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 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
0
|
kh: Symmetry Understanding of 3D Shapes via Chirality Disentanglement
|
[
"Weikang Wang",
"Tobias Weißberg",
"Nafie El Amrani",
"Florian Bernard"
] |
https://openaccess.thecvf.com/content/ICCV2025/html/Wang_kh_Symmetry_Understanding_of_3D_Shapes_via_Chirality_Disentanglement_ICCV_2025_paper.html
|
https://openaccess.thecvf.com/content/ICCV2025/papers/Wang_kh_Symmetry_Understanding_of_3D_Shapes_via_Chirality_Disentanglement_ICCV_2025_paper.pdf
|
https://openaccess.thecvf.com/content/ICCV2025/supplemental/Wang_kh_Symmetry_Understanding_ICCV_2025_supplemental.pdf
|
@InProceedings{Wang_2025_ICCV,
author = {Wang, Weikang and Wei{\ss}berg, Tobias and El Amrani, Nafie and Bernard, Florian},
title = {kh: Symmetry Understanding of 3D Shapes via Chirality Disentanglement},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2025},
pages = {28292-28302}
}
|
Chirality information (i.e. information that allows distinguishing left from right) is ubiquitous for various data modes in computer vision, including images, videos, point clouds, and meshes. While chirality has been extensively studied in the image domain, its exploration in shape analysis (such as point clouds and meshes) remains underdeveloped. Although many shape vertex descriptors have shown appealing properties (e.g. robustness to rigid-body transformations), they are often not able to disambiguate between left and right symmetric parts. Considering the ubiquity of chirality information in different shape analysis problems and the lack of chirality-aware features within current shape descriptors, developing a chirality feature extractor becomes necessary and urgent. Based on the recent Diff3F framework, we propose an unsupervised chirality feature extraction pipeline to decorate shape vertices with chirality-aware information, extracted from 2D foundation models. We evaluated the extracted chirality features through quantitative and qualitative experiments across diverse datasets. Results from downstream tasks including left-right disentanglement, shape matching, and part segmentation demonstrate their effectiveness and practical utility. Project page: https://wei-kang-wang.github.io/chirality/
| null | null | null | null |
[] |
[] |
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] |
1
|
Efficient Adaptation of Pre-trained Vision Transformer underpinned by Approximately Orthogonal Fine-Tuning Strategy
|
[
"Yiting Yang",
"Hao Luo",
"Yuan Sun",
"Qingsen Yan",
"Haokui Zhang",
"Wei Dong",
"Guoqing Wang",
"Peng Wang",
"Yang Yang",
"Hengtao Shen"
] |
https://openaccess.thecvf.com/content/ICCV2025/html/Yang_Efficient_Adaptation_of_Pre-trained_Vision_Transformer_underpinned_by_Approximately_Orthogonal_ICCV_2025_paper.html
|
https://openaccess.thecvf.com/content/ICCV2025/papers/Yang_Efficient_Adaptation_of_Pre-trained_Vision_Transformer_underpinned_by_Approximately_Orthogonal_ICCV_2025_paper.pdf
|
https://openaccess.thecvf.com/content/ICCV2025/supplemental/Yang_Efficient_Adaptation_of_ICCV_2025_supplemental.zip
|
@InProceedings{Yang_2025_ICCV,
author = {Yang, Yiting and Luo, Hao and Sun, Yuan and Yan, Qingsen and Zhang, Haokui and Dong, Wei and Wang, Guoqing and Wang, Peng and Yang, Yang and Shen, Hengtao},
title = {Efficient Adaptation of Pre-trained Vision Transformer underpinned by Approximately Orthogonal Fine-Tuning Strategy},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2025},
pages = {4878-4887}
}
|
A prevalent approach in Parameter-Efficient Fine-Tuning (PEFT) of pre-trained Vision Transformers (ViT) involves freezing the majority of the backbone parameters and solely learning low-rank adaptation weight matrices to accommodate downstream tasks. These low-rank matrices are commonly derived through the multiplication structure of down-projection and up-projection matrices, exemplified by methods such as LoRA and Adapter. In this study, we observe an approximate orthogonality among any two row or column vectors within any weight matrix of the backbone parameters; however, this property is absent in the vectors of the down/up-projection matrices. Approximate orthogonality implies a reduction in the upper bound of the model's generalization error, signifying that the model possesses enhanced generalization capability. If the fine-tuned down/up-projection matrices were to exhibit this same property as the pre-trained backbone matrices, could the generalization capability of fine-tuned ViTs be further augmented? To address this question, we propose an Approximately Orthogonal Fine-Tuning (AOFT) strategy for representing the low-rank weight matrices. This strategy employs a single learnable vector to generate a set of approximately orthogonal vectors, which form the down/up-projection matrices, thereby aligning the properties of these matrices with those of the backbone. Extensive experimental results demonstrate that our method achieves competitive performance across a range of downstream image classification tasks, confirming the efficacy of the enhanced generalization capability embedded in the down/up-projection matrices. Our code is available at link.
|
2507.13260
|
This paper is accepted by ICCV 2025
| null | null |
[] |
[] |
[] |
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] |
2
|
MM-IFEngine: Towards Multimodal Instruction Following
|
[
"Shengyuan Ding",
"Shenxi Wu",
"Xiangyu Zhao",
"Yuhang Zang",
"Haodong Duan",
"Xiaoyi Dong",
"Pan Zhang",
"Yuhang Cao",
"Dahua Lin",
"Jiaqi Wang"
] |
https://openaccess.thecvf.com/content/ICCV2025/html/Ding_MM-IFEngine_Towards_Multimodal_Instruction_Following_ICCV_2025_paper.html
|
https://openaccess.thecvf.com/content/ICCV2025/papers/Ding_MM-IFEngine_Towards_Multimodal_Instruction_Following_ICCV_2025_paper.pdf
|
https://openaccess.thecvf.com/content/ICCV2025/supplemental/Ding_MM-IFEngine_Towards_Multimodal_ICCV_2025_supplemental.pdf
|
@InProceedings{Ding_2025_ICCV,
author = {Ding, Shengyuan and Wu, Shenxi and Zhao, Xiangyu and Zang, Yuhang and Duan, Haodong and Dong, Xiaoyi and Zhang, Pan and Cao, Yuhang and Lin, Dahua and Wang, Jiaqi},
title = {MM-IFEngine: Towards Multimodal Instruction Following},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2025},
pages = {1099-1109}
}
|
The Instruction Following (IF) ability measures how well Multi-modal Large Language Models (MLLMs) understand exactly what users are telling them and doing it right.Existing multimodal instruction following training data is scarce, the benchmarks are simple with atomic instructions, and the evaluation strategies are imprecise for tasks demanding exact output constraints.To address this, we present MM-IFEngine, an effective pipeline to generate high-quality image-instruction pairs.Our MM-IFEngine pipeline yields large-scale, diverse, and high-quality training data MM-IFInstruct-23k, which is suitable for Supervised Fine-Tuning (SFT) and extended as MM-IFDPO-23k for Direct Preference Optimization (DPO).We further introduce MM-IFEval, a challenging and diverse multi-modal instruction-following benchmark that includes (1) both textual constraints for output responses and visual constraints tied to the input images, and (2) a comprehensive evaluation pipeline incorporating rule-based assessment and LLM-as-a-Judge evaluation.We conduct SFT and DPO experiments and demonstrate that fine-tuning MLLMs on MM-IFInstruct-23k and MM-IFDPO-23k achieve notable gains on various IF benchmarks, such as MM-IFEval (+11.8%), MIA (+7.7%), and IFEval (+10.5%).
| null | null | null | null |
[] |
[] |
[] |
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] |
3
|
Who is a Better Talker: Subjective and Objective Quality Assessment for AI-Generated Talking Heads
|
[
"Yingjie Zhou",
"Jiezhang Cao",
"Zicheng Zhang",
"Farong Wen",
"Yanwei Jiang",
"Jun Jia",
"Xiaohong Liu",
"Xiongkuo Min",
"Guangtao Zhai"
] |
https://openaccess.thecvf.com/content/ICCV2025/html/Zhou_Who_is_a_Better_Talker_Subjective_and_Objective_Quality_Assessment_ICCV_2025_paper.html
|
https://openaccess.thecvf.com/content/ICCV2025/papers/Zhou_Who_is_a_Better_Talker_Subjective_and_Objective_Quality_Assessment_ICCV_2025_paper.pdf
|
https://openaccess.thecvf.com/content/ICCV2025/supplemental/Zhou_Who_is_a_ICCV_2025_supplemental.pdf
|
@InProceedings{Zhou_2025_ICCV,
author = {Zhou, Yingjie and Cao, Jiezhang and Zhang, Zicheng and Wen, Farong and Jiang, Yanwei and Jia, Jun and Liu, Xiaohong and Min, Xiongkuo and Zhai, Guangtao},
title = {Who is a Better Talker: Subjective and Objective Quality Assessment for AI-Generated Talking Heads},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2025},
pages = {12201-12211}
}
|
Speech-driven methods for portraits are figuratively known as "Talkers" because of their capability to synthesize speaking mouth shapes and facial movements. Especially with the rapid development of the Text-to-Image (T2I) models, AI-Generated Talking Heads (AGTHs) have gradually become an emerging digital human media. However, challenges persist regarding the quality of these talkers and AGTHs they generate, and comprehensive studies addressing these issues remain limited. To address this gap, this paper presents the largest AGTH quality assessment dataset THQA-10K to date, which selects 12 prominent T2I models and 14 advanced talkers to generate AGTHs for 14 prompts. After excluding instances where AGTH generation is unsuccessful, the THQA-10K dataset contains 10,457 AGTHs, which provides rich material for AGTH quality assessment. Then, volunteers are recruited to subjectively rate the AGTHs and give the corresponding distortion categories. In our analysis for subjective experimental results, we evaluate the performance of talkers in terms of generalizability and quality, and also expose the distortions of existing AGTHs. Finally, an objective quality assessment method based on the first frame, Y-T slice and tone-lip consistency is proposed. Experimental results show that this method can achieve state-of-the-art (SOTA) performance in AGTH quality assessment. The work is released at https://github.com/zyj-2000/Talker.
|
2507.23343
| null |
https://github.com/zyj-2000/Talker
| null |
[] |
[] |
[] |
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] |
4
|
LayerAnimate: Layer-level Control for Animation
|
[
"Yuxue Yang",
"Lue Fan",
"Zuzeng Lin",
"Feng Wang",
"Zhaoxiang Zhang"
] |
https://openaccess.thecvf.com/content/ICCV2025/html/Yang_LayerAnimate_Layer-level_Control_for_Animation_ICCV_2025_paper.html
|
https://openaccess.thecvf.com/content/ICCV2025/papers/Yang_LayerAnimate_Layer-level_Control_for_Animation_ICCV_2025_paper.pdf
|
https://openaccess.thecvf.com/content/ICCV2025/supplemental/Yang_LayerAnimate_Layer-level_Control_ICCV_2025_supplemental.pdf
|
@InProceedings{Yang_2025_ICCV,
author = {Yang, Yuxue and Fan, Lue and Lin, Zuzeng and Wang, Feng and Zhang, Zhaoxiang},
title = {LayerAnimate: Layer-level Control for Animation},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2025},
pages = {10865-10874}
}
|
Traditional animation production decomposes visual elements into discrete layers to enable independent processing for sketching, refining, coloring, and in-betweening. Existing anime generation video methods typically treat animation as a distinct data domain different from real-world videos, lacking fine-grained control at the layer level. To bridge this gap, we introduce LayerAnimate, a novel video diffusion framework with layer-aware architecture that empowers the manipulation of layers through layer-level controls. The development of a layer-aware framework faces a significant data scarcity challenge due to the commercial sensitivity of professional animation assets. To address the limitation, we propose a data curation pipeline featuring Automated Element Segmentation and Motion-based Hierarchical Merging. Through quantitative and qualitative comparisons and user study, we demonstrate that LayerAnimate outperforms current methods in terms of animation quality, control precision, and usability, making it an effective tool for both professional animators and amateur enthusiasts. This framework opens up new possibilities for layer-level animation applications and creative flexibility. Our code is available at https://layeranimate.github.io.
|
2501.08295
|
Project page: https://layeranimate.github.io
| null |
https://layeranimate.github.io
|
[
"IamCreateAI/LayerAnimate"
] |
[
"Yuppie1204/LayerAnimate-Mix"
] |
[] |
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] |
5
|
Towards a Unified Copernicus Foundation Model for Earth Vision
|
[
"Yi Wang",
"Zhitong Xiong",
"Chenying Liu",
"Adam J. Stewart",
"Thomas Dujardin",
"Nikolaos Ioannis Bountos",
"Angelos Zavras",
"Franziska Gerken",
"Ioannis Papoutsis",
"Laura Leal-Taixé",
"Xiao Xiang Zhu"
] |
https://openaccess.thecvf.com/content/ICCV2025/html/Wang_Towards_a_Unified_Copernicus_Foundation_Model_for_Earth_Vision_ICCV_2025_paper.html
|
https://openaccess.thecvf.com/content/ICCV2025/papers/Wang_Towards_a_Unified_Copernicus_Foundation_Model_for_Earth_Vision_ICCV_2025_paper.pdf
|
https://openaccess.thecvf.com/content/ICCV2025/supplemental/Wang_Towards_a_Unified_ICCV_2025_supplemental.pdf
|
@InProceedings{Wang_2025_ICCV,
author = {Wang, Yi and Xiong, Zhitong and Liu, Chenying and Stewart, Adam J. and Dujardin, Thomas and Bountos, Nikolaos Ioannis and Zavras, Angelos and Gerken, Franziska and Papoutsis, Ioannis and Leal-Taix\'e, Laura and Zhu, Xiao Xiang},
title = {Towards a Unified Copernicus Foundation Model for Earth Vision},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2025},
pages = {9888-9899}
}
|
Advances in Earth observation (EO) foundation models have unlocked the potential of big satellite data to learn generic representations from space, benefiting a wide range of downstream applications crucial to our planet. However, most existing efforts remain limited to fixed spectral sensors, focus solely on the Earth's surface, and overlook valuable metadata beyond imagery. In this work, we take a step towards next-generation EO foundation models with three key components: 1) Copernicus-Pretrain, a massive-scale pretraining dataset that integrates 18.7M aligned images from all major Copernicus Sentinel missions, spanning from the Earth's surface to its atmosphere; 2) Copernicus-FM, a unified foundation model capable of processing any spectral or non-spectral sensor modality using extended dynamic hypernetworks and flexible metadata encoding; and 3) Copernicus-Bench, a systematic evaluation benchmark with 15 hierarchical downstream tasks ranging from preprocessing to specialized applications for each Sentinel mission. Our dataset, model, and benchmark greatly improve the scalability, versatility, and multimodal adaptability of EO foundation models, while also creating new opportunities to connect EO, weather, and climate research. Codes at https://github.com/zhu-xlab/Copernicus-FM.
|
2503.11849
|
Accepted to ICCV 2025. 33 pages, 34 figures
|
https://github.com/zhu-xlab/Copernicus-FM
| null |
[] |
[
"wangyi111/Copernicus-FM"
] |
[
"wangyi111/Copernicus-Pretrain"
] |
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] |
6
|
ROADWork: A Dataset and Benchmark for Learning to Recognize, Observe, Analyze and Drive Through Work Zones
|
[
"Anurag Ghosh",
"Shen Zheng",
"Robert Tamburo",
"Khiem Vuong",
"Juan Alvarez-Padilla",
"Hailiang Zhu",
"Michael Cardei",
"Nicholas Dunn",
"Christoph Mertz",
"Srinivasa G. Narasimhan"
] |
https://openaccess.thecvf.com/content/ICCV2025/html/Ghosh_ROADWork_A_Dataset_and_Benchmark_for_Learning_to_Recognize_Observe_ICCV_2025_paper.html
|
https://openaccess.thecvf.com/content/ICCV2025/papers/Ghosh_ROADWork_A_Dataset_and_Benchmark_for_Learning_to_Recognize_Observe_ICCV_2025_paper.pdf
|
https://openaccess.thecvf.com/content/ICCV2025/supplemental/Ghosh_ROADWork_A_Dataset_ICCV_2025_supplemental.pdf
|
@InProceedings{Ghosh_2025_ICCV,
author = {Ghosh, Anurag and Zheng, Shen and Tamburo, Robert and Vuong, Khiem and Alvarez-Padilla, Juan and Zhu, Hailiang and Cardei, Michael and Dunn, Nicholas and Mertz, Christoph and Narasimhan, Srinivasa G.},
title = {ROADWork: A Dataset and Benchmark for Learning to Recognize, Observe, Analyze and Drive Through Work Zones},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2025},
pages = {6132-6142}
}
|
Perceiving and autonomously navigating through work zones is a challenging and under-explored problem. Open datasets for this long-tailed scenario are scarce. We propose the ROADWork dataset to learn to recognize, observe, analyze, and drive through work zones. State-of-the-art foundation models fail when applied to work zones. Fine-tuning models on our dataset significantly improves perception and navigation in work zones. With ROADWork, we discover new work zone images with higher precision (+32.5%) at a much higher rate (12.8x) around the world. Open-vocabulary methods fail too, whereas fine-tuned detectors improve performance (+32.2 AP).Vision-Language Models (VLMs) struggle to describe work zones, but fine-tuning substantially improves performance (+36.7 SPICE). Beyond fine-tuning, we show the value of simple techniques. Video label propagation provides additional gains (+2.6 AP) for instance segmentation. While reading work zone signs, composing a detector and text spotter via crop-scaling improves performance (+14.2% 1-NED). Composing work zone detections to provide context further reduces hallucinations (+3.9 SPICE) in VLMs. We predict navigational goals and compute drivable paths from work zone videos. Incorporating road work semantics ensures 53.6% goals have angular error (AE) < 0.5 (+9.9%) and 75.3% pathways have AE < 0.5 (+8.1%).
