_Benchmarking_and_Improving_Video_Diffusion_Transformers_For_ICCV_2025_paper.html
|
https://openaccess.thecvf.com/content/ICCV2025/papers/Shi_Decouple_and_Track_Benchmarking_and_Improving_Video_Diffusion_Transformers_For_ICCV_2025_paper.pdf
|
https://openaccess.thecvf.com/content/ICCV2025/supplemental/Shi_Decouple_and_Track_ICCV_2025_supplemental.zip
|
@InProceedings{Shi_2025_ICCV,
author = {Shi, Qingyu and Wu, Jianzong and Bai, Jinbin and Zhang, Jiangning and Qi, Lu and Tong, Yunhai and Li, Xiangtai},
title = {Decouple and Track: Benchmarking and Improving Video Diffusion Transformers For Motion Transfer},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2025},
pages = {10995-11005}
}
|
The motion transfer task aims to transfer motion from a source video to newly generated videos, requiring the model to decouple motion from appearance. Previous diffusion-based methods primarily rely on separate spatial and temporal attention mechanisms within the 3D U-Net. In contrast, state-of-the-art video Diffusion Transformers (DiT) models use 3D full attention, which does not explicitly separate temporal and spatial information. Thus, the interaction between spatial and temporal dimensions makes decoupling motion and appearance more challenging for DiT models. In this paper, we propose DeT, a method that adapts DiT models to improve motion transfer ability. Our approach introduces a simple yet effective temporal kernel to smooth DiT features along the temporal dimension, facilitating the decoupling of foreground motion from background appearance. Meanwhile, the temporal kernel effectively captures temporal variations in DiT features, which are closely related to motion. Moreover, we introduce explicit supervision along dense trajectories in the latent feature space to further enhance motion consistency. Additionally, we present MTBench, a general and challenging benchmark for motion transfer. We also introduce a hybrid motion fidelity metric that considers both the global and local motion similarity. Therefore, our work provides a more comprehensive evaluation than previous works. Extensive experiments on MTBench demonstrate that DeT achieves the best trade-off between motion fidelity and edit fidelity.
|
2503.17350
|
ICCV 2025
| null |
https://shi-qingyu.github.io/DeT.github.io/
|
[] |
[] |
[] |
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] |
196
|
MaskControl: Spatio-Temporal Control for Masked Motion Synthesis
|
[
"Ekkasit Pinyoanuntapong",
"Muhammad Saleem",
"Korrawe Karunratanakul",
"Pu Wang",
"Hongfei Xue",
"Chen Chen",
"Chuan Guo",
"Junli Cao",
"Jian Ren",
"Sergey Tulyakov"
] |
https://openaccess.thecvf.com/content/ICCV2025/html/Pinyoanuntapong_MaskControl_Spatio-Temporal_Control_for_Masked_Motion_Synthesis_ICCV_2025_paper.html
|
https://openaccess.thecvf.com/content/ICCV2025/papers/Pinyoanuntapong_MaskControl_Spatio-Temporal_Control_for_Masked_Motion_Synthesis_ICCV_2025_paper.pdf
|
https://openaccess.thecvf.com/content/ICCV2025/supplemental/Pinyoanuntapong_MaskControl_Spatio-Temporal_Control_ICCV_2025_supplemental.pdf
|
@InProceedings{Pinyoanuntapong_2025_ICCV,
author = {Pinyoanuntapong, Ekkasit and Saleem, Muhammad and Karunratanakul, Korrawe and Wang, Pu and Xue, Hongfei and Chen, Chen and Guo, Chuan and Cao, Junli and Ren, Jian and Tulyakov, Sergey},
title = {MaskControl: Spatio-Temporal Control for Masked Motion Synthesis},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2025},
pages = {9955-9965}
}
|
Recent advances in motion diffusion models have enabled spatially controllable text-to-motion generation. However, these models struggle to achieve high-precision control while maintaining high-quality motion generation. To address these challenges, we propose MaskControl, the first approach to introduce controllability to the generative masked motion model. Our approach introduces two key innovations. First, Logits Regularizer implicitly perturbs logits at training time to align the distribution of motion tokens with the controlled joint positions, while regularizing the categorical token prediction to ensure high-fidelity generation. Second, Logit Optimization explicitly optimizes the predicted logits during inference time, directly reshaping the token distribution that forces the generated motion to accurately align with the controlled joint positions. Moreover, we introduce Differentiable Expectation Sampling (DES) to combat the non-differential distribution sampling process encountered by logits regularizer and optimization. Extensive experiments demonstrate that MaskControl outperforms state-of-the-art methods, achieving superior motion quality (FID decreases by 77%) and higher control precision (average error 0.91 vs. 1.08). Additionally, MaskControl enables diverse applications, including any-joint-any-frame control, body-part timeline control, and zero-shot objective control. Video visualization can be found at https://anonymous-ai-agent.github.io/CAM
|
2410.10780
|
Camera Ready Version. ICCV2025 (Oral). Change name from ControlMM to
MaskControl. project page https://exitudio.github.io/ControlMM-page
| null | null |
[] |
[] |
[] |
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] |
197
|
Evidential Knowledge Distillation
|
[
"Liangyu Xiang",
"Junyu Gao",
"Changsheng Xu"
] |
https://openaccess.thecvf.com/content/ICCV2025/html/Xiang_Evidential_Knowledge_Distillation_ICCV_2025_paper.html
|
https://openaccess.thecvf.com/content/ICCV2025/papers/Xiang_Evidential_Knowledge_Distillation_ICCV_2025_paper.pdf
|
https://openaccess.thecvf.com/content/ICCV2025/supplemental/Xiang_Evidential_Knowledge_Distillation_ICCV_2025_supplemental.pdf
|
@InProceedings{Xiang_2025_ICCV,
author = {Xiang, Liangyu and Gao, Junyu and Xu, Changsheng},
title = {Evidential Knowledge Distillation},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2025},
pages = {2814-2824}
}
|
Existing logit-based knowledge distillation methods typically employ singularly deterministic categorical distributions, which eliminates the inherent uncertainty in network predictions and thereby limiting the effective transfer of knowledge. To address this limitation, we introduce distribution-based probabilistic modeling as a more comprehensive representation of network knowledge. Specifically, we regard the categorical distribution as a random variable and leverage deep neural networks to predict its distribution, representing it as an evidential second-order distribution. Based on the second-oder modeling, we propose Evidential Knowledge Distillation (EKD) which distills both the expectation of the teacher distribution and the distribution itself into the student. The expectation captures the macroscopic characteristics of the distribution, while the distribution itself conveys microscopic information about the classification boundaries. Additionally, we theoretically show that EKD's distillation objective provides an upper bound on the student's expected risk when treating the teacher's predictions as ground truth. Extensive experiments on several standard benchmarks across various teacher-student network pairs highlight the effectiveness and superior performance of EKD. Our code is available at https://github.com/lyxiang-casia/EKD.