| null | null | null | null |
[] |
[] |
[] |
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] |
7
|
Gradient Decomposition and Alignment for Incremental Object Detection
|
[
"Wenlong Luo",
"Shizhou Zhang",
"De Cheng",
"Yinghui Xing",
"Guoqiang Liang",
"Peng Wang",
"Yanning Zhang"
] |
https://openaccess.thecvf.com/content/ICCV2025/html/Luo_Gradient_Decomposition_and_Alignment_for_Incremental_Object_Detection_ICCV_2025_paper.html
|
https://openaccess.thecvf.com/content/ICCV2025/papers/Luo_Gradient_Decomposition_and_Alignment_for_Incremental_Object_Detection_ICCV_2025_paper.pdf
|
https://openaccess.thecvf.com/content/ICCV2025/supplemental/Luo_Gradient_Decomposition_and_ICCV_2025_supplemental.pdf
|
@InProceedings{Luo_2025_ICCV,
author = {Luo, Wenlong and Zhang, Shizhou and Cheng, De and Xing, Yinghui and Liang, Guoqiang and Wang, Peng and Zhang, Yanning},
title = {Gradient Decomposition and Alignment for Incremental Object Detection},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2025},
pages = {4486-4495}
}
|
Incremental object detection (IOD) is crucial for enabling AI systems to continuously learn new object classes over time while retaining knowledge of previously learned categories, allowing model to adapt to dynamic environments without forgetting prior information.Existing IOD methods primarily employ knowledge distillation to mitigate catastrophic forgetting, yet these approaches overlook class overlap issues, often resulting in suboptimal performance. In this paper, we propose a novel framework for IOD that leverages a decoupled gradient alignment technique on top of the specially proposed pseudo-labeling strategy. Our method employs a Gaussian Mixture Model to accurately estimate pseudo-labels of previously learned objects in current training images, effectively functioning as a knowledge-replay mechanism. This strategy reinforces prior knowledge retention and prevents the misclassification of unannotated foreground objects from earlier classes as background. Furthermore, we introduce an adaptive gradient decomposition and alignment method to maintain model stability while facilitating positive knowledge transfer. By aligning gradients from both old and new classes, our approach preserves previously learned knowledge while enhancing plasticity for new tasks. Extensive experiments on two IOD benchmarks demonstrate the effectiveness of the proposed method, achieving superior performances to state-of-the-art methods.
| null | null | null | null |
[] |
[] |
[] |
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] |
8
|
One Polyp Identifies All: One-Shot Polyp Segmentation with SAM via Cascaded Priors and Iterative Prompt Evolution
|
[
"Xinyu Mao",
"Xiaohan Xing",
"Fei Meng",
"Jianbang Liu",
"Fan Bai",
"Qiang Nie",
"Max Meng"
] |
https://openaccess.thecvf.com/content/ICCV2025/html/Mao_One_Polyp_Identifies_All_One-Shot_Polyp_Segmentation_with_SAM_via_ICCV_2025_paper.html
|
https://openaccess.thecvf.com/content/ICCV2025/papers/Mao_One_Polyp_Identifies_All_One-Shot_Polyp_Segmentation_with_SAM_via_ICCV_2025_paper.pdf
|
https://openaccess.thecvf.com/content/ICCV2025/supplemental/Mao_One_Polyp_Identifies_ICCV_2025_supplemental.pdf
|
@InProceedings{Mao_2025_ICCV,
author = {Mao, Xinyu and Xing, Xiaohan and Meng, Fei and Liu, Jianbang and Bai, Fan and Nie, Qiang and Meng, Max},
title = {One Polyp Identifies All: One-Shot Polyp Segmentation with SAM via Cascaded Priors and Iterative Prompt Evolution},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2025},
pages = {24182-24191}
}
|
Polyp segmentation is vital for early colorectal cancer detection, yet traditional fully supervised methods struggle with morphological variability and domain shifts, requiring frequent retraining. Additionally, reliance on large-scale annotations is a major bottleneck due to the time-consuming and error-prone nature of polyp boundary labeling. Recently, vision foundation models like Segment Anything Model (SAM) have demonstrated strong generalizability and fine-grained boundary detection with sparse prompts, effectively addressing key polyp segmentation challenges. However, SAM's prompt-dependent nature limits automation in medical applications, since manually inputting prompts for each image is labor-intensive and time-consuming. We propose OP-SAM, a One-shot Polyp segmentation framework based on SAM that automatically generates prompts from a single annotated image, ensuring accurate and generalizable segmentation without additional annotation burdens. Our method introduces Correlation-based Prior Generation (CPG) for semantic label transfer and Scale-cascaded Prior Fusion (SPF) to adapt to polyp size variations as well as filter out noisy transfers. Instead of dumping all prompts at once, we devise Euclidean Prompt Evolution (EPE) for iterative prompt refinement, progressively enhancing segmentation quality. Extensive evaluations across five datasets validate OP-SAM's effectiveness. Notably, on Kvasir, it achieves 76.93% IoU, surpassing the state-of-the-art by 11.44%.
|
2507.16337
|
accepted by ICCV2025
| null | null |
[] |
[] |
[] |
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] |
9
|
Gradient Extrapolation for Debiased Representation Learning
|
[
"Ihab Asaad",
"Maha Shadaydeh",
"Joachim Denzler"
] |
https://openaccess.thecvf.com/content/ICCV2025/html/Asaad_Gradient_Extrapolation_for_Debiased_Representation_Learning_ICCV_2025_paper.html
|
https://openaccess.thecvf.com/content/ICCV2025/papers/Asaad_Gradient_Extrapolation_for_Debiased_Representation_Learning_ICCV_2025_paper.pdf
|
https://openaccess.thecvf.com/content/ICCV2025/supplemental/Asaad_Gradient_Extrapolation_for_ICCV_2025_supplemental.pdf
|
@InProceedings{Asaad_2025_ICCV,
author = {Asaad, Ihab and Shadaydeh, Maha and Denzler, Joachim},
title = {Gradient Extrapolation for Debiased Representation Learning},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2025},
pages = {3819-3829}
}
|
Machine learning classification models trained with empirical risk minimization (ERM) often inadvertently rely on spurious correlations. When absent in the test data, these unintended associations between non-target attributes and target labels lead to poor generalization. This paper addresses this problem from a model optimization perspective and proposes a novel method, Gradient Extrapolation for Debiased Representation Learning (GERNE), designed to learn debiased representations in both known and unknown attribute training cases. GERNE uses two distinct batches with different amounts of spurious correlations and defines the target gradient as a linear extrapolation of the gradients computed from each batch's loss. Our analysis shows that when the extrapolated gradient points toward the batch gradient with fewer spurious correlations, it effectively guides training toward learning a debiased model. GERNE serves as a general framework for debiasing, encompassing ERM and Resampling methods as special cases. We derive the theoretical upper and lower bounds of the extrapolation factor employed by GERNE. By tuning this factor, GERNE can adapt to maximize either Group-Balanced Accuracy (GBA) or Worst-Group Accuracy (WGA). We validate GERNE on five vision and one NLP benchmarks, demonstrating competitive and often superior performance compared to state-of-the-art baselines. The project page is available at: https://gerne-debias.github.io/.
|
2503.13236
|
Accepted at International Conference on Computer Vision, ICCV 2025
| null |
https://gerne-debias.github.io/
|
[] |
[] |
[] |
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] |
10
|
From Gaze to Movement: Predicting Visual Attention for Autonomous Driving Human-Machine Interaction based on Programmatic Imitation Learning
|
[
"Yexin Huang",
"Yongbin Lin",
"Lishengsa Yue",
"Zhihong Yao",
"Jie Wang"
] |
https://openaccess.thecvf.com/content/ICCV2025/html/Huang_From_Gaze_to_Movement_Predicting_Visual_Attention_for_Autonomous_Driving_ICCV_2025_paper.html
|
https://openaccess.thecvf.com/content/ICCV2025/papers/Huang_From_Gaze_to_Movement_Predicting_Visual_Attention_for_Autonomous_Driving_ICCV_2025_paper.pdf
|
https://openaccess.thecvf.com/content/ICCV2025/supplemental/Huang_From_Gaze_to_ICCV_2025_supplemental.pdf
|
@InProceedings{Huang_2025_ICCV,
author = {Huang, Yexin and Lin, Yongbin and Yue, Lishengsa and Yao, Zhihong and Wang, Jie},
title = {From Gaze to Movement: Predicting Visual Attention for Autonomous Driving Human-Machine Interaction based on Programmatic Imitation Learning},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2025},
pages = {26146-26155}
}
|
Human-machine interaction technology requires not only the distribution of human visual attention but also the prediction of the gaze point trajectory. We introduce PILOT, a programmatic imitation learning approach that predicts a driver's eye movements based on a set of rule-based conditions. These conditions--derived from driving operations and traffic flow characteristics--define how gaze shifts occur. They are initially identified through incremental synthesis, a heuristic search method, and then refined via L-BFGS, a numerical optimization technique. These human-readable rules enable us to understand drivers' eye movement patterns and make efficient and explainable predictions. We also propose DATAD, a dataset that covers 12 types of autonomous driving takeover scenarios, collected from 60 participants and comprising approximately 600,000 frames of gaze point data. Compared to existing eye-tracking datasets, DATAD includes additional driving metrics and surrounding traffic flow characteristics, providing richer contextual information for modeling gaze behavior. Experimental evaluations of PILOT on DATAD demonstrate superior accuracy and faster prediction speeds compared to four baseline models. Specifically, PILOT reduces the MSE of predicted trajectories by 38.59% to 88.02% and improves the accuracy of gaze object predictions by 6.90% to 55.06%. Moreover, PILOT achieves these gains with approximately 30% lower prediction time, offering both more accurate and more efficient eye movement prediction.
| null | null | null | null |
[] |
[] |
[] |
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] |
11
|
Less-to-More Generalization: Unlocking More Controllability by In-Context Generation
|
[
"Shaojin Wu",
"Mengqi Huang",
"Wenxu Wu",
"Yufeng Cheng",
"Fei Ding",
"Qian He"
] |
https://openaccess.thecvf.com/content/ICCV2025/html/Wu_Less-to-More_Generalization_Unlocking_More_Controllability_by_In-Context_Generation_ICCV_2025_paper.html
|
https://openaccess.thecvf.com/content/ICCV2025/papers/Wu_Less-to-More_Generalization_Unlocking_More_Controllability_by_In-Context_Generation_ICCV_2025_paper.pdf
|
https://openaccess.thecvf.com/content/ICCV2025/supplemental/Wu_Less-to-More_Generalization_Unlocking_ICCV_2025_supplemental.pdf
|
@InProceedings{Wu_2025_ICCV,
author = {Wu, Shaojin and Huang, Mengqi and Wu, Wenxu and Cheng, Yufeng and Ding, Fei and He, Qian},
title = {Less-to-More Generalization: Unlocking More Controllability by In-Context Generation},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2025},
pages = {18682-18692}
}
|
Although subject-driven generation has been extensively explored in image generation due to its wide applications, it still has challenges in data scalability and subject expansibility. For the first challenge, moving from curating single-subject datasets to multiple-subject ones and scaling them is particularly difficult. For the second, most recent methods center on single-subject generation, making it hard to apply when dealing with multi-subject scenarios. In this study, we propose a highly-consistent data synthesis pipeline to tackle these challenges. This pipeline harnesses the intrinsic in-context generation capabilities of diffusion transformers and generates high-consistent multi-subject paired data. Additionally, we introduce UNO, a multi-subject driven customization architecture based on a diffusion transformer. UNO incorporates a progressive cross-modal alignment training paradigm that progresses from simpler single-subject conditioning to more complex multi-subject conditioning. Along with this, a universal rotary position embedding (UnoPE) adjusts the position indices. Extensive experiments show that our method can achieve high consistency while ensuring controllability in both single-subject and multi-subject driven generation. Code and model: https://github.com/bytedance/UNO.
|
2504.02160
|
Project page: https://bytedance.github.io/UNO Code and model:
https://github.com/bytedance/UNO
| null | null |
[
"bytedance-research/UNO-FLUX"
] |
[
"bytedance-research/UNO"
] |
[
"bytedance-research/UNO-1M"
] |
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] |
12
|
Improving Large Vision and Language Models by Learning from a Panel of Peers
|
[
"Jefferson Hernandez",
"Jing Shi",
"Simon Jenni",
"Vicente Ordonez",
"Kushal Kafle"
] |
https://openaccess.thecvf.com/content/ICCV2025/html/Hernandez_Improving_Large_Vision_and_Language_Models_by_Learning_from_a_ICCV_2025_paper.html
|
https://openaccess.thecvf.com/content/ICCV2025/papers/Hernandez_Improving_Large_Vision_and_Language_Models_by_Learning_from_a_ICCV_2025_paper.pdf
|
https://openaccess.thecvf.com/content/ICCV2025/supplemental/Hernandez_Improving_Large_Vision_ICCV_2025_supplemental.pdf
|
@InProceedings{Hernandez_2025_ICCV,
author = {Hernandez, Jefferson and Shi, Jing and Jenni, Simon and Ordonez, Vicente and Kafle, Kushal},
title = {Improving Large Vision and Language Models by Learning from a Panel of Peers},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2025},
pages = {1402-1412}
}
|
Traditional alignment methods for Large Vision and Language Models (LVLMs) primarily rely on human-curated preference data. Human-generated preference data is costly; machine-generated preference data is limited in quality; and self-supervised preference data often introduces hallucinations. To overcome these limitations, we propose a novel Panel-of-Peers learning framework inspired by collaborative learning among humans. This approach leverages a panel of LVLMs, each evaluating and learning from their collective outputs through an iterative self-improvement process. By simulating a peer review system, our models generate, assess, and refine outputs in response to a curated set of prompts, mimicking a classroom learning environment. We demonstrate that this methodology enhances model performance without requiring extensive human-labeled datasets. Our experiments show significant improvement across multiple benchmarks, demonstrating the potential of peer evaluations as a scalable alternative to self-supervised alignment. Notably, we show that Panel-of-Peers increases the average score on fifteen benchmarks from 48% to 57%.
|
2509.01610
|
Accepted at ICCV 2025
| null | null |
[] |
[] |
[] |
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] |
13
|
Federated Representation Angle Learning
|
[
"Liping Yi",
"Han Yu",
"Gang Wang",
"Xiaoguang Liu",
"Xiaoxiao Li"
] |
https://openaccess.thecvf.com/content/ICCV2025/html/Yi_Federated_Representation_Angle_Learning_ICCV_2025_paper.html
|
https://openaccess.thecvf.com/content/ICCV2025/papers/Yi_Federated_Representation_Angle_Learning_ICCV_2025_paper.pdf
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@InProceedings{Yi_2025_ICCV,
author = {Yi, Liping and Yu, Han and Wang, Gang and Liu, Xiaoguang and Li, Xiaoxiao},
title = {Federated Representation Angle Learning},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2025},
pages = {1314-1324}
}
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Model-heterogeneous federated learning (MHFL) is a challenging FL paradigm designed to allow FL clients to train structurally heterogeneous models under the coordination of an FL server. Existing MHFL methods face significant limitations when it comes to transferring global knowledge to clients as a result of sharing only partial homogeneous model parameters or calculating distance loss, leading to inferior model generalization. To bridge this gap, we propose a novel model-heterogeneous Federated learning method with Representation Angle Learning (FedRAL). It consists of three innovative designs: (1) We first introduce representation angle learning into MHFL. Specifically, we embed a homogeneous square matrix into the local heterogeneous model of each client, which learns the angle information of local representations. These homogeneous representation angle square matrices are aggregated on the server to fuse representation angle knowledge shared by clients for enhancing the generalization of local representations. (2) As different clients might have heterogeneous system resources, we propose an adaptive diagonal sparsification strategy to reduce the numbers of the parameters of representation angle square matrices uploaded to the server, to improve FL communication efficiency. (3) To enable the effective fusion of sparsified homogeneous local representation angle square matrices, we design an element-wise weighted aggregation approach. Experiments on 4 benchmark datasets under 2 types of non-IID divisions over 6 state-of-the-art baselines demonstrate that FedRAL achieves the best performance. It improves test accuracy, communication efficiency and computational efficiency by up to 5.03%, 12.43x and 6.49x, respectively.
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] |
14
|
Why LVLMs Are More Prone to Hallucinations in Longer Responses: The Role of Context
|
[
"Ge Zheng",
"Jiaye Qian",
"Jiajin Tang",
"Sibei Yang"
] |
https://openaccess.thecvf.com/content/ICCV2025/html/Zheng_Why_LVLMs_Are_More_Prone_to_Hallucinations_in_Longer_Responses_ICCV_2025_paper.html
|
https://openaccess.thecvf.com/content/ICCV2025/papers/Zheng_Why_LVLMs_Are_More_Prone_to_Hallucinations_in_Longer_Responses_ICCV_2025_paper.pdf
|
https://openaccess.thecvf.com/content/ICCV2025/supplemental/Zheng_Why_LVLMs_Are_ICCV_2025_supplemental.pdf
|
@InProceedings{Zheng_2025_ICCV,
author = {Zheng, Ge and Qian, Jiaye and Tang, Jiajin and Yang, Sibei},
title = {Why LVLMs Are More Prone to Hallucinations in Longer Responses: The Role of Context},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2025},
pages = {4101-4113}
}
|
Large Vision-Language Models (LVLMs) have made significant progress in recent years but are also prone to hallucination issues. They exhibit more hallucinations in longer, free-form responses, often attributed to accumulated uncertainties. In this paper, we ask: Does increased hallucination result solely from length-induced errors, or is there a deeper underlying mechanism? After a series of preliminary experiments and findings, we suggest that the risk of hallucinations is not caused by length itself but by the increased reliance on context for coherence and completeness in longer responses. Building on these insights, we propose a novel "induce-detect-suppress" framework that actively induces hallucinations through deliberately designed contexts, leverages induced instances for early detection of high-risk cases, and ultimately suppresses potential object-level hallucinations during actual decoding. Our approach achieves consistent, significant improvements across all benchmarks, demonstrating its efficacy. The strong detection and improved hallucination mitigation not only validate our framework but, more importantly, re-validate our hypothesis on context. Rather than solely pursuing performance gains, this study aims to provide new insights and serves as a first step toward a deeper exploration of hallucinations in LVLMs' longer responses.
| null | null | null | null |
[] |
[] |
[] |
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] |
15
|
Training-Free Personalization via Retrieval and Reasoning on Fingerprints
|
[
"Deepayan Das",
"Davide Talon",
"Yiming Wang",
"Massimiliano Mancini",
"Elisa Ricci"
] |
https://openaccess.thecvf.com/content/ICCV2025/html/Das_Training-Free_Personalization_via_Retrieval_and_Reasoning_on_Fingerprints_ICCV_2025_paper.html
|
https://openaccess.thecvf.com/content/ICCV2025/papers/Das_Training-Free_Personalization_via_Retrieval_and_Reasoning_on_Fingerprints_ICCV_2025_paper.pdf
|
https://openaccess.thecvf.com/content/ICCV2025/supplemental/Das_Training-Free_Personalization_via_ICCV_2025_supplemental.pdf
|
@InProceedings{Das_2025_ICCV,
author = {Das, Deepayan and Talon, Davide and Wang, Yiming and Mancini, Massimiliano and Ricci, Elisa},
title = {Training-Free Personalization via Retrieval and Reasoning on Fingerprints},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2025},
pages = {9683-9692}
}
|
Vision Language Models (VLMs) have lead to major improvements in multimodal reasoning, yet they still struggle to understand user-specific concepts. Existing personalization methods address this limitation butheavily rely on training procedures, that can be either costly or unpleasant to individual users.We depart from existing work, and for the first time explore the training-free setting in the context of personalization. We propose a novel method, Retrieval and Reasoning for Personalization (R2P), leveraging internal knowledge of VLMs. First, we leverage VLMs to extract the concept fingerprint, i.e., key attributes uniquely defining the concept within its semantic class. When a query arrives, the most similar fingerprints are retrieved and scored via chain of thought reasoning. To reduce the risk of hallucinations, the scores are validated through cross-modal verification at the attribute level:in case of a discrepancy between the scores, R2P refines the concept association viapairwise multimodal matching, where the retrieved fingerprints and their images aredirectly compared with the query.We validate R2P on two publicly available benchmarks and a newly introduced dataset, Personal Concepts with Visual Ambiguity (PerVA), for concept identification highlighting challenges in visual ambiguity. R2P consistently outperforms state-of-the-art approaches on various downstream tasks across all benchmarks.
|
2503.18623
| null | null |
https://deepayan137.github.io/papers/training-free-personalization.html
|
[] |
[] |
[] |
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] |
16
|
How Far are AI-generated Videos from Simulating the 3D Visual World: A Learned 3D Evaluation Approach
|
[
"Chirui Chang",
"Jiahui Liu",
"Zhengzhe Liu",
"Xiaoyang Lyu",
"Yi-Hua Huang",
"Xin Tao",
"Pengfei Wan",
"Di Zhang",
"Xiaojuan Qi"
] |
https://openaccess.thecvf.com/content/ICCV2025/html/Chang_How_Far_are_AI-generated_Videos_from_Simulating_the_3D_Visual_ICCV_2025_paper.html
|
https://openaccess.thecvf.com/content/ICCV2025/papers/Chang_How_Far_are_AI-generated_Videos_from_Simulating_the_3D_Visual_ICCV_2025_paper.pdf
|
https://openaccess.thecvf.com/content/ICCV2025/supplemental/Chang_How_Far_are_ICCV_2025_supplemental.pdf
|
@InProceedings{Chang_2025_ICCV,
author = {Chang, Chirui and Liu, Jiahui and Liu, Zhengzhe and Lyu, Xiaoyang and Huang, Yi-Hua and Tao, Xin and Wan, Pengfei and Zhang, Di and Qi, Xiaojuan},
title = {How Far are AI-generated Videos from Simulating the 3D Visual World: A Learned 3D Evaluation Approach},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2025},
pages = {10307-10317}
}
|
Recent advancements in video diffusion models enable the generation of photorealistic videos with impressive 3D consistency and temporal coherence. However, the extent to which these AI-generated videos simulate the 3D visual world remains underexplored. In this paper, we introduce Learned 3D Evaluation (L3DE), an objective, quantifiable, and interpretable method for assessing AI-generated videos' ability to simulate the real world in terms of 3D visual qualities and consistencies, without requiring manually labeled defects or quality annotations. Instead of relying on 3D reconstruction, which is prone to failure with in-the-wild videos, L3DE employs a 3D convolutional network, trained on monocular 3D cues of motion, depth, and appearance, to distinguish real from synthetic videos. Confidence scores from L3DE quantify the gap between real and synthetic videos in terms of 3D visual coherence, while a gradient-based visualization pinpoints unrealistic regions, improving interpretability. We validate L3DE through extensive experiments, demonstrating strong alignment with 3D reconstruction quality and human judgments. Our evaluations on leading generative models (e.g., Sora, MiniMax, and Kling) reveal persistent simulation gaps and subtle inconsistencies. Beyond generative video assessment, L3DE extends to broader applications: benchmarking video generation models, serving as a deepfake detector, and enhancing video synthesis by inpainting flagged inconsistencies.
|
2406.19568
| null | null | null |
[] |
[] |
[] |
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] |
17
|
Rethinking Detecting Salient and Camouflaged Objects in Unconstrained Scenes
|
[
"Zhangjun Zhou",
"Yiping Li",
"Chunlin Zhong",
"Jianuo Huang",
"Jialun Pei",
"Hua Li",
"He Tang"
] |
https://openaccess.thecvf.com/content/ICCV2025/html/Zhou_Rethinking_Detecting_Salient_and_Camouflaged_Objects_in_Unconstrained_Scenes_ICCV_2025_paper.html
|
https://openaccess.thecvf.com/content/ICCV2025/papers/Zhou_Rethinking_Detecting_Salient_and_Camouflaged_Objects_in_Unconstrained_Scenes_ICCV_2025_paper.pdf
|
https://openaccess.thecvf.com/content/ICCV2025/supplemental/Zhou_Rethinking_Detecting_Salient_ICCV_2025_supplemental.pdf
|
@InProceedings{Zhou_2025_ICCV,
author = {Zhou, Zhangjun and Li, Yiping and Zhong, Chunlin and Huang, Jianuo and Pei, Jialun and Li, Hua and Tang, He},
title = {Rethinking Detecting Salient and Camouflaged Objects in Unconstrained Scenes},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2025},
pages = {22372-22382}
}
|
While the human visual system employs distinct mechanisms to perceive salient and camouflaged objects, existing models struggle to disentangle these tasks. Specifically, salient object detection (SOD) models frequently misclassify camouflaged objects as salient, while camouflaged object detection (COD) models conversely misinterpret salient objects as camouflaged. We hypothesize that this can be attributed to two factors: (i) the specific annotation paradigm of current SOD and COD datasets, and (ii) the lack of explicit aspect relationship modeling in current models. Prevalent SOD/COD datasets enforce a mutual exclusivity constraint, assuming scenes contain either salient or camouflaged objects, which poorly aligns with the real world. Furthermore, current SOD/COD methods are primarily designed for these highly constrained datasets and lack explicit modeling of the relationship between salient and camouflaged objects. In this paper, to promote the development of unconstrained salient and camouflaged object detection, we construct a large-scale dataset, USC12K, which features comprehensive labels and four different scenes that cover all possible logical existence scenarios of both salient and camouflaged objects. To explicitly model the relationship between salient and camouflaged objects, we propose a model called USCNet, which introduces two distinct prompt query mechanisms for modeling inter-sample and intra-sample aspect relationships. Additionally, to assess the model's ability to distinguish between salient and camouflaged objects, we design an evaluation metric called CSCS. The proposed method achieves state-of-the-art performance across all scenes in various metrics. Code and dataset are available at https://github.com/ssecv/USCNet.
|
2412.10943
|
17 pages, 11 figures
|
https://github.com/ssecv/USCNet
| null |
[] |
[] |
[] |
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] |
18
|
OccluGaussian: Occlusion-Aware Gaussian Splatting for Large Scene Reconstruction and Rendering
|
[
"Shiyong Liu",
"Xiao Tang",
"Zhihao Li",
"Yingfan He",
"Chongjie Ye",
"Jianzhuang Liu",
"Binxiao Huang",
"Shunbo Zhou",
"Xiaofei Wu"
] |
https://openaccess.thecvf.com/content/ICCV2025/html/Liu_OccluGaussian_Occlusion-Aware_Gaussian_Splatting_for_Large_Scene_Reconstruction_and_Rendering_ICCV_2025_paper.html
|
https://openaccess.thecvf.com/content/ICCV2025/papers/Liu_OccluGaussian_Occlusion-Aware_Gaussian_Splatting_for_Large_Scene_Reconstruction_and_Rendering_ICCV_2025_paper.pdf
|
https://openaccess.thecvf.com/content/ICCV2025/supplemental/Liu_OccluGaussian_Occlusion-Aware_Gaussian_ICCV_2025_supplemental.pdf
|
@InProceedings{Liu_2025_ICCV,
author = {Liu, Shiyong and Tang, Xiao and Li, Zhihao and He, Yingfan and Ye, Chongjie and Liu, Jianzhuang and Huang, Binxiao and Zhou, Shunbo and Wu, Xiaofei},
title = {OccluGaussian: Occlusion-Aware Gaussian Splatting for Large Scene Reconstruction and Rendering},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2025},
pages = {26643-26652}
}
|
In large-scale scene reconstruction using 3D Gaussian splatting, it is common to partition the scene into multiple smaller regions and reconstruct them individually. However, existing division methods are occlusion-agnostic, meaning that each region may contain areas with severe occlusions. As a result, the cameras within those regions are less correlated, leading to a low average contribution to the overall reconstruction. In this paper, we propose an occlusion-aware scene division strategy that clusters training cameras based on their positions and co-visibilities to acquire multiple regions. Cameras in such regions exhibit stronger correlations and a higher average contribution, facilitating high-quality scene reconstruction. We further propose a region-based rendering technique to accelerate large scene rendering, which culls Gaussians invisible to the region where the viewpoint is located. Such a technique significantly speeds up the rendering without compromising quality. Extensive experiments on multiple large scenes show that our method achieves superior reconstruction results with faster rendering speeds compared to existing state-of-the-art approaches. Project page: https://occlugaussian.github.io.
|
2503.16177
|
Project website: https://occlugaussian.github.io
| null |
https://occlugaussian.github.io/
|
[] |
[] |
[] |
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] |
19
|
VisionMath: Vision-Form Mathematical Problem-Solving
|
[
"Zongyang Ma",
"Yuxin Chen",
"Ziqi Zhang",
"Zhongang Qi",
"Chunfeng Yuan",
"Shaojie Zhu",
"Chengxiang Zhuo",
"Bing Li",
"Ye Liu",
"Zang Li",
"Ying Shan",
"Weiming Hu"
] |
https://openaccess.thecvf.com/content/ICCV2025/html/Ma_VisionMath_Vision-Form_Mathematical_Problem-Solving_ICCV_2025_paper.html
|
https://openaccess.thecvf.com/content/ICCV2025/papers/Ma_VisionMath_Vision-Form_Mathematical_Problem-Solving_ICCV_2025_paper.pdf
|
https://openaccess.thecvf.com/content/ICCV2025/supplemental/Ma_VisionMath_Vision-Form_Mathematical_ICCV_2025_supplemental.pdf
|
@InProceedings{Ma_2025_ICCV,
author = {Ma, Zongyang and Chen, Yuxin and Zhang, Ziqi and Qi, Zhongang and Yuan, Chunfeng and Zhu, Shaojie and Zhuo, Chengxiang and Li, Bing and Liu, Ye and Li, Zang and Shan, Ying and Hu, Weiming},
title = {VisionMath: Vision-Form Mathematical Problem-Solving},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2025},
pages = {1162-1172}
}
|
Mathematical problems in real-world scenarios are often presented in a purely vision-form, where textual problem statement and accompanying math figures, e.g., geometry figures and functional graphs, are integrated into a single image. This vision-form problem-solving task requires precise comprehension and reasoning on both textual and graphical elements in the images, posing significant challenge to current Multimodal Large Language Models (MLLMs), which process text and math figures in isolation. In this work, we propose VisionMath, the first exploration for vision-form mathematical problem-solving model, which employs a three-stage progressive multimodal reasoning alignment strategy to systematically enhance task-specific capabilities. Building upon a LLM proficient in unimodal mathematical reasoning, VisionMath first establishes foundational OCR capabilities through capturing rendered mathematical problem images. Subsequently, the model develops comprehensive understanding of figure structures and properties via learning from figure descriptions and mathematical educational videos. Finally, the model's reasoning capacity is activated using carefully constructed visual-form problem-solving datasets VisionMath-IT with chain-of-thought annotations. For comprehensive evaluation, we construct multilingual benchmarks covering diverse problem types, including geometry, algebra, function problems in both English and Chinese. Our model weights, data and code will be made available at https://github.com/mengqiDyangge/VisionMath.
| null | null | null | null |
[] |
[] |
[] |
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] |
20
|
Unsupervised RGB-D Point Cloud Registration for Scenes with Low Overlap and Photometric Inconsistency
|
[
"Yejun Shou",
"Haocheng Wang",
"Lingfeng Shen",
"Qian Zheng",
"Gang Pan",
"Yanlong Cao"
] |
https://openaccess.thecvf.com/content/ICCV2025/html/Shou_Unsupervised_RGB-D_Point_Cloud_Registration_for_Scenes_with_Low_Overlap_ICCV_2025_paper.html
|
https://openaccess.thecvf.com/content/ICCV2025/papers/Shou_Unsupervised_RGB-D_Point_Cloud_Registration_for_Scenes_with_Low_Overlap_ICCV_2025_paper.pdf
|
https://openaccess.thecvf.com/content/ICCV2025/supplemental/Shou_Unsupervised_RGB-D_Point_ICCV_2025_supplemental.pdf
|
@InProceedings{Shou_2025_ICCV,
author = {Shou, Yejun and Wang, Haocheng and Shen, Lingfeng and Zheng, Qian and Pan, Gang and Cao, Yanlong},
title = {Unsupervised RGB-D Point Cloud Registration for Scenes with Low Overlap and Photometric Inconsistency},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2025},
pages = {24868-24877}
}
|
Point cloud registration is a fundamental task in 3D vision, playing a crucial role in various fields. With the rapid advancement of RGB-D sensors, unsupervised point cloud registration methods based on RGB-D sequences have demonstrated excellent performance. However, existing methods struggle in scenes with low overlap and photometric inconsistency. Low overlap results in numerous correspondence outliers, while photometric inconsistency hinders the model's ability to extract discriminative features. To address these challenges, we first propose the Overlapping Constraint for Inliers Detection (OCID) module, which filters and optimizes the initial correspondence set using an overlappping constraint. This module robustly selects reliable correspondences within the overlapping region while maintaining a balance between accuracy and efficiency. Additionally, we introduce a novel scene representation, 3DGS, which integrates both geometric and texture information, making it particularly well-suited for RGB-D registration tasks. Building on this, we propose the Gaussian Rendering for Photometric Adaptation (GRPA) module, which refines the geometric transformation and enhances the model's adaptability to scenes with inconsistent photometric information. Extensive experiments on ScanNet and ScanNet1500 demonstrate that our method achieves state-of-the-art performance.
| null | null | null | null |
[] |
[] |
[] |
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] |
21
|
CWNet: Causal Wavelet Network for Low-Light Image Enhancement
|
[
"Tongshun Zhang",
"Pingping Liu",
"Yubing Lu",
"Mengen Cai",
"Zijian Zhang",
"Zhe Zhang",
"Qiuzhan Zhou"
] |
https://openaccess.thecvf.com/content/ICCV2025/html/Zhang_CWNet_Causal_Wavelet_Network_for_Low-Light_Image_Enhancement_ICCV_2025_paper.html
|
https://openaccess.thecvf.com/content/ICCV2025/papers/Zhang_CWNet_Causal_Wavelet_Network_for_Low-Light_Image_Enhancement_ICCV_2025_paper.pdf
|
https://openaccess.thecvf.com/content/ICCV2025/supplemental/Zhang_CWNet_Causal_Wavelet_ICCV_2025_supplemental.pdf
|
@InProceedings{Zhang_2025_ICCV,
author = {Zhang, Tongshun and Liu, Pingping and Lu, Yubing and Cai, Mengen and Zhang, Zijian and Zhang, Zhe and Zhou, Qiuzhan},
title = {CWNet: Causal Wavelet Network for Low-Light Image Enhancement},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2025},
pages = {8789-8799}
}
|
Traditional Low-Light Image Enhancement (LLIE) methods primarily focus on uniform brightness adjustment, often neglecting instance-level semantic information and the inherent characteristics of different features. To address these limitations, we propose CWNet (Causal Wavelet Network), a novel architecture that leverages wavelet transforms for causal reasoning. Specifically, our approach comprises two key components: 1) Inspired by the concept of intervention in causality, we adopt a causal reasoning perspective to reveal the underlying causal relationships in low-light enhancement. From a global perspective, we employ a metric learning strategy to ensure causal embeddings adhere to causal principles, separating them from non-causal confounding factors while focusing on the invariance of causal factors. At the local level, we introduce an instance-level CLIP semantic loss to precisely maintain causal factor consistency. 2) Based on our causal analysis, we present a wavelet transform-based backbone network that effectively optimizes the recovery of frequency information, ensuring precise enhancement tailored to the specific attributes of wavelet transforms. Extensive experiments demonstrate that CWNet significantly outperforms current state-of-the-art methods across multiple datasets, showcasing its robust performance across diverse scenes. Code is available at https://github.com/bywlzts/CWNet-Causal-Wavelet-Network.
|
2507.10689
|
Accepted by ICCV 2025
|
https://github.com/bywlzts/CWNet-Causal-Wavelet-Network
| null |
[] |
[] |
[] |
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] |
22
|
Demeter: A Parametric Model of Crop Plant Morphology from the Real World
|
[
"Tianhang Cheng",
"Albert J. Zhai",
"Evan Z. Chen",
"Rui Zhou",
"Yawen Deng",
"Zitong Li",
"Kejie Zhao",
"Janice Shiu",
"Qianyu Zhao",
"Yide Xu",
"Xinlei Wang",
"Yuan Shen",
"Sheng Wang",
"Lisa Ainsworth",
"Kaiyu Guan",
"Shenlong Wang"
] |
https://openaccess.thecvf.com/content/ICCV2025/html/Cheng_Demeter_A_Parametric_Model_of_Crop_Plant_Morphology_from_the_ICCV_2025_paper.html
|
https://openaccess.thecvf.com/content/ICCV2025/papers/Cheng_Demeter_A_Parametric_Model_of_Crop_Plant_Morphology_from_the_ICCV_2025_paper.pdf
|
https://openaccess.thecvf.com/content/ICCV2025/supplemental/Cheng_Demeter_A_Parametric_ICCV_2025_supplemental.pdf
|
@InProceedings{Cheng_2025_ICCV,
author = {Cheng, Tianhang and Zhai, Albert J. and Chen, Evan Z. and Zhou, Rui and Deng, Yawen and Li, Zitong and Zhao, Kejie and Shiu, Janice and Zhao, Qianyu and Xu, Yide and Wang, Xinlei and Shen, Yuan and Wang, Sheng and Ainsworth, Lisa and Guan, Kaiyu and Wang, Shenlong},
title = {Demeter: A Parametric Model of Crop Plant Morphology from the Real World},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2025},
pages = {28740-28751}
}
|
Learning 3D parametric shape models of objects has gained popularity in vision and graphics and has showed broad utility in 3D reconstruction, generation, understanding, and simulation. While powerful models exist for humans and animals, equally expressive approaches for modeling plants are lacking. In this work, we present Demeter, a data-driven parametric model that encodes key factors of a plant morphology, including topology, shape, articulation, and deformation into a compact learned representation. Unlike previous parametric models, Demeter handles varying shape topology across various species and models three sources of shape variation: articulation, subcomponent shape variation, and non-rigid deformation. To advance crop plant modeling, we collected a large-scale, ground-truthed dataset from a soybean farm as a testbed. Experiments show that Demeter effectively synthesizes shapes, reconstructs structures, and simulates biophysical processes. Code and data will be open-sourced.