| null | null | null | null |
[] |
[] |
[] |
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] |
198
|
Straighten Viscous Rectified Flow via Noise Optimization
|
[
"Jimin Dai",
"Jiexi Yan",
"Jian Yang",
"Lei Luo"
] |
https://openaccess.thecvf.com/content/ICCV2025/html/Dai_Straighten_Viscous_Rectified_Flow_via_Noise_Optimization_ICCV_2025_paper.html
|
https://openaccess.thecvf.com/content/ICCV2025/papers/Dai_Straighten_Viscous_Rectified_Flow_via_Noise_Optimization_ICCV_2025_paper.pdf
|
https://openaccess.thecvf.com/content/ICCV2025/supplemental/Dai_Straighten_Viscous_Rectified_ICCV_2025_supplemental.pdf
|
@InProceedings{Dai_2025_ICCV,
author = {Dai, Jimin and Yan, Jiexi and Yang, Jian and Luo, Lei},
title = {Straighten Viscous Rectified Flow via Noise Optimization},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2025},
pages = {15005-15014}
}
|
The Reflow operation aims to straighten the inference trajectories of the rectified flow during training by constructing deterministic couplings between noises and images, thereby improving the quality of generated images in single-step or few-step generation. However, we identify critical limitations in Reflow, particularly its inability to rapidly generate high-quality images due to a distribution gap between images in its constructed deterministic couplings and real images. To address these shortcomings, we propose a novel alternative called Straighten Viscous Rectified Flow via Noise Optimization (VRFNO), which is a joint training framework integrating an encoder and a neural velocity field. VRFNO introduces two key innovations: (1) a historical velocity term that enhances trajectory distinction, enabling the model to more accurately predict the velocity of the current trajectory, and (2) the noise optimization through reparameterization to form optimized couplings with real images which are then utilized for training, effectively mitigating errors caused by Reflow's limitations. Comprehensive experiments on synthetic data and real datasets with varying resolutions show that VRFNO significantly mitigates the limitations of Reflow, achieving state-of-the-art performance in both one-step and few-step generation tasks.
|
2507.10218
| null | null | null |
[] |
[] |
[] |
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] |
199
|
A Framework for Double-Blind Federated Adaptation of Foundation Models
|
[
"Nurbek Tastan",
"Karthik Nandakumar"
] |
https://openaccess.thecvf.com/content/ICCV2025/html/Tastan_A_Framework_for_Double-Blind_Federated_Adaptation_of_Foundation_Models_ICCV_2025_paper.html
|
https://openaccess.thecvf.com/content/ICCV2025/papers/Tastan_A_Framework_for_Double-Blind_Federated_Adaptation_of_Foundation_Models_ICCV_2025_paper.pdf
|
https://openaccess.thecvf.com/content/ICCV2025/supplemental/Tastan_A_Framework_for_ICCV_2025_supplemental.pdf
|
@InProceedings{Tastan_2025_ICCV,
author = {Tastan, Nurbek and Nandakumar, Karthik},
title = {A Framework for Double-Blind Federated Adaptation of Foundation Models},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2025},
pages = {923-933}
}
|
Foundation models (FMs) excel in zero-shot tasks but benefit from task-specific adaptation. However, privacy concerns prevent data sharing among multiple data owners, and proprietary restrictions prevent the learning service provider (LSP) from sharing the FM. In this work, we propose BlindFed, a framework enabling collaborative FM adaptation while protecting both parties: data owners do not access the FM or each other's data, and the LSP does not see sensitive task data. BlindFed relies on fully homomorphic encryption (FHE) and consists of three key innovations: (i) FHE-friendly architectural modifications via polynomial approximations and low-rank adapters, (ii) a two-stage split learning approach combining offline knowledge distillation and online encrypted inference for adapter training without backpropagation through the FM, and (iii) a privacy-boosting scheme using sample permutations and stochastic block sampling to mitigate model extraction attacks. Empirical results on four image classification datasets demonstrate the practical feasibility of the BlindFed framework, albeit at a high communication cost and large computational complexity for the LSP. The code can be found at https://github.com/tnurbek/blindfed.
|
2502.01289
| null | null | null |
[] |
[] |
[] |
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] |
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