| null | null | null | null |
[] |
[] |
[] |
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0.028755217790603638,
-0.03109605424106121,
-0.004951639100909233,
-0.012302389368414879,
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] |
23
|
VideoLLaMB: Long Streaming Video Understanding with Recurrent Memory Bridges
|
[
"Yuxuan Wang",
"Yiqi Song",
"Cihang Xie",
"Yang Liu",
"Zilong Zheng"
] |
https://openaccess.thecvf.com/content/ICCV2025/html/Wang_VideoLLaMB_Long_Streaming_Video_Understanding_with_Recurrent_Memory_Bridges_ICCV_2025_paper.html
|
https://openaccess.thecvf.com/content/ICCV2025/papers/Wang_VideoLLaMB_Long_Streaming_Video_Understanding_with_Recurrent_Memory_Bridges_ICCV_2025_paper.pdf
|
https://openaccess.thecvf.com/content/ICCV2025/supplemental/Wang_VideoLLaMB_Long_Streaming_ICCV_2025_supplemental.pdf
|
@InProceedings{Wang_2025_ICCV,
author = {Wang, Yuxuan and Song, Yiqi and Xie, Cihang and Liu, Yang and Zheng, Zilong},
title = {VideoLLaMB: Long Streaming Video Understanding with Recurrent Memory Bridges},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2025},
pages = {24170-24181}
}
|
Recent advancements in large-scale video-language models have shown significant potential for real-time planning and detailed interactions. However, their high computational demands and the scarcity of annotated datasets limit their practicality for academic researchers. In this work, we introduce VideoLLaMB, a novel and efficient framework for long video understanding that leverages recurrent memory bridges and temporal memory tokens to enable seamless encoding of entire video sequences with preserved semantic continuity. Central to our approach is a SceneTiling algorithm that segments videos into coherent semantic units, facilitating robust understanding across tasks without requiring additional training. VideoLLaMB achieves state-of-the-art performance, surpassing existing models by 4.2 points on four VideoQA benchmarks and by 2.06 points on egocentric planning tasks. Notably, it maintains strong performance under extreme video length scaling (up to 8x) and excels at fine-grained frame retrieval on our proposed Needle in a Video Haystack (NIAVH) benchmark. With linear GPU memory scaling, VideoLLaMB processes up to 320 frames using a single Nvidia A100 GPU, despite being trained on only 16 frames--offering an unprecedented balance of accuracy, scalability, and cost-effectiveness. This makes it highly accessible and practical for the academic community.
|
2409.01071
|
To appear at ICCV 2025
| null | null |
[] |
[
"ColorfulAI/videollamb-llava-1.5-7b"
] |
[] |
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] |
24
|
Automated Red Teaming for Text-to-Image Models through Feedback-Guided Prompt Iteration with Vision-Language Models
|
[
"Wei Xu",
"Kangjie Chen",
"Jiawei Qiu",
"Yuyang Zhang",
"Run Wang",
"Jin Mao",
"Tianwei Zhang",
"Lina Wang"
] |
https://openaccess.thecvf.com/content/ICCV2025/html/Xu_Automated_Red_Teaming_for_Text-to-Image_Models_through_Feedback-Guided_Prompt_Iteration_ICCV_2025_paper.html
|
https://openaccess.thecvf.com/content/ICCV2025/papers/Xu_Automated_Red_Teaming_for_Text-to-Image_Models_through_Feedback-Guided_Prompt_Iteration_ICCV_2025_paper.pdf
|
https://openaccess.thecvf.com/content/ICCV2025/supplemental/Xu_Automated_Red_Teaming_ICCV_2025_supplemental.pdf
|
@InProceedings{Xu_2025_ICCV,
author = {Xu, Wei and Chen, Kangjie and Qiu, Jiawei and Zhang, Yuyang and Wang, Run and Mao, Jin and Zhang, Tianwei and Wang, Lina},
title = {Automated Red Teaming for Text-to-Image Models through Feedback-Guided Prompt Iteration with Vision-Language Models},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2025},
pages = {18575-18584}
}
|
Text-to-image models have achieved remarkable progress in generating high-quality images from textual prompts, yet their potential for misuse like generating unsafe content remains a critical concern. Existing safety mechanisms, such as filtering and fine-tuning, remain insufficient in preventing vulnerabilities exposed by adversarial prompts. To systematically evaluate these weaknesses, we propose an automated red-teaming framework, Feedback-Guided Prompt Iteration (FGPI), which utilizes a Vision-Language Model (VLM) as the red-teaming agent following a feedback-guide-rewrite paradigm for iterative prompt optimization. The red-teaming VLM analyzes prompt-image pairs based on evaluation results, provides feedback and modification strategies to enhance adversarial effectiveness while preserving safety constraints, and iteratively improves prompts. To enable this functionality, we construct a multi-turn conversational VQA dataset with over 6,000 instances, covering seven attack types and facilitating the fine-tuning of the red-teaming VLM. Extensive experiments demonstrate the effectiveness of our approach, achieving over 90% attack success rate within five iterations while maintaining prompt stealthiness and safety. The experiments also validate the adaptability, diversity, transferability, and explainability of FGPI. The source code and dataset are available at https://github.com/Weiww-Xu/FGPI.
| null | null | null | null |
[] |
[] |
[] |
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] |
25
|
CoA-VLA: Improving Vision-Language-Action Models via Visual-Text Chain-of-Affordance
|
[
"Jinming Li",
"Yichen Zhu",
"Zhibin Tang",
"Junjie Wen",
"Minjie Zhu",
"Xiaoyu Liu",
"Chengmeng Li",
"Ran Cheng",
"Yaxin Peng",
"Yan Peng",
"Feifei Feng"
] |
https://openaccess.thecvf.com/content/ICCV2025/html/Li_CoA-VLA_Improving_Vision-Language-Action_Models_via_Visual-Text_Chain-of-Affordance_ICCV_2025_paper.html
|
https://openaccess.thecvf.com/content/ICCV2025/papers/Li_CoA-VLA_Improving_Vision-Language-Action_Models_via_Visual-Text_Chain-of-Affordance_ICCV_2025_paper.pdf
|
https://openaccess.thecvf.com/content/ICCV2025/supplemental/Li_CoA-VLA_Improving_Vision-Language-Action_ICCV_2025_supplemental.pdf
|
@InProceedings{Li_2025_ICCV,
author = {Li, Jinming and Zhu, Yichen and Tang, Zhibin and Wen, Junjie and Zhu, Minjie and Liu, Xiaoyu and Li, Chengmeng and Cheng, Ran and Peng, Yaxin and Peng, Yan and Feng, Feifei},
title = {CoA-VLA: Improving Vision-Language-Action Models via Visual-Text Chain-of-Affordance},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2025},
pages = {9759-9769}
}
|
Robot foundation models, particularly Vision-Language-Action (VLA) models, have garnered significant attention for their ability to enhance robot policy learning, greatly improving robot's generalization and robustness. OpenAI's recent model, O1, showcased impressive capabilities in solving complex problems by utilizing extensive reasoning chains. This prompts an important question: can robot models achieve better performance in multi-task, complex environments by reviewing prior observations and then providing task-specific reasoning to guide action prediction?In this paper, we introduce Chain-of-Affordance (CoA-VLA), a novel approach to scaling robot models by incorporating reasoning in the format of sequential robot affordances to facilitate task completion. Specifically, we prompt the model to consider the following four types of affordances before taking action: (1) object affordance-- what object to manipulate and where it is; (2) grasp affordance -- the specific object part to grasp; (3) spatial affordance -- the optimal space to place the object; and (4) movement affordance-- the collision-free path for movement. We further transform each affordance into two prompting formats: visual affordance and textual affordance. We introduce a novel vision-language co-injection module that integrates this knowledge into the policy network. This allows the robot to leverage essential contextual information during action inference, resulting in improved precision and robustness. Our experiments demonstrate that CoA-VLA outperforms state-of-the-art robot foundation models, including OpenVLA and Octo, on a variety of tasks. Furthermore, CoA-VLA exhibits strong generalization capabilities, including recognizing unseen object poses, identifying free space, and avoiding obstacles in novel environments.
| null | null | null | null |
[] |
[] |
[] |
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] |
26
|
HiERO: Understanding the Hierarchy of Human Behavior Enhances Reasoning on Egocentric Videos
|
[
"Simone Alberto Peirone",
"Francesca Pistilli",
"Giuseppe Averta"
] |
https://openaccess.thecvf.com/content/ICCV2025/html/Peirone_HiERO_Understanding_the_Hierarchy_of_Human_Behavior_Enhances_Reasoning_on_ICCV_2025_paper.html
|
https://openaccess.thecvf.com/content/ICCV2025/papers/Peirone_HiERO_Understanding_the_Hierarchy_of_Human_Behavior_Enhances_Reasoning_on_ICCV_2025_paper.pdf
|
https://openaccess.thecvf.com/content/ICCV2025/supplemental/Peirone_HiERO_Understanding_the_ICCV_2025_supplemental.pdf
|
@InProceedings{Peirone_2025_ICCV,
author = {Peirone, Simone Alberto and Pistilli, Francesca and Averta, Giuseppe},
title = {HiERO: Understanding the Hierarchy of Human Behavior Enhances Reasoning on Egocentric Videos},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2025},
pages = {19862-19871}
}
|
Human activities are particularly complex and variable, and this makes challenging for deep learning models to reason about them. However, we note that such variability does have an underlying structure, composed of a hierarchy of patterns of related actions. We argue that such structure can emerge naturally from unscripted videos of human activities, and can be leveraged to better reason about their content. We present HiERO, a weakly-supervised method to enrich video segments features with the corresponding hierarchical activity threads. By aligning video clips with their narrated descriptions, HiERO infers contextual, semantic and temporal reasoning with an hierarchical architecture. We prove the potential of our enriched features with multiple video-text alignment benchmarks (EgoMCQ, EgoNLQ) with minimal additional training, and in zero-shot for procedure learning tasks (EgoProceL and Ego4D Goal-Step). Notably, HiERO achieves state-of-the-art performance in all the benchmarks, and for procedure learning tasks it outperforms fully-supervised methods by a large margin (+12.5% F1 on EgoProceL) in zero shot. Our results prove the relevance of using knowledge of the hierarchy of human activities for multiple reasoning tasks in egocentric vision.
|
2505.12911
|
Project page https://github.com/sapeirone/hiero
| null |
https://sapeirone.github.io/HiERO/
|
[] |
[] |
[] |
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] |
27
|
FVGen: Accelerating Novel-View Synthesis with Adversarial Video Diffusion Distillation
|
[
"Wenbin Teng",
"Gonglin Chen",
"Haiwei Chen",
"Yajie Zhao"
] |
https://openaccess.thecvf.com/content/ICCV2025/html/Teng_FVGen_Accelerating_Novel-View_Synthesis_with_Adversarial_Video_Diffusion_Distillation_ICCV_2025_paper.html
|
https://openaccess.thecvf.com/content/ICCV2025/papers/Teng_FVGen_Accelerating_Novel-View_Synthesis_with_Adversarial_Video_Diffusion_Distillation_ICCV_2025_paper.pdf
|
https://openaccess.thecvf.com/content/ICCV2025/supplemental/Teng_FVGen_Accelerating_Novel-View_ICCV_2025_supplemental.pdf
|
@InProceedings{Teng_2025_ICCV,
author = {Teng, Wenbin and Chen, Gonglin and Chen, Haiwei and Zhao, Yajie},
title = {FVGen: Accelerating Novel-View Synthesis with Adversarial Video Diffusion Distillation},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2025},
pages = {26095-26105}
}
|
Recent progress in 3D reconstruction has enabled realistic 3D models from dense image captures, yet challenges persist with sparse views, often leading to artifacts in unseen areas. Recent works leverage Video Diffusion Models (VDMs) to generate dense observations, filling the gaps when only sparse views are available for 3D reconstruction tasks. A significant limitation of these methods is their slow sampling speed when using VDMs. In this paper, we present FVGen, a novel framework that addresses this challenge by enabling fast novel view synthesis using VDMs in as few as 4 sampling steps. We propose a novel video diffusion model distillation method that distills a multi-step denoising teacher model into a few-step denoising student model using Generative Adversarial Networks (GANs) and softened reverse KL-divergence minimization. Extensive experiments on real-world datasets show that, compared to prior works, our framework generates the same number of novel views with similar (or even better) visual quality while reducing sampling time by more than 90%. FVGen significantly improves time efficiency for downstream reconstruction tasks, particularly when working with sparse input views (more than 2) where pre-trained VDMs need to be run multiple times to achieve better spatial coverage. Our code will be released upon acceptance of the paper.
|
2508.06392
| null | null | null |
[] |
[] |
[] |
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] |
28
|
ZFusion: Efficient Deep Compositional Zero-shot Learning for Blind Image Super-Resolution with Generative Diffusion Prior
|
[
"Alireza Esmaeilzehi",
"Hossein Zaredar",
"Yapeng Tian",
"Laleh Seyyed-Kalantari"
] |
https://openaccess.thecvf.com/content/ICCV2025/html/Esmaeilzehi_ZFusion_Efficient_Deep_Compositional_Zero-shot_Learning_for_Blind_Image_Super-Resolution_ICCV_2025_paper.html
|
https://openaccess.thecvf.com/content/ICCV2025/papers/Esmaeilzehi_ZFusion_Efficient_Deep_Compositional_Zero-shot_Learning_for_Blind_Image_Super-Resolution_ICCV_2025_paper.pdf
|
https://openaccess.thecvf.com/content/ICCV2025/supplemental/Esmaeilzehi_ZFusion_Efficient_Deep_ICCV_2025_supplemental.pdf
|
@InProceedings{Esmaeilzehi_2025_ICCV,
author = {Esmaeilzehi, Alireza and Zaredar, Hossein and Tian, Yapeng and Seyyed-Kalantari, Laleh},
title = {ZFusion: Efficient Deep Compositional Zero-shot Learning for Blind Image Super-Resolution with Generative Diffusion Prior},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2025},
pages = {12338-12348}
}
|
Deep blind image super resolution (Blind SR) schemes strive to provide high performances under various image degradation processes. Despite the significant advancement in the area of Blind SR, the performances of these methods still may not be as high as one would desire in the case of real-world degradation operations. In this paper, we develop a novel diffusion-based Blind SR method, which, by leveraging compositional zero-shot learning, is able to provide superior performances for both synthetic and real-world unknown degradation processes. Specifically, we first extract both synthetic and real-world degradation embeddings from the input visual signal in a compositional zero-shot fashion. Next, we have efficiently embedded such degradation embeddings in the architecture of our diffusion-based scheme for guiding the diffusion feature generation process. The results of extensive experiments have demonstrated the effectiveness of the proposed Blind SR method over the state-of-the-art algorithms. Our source code and pre-trained models will be publicly available.
| null | null | null | null |
[] |
[] |
[] |
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] |
29
|
Doodle Your Keypoints: Sketch-Based Few-Shot Keypoint Detection
|
[
"Subhajit Maity",
"Ayan Kumar Bhunia",
"Subhadeep Koley",
"Pinaki Nath Chowdhury",
"Aneeshan Sain",
"Yi-Zhe Song"
] |
https://openaccess.thecvf.com/content/ICCV2025/html/Maity_Doodle_Your_Keypoints_Sketch-Based_Few-Shot_Keypoint_Detection_ICCV_2025_paper.html
|
https://openaccess.thecvf.com/content/ICCV2025/papers/Maity_Doodle_Your_Keypoints_Sketch-Based_Few-Shot_Keypoint_Detection_ICCV_2025_paper.pdf
|
https://openaccess.thecvf.com/content/ICCV2025/supplemental/Maity_Doodle_Your_Keypoints_ICCV_2025_supplemental.pdf
|
@InProceedings{Maity_2025_ICCV,
author = {Maity, Subhajit and Bhunia, Ayan Kumar and Koley, Subhadeep and Chowdhury, Pinaki Nath and Sain, Aneeshan and Song, Yi-Zhe},
title = {Doodle Your Keypoints: Sketch-Based Few-Shot Keypoint Detection},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2025},
pages = {284-296}
}
|
Keypoint detection, integral to modern machine perception, faces challenges in few-shot learning, particularly when source data from the same distribution as the query is unavailable. This gap is addressed by leveraging sketches, a popular form of human expression, providing a source-free alternative. However, challenges arise in mastering cross-modal embeddings and handling user-specific sketch styles. Our proposed framework overcomes these hurdles with a prototypical setup, combined with a grid-based locator and prototypical domain adaptation. We also demonstrate success in few-shot convergence across novel keypoints and classes through extensive experiments.
|
2507.07994
|
Accepted at ICCV 2025. Project Page: https://subhajitmaity.me/DYKp
| null | null |
[] |
[] |
[] |
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] |
30
|
Open-Vocabulary Octree-Graph for 3D Scene Understanding
|
[
"Zhigang Wang",
"Yifei Su",
"Chenhui Li",
"Dong Wang",
"Yan Huang",
"Xuelong Li",
"Bin Zhao"
] |
https://openaccess.thecvf.com/content/ICCV2025/html/Wang_Open-Vocabulary_Octree-Graph_for_3D_Scene_Understanding_ICCV_2025_paper.html
|
https://openaccess.thecvf.com/content/ICCV2025/papers/Wang_Open-Vocabulary_Octree-Graph_for_3D_Scene_Understanding_ICCV_2025_paper.pdf
| null |
@InProceedings{Wang_2025_ICCV,
author = {Wang, Zhigang and Su, Yifei and Li, Chenhui and Wang, Dong and Huang, Yan and Li, Xuelong and Zhao, Bin},
title = {Open-Vocabulary Octree-Graph for 3D Scene Understanding},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2025},
pages = {7037-7047}
}
|
Open-vocabulary 3D scene understanding is indispensable for embodied agents. Recent works leverage pretrained vision-language models (VLMs) for object segmentation and project them to point clouds to build 3D maps. Despite progress, a point cloud is a set of unordered coordinates that requires substantial storage space and can not directly convey occupancy information or spatial relation, making existing methods inefficient for downstream tasks, e.g., path planning and complex text-based object retrieval. To address these issues, we propose Octree-Graph, a novel scene representation for open-vocabulary 3D scene understanding. Specifically, a Chronological Group-wise Segment Merging (CGSM) strategy and an Instance Feature Aggregation (IFA) algorithm are first designed to get 3D instances and corresponding semantic features. Subsequently, an adaptive-octree structure is developed that stores semantics and depicts the occupancy of an object adjustably according to its shape. Finally, the Octree-Graph is constructed where each adaptive-octree acts as a graph node, and edges describe the spatial relations among nodes. Extensive experiments on various tasks are conducted on several widely-used datasets, demonstrating the versatility and effectiveness of our method. Code is available at https://github.com/yifeisu/OV-Octree-Graph.
|
2411.16253
|
11pages,7figures
| null | null |
[] |
[] |
[] |
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] |
31
|
FlexGen: Flexible Multi-View Generation from Text and Image Inputs
|
[
"Xinli Xu",
"Wenhang Ge",
"Jiantao Lin",
"Jiawei Feng",
"Lie Xu",
"Hanfeng Zhao",
"Shunsi Zhang",
"Ying-Cong Chen"
] |
https://openaccess.thecvf.com/content/ICCV2025/html/Xu_FlexGen_Flexible_Multi-View_Generation_from_Text_and_Image_Inputs_ICCV_2025_paper.html
|
https://openaccess.thecvf.com/content/ICCV2025/papers/Xu_FlexGen_Flexible_Multi-View_Generation_from_Text_and_Image_Inputs_ICCV_2025_paper.pdf
|
https://openaccess.thecvf.com/content/ICCV2025/supplemental/Xu_FlexGen_Flexible_Multi-View_ICCV_2025_supplemental.pdf
|
@InProceedings{Xu_2025_ICCV,
author = {Xu, Xinli and Ge, Wenhang and Lin, Jiantao and Feng, Jiawei and Xu, Lie and Zhao, Hanfeng and Zhang, Shunsi and Chen, Ying-Cong},
title = {FlexGen: Flexible Multi-View Generation from Text and Image Inputs},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2025},
pages = {18714-18724}
}
|
In this work, we introduce FlexGen, a flexible framework designed to generate controllable and consistent multi-view images, conditioned on a single-view image, or a text prompt, or both. FlexGen tackles the challenges of controllable multi-view synthesis through additional conditioning on 3D-aware text annotations. We utilize the strong reasoning capabilities of GPT-4V to generate 3D-aware text annotations. By analyzing four orthogonal views of an object arranged as tiled multi-view images, GPT-4V can produce text annotations that include 3D-aware information with spatial relationship. By integrating the control signal with proposed adaptive dual-control module, our model can generate multi-view images that correspond to the specified text. FlexGen supports multiple controllable capabilities, allowing users to modify text prompts to generate reasonable and corresponding unseen parts. Additionally, users can influence attributes such as appearance and material properties, including metallic and roughness. Extensive experiments demonstrate that our approach offers enhanced multiple controllability, marking a significant advancement over existing multi-view diffusion models. This work has substantial implications for fields requiring rapid and flexible 3D content creation, including game development, animation, and virtual reality.
|
2410.10745
|
16 pages, 13 figures
| null | null |
[] |
[] |
[] |
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] |
32
|
SummDiff: Generative Modeling of Video Summarization with Diffusion
|
[
"Kwanseok Kim",
"Jaehoon Hahm",
"Sumin Kim",
"Jinhwan Sul",
"Byunghak Kim",
"Joonseok Lee"
] |
https://openaccess.thecvf.com/content/ICCV2025/html/Kim_SummDiff_Generative_Modeling_of_Video_Summarization_with_Diffusion_ICCV_2025_paper.html
|
https://openaccess.thecvf.com/content/ICCV2025/papers/Kim_SummDiff_Generative_Modeling_of_Video_Summarization_with_Diffusion_ICCV_2025_paper.pdf
|
https://openaccess.thecvf.com/content/ICCV2025/supplemental/Kim_SummDiff_Generative_Modeling_ICCV_2025_supplemental.pdf
|
@InProceedings{Kim_2025_ICCV,
author = {Kim, Kwanseok and Hahm, Jaehoon and Kim, Sumin and Sul, Jinhwan and Kim, Byunghak and Lee, Joonseok},
title = {SummDiff: Generative Modeling of Video Summarization with Diffusion},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2025},
pages = {15096-15106}
}
|
Video summarization is a task of shortening a video by choosing a subset of frames while preserving its essential moments. Despite the innate subjectivity of the task, previous works have deterministically regressed to an averaged frame score over multiple raters, ignoring the inherent subjectivity of what constitutes a "good" summary. We propose a novel problem formulation by framing video summarization as a conditional generation task, allowing a model to learn the distribution of good summaries and to generate multiple plausible summaries that better reflect varying human perspectives. Adopting diffusion models for the first time in video summarization, our proposed method, SummDiff, dynamically adapts to visual contexts and generates multiple candidate summaries conditioned on the input video. Extensive experiments demonstrate that SummDiff not only achieves the state-of-the-art performance on various benchmarks but also produces summaries that closely align with individual annotator preferences. Moreover, we provide a deeper insight with novel metrics from an analysis of the knapsack, which is an important last step of generating summaries but has been overlooked in evaluation.
|
2510.08458
| null | null | null |
[] |
[] |
[] |
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-0.002196788089349866,
-0.002514343010261655,
-0.050719503313302994,
-0.05019928887486458,
-0.025821620598435402,
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] |
33
|
FlowDPS : Flow-Driven Posterior Sampling for Inverse Problems
|
[
"Jeongsol Kim",
"Bryan Sangwoo Kim",
"Jong Chul Ye"
] |
https://openaccess.thecvf.com/content/ICCV2025/html/Kim_FlowDPS__Flow-Driven_Posterior_Sampling_for_Inverse_Problems_ICCV_2025_paper.html
|
https://openaccess.thecvf.com/content/ICCV2025/papers/Kim_FlowDPS__Flow-Driven_Posterior_Sampling_for_Inverse_Problems_ICCV_2025_paper.pdf
|
https://openaccess.thecvf.com/content/ICCV2025/supplemental/Kim_FlowDPS__Flow-Driven_ICCV_2025_supplemental.pdf
|
@InProceedings{Kim_2025_ICCV,
author = {Kim, Jeongsol and Kim, Bryan Sangwoo and Ye, Jong Chul},
title = {FlowDPS : Flow-Driven Posterior Sampling for Inverse Problems},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2025},
pages = {12328-12337}
}
|
Flow matching is a recent state-of-the-art framework for generative modeling based on ordinary differential equations (ODEs). While closely related to diffusion models, it provides a more general perspective on generative modeling.Although inverse problem solving has been extensively explored using diffusion models, it has not been rigorously examined within the broader context of flow models. Therefore, here we extend the diffusion inverse solvers (DIS)-- which perform posterior sampling by combining a denoising diffusion prior with an likelihood gradient--into the flow framework. Specifically, by driving the flow-version of Tweedie's formula, we decompose the flow ODE into two components: one for clean image estimation and the other for noise estimation.By integrating the likelihood gradient and stochastic noise into each component, respectively, we demonstrate that posterior sampling for inverse problem solving can be effectively achieved using flows. Our proposed solver, Flow-Driven Posterior Sampling (FlowDPS), can also be seamlessly integrated into a latent flow model with a transformer architecture. Across four linear inverse problems, we confirm that FlowDPS outperforms state-of-the-art alternatives, all without requiring additional training.
|
2503.08136
| null | null | null |
[] |
[] |
[] |
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] |
34
|
Head2Body: Body Pose Generation from Multi-sensory Head-mounted Inputs
|
[
"Minh Tran",
"Hongda Mao",
"Qingshuang Chen",
"Yelin Kim"
] |
https://openaccess.thecvf.com/content/ICCV2025/html/Tran_Head2Body_Body_Pose_Generation_from_Multi-sensory_Head-mounted_Inputs_ICCV_2025_paper.html
|
https://openaccess.thecvf.com/content/ICCV2025/papers/Tran_Head2Body_Body_Pose_Generation_from_Multi-sensory_Head-mounted_Inputs_ICCV_2025_paper.pdf
|
https://openaccess.thecvf.com/content/ICCV2025/supplemental/Tran_Head2Body_Body_Pose_ICCV_2025_supplemental.pdf
|
@InProceedings{Tran_2025_ICCV,
author = {Tran, Minh and Mao, Hongda and Chen, Qingshuang and Kim, Yelin},
title = {Head2Body: Body Pose Generation from Multi-sensory Head-mounted Inputs},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2025},
pages = {6849-6858}
}
|
Generating body pose from head-mounted, egocentric inputs is essential for immersive VR/AR and assistive technologies, as it supports more natural interactions. However, the task is challenging due to limited visibility of body parts in first-person views and the sparseness of sensory data, with only a single device placed on the head. To address these challenges, we introduce Head2Body, a novel framework for body pose estimation that effectively combines IMU and visual data. First, we introduce a pre-trained IMU encoder, trained on over 1,700 hours of head-IMU data from wearable eyeglasses, to better capture detailed temporal motion cues given limited labeled egocentric pose data. For visual processing, we leverage large vision-language models (LVLMs) to segment body parts that appear sporadically in video frames to improve visual feature extraction. To better guide the pose generation process with sparse signals from only head-mounted devices, we incorporates a Vector Quantized Variational Autoencoder (VQ-VAE) to represent poses as discrete tokens, which capture high-frequency motion patterns and provide a more structured representation of body pose. Our experiments demonstrate the effectiveness of the proposed approach, yielding 6-13% gains over state-of-the-art baselines on four datasets: AMASS, KinPoly, GIMO, and EgoExo4D. By capturing subtle temporal dynamics and leveraging complementary sensory data, our approach advances accurate egocentric body pose estimation and sets a new benchmark for multi-modal, first-person motion tracking.
| null | null | null | null |
[] |
[] |
[] |
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] |
35
|
Closed-Loop Transfer for Weakly-supervised Affordance Grounding
|
[
"Jiajin Tang",
"Zhengxuan Wei",
"Ge Zheng",
"Sibei Yang"
] |
https://openaccess.thecvf.com/content/ICCV2025/html/Tang_Closed-Loop_Transfer_for_Weakly-supervised_Affordance_Grounding_ICCV_2025_paper.html
|
https://openaccess.thecvf.com/content/ICCV2025/papers/Tang_Closed-Loop_Transfer_for_Weakly-supervised_Affordance_Grounding_ICCV_2025_paper.pdf
| null |
@InProceedings{Tang_2025_ICCV,
author = {Tang, Jiajin and Wei, Zhengxuan and Zheng, Ge and Yang, Sibei},
title = {Closed-Loop Transfer for Weakly-supervised Affordance Grounding},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2025},
pages = {9530-9539}
}
|
Humans can perform previously unexperienced interactions with novel objects simply by observing others engage with them. Weakly-supervised affordance grounding mimics this process by learning to locate object regions that enable actions on egocentric images, using exocentric interaction images with image-level annotations. However, extracting affordance knowledge solely from exocentric images and transferring it one-way to egocentric images limits the applicability of previous works in complex interaction scenarios. Instead, this study introduces LoopTrans, a novel closed-loop framework that not only transfers knowledge from exocentric to egocentric but also transfers back to enhance exocentric knowledge extraction. Within LoopTrans, several innovative mechanisms are introduced, including unified cross-modal localization and denoising knowledge distillation, to bridge domain gaps between object-centered egocentric and interaction-centered exocentric images while enhancing knowledge transfer. Experiments show that LoopTrans achieves consistent improvements across all metrics on image and video benchmarks, even handling challenging scenarios where object interaction regions are fully occluded by the human body.
| null | null | null | null |
[] |
[] |
[] |
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] |
36
|
OminiControl: Minimal and Universal Control for Diffusion Transformer
|
[
"Zhenxiong Tan",
"Songhua Liu",
"Xingyi Yang",
"Qiaochu Xue",
"Xinchao Wang"
] |
https://openaccess.thecvf.com/content/ICCV2025/html/Tan_OminiControl_Minimal_and_Universal_Control_for_Diffusion_Transformer_ICCV_2025_paper.html
|
https://openaccess.thecvf.com/content/ICCV2025/papers/Tan_OminiControl_Minimal_and_Universal_Control_for_Diffusion_Transformer_ICCV_2025_paper.pdf
|
https://openaccess.thecvf.com/content/ICCV2025/supplemental/Tan_OminiControl_Minimal_and_ICCV_2025_supplemental.pdf
|
@InProceedings{Tan_2025_ICCV,
author = {Tan, Zhenxiong and Liu, Songhua and Yang, Xingyi and Xue, Qiaochu and Wang, Xinchao},
title = {OminiControl: Minimal and Universal Control for Diffusion Transformer},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2025},
pages = {14940-14950}
}
|
We present OminiControl, a novel approach that rethinks how image conditions are integrated into Diffusion Transformer (DiT) architectures. Current image conditioning methods either introduce substantial parameter overhead or handle only specific control tasks effectively, limiting their practical versatility. OminiControl addresses these limitations through three key innovations: (1) a minimal architectural design that leverages the DiT's own VAE encoder and transformer blocks, requiring just 0.1% additional parameters; (2) a unified sequence processing strategy that combines condition tokens with image tokens for flexible token interactions; and (3) a dynamic position encoding mechanism that adapts to both spatially-aligned and non-aligned control tasks. Our extensive experiments show that this streamlined approach not only matches but surpasses the performance of specialized methods across multiple conditioning tasks. To overcome data limitations in subject-driven generation, we also introduce Subjects200K, a large-scale dataset of identity-consistent image pairs synthesized using DiT models themselves. This work demonstrates that effective image control can be achieved without architectural complexity, opening new possibilities for efficient and versatile image generation systems.
|
2411.15098
|
Accepted to ICCV 2025
| null | null |
[
"Yuanshi/OminiControl"
] |
[] |
[
"Yuanshi/Subjects200K"
] |
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] |
37
|
Zeroth-Order Fine-Tuning of LLMs in Random Subspaces
|
[
"Ziming Yu",
"Pan Zhou",
"Sike Wang",
"Jia Li",
"Mi Tian",
"Hua Huang"
] |
https://openaccess.thecvf.com/content/ICCV2025/html/Yu_Zeroth-Order_Fine-Tuning_of_LLMs_in_Random_Subspaces_ICCV_2025_paper.html
|
https://openaccess.thecvf.com/content/ICCV2025/papers/Yu_Zeroth-Order_Fine-Tuning_of_LLMs_in_Random_Subspaces_ICCV_2025_paper.pdf
|
https://openaccess.thecvf.com/content/ICCV2025/supplemental/Yu_Zeroth-Order_Fine-Tuning_of_ICCV_2025_supplemental.pdf
|
@InProceedings{Yu_2025_ICCV,
author = {Yu, Ziming and Zhou, Pan and Wang, Sike and Li, Jia and Tian, Mi and Huang, Hua},
title = {Zeroth-Order Fine-Tuning of LLMs in Random Subspaces},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2025},
pages = {4475-4485}
}
|
Fine-tuning Large Language Models (LLMs) has proven effective for a variety of downstream tasks. However, as LLMs grow in size, the memory demands for backpropagation become increasingly prohibitive. Zeroth-order (ZO) optimization methods offer a memory-efficient alternative by using forward passes to estimate gradients, but the variance of gradient estimates typically scales linearly with the model's parameter dimension--a significant issue for LLMs. In this paper, we propose the random Subspace Zeroth-order (SubZero) optimization to address the challenges posed by LLMs' high dimensionality. We introduce a low-rank perturbation tailored for LLMs that significantly reduces memory consumption while improving performance. Additionally, we prove that our gradient estimation closely approximates the backpropagation gradient, exhibits lower variance than traditional ZO methods, and ensures convergence when combined with SGD. Experimental results show that SubZero enhances fine-tuning performance and achieves faster convergence compared to standard ZO approaches like MeZO across various language modeling tasks. Code is available at https://github.com/zimingyy/SubZero.
|
2410.08989
|
ICCV 2025 camera-ready version
|
https://github.com/zimingyy/SubZero
| null |
[] |
[] |
[] |
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] |
38
|
G2D: Boosting Multimodal Learning with Gradient-Guided Distillation
|
[
"Mohammed Rakib",
"Arunkumar Bagavathi"
] |
https://openaccess.thecvf.com/content/ICCV2025/html/Rakib_G2D_Boosting_Multimodal_Learning_with_Gradient-Guided_Distillation_ICCV_2025_paper.html
|
https://openaccess.thecvf.com/content/ICCV2025/papers/Rakib_G2D_Boosting_Multimodal_Learning_with_Gradient-Guided_Distillation_ICCV_2025_paper.pdf
|
https://openaccess.thecvf.com/content/ICCV2025/supplemental/Rakib_G2D_Boosting_Multimodal_ICCV_2025_supplemental.pdf
|
@InProceedings{Rakib_2025_ICCV,
author = {Rakib, Mohammed and Bagavathi, Arunkumar},
title = {G2D: Boosting Multimodal Learning with Gradient-Guided Distillation},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2025},
pages = {4059-4068}
}
|
Multimodal learning aims to leverage information from diverse data modalities to achieve more comprehensive performance. However, conventional multimodal models often suffer from modality imbalance, where one or a few modalities dominate model optimization, leading to suboptimal feature representation and underutilization of weak modalities. To address this challenge, we introduce Gradient-Guided Distillation (G^ 2 D), a knowledge distillation framework that optimizes the multimodal model with a custom-built loss function that fuses both unimodal and multimodal objectives. G^ 2 D further incorporates a dynamic sequential modality prioritization (SMP) technique in the learning process to ensure each modality leads the learning process, avoiding the pitfall of stronger modalities overshadowing weaker ones. We validate G^ 2 D on multiple real-world datasets and show that G^ 2 D amplifies the significance of weak modalities while training and outperforms state-of-the-art methods in classification and regression tasks. Our code is available \href https://github.com/rAIson-Lab/G2D here .
|
2506.21514
|
Accepted at ICCV 2025
|
https://github.com/rAIson-Lab/G2D
| null |
[] |
[] |
[] |
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] |
39
|
AIComposer: Any Style and Content Image Composition via Feature Integration
|
[
"Haowen Li",
"Zhenfeng Fan",
"Zhang Wen",
"Zhengzhou Zhu",
"Yunjin Li"
] |
https://openaccess.thecvf.com/content/ICCV2025/html/Li_AIComposer_Any_Style_and_Content_Image_Composition_via_Feature_Integration_ICCV_2025_paper.html
|
https://openaccess.thecvf.com/content/ICCV2025/papers/Li_AIComposer_Any_Style_and_Content_Image_Composition_via_Feature_Integration_ICCV_2025_paper.pdf
|
https://openaccess.thecvf.com/content/ICCV2025/supplemental/Li_AIComposer_Any_Style_ICCV_2025_supplemental.zip
|
@InProceedings{Li_2025_ICCV,
author = {Li, Haowen and Fan, Zhenfeng and Wen, Zhang and Zhu, Zhengzhou and Li, Yunjin},
title = {AIComposer: Any Style and Content Image Composition via Feature Integration},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2025},
pages = {16840-16850}
}
|
Image composition has advanced significantly with large-scale pre-trained T2I diffusion models. Despite progress in same-domain composition, cross-domain composition remains under-explored. The main challenges are the stochastic nature of diffusion models and the style gap between input images, leading to failures and artifacts. Additionally, heavy reliance on text prompts limits practical applications. This paper presents the first cross-domain image composition method that does not require text prompts, allowing natural stylization and seamless compositions. Our method is efficient and robust, preserving the diffusion prior, as it involves minor steps for backward inversion and forward denoising without training the diffuser. Our method also uses a simple multilayer perceptron network to integrate CLIP features from foreground and background, manipulating diffusion with a local cross-attention strategy. It effectively preserves foreground content while enabling stable stylization without a pre-stylization network. Finally, we create a benchmark dataset with diverse contents and styles for fair evaluation, addressing the lack of testing datasets for cross-domain image composition. Our method outperforms state-of-the-art techniques in both qualitative and quantitative evaluations, significantly improving the LPIPS score by 30.5% and the CSD metric by 18.1%. We believe our method will advance future research and applications. Code and benchmark at https://github.com/sherlhw/AIComposer.
|
2507.20721
| null |
https://github.com/sherlhw/AIComposer
| null |
[] |
[] |
[] |
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] |
40
|
PAN-Crafter: Learning Modality-Consistent Alignment for PAN-Sharpening
|
[
"Jeonghyeok Do",
"Sungpyo Kim",
"Geunhyuk Youk",
"Jaehyup Lee",
"Munchurl Kim"
] |
https://openaccess.thecvf.com/content/ICCV2025/html/Do_PAN-Crafter_Learning_Modality-Consistent_Alignment_for_PAN-Sharpening_ICCV_2025_paper.html
|
https://openaccess.thecvf.com/content/ICCV2025/papers/Do_PAN-Crafter_Learning_Modality-Consistent_Alignment_for_PAN-Sharpening_ICCV_2025_paper.pdf
|
https://openaccess.thecvf.com/content/ICCV2025/supplemental/Do_PAN-Crafter_Learning_Modality-Consistent_ICCV_2025_supplemental.pdf
|
@InProceedings{Do_2025_ICCV,
author = {Do, Jeonghyeok and Kim, Sungpyo and Youk, Geunhyuk and Lee, Jaehyup and Kim, Munchurl},
title = {PAN-Crafter: Learning Modality-Consistent Alignment for PAN-Sharpening},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2025},
pages = {4242-4252}
}
|
PAN-sharpening aims to fuse high-resolution panchromatic (PAN) images with low-resolution multi-spectral (MS) images to generate high-resolution multi-spectral (HRMS) outputs. However, cross-modality misalignment---caused by sensor placement, acquisition timing, and resolution disparity---induces a fundamental challenge. Conventional deep learning methods assume perfect pixel-wise alignment and rely on per-pixel reconstruction losses, leading to spectral distortion, double edges, and blurring when misalignment is present. To address this, we propose PAN-Crafter, a modality-consistent alignment framework that explicitly mitigates the misalignment gap between PAN and MS modalities. At its core, Modality-Adaptive Reconstruction (MARs) enables a single network to jointly reconstruct HRMS and PAN images, leveraging PAN's high-frequency details as auxiliary self-supervision. Additionally, we introduce Cross-Modality Alignment-Aware Attention (CM3A), a novel mechanism that bidirectionally aligns MS texture to PAN structure and vice versa, enabling adaptive feature refinement across modalities. Extensive experiments on multiple benchmark datasets demonstrate that our PAN-Crafter outperforms the most recent state-of-the-art method in all metrics, even with 50.11xfaster inference time and 0.63xthe memory size. Furthermore, it demonstrates strong generalization performance on unseen satellite datasets, showing its robustness across different conditions.
| null | null | null |
https://kaist-viclab.github.io/PAN-Crafter_site/
|
[] |
[] |
[] |
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] |
41
|
M2SFormer: Multi-Spectral and Multi-Scale Attention with Edge-Aware Difficulty Guidance for Image Forgery Localization
|
[
"Ju-Hyeon Nam",
"Dong-Hyun Moon",
"Sang-Chul Lee"
] |
https://openaccess.thecvf.com/content/ICCV2025/html/Nam_M2SFormer_Multi-Spectral_and_Multi-Scale_Attention_with_Edge-Aware_Difficulty_Guidance_for_ICCV_2025_paper.html
|
https://openaccess.thecvf.com/content/ICCV2025/papers/Nam_M2SFormer_Multi-Spectral_and_Multi-Scale_Attention_with_Edge-Aware_Difficulty_Guidance_for_ICCV_2025_paper.pdf
|
https://openaccess.thecvf.com/content/ICCV2025/supplemental/Nam_M2SFormer_Multi-Spectral_and_ICCV_2025_supplemental.pdf
|
@InProceedings{Nam_2025_ICCV,
author = {Nam, Ju-Hyeon and Moon, Dong-Hyun and Lee, Sang-Chul},
title = {M2SFormer: Multi-Spectral and Multi-Scale Attention with Edge-Aware Difficulty Guidance for Image Forgery Localization},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2025},
pages = {15927-15938}
}
|
Image editing techniques have rapidly advanced, facilitating both innovative use cases and malicious manipulation of digital images. Deep learning-based methods have recently achieved high accuracy in pixel-level forgery localization, yet they frequently struggle with computational overhead and limited representation power, particularly for subtle or complex tampering. In this paper, we propose M2SFormer, a novel Transformer encoder-based framework designed to overcome these challenges. Unlike approaches that process spatial and frequency cues separately, M2SFormer unifies multi-frequency and multi-scale attentions in the skip connection, harnessing global context to better capture diverse forgery artifacts. Additionally, our framework addresses the loss of fine detail during upsampling by utilizing a global prior map--a curvature metric indicating the difficulty of forgery localization--which then guides a difficulty-guided attention module to preserve subtle manipulations more effectively. Extensive experiments on multiple benchmark datasets demonstrate that M2SFormer outperforms existing state-of-the-art models, offering superior generalization in detecting and localizing forgeries across unseen domains. Our M2SFormer code is available in Github Link.
|
2506.20922
|
Accepted in International Conference on Computer Vision (ICCV) 2025
| null | null |
[] |
[] |
[] |
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] |
42
|
Pinco: Position-induced Consistent Adapter for Diffusion Transformer in Foreground-conditioned Inpainting
|
[
"Guangben Lu",
"Yuzhen Du",
"Yizhe Tang",
"Zhimin Sun",
"Ran Yi",
"Yifan Qi",
"Tianyi Wang",
"Lizhuang Ma",
"Fangyuan Zou"
] |
https://openaccess.thecvf.com/content/ICCV2025/html/Lu_Pinco_Position-induced_Consistent_Adapter_for_Diffusion_Transformer_in_Foreground-conditioned_Inpainting_ICCV_2025_paper.html
|
https://openaccess.thecvf.com/content/ICCV2025/papers/Lu_Pinco_Position-induced_Consistent_Adapter_for_Diffusion_Transformer_in_Foreground-conditioned_Inpainting_ICCV_2025_paper.pdf
|
https://openaccess.thecvf.com/content/ICCV2025/supplemental/Lu_Pinco_Position-induced_Consistent_ICCV_2025_supplemental.pdf
|
@InProceedings{Lu_2025_ICCV,
author = {Lu, Guangben and Du, Yuzhen and Tang, Yizhe and Sun, Zhimin and Yi, Ran and Qi, Yifan and Wang, Tianyi and Ma, Lizhuang and Zou, Fangyuan},
title = {Pinco: Position-induced Consistent Adapter for Diffusion Transformer in Foreground-conditioned Inpainting},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2025},
pages = {15266-15276}
}
|
Foreground-conditioned inpainting aims to seamlessly fill the background region of an image by utilizing the provided foreground subject and a text description. While existing T2I-based image inpainting methods can be applied to this task, they suffer from issues of subject shape expansion, distortion, or impaired ability to align with the text description, resulting in inconsistencies between the visual elements and the text description. To address these challenges, we propose Pinco, a plug-and-play foreground-conditioned inpainting adapter that generates high-quality backgrounds with good text alignment while effectively preserving the shape of the foreground subject. Firstly, we design a Self-Consistent Adapter that integrates the foreground subject features into the layout-related self-attention layer, which helps to alleviate conflicts between the text and subject features by ensuring that the model can effectively consider the foreground subject's characteristics while processing the overall image layout. Secondly, we design a Decoupled Image Feature Extraction method that employs distinct architectures to extract semantic and spatial features separately, significantly improving subject feature extraction and ensuring high-quality preservation of the subject's shape. Thirdly, to ensure precise utilization of the extracted features and to focus attention on the subject region, we introduce a Shared Positional Embedding Anchor, greatly improving the model's understanding of subject features and boosting training efficiency. Extensive experiments demonstrate that our method achieves superior performance and efficiency in foreground-conditioned inpainting.
|
2412.03812
| null | null | null |
[] |
[] |
[] |
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] |
43
|
ReconDreamer++: Harmonizing Generative and Reconstructive Models for Driving Scene Representation
|
[
"Guosheng Zhao",
"Xiaofeng Wang",
"Chaojun Ni",
"Zheng Zhu",
"Wenkang Qin",
"Guan Huang",
"Xingang Wang"
] |
https://openaccess.thecvf.com/content/ICCV2025/html/Zhao_ReconDreamer_Harmonizing_Generative_and_Reconstructive_Models_for_Driving_Scene_Representation_ICCV_2025_paper.html
|
https://openaccess.thecvf.com/content/ICCV2025/papers/Zhao_ReconDreamer_Harmonizing_Generative_and_Reconstructive_Models_for_Driving_Scene_Representation_ICCV_2025_paper.pdf
|
https://openaccess.thecvf.com/content/ICCV2025/supplemental/Zhao_ReconDreamer_Harmonizing_Generative_ICCV_2025_supplemental.zip
|
@InProceedings{Zhao_2025_ICCV,
author = {Zhao, Guosheng and Wang, Xiaofeng and Ni, Chaojun and Zhu, Zheng and Qin, Wenkang and Huang, Guan and Wang, Xingang},
title = {ReconDreamer++: Harmonizing Generative and Reconstructive Models for Driving Scene Representation},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2025},
pages = {26718-26728}
}
|
Combining reconstruction models with generative models has emerged as a promising paradigm for closed-loop simulation in autonomous driving. For example, ReconDreamer has demonstrated remarkable success in rendering large-scale maneuvers. However, a significant gap remains between the generated data and real-world sensor observations, particularly in terms of fidelity for structured elements, such as the ground surface. To address these challenges, we propose ReconDreamer++, an enhanced framework that significantly improves the overall rendering quality by mitigating the domain gap and refining the representation of the ground surface.Specifically, ReconDreamer++ introduces the Novel Trajectory Deformable Network (NTDNet), which leverages learnable spatial deformation mechanisms to bridge the domain gap between synthesized novel views and original sensor observations. Moreover, for structured elements such as the ground surface, we preserve geometric prior knowledge in 3D Gaussians, andthe optimization process focuses on refining appearance attributes while preserving the underlying geometric structure. Experimental evaluations conducted on multiple datasets (Waymo, nuScenes, PandaSet, and EUVS) confirm the superior performance of ReconDreamer++. Specifically, on Waymo, ReconDreamer++ achieves performance comparable to Street Gaussians for the original trajectory while significantly outperforming ReconDreamer on novel trajectories. In particular, it achieves substantial improvements, including a 6.1% increase in NTA-IoU, a 23. 0% improvement in FID, and a remarkable 4.5% gain in the ground surface metric NTL-IoU, highlighting its effectiveness in accurately reconstructing structured elements such as the road surface.
|
2503.18438
|
Project Page: https://recondreamer-plus.github.io/
| null | null |
[] |
[] |
[] |
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] |
44
|
SyncDiff: Synchronized Motion Diffusion for Multi-Body Human-Object Interaction Synthesis
|
[
"Wenkun He",
"Yun Liu",
"Ruitao Liu",
"Li Yi"
] |
https://openaccess.thecvf.com/content/ICCV2025/html/He_SyncDiff_Synchronized_Motion_Diffusion_for_Multi-Body_Human-Object_Interaction_Synthesis_ICCV_2025_paper.html
|
https://openaccess.thecvf.com/content/ICCV2025/papers/He_SyncDiff_Synchronized_Motion_Diffusion_for_Multi-Body_Human-Object_Interaction_Synthesis_ICCV_2025_paper.pdf
|
https://openaccess.thecvf.com/content/ICCV2025/supplemental/He_SyncDiff_Synchronized_Motion_ICCV_2025_supplemental.zip
|
@InProceedings{He_2025_ICCV,
author = {He, Wenkun and Liu, Yun and Liu, Ruitao and Yi, Li},
title = {SyncDiff: Synchronized Motion Diffusion for Multi-Body Human-Object Interaction Synthesis},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2025},
pages = {11731-11743}
}
|
Synthesizing realistic human-object interaction motions is a critical problem in VR/AR and human animation. Unlike the commonly studied scenarios involving a single human or hand interacting with one object, we address a more generic multi-body setting with arbitrary numbers of humans, hands, and objects. The high correlations and mutual influences among bodies leads to two major challenges, for which we propose solutions. First, to satisfy the high demands for synchronization of different body motions, we mathematically derive a new set of alignment scores during the training process, and use maximum likelihood sampling on a dynamic graphical model for explicit synchronization during inference. Second, the high-frequency interactions between objects are often overshadowed by the large-scale low-frequency movements. To address this, we introduce frequency decomposition and explicitly represent high-frequency components in the frequency domain. Extensive experiments across five datasets with various multi-body configurations demonstrate the superiority of SyncDiff over existing state-of-the-art motion synthesis methods.
|
2412.20104
|
27 pages, 10 figures, 20 tables. Accepted by ICCV 2025
| null | null |
[] |
[] |
[] |
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] |
45
|
Rethinking Few Shot CLIP Benchmarks: A Critical Analysis in the Inductive Setting
|
[
"Alexey Kravets",
"Da Chen",
"Vinay P. Namboodiri"
] |
https://openaccess.thecvf.com/content/ICCV2025/html/Kravets_Rethinking_Few_Shot_CLIP_Benchmarks_A_Critical_Analysis_in_the_ICCV_2025_paper.html
|
https://openaccess.thecvf.com/content/ICCV2025/papers/Kravets_Rethinking_Few_Shot_CLIP_Benchmarks_A_Critical_Analysis_in_the_ICCV_2025_paper.pdf
|
https://openaccess.thecvf.com/content/ICCV2025/supplemental/Kravets_Rethinking_Few_Shot_ICCV_2025_supplemental.pdf
|
@InProceedings{Kravets_2025_ICCV,
author = {Kravets, Alexey and Chen, Da and Namboodiri, Vinay P.},
title = {Rethinking Few Shot CLIP Benchmarks: A Critical Analysis in the Inductive Setting},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2025},
pages = {1902-1911}
}
|
CLIP is a foundational model with transferable classification performance in the few-shot setting. Several methods have shown improved performance of CLIP using few-shot examples. However, so far all these techniques have been benchmarked using standard few-shot datasets. We argue that this mode of evaluation does not provide a true indication of the inductive generalization ability using few-shot examples. As most datasets have been seen by the CLIP model, the resultant setting can be termed as partially transductive. To solve this, we propose a pipeline that uses an unlearning technique to obtain true inductive baselines. In this new inductive setting, methods show a significant drop in performance (-55% on average among 13 baselines with multiple datasets). We validate the unlearning technique using oracle baselines. An improved few-shot classification technique is proposed that consistently obtains state-of-the-art performance over 13 other recent baseline methods on a comprehensive analysis with 5880 experiments - varying the datasets, differing number of few-shot examples, unlearning setting, and with different seeds. Thus, we identify the issue with the evaluation of CLIP-based few-shot classification, provide a solution using unlearning, propose new benchmarks, and provide an improved method.
|
2507.20834
| null | null | null |
[] |
[] |
[] |
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] |
46
|
Mind the Gap: Aligning Vision Foundation Models to Image Feature Matching
|
[
"Yuhan Liu",
"Jingwen Fu",
"Yang Wu",
"Kangyi Wu",
"Pengna Li",
"Jiayi Wu",
"Sanping Zhou",
"Jingmin Xin"
] |
https://openaccess.thecvf.com/content/ICCV2025/html/Liu_Mind_the_Gap_Aligning_Vision_Foundation_Models_to_Image_Feature_ICCV_2025_paper.html
|
https://openaccess.thecvf.com/content/ICCV2025/papers/Liu_Mind_the_Gap_Aligning_Vision_Foundation_Models_to_Image_Feature_ICCV_2025_paper.pdf
|
https://openaccess.thecvf.com/content/ICCV2025/supplemental/Liu_Mind_the_Gap_ICCV_2025_supplemental.pdf
|
@InProceedings{Liu_2025_ICCV,
author = {Liu, Yuhan and Fu, Jingwen and Wu, Yang and Wu, Kangyi and Li, Pengna and Wu, Jiayi and Zhou, Sanping and Xin, Jingmin},
title = {Mind the Gap: Aligning Vision Foundation Models to Image Feature Matching},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2025},
pages = {20313-20323}
}
|
Leveraging the vision foundation models has emerged as a mainstream paradigm that improves the performance of image feature matching. However, previous works have ignored the misalignment when introducing the foundation models into feature matching. The misalignment arises from the discrepancy between the foundation models focusing on single-image understanding and the cross-image understanding requirement of feature matching. Specifically, 1) the embeddings derived from commonly used foundation models exhibit discrepancies with the optimal embeddings required for feature matching; 2) lacking an effective mechanism to leverage the single-image understanding ability into cross-image understanding. A significant consequence of the misalignment is they struggle when addressing multi-instance feature matching problems. To address this, we introduce a simple but effective framework, called IMD (Image feature Matching with a pre-trained Diffusion model) with two parts: 1) Unlike the dominant solutions employing contrastive-learning based foundation models that emphasize global semantics, we integrate the generative-based diffusion models to effectively capture instance-level details. 2) We leverage the prompt mechanism in generative model as a natural tunnel, propose a novel cross-image interaction prompting module to facilitate bidirectional information interaction between image pairs. To more accurately measure the misalignment, we propose a new benchmark called IMIM, which focuses on multi-instance scenarios. Our proposed IMD establishes a new state-of-the-art in commonly evaluated benchmarks, and the superior improvement 12% in IMIM indicates our method efficiently mitigates the misalignment.
|
2507.10318
|
Accepted by ICCV 2025
| null | null |
[] |
[] |
[] |
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] |
47
|
CoStoDet-DDPM: Collaborative Training of Stochastic and Deterministic Models Improves Surgical Workflow Anticipation and Recognition
|
[
"Kaixiang Yang",
"Xin Li",
"Qiang Li",
"Zhiwei Wang"
] |
https://openaccess.thecvf.com/content/ICCV2025/html/Yang_CoStoDet-DDPM_Collaborative_Training_of_Stochastic_and_Deterministic_Models_Improves_Surgical_ICCV_2025_paper.html
|
https://openaccess.thecvf.com/content/ICCV2025/papers/Yang_CoStoDet-DDPM_Collaborative_Training_of_Stochastic_and_Deterministic_Models_Improves_Surgical_ICCV_2025_paper.pdf
|
https://openaccess.thecvf.com/content/ICCV2025/supplemental/Yang_CoStoDet-DDPM_Collaborative_Training_ICCV_2025_supplemental.pdf
|
@InProceedings{Yang_2025_ICCV,
author = {Yang, Kaixiang and Li, Xin and Li, Qiang and Wang, Zhiwei},
title = {CoStoDet-DDPM: Collaborative Training of Stochastic and Deterministic Models Improves Surgical Workflow Anticipation and Recognition},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2025},
pages = {23741-23751}
}
|
Anticipating and recognizing surgical workflows are critical for intelligent surgical assistance systems. However, existing methods rely on deterministic decision-making, struggling to generalize across the large anatomical and procedural variations inherent in real-world surgeries. In this paper, we introduce an innovative framework that incorporates stochastic modeling through a denoising diffusion probabilistic model (DDPM) into conventional deterministic learning for surgical workflow analysis. At the heart of our approach is a collaborative co-training paradigm: the DDPM branch captures procedural uncertainties to enrich feature representations, while the task branch focuses on predicting surgical phases and instrument usage. Theoretically, we demonstrate that this mutual refinement mechanism benefits both branches: the DDPM reduces prediction errors in uncertain scenarios, and the task branch directs the DDPM toward clinically meaningful representations. Notably, the DDPM branch is discarded during inference, enabling real-time predictions without sacrificing accuracy. Experiments on the Cholec80 dataset show that for the anticipation task, our method achieves a 16% reduction in eMAE compared to state-of-the-art approaches, and for phase recognition, it improves the Jaccard score by 1.0%. Additionally, on the AutoLaparo dataset, our method achieves a 1.5% improvement in the Jaccard score for phase recognition, while also exhibiting robust generalization to patient-specific variations. Our code and weight will be available.
| null | null | null | null |
[] |
[] |
[] |
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] |
48
|
GSOT3D: Towards Generic 3D Single Object Tracking in the Wild
|
[
"Yifan Jiao",
"Yunhao Li",
"Junhua Ding",
"Qing Yang",
"Song Fu",
"Heng Fan",
"Libo Zhang"
] |
https://openaccess.thecvf.com/content/ICCV2025/html/Jiao_GSOT3D_Towards_Generic_3D_Single_Object_Tracking_in_the_Wild_ICCV_2025_paper.html
|
https://openaccess.thecvf.com/content/ICCV2025/papers/Jiao_GSOT3D_Towards_Generic_3D_Single_Object_Tracking_in_the_Wild_ICCV_2025_paper.pdf
|
https://openaccess.thecvf.com/content/ICCV2025/supplemental/Jiao_GSOT3D_Towards_Generic_ICCV_2025_supplemental.pdf
|
@InProceedings{Jiao_2025_ICCV,
author = {Jiao, Yifan and Li, Yunhao and Ding, Junhua and Yang, Qing and Fu, Song and Fan, Heng and Zhang, Libo},
title = {GSOT3D: Towards Generic 3D Single Object Tracking in the Wild},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2025},
pages = {5469-5478}
}
|
In this paper, we present a novel benchmark, GSOT3D, that aims at facilitating development of generic 3D single object tracking (SOT) in the wild. Specifically, GSOT3D offers 620 sequences with 123K frames, and covers a wide selection of 54 object categories. Each sequence is offered with multiple modalities, including the point cloud (PC), RGB image, and depth. This allows GSOT3D to support various 3D tracking tasks, such as single-modal 3D SOT on PC and multi-modal 3D SOT on RGB-PC or RGB-D, and thus greatly broadens research directions for 3D object tracking. To provide high-quality per-frame 3D annotations, all sequences are labeled manually with multiple rounds of meticulous inspection and refinement. To our best knowledge, GSOT3D is the largest benchmark dedicated to various generic 3D object tracking tasks. To understand how existing 3D trackers perform and to provide comparisons for future research on GSOT3D, we assess eight representative point cloud-based tracking models. Our evaluation results exhibit that these models heavily degrade on GSOT3D, and more efforts are required for robust and generic 3D object tracking. Besides, to encourage future research, we present a simple yet effective generic 3D tracker, named PROT3D, that localizes the target object via a progressive spatial-temporal network and outperforms all current solutions by a large margin. By releasing GSOT3D, we expect to advance further 3D tracking in future research and applications. Our benchmark and model as well as the evaluation toolkit and results are publicly available at https://github.com/ailovejinx/GSOT3D.
|
2412.02129
|
14 pages, 12 figures
|
https://github.com/ailovejinx/GSOT3D
| null |
[] |
[] |
[
"Ailovejinx/GSOT3D"
] |
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] |
49
|
UnZipLoRA: Separating Content and Style from a Single Image
|
[
"Chang Liu",
"Viraj Shah",
"Aiyu Cui",
"Svetlana Lazebnik"
] |
https://openaccess.thecvf.com/content/ICCV2025/html/Liu_UnZipLoRA_Separating_Content_and_Style_from_a_Single_Image_ICCV_2025_paper.html
|
https://openaccess.thecvf.com/content/ICCV2025/papers/Liu_UnZipLoRA_Separating_Content_and_Style_from_a_Single_Image_ICCV_2025_paper.pdf
|
https://openaccess.thecvf.com/content/ICCV2025/supplemental/Liu_UnZipLoRA_Separating_Content_ICCV_2025_supplemental.pdf
|
@InProceedings{Liu_2025_ICCV,
author = {Liu, Chang and Shah, Viraj and Cui, Aiyu and Lazebnik, Svetlana},
title = {UnZipLoRA: Separating Content and Style from a Single Image},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2025},
pages = {16776-16785}
}
|
This paper introduces UnZipLoRA, a method for decomposing an image into its constituent subject and style, represented as two distinct LoRAs (Low-Rank Adaptations). Unlike existing personalization techniques that focus on either subject or style in isolation, or require separate training sets for each, UnZipLoRA disentangles these elements from a single image by training both the LoRAs simultaneously. UnZipLoRA ensures that the resulting LoRAs are compatible, i.e., they can be seamlessly combined using direct addition. UnZipLoRA enables independent manipulation and recontextualization of subject and style, including generating variations of each, applying the extracted style to new subjects, and recombining them to reconstruct the original image or create novel variations. To address the challenge of subject and style entanglement, UnZipLoRA employs a novel prompt separation technique, as well as column and block separation strategies to accurately preserve the characteristics of subject and style, and ensure compatibility between the learned LoRAs. Evaluation with human studies and quantitative metrics demonstrates UnZipLoRA's effectiveness compared to other state-of-the-art methods, including DreamBooth-LoRA, Inspiration Tree, and B-LoRA.
|
2412.04465
|
Project page: https://unziplora.github.io
| null | null |
[] |
[] |
[] |
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] |
50
|
What You Have is What You Track: Adaptive and Robust Multimodal Tracking
|
[
"Yuedong Tan",
"Jiawei Shao",
"Eduard Zamfir",
"Ruanjun Li",
"Zhaochong An",
"Chao Ma",
"Danda Paudel",
"Luc Van Gool",
"Radu Timofte",
"Zongwei Wu"
] |
https://openaccess.thecvf.com/content/ICCV2025/html/Tan_What_You_Have_is_What_You_Track_Adaptive_and_Robust_ICCV_2025_paper.html
|
https://openaccess.thecvf.com/content/ICCV2025/papers/Tan_What_You_Have_is_What_You_Track_Adaptive_and_Robust_ICCV_2025_paper.pdf
|
https://openaccess.thecvf.com/content/ICCV2025/supplemental/Tan_What_You_Have_ICCV_2025_supplemental.pdf
|
@InProceedings{Tan_2025_ICCV,
author = {Tan, Yuedong and Shao, Jiawei and Zamfir, Eduard and Li, Ruanjun and An, Zhaochong and Ma, Chao and Paudel, Danda and Van Gool, Luc and Timofte, Radu and Wu, Zongwei},
title = {What You Have is What You Track: Adaptive and Robust Multimodal Tracking},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2025},
pages = {3455-3465}
}
|
Multimodal data is known to be helpful for visual tracking by improving robustness to appearance variations. However, sensor synchronization challenges often compromise data availability, particularly in video settings where shortages can be temporal. Despite its importance, this area remains underexplored. In this paper, we present the first comprehensive study on tracker performance with temporally incomplete multimodal data. Unsurprisingly, under such a circumstance, existing trackers exhibit significant performance degradation, as their rigid architectures lack the adaptability needed to effectively handle missing modalities.To address these limitations, we propose a flexible framework for robust multimodal tracking. We venture that a tracker should dynamically activate computational units based on missing data rates. This is achieved through a novel Heterogeneous Mixture-of-Experts fusion mechanism with adaptive complexity, coupled with a video-level masking strategy that ensures both temporal consistency and spatial completeness -- critical for effective video tracking. Surprisingly, our model not only adapts to varying missing rates but also adjusts to scene complexity. Extensive experiments show that our model achieves SOTA performance across 9 benchmarks, excelling in both conventional complete and missing modality settings. The code and benchmark will be made publicly available.
|
2507.05899
|
ICCV2025 accepted
|
https://github.com/supertyd/FlexTrack
| null |
[] |
[] |
[] |
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] |
51
|
RareCLIP: Rarity-aware Online Zero-shot Industrial Anomaly Detection
|
[
"Jianfang He",
"Min Cao",
"Silong Peng",
"Qiong Xie"
] |
https://openaccess.thecvf.com/content/ICCV2025/html/He_RareCLIP_Rarity-aware_Online_Zero-shot_Industrial_Anomaly_Detection_ICCV_2025_paper.html
|
https://openaccess.thecvf.com/content/ICCV2025/papers/He_RareCLIP_Rarity-aware_Online_Zero-shot_Industrial_Anomaly_Detection_ICCV_2025_paper.pdf
|
https://openaccess.thecvf.com/content/ICCV2025/supplemental/He_RareCLIP_Rarity-aware_Online_ICCV_2025_supplemental.pdf
|
@InProceedings{He_2025_ICCV,
author = {He, Jianfang and Cao, Min and Peng, Silong and Xie, Qiong},
title = {RareCLIP: Rarity-aware Online Zero-shot Industrial Anomaly Detection},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2025},
pages = {24478-24487}
}
|
Large vision-language models such as CLIP have made significant strides in zero-shot anomaly detection through prompt engineering. However, most existing methods typically process each test image individually, ignoring the practical rarity of abnormal patches in real-world scenarios. Although some batch-based approaches exploit the rarity by processing multiple samples concurrently, they generally introduce unacceptable latency for real-time applications. To mitigate these limitations, we propose RareCLIP, a novel online zero-shot anomaly detection framework that enables sequential image processing in real-time without requiring prior knowledge of the target domain. RareCLIP capitalizes on the zero-shot capabilities of CLIP and integrates a dynamic test-time rarity estimation mechanism. A key innovation of our framework is the introduction of a prototype patch feature memory bank, which aggregates representative features from historical observations and continuously updates their corresponding rarity measures. For each incoming image patch, RareCLIP computes a rarity score by aggregating the rarity measures of its nearest neighbors within the memory bank. Moreover, we introduce a prototype sampling strategy based on dissimilarity to enhance computational efficiency, as well as a similarity calibration strategy to enhance the robustness of rarity estimation. Extensive experiments demonstrate that RareCLIP attains state-of-the-art performance with 98.2% image-level AUROC on MVTec AD and 94.4% on VisA, while achieving a latency of 59.4 ms. Code is available at https://github.com/hjf02/RareCLIP.
| null | null | null | null |
[] |
[] |
[] |
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] |
52
|
AdaDCP: Learning an Adapter with Discrete Cosine Prior for Clear-to-Adverse Domain Generalization
|
[
"Qi Bi",
"Yixian Shen",
"Jingjun Yi",
"Gui-Song Xia"
] |
https://openaccess.thecvf.com/content/ICCV2025/html/Bi_AdaDCP_Learning_an_Adapter_with_Discrete_Cosine_Prior_for_Clear-to-Adverse_ICCV_2025_paper.html
|
https://openaccess.thecvf.com/content/ICCV2025/papers/Bi_AdaDCP_Learning_an_Adapter_with_Discrete_Cosine_Prior_for_Clear-to-Adverse_ICCV_2025_paper.pdf
| null |
@InProceedings{Bi_2025_ICCV,
author = {Bi, Qi and Shen, Yixian and Yi, Jingjun and Xia, Gui-Song},
title = {AdaDCP: Learning an Adapter with Discrete Cosine Prior for Clear-to-Adverse Domain Generalization},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2025},
pages = {12997-13008}
}
|
Vision Foundation Model (VFM) provides an inherent generalization ability to unseen domains for downstream tasks. However, fine-tuning VFM to parse various adverse scenes (e.g., fog, snow, night) is particularly challenging, as these samples are difficult to collect. Using easy-to-acquire clear scenes as the source domain is a feasible solution, but a huge domain gap exists between clear and adverse scenes due to their dramatically different appearances. To address this challenge, this paper proposes AdaDCP, a VFM adapter with discrete cosine prior. The innovation originates from the observation that, the frequency components from a VFM exhibit either variant or invariant properties on adverse weather conditions after discrete cosine transform. Technically, the weather-invariant property learning preceives most of the scene content that is invariant to the adverse condition. The weather-variant property learning, in contrast, perceives the weather-specific information from different types of adverse conditions. Finally, the weather-invariant property alignment implicitly enforces the weather-variant components to incorporate the weather-invariant information, therefore mitigating the clear-to-adverse domain gap. Experiments conducted on eight unseen adverse scene segmentation datasets show its state-of-the-art performance.
| null | null | null | null |
[] |
[] |
[] |
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] |
53
|
HERMES: A Unified Self-Driving World Model for Simultaneous 3D Scene Understanding and Generation
|
[
"Xin Zhou",
"Dingkang Liang",
"Sifan Tu",
"Xiwu Chen",
"Yikang Ding",
"Dingyuan Zhang",
"Feiyang Tan",
"Hengshuang Zhao",
"Xiang Bai"
] |
https://openaccess.thecvf.com/content/ICCV2025/html/Zhou_HERMES_A_Unified_Self-Driving_World_Model_for_Simultaneous_3D_Scene_ICCV_2025_paper.html
|
https://openaccess.thecvf.com/content/ICCV2025/papers/Zhou_HERMES_A_Unified_Self-Driving_World_Model_for_Simultaneous_3D_Scene_ICCV_2025_paper.pdf
|
https://openaccess.thecvf.com/content/ICCV2025/supplemental/Zhou_HERMES_A_Unified_ICCV_2025_supplemental.pdf
|
@InProceedings{Zhou_2025_ICCV,
author = {Zhou, Xin and Liang, Dingkang and Tu, Sifan and Chen, Xiwu and Ding, Yikang and Zhang, Dingyuan and Tan, Feiyang and Zhao, Hengshuang and Bai, Xiang},
title = {HERMES: A Unified Self-Driving World Model for Simultaneous 3D Scene Understanding and Generation},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2025},
pages = {27817-27827}
}
|
Driving World Models (DWMs) have become essential for autonomous driving by enabling future scene prediction. However, existing DWMs are limited to scene generation and fail to incorporate scene understanding, which involves interpreting and reasoning about the driving environment. In this paper, we present a unified Driving World Model named HERMES. We seamlessly integrate 3D scene understanding and future scene evolution (generation) through a unified framework in driving scenarios. Specifically, HERMES leverages a Bird's-Eye View (BEV) representation to consolidate multi-view spatial information while preserving geometric relationships and interactions. We also introduce world queries, which incorporate world knowledge into BEV features via causal attention in the Large Language Model, enabling contextual enrichment for understanding and generation tasks. We conduct comprehensive studies on nuScenes and OmniDrive-nuScenes datasets to validate the effectiveness of our method. HERMES achieves state-of-the-art performance, reducing generation error by 32.4% and improving understanding metrics such as CIDEr by 8.0%. The model and code will be publicly released at https://github.com/LMD0311/HERMES.
|
2501.14729
|
Accepted by ICCV 2025. The code is available at
https://github.com/LMD0311/HERMES
|
https://github.com/LMD0311/HERMES
| null |
[] |
[
"LMD0311/HERMES"
] |
[] |
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] |
54
|
ArgMatch: Adaptive Refinement Gathering for Efficient Dense Matching
|
[
"Yuxin Deng",
"Kaining Zhang",
"Linfeng Tang",
"Jiaqi Yang",
"Jiayi Ma"
] |
https://openaccess.thecvf.com/content/ICCV2025/html/Deng_ArgMatch_Adaptive_Refinement_Gathering_for_Efficient_Dense_Matching_ICCV_2025_paper.html
|
https://openaccess.thecvf.com/content/ICCV2025/papers/Deng_ArgMatch_Adaptive_Refinement_Gathering_for_Efficient_Dense_Matching_ICCV_2025_paper.pdf
|
https://openaccess.thecvf.com/content/ICCV2025/supplemental/Deng_ArgMatch_Adaptive_Refinement_ICCV_2025_supplemental.pdf
|
@InProceedings{Deng_2025_ICCV,
author = {Deng, Yuxin and Zhang, Kaining and Tang, Linfeng and Yang, Jiaqi and Ma, Jiayi},
title = {ArgMatch: Adaptive Refinement Gathering for Efficient Dense Matching},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2025},
pages = {27369-27379}
}
|
Establishing dense correspondences is crucial yet computationally demanding in multi-view tasks. Although coarse-to-fine schemes mitigate computational costs, their efficiency remains limited by the substantial demands of heavy feature extractors and global matchers. In this paper, we propose Adaptive Refinement Gathering, a refinement pipeline that reduces reliance on these costly components without sacrificing accuracy. The pipeline consists of (i) a content-aware offset estimator that leverages content information for lightweight correlation volume encoding and decoding; (ii) a locally consistent match rectifier robust to large global initial errors; (iii) a locally consistent upsampler that yields fewer artifacts around depth-discontinuous edges. Additionally, we introduce an adaptive gating strategy that, in conjunction with local consistency, dynamically modulates the contribution of different components and pixels. This enables adaptive gradient backpropagation and allows the network to fully exploit its capacity. Compared to the state-of-the-art, our lightweight network, termed ArgMatch, achieves competitive performance in serval tasks, while significantly reducing the computational cost. Codes are available in https://github.com/ACuOoOoO/argmatch.
| null | null | null | null |
[] |
[] |
[] |
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] |
55
|
Enhancing Image Restoration Transformer via Adaptive Translation Equivariance
|
[
"JiaKui Hu",
"Zhengjian Yao",
"Lujia Jin",
"Hangzhou He",
"Yanye Lu"
] |
https://openaccess.thecvf.com/content/ICCV2025/html/Hu_Enhancing_Image_Restoration_Transformer_via_Adaptive_Translation_Equivariance_ICCV_2025_paper.html
|
https://openaccess.thecvf.com/content/ICCV2025/papers/Hu_Enhancing_Image_Restoration_Transformer_via_Adaptive_Translation_Equivariance_ICCV_2025_paper.pdf
|
https://openaccess.thecvf.com/content/ICCV2025/supplemental/Hu_Enhancing_Image_Restoration_ICCV_2025_supplemental.pdf
|
@InProceedings{Hu_2025_ICCV,
author = {Hu, JiaKui and Yao, Zhengjian and Jin, Lujia and He, Hangzhou and Lu, Yanye},
title = {Enhancing Image Restoration Transformer via Adaptive Translation Equivariance},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2025},
pages = {16047-16057}
}
|
Translation equivariance is a fundamental inductive bias in image restoration, ensuring that translated inputs produce translated outputs. Attention mechanisms in modern restoration transformers undermine this property, adversely impacting both training convergence and generalization. To alleviate this issue, we propose two key strategies for incorporating translation equivariance: slide indexing and component stacking. Slide indexing maintains operator responses at fixed positions, with sliding window attention being a notable example, while component stacking enables the arrangement of translation-equivariant operators in parallel or sequentially, thereby building complex architectures while preserving translation equivariance. However, these strategies still create a dilemma in model design between the high computational cost of self-attention and the fixed receptive field associated with sliding window attention. To address this, we develop an adaptive sliding indexing mechanism to efficiently select key-value pairs for each query, which are then concatenated in parallel with globally aggregated key-value pairs. The designed network, called the Translation Equivariance Adaptive Transformer (TEAFormer), is assessed across a variety of image restoration tasks. The results highlight its superiority in terms of effectiveness, training convergence, and generalization.
|
2506.18520
| null | null | null |
[] |
[] |
[] |
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] |
56
|
Free4D: Tuning-free 4D Scene Generation with Spatial-Temporal Consistency
|
[
"Tianqi Liu",
"Zihao Huang",
"Zhaoxi Chen",
"Guangcong Wang",
"Shoukang Hu",
"Liao Shen",
"Huiqiang Sun",
"Zhiguo Cao",
"Wei Li",
"Ziwei Liu"
] |
https://openaccess.thecvf.com/content/ICCV2025/html/Liu_Free4D_Tuning-free_4D_Scene_Generation_with_Spatial-Temporal_Consistency_ICCV_2025_paper.html
|
https://openaccess.thecvf.com/content/ICCV2025/papers/Liu_Free4D_Tuning-free_4D_Scene_Generation_with_Spatial-Temporal_Consistency_ICCV_2025_paper.pdf
|
https://openaccess.thecvf.com/content/ICCV2025/supplemental/Liu_Free4D_Tuning-free_4D_ICCV_2025_supplemental.pdf
|
@InProceedings{Liu_2025_ICCV,
author = {Liu, Tianqi and Huang, Zihao and Chen, Zhaoxi and Wang, Guangcong and Hu, Shoukang and Shen, Liao and Sun, Huiqiang and Cao, Zhiguo and Li, Wei and Liu, Ziwei},
title = {Free4D: Tuning-free 4D Scene Generation with Spatial-Temporal Consistency},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2025},
pages = {25571-25582}
}
|
We present Free4D, a novel tuning-free framework for 4D scene generation from a single image. Existing methods either focus on object-level generation, making scene-level generation infeasible, or rely on large-scale multi-view video datasets for expensive training, with limited generalization ability due to the scarcity of 4D scene data. In contrast, our key insight is to distill pre-trained foundation models for consistent 4D scene representation, which offers promising advantages such as efficiency and generalizability. 1) To achieve this, we first animate the input image using image-to-video diffusion models followed by 4D geometric structure initialization. 2) To turn this coarse structure into spatial-temporal consistent multi-view videos, we design an adaptive guidance mechanism with a point-guided denoising strategy for spatial consistency and a novel latent replacement strategy for temporal coherence. 3) To lift these generated observations into consistent 4D representation, we propose a modulation-based refinement to mitigate inconsistencies while fully leveraging the generated information. The resulting 4D representation enables real-time, controllable rendering, marking a significant advancement in single-image-based 4D scene generation.
|
2503.20785
|
Project Page: https://free4d.github.io/ , Code:
https://github.com/TQTQliu/Free4D
| null | null |
[] |
[] |
[] |
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] |
57
|
Generative Zoo
|
[
"Tomasz Niewiadomski",
"Anastasios Yiannakidis",
"Hanz Cuevas-Velasquez",
"Soubhik Sanyal",
"Michael J. Black",
"Silvia Zuffi",
"Peter Kulits"
] |
https://openaccess.thecvf.com/content/ICCV2025/html/Niewiadomski_Generative_Zoo_ICCV_2025_paper.html
|
https://openaccess.thecvf.com/content/ICCV2025/papers/Niewiadomski_Generative_Zoo_ICCV_2025_paper.pdf
|
https://openaccess.thecvf.com/content/ICCV2025/supplemental/Niewiadomski_Generative_Zoo_ICCV_2025_supplemental.pdf
|
@InProceedings{Niewiadomski_2025_ICCV,
author = {Niewiadomski, Tomasz and Yiannakidis, Anastasios and Cuevas-Velasquez, Hanz and Sanyal, Soubhik and Black, Michael J. and Zuffi, Silvia and Kulits, Peter},
title = {Generative Zoo},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2025},
pages = {8492-8502}
}
|
The model-based estimation of 3D animal pose and shape from images enables computational modeling of animal behavior. Training models for this purpose requires large amounts of labeled image data with precise pose and shape annotations. However, capturing such data requires the use of multi-view or marker-based motion-capture systems, which are impractical to adapt to wild animals in situ and impossible to scale across a comprehensive set of animal species. Some have attempted to address the challenge of procuring training data by pseudo-labeling individual real-world images through manual 2D annotation, followed by 3D-parameter optimization to those labels. While this approach may produce silhouette-aligned samples, the obtained pose and shape parameters are often implausible due to the ill-posed nature of the monocular fitting problem. Sidestepping real-world ambiguity, others have designed complex synthetic-data-generation pipelines leveraging game engines and collections of artist-designed 3D assets. Such engines yield perfect ground-truth labels but are often lacking in visual realism and require considerable manual effort to adapt to new species or environments. We propose an alternative approach to synthetic-data generation: rendering with a conditional image-generation model. We introduce a pipeline that samples a diverse set of poses and shapes for a variety of mammalian quadrupeds and generates realistic images with corresponding ground-truth pose and shape parameters. To demonstrate the scalability of our approach, we introduce GenZoo, a synthetic dataset containing one million images of distinct subjects. We train a 3D pose and shape regressor on GenZoo, which achieves state-of-the-art performance on a real-world multi-species 3D animal pose and shape estimation benchmark, despite being trained solely on synthetic data. We release our data and pipeline at https://genzoo.is.tue.mpg.de.
|
2412.08101
|
12 pages; project page: https://genzoo.is.tue.mpg.de
| null | null |
[] |
[] |
[] |
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] |
58
|
Any-SSR: How Recursive Least Squares Works in Continual Learning of Large Language Model
|
[
"Kai Tong",
"Kang Pan",
"Xiao Zhang",
"Erli Meng",
"Run He",
"Yawen Cui",
"Nuoyan Guo",
"Huiping Zhuang"
] |
https://openaccess.thecvf.com/content/ICCV2025/html/Tong_Any-SSR_How_Recursive_Least_Squares_Works_in_Continual_Learning_of_ICCV_2025_paper.html
|
https://openaccess.thecvf.com/content/ICCV2025/papers/Tong_Any-SSR_How_Recursive_Least_Squares_Works_in_Continual_Learning_of_ICCV_2025_paper.pdf
|
https://openaccess.thecvf.com/content/ICCV2025/supplemental/Tong_Any-SSR_How_Recursive_ICCV_2025_supplemental.pdf
|
@InProceedings{Tong_2025_ICCV,
author = {Tong, Kai and Pan, Kang and Zhang, Xiao and Meng, Erli and He, Run and Cui, Yawen and Guo, Nuoyan and Zhuang, Huiping},
title = {Any-SSR: How Recursive Least Squares Works in Continual Learning of Large Language Model},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2025},
pages = {3047-3057}
}
|
Large Language Models (LLMs) possess encompassing capabilities that can process diverse language-related tasks. However, finetuning on LLMs will diminish this general skills and continual finetuning will further cause severe degradation on accumulated knowledge. Recently, Continual Learning (CL) in Large Language Models (LLMs) arises which aims to continually adapt the LLMs to new tasks while maintaining previously learned knowledge and inheriting general skills. Existing techniques either leverage previous data to replay, leading to extra computational costs, or utilize a single parameter-efficient module to learn the downstream task, constraining new knowledge absorption with interference between different tasks. Toward these issues, this paper proposes Analytic Subspace Routing(Any-SSR) to address these challenges. For each task, we isolate the learning within a subspace of deep layers' features via low-rank adaptation, eliminating knowledge interference between different tasks. Additionally, we propose an analytic routing mechanism to properly utilize knowledge learned in different subspaces. Our approach employs Recursive Least Squares to train a multi-task router model, allowing the router to dynamically adapt to incoming data without requiring access to historical data. Also, the router effectively assigns the current task to an appropriate subspace and has a non-forgetting property of previously learned tasks with a solid theoretical guarantee. Experimental results demonstrate that our method achieves near-perfect retention of prior knowledge while seamlessly integrating new information, effectively overcoming the core limitations of existing methods. Our code is available at https://github.com/ZHUANGHP/Any-SSR
| null | null | null | null |
[] |
[] |
[] |
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] |
59
|
Instruction-Oriented Preference Alignment for Enhancing Multi-Modal Comprehension Capability of MLLMs
|
[
"Zitian Wang",
"Yue Liao",
"Kang Rong",
"Fengyun Rao",
"Yibo Yang",
"Si Liu"
] |
https://openaccess.thecvf.com/content/ICCV2025/html/Wang_Instruction-Oriented_Preference_Alignment_for_Enhancing_Multi-Modal_Comprehension_Capability_of_MLLMs_ICCV_2025_paper.html
|
https://openaccess.thecvf.com/content/ICCV2025/papers/Wang_Instruction-Oriented_Preference_Alignment_for_Enhancing_Multi-Modal_Comprehension_Capability_of_MLLMs_ICCV_2025_paper.pdf
|
https://openaccess.thecvf.com/content/ICCV2025/supplemental/Wang_Instruction-Oriented_Preference_Alignment_ICCV_2025_supplemental.pdf
|
@InProceedings{Wang_2025_ICCV,
author = {Wang, Zitian and Liao, Yue and Rong, Kang and Rao, Fengyun and Yang, Yibo and Liu, Si},
title = {Instruction-Oriented Preference Alignment for Enhancing Multi-Modal Comprehension Capability of MLLMs},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2025},
pages = {2010-2021}
}
|
Preference alignment has emerged as an effective strategy to enhance the performance of Multimodal Large Language Models (MLLMs) following supervised fine-tuning. While existing preference alignment methods predominantly target hallucination factors, they overlook the factors essential for multi-modal comprehension capabilities, often narrowing their improvements on hallucination mitigation. To bridge this gap, we propose Instruction-oriented Preference Alignment (IPA), a scalable framework designed to automatically construct alignment preferences grounded in instruction fulfillment efficacy. Our method involves an automated preference construction coupled with a dedicated verification process that identifies instruction-oriented factors, avoiding significant variability in response representations. Additionally, IPA incorporates a progressive preference collection pipeline, further recalling challenging samples through model self-evolution and reference-guided refinement. Experiments conducted on Qwen2VL-7B demonstrate IPA's effectiveness across multiple benchmarks, including hallucination evaluation, visual question answering, and text understanding tasks, highlighting its capability to enhance general comprehension.
|
2503.20309
|
Accepted by ICCV 2025
| null | null |
[] |
[] |
[] |
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] |
60
|
RapVerse: Coherent Vocals and Whole-Body Motion Generation from Text
|
[
"Jiaben Chen",
"Xin Yan",
"Yihang Chen",
"Siyuan Cen",
"Zixin Wang",
"Qinwei Ma",
"Haoyu Zhen",
"Kaizhi Qian",
"Lie Lu",
"Chuang Gan"
] |
https://openaccess.thecvf.com/content/ICCV2025/html/Chen_RapVerse_Coherent_Vocals_and_Whole-Body_Motion_Generation_from_Text_ICCV_2025_paper.html
|
https://openaccess.thecvf.com/content/ICCV2025/papers/Chen_RapVerse_Coherent_Vocals_and_Whole-Body_Motion_Generation_from_Text_ICCV_2025_paper.pdf
|
https://openaccess.thecvf.com/content/ICCV2025/supplemental/Chen_RapVerse_Coherent_Vocals_ICCV_2025_supplemental.pdf
|
@InProceedings{Chen_2025_ICCV,
author = {Chen, Jiaben and Yan, Xin and Chen, Yihang and Cen, Siyuan and Wang, Zixin and Ma, Qinwei and Zhen, Haoyu and Qian, Kaizhi and Lu, Lie and Gan, Chuang},
title = {RapVerse: Coherent Vocals and Whole-Body Motion Generation from Text},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2025},
pages = {10097-10107}
}
|
In this work, we introduce a challenging task for simultaneously generating 3D holistic body motions and singing vocals directly from textual lyrics inputs, advancing beyond existing works that typically address these two modalities in isolation. To facilitate this, we first collect the RapVerse dataset, a large dataset containing synchronous rapping vocals, lyrics, and high-quality 3D holistic body meshes. With the RapVerse dataset, we investigate the extent to which scaling autoregressive multimodal transformers across language, audio, and motion can enhance the coherent and realistic generation of vocals and whole-body human motions. For modality unification, a vector-quantized variational autoencoder is employed to encode whole-body motion sequences into discrete motion tokens, while a vocal-to-unit model is leveraged to obtain quantized audio tokens preserving content, prosodic information and singer identity. By jointly performing transformer modeling on these three modalities in a unified way, our framework ensures a seamless and realistic blend of vocals and human motions. Extensive experiments demonstrate that our unified generation framework not only produces coherent and realistic singing vocals alongside human motions directly from textual inputs, but also rivals the performance of specialized single-modality generation systems, establishing new benchmarks for joint vocal-motion generation.
|
2405.20336
|
Project website: https://vis-www.cs.umass.edu/RapVerse
| null | null |
[] |
[] |
[] |
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] |
61
|
MoFRR: Mixture of Diffusion Models for Face Retouching Restoration
|
[
"Jiaxin Liu",
"Qichao Ying",
"Zhenxing Qian",
"Sheng Li",
"Runqi Zhang",
"Jian Liu",
"Xinpeng Zhang"
] |
https://openaccess.thecvf.com/content/ICCV2025/html/Liu_MoFRR_Mixture_of_Diffusion_Models_for_Face_Retouching_Restoration_ICCV_2025_paper.html
|
https://openaccess.thecvf.com/content/ICCV2025/papers/Liu_MoFRR_Mixture_of_Diffusion_Models_for_Face_Retouching_Restoration_ICCV_2025_paper.pdf
| null |
@InProceedings{Liu_2025_ICCV,
author = {Liu, Jiaxin and Ying, Qichao and Qian, Zhenxing and Li, Sheng and Zhang, Runqi and Liu, Jian and Zhang, Xinpeng},
title = {MoFRR: Mixture of Diffusion Models for Face Retouching Restoration},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2025},
pages = {12842-12851}
}
|
The widespread use of face retouching on social media platforms raises concerns about the authenticity of face images. While existing methods focus on detecting face retouching, how to accurately recover the original faces from the retouched ones has yet to be answered. This paper introduces Face Retouching Restoration (FRR), a novel computer vision task aimed at restoring original faces from their retouched counterparts. FRR differs from traditional image restoration tasks by addressing the complex retouching operations with various types and degrees, which focuses more on the restoration of the low-frequency information of the faces. To tackle this challenge, we propose MoFRR, Mixture of Diffusion Models for FRR. Inspired by DeepSeek's expert isolation strategy, the MoFRR uses sparse activation of specialized experts handling distinct retouching types and the engagement of a shared expert dealing with universal retouching traces. Each specialized expert follows a dual-branch structure with a DDIM-based low-frequency branch guided by an Iterative Distortion Evaluation Module (IDEM) and a Cross-Attention-based High-Frequency branch (HFCAM) for detail refinement. Extensive experiments on a newly constructed face retouching dataset, RetouchingFFHQ++, demonstrate the effectiveness of MoFRR for FRR.
|
2507.19770
| null | null | null |
[] |
[] |
[] |
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] |
62
|
SFUOD: Source-Free Unknown Object Detection
|
[
"Keon-Hee Park",
"Seun-An Choe",
"Gyeong-Moon Park"
] |
https://openaccess.thecvf.com/content/ICCV2025/html/Park_SFUOD_Source-Free_Unknown_Object_Detection_ICCV_2025_paper.html
|
https://openaccess.thecvf.com/content/ICCV2025/papers/Park_SFUOD_Source-Free_Unknown_Object_Detection_ICCV_2025_paper.pdf
|
https://openaccess.thecvf.com/content/ICCV2025/supplemental/Park_SFUOD_Source-Free_Unknown_ICCV_2025_supplemental.pdf
|
@InProceedings{Park_2025_ICCV,
author = {Park, Keon-Hee and Choe, Seun-An and Park, Gyeong-Moon},
title = {SFUOD: Source-Free Unknown Object Detection},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2025},
pages = {3499-3508}
}
|
Source-free object detection adapts a detector pre-trained on a source domain to an unlabeled target domain without requiring access to labeled source data. While this setting is practical as it eliminates the need for the source dataset during domain adaptation, it operates under the restrictive assumption that only pre-defined objects from the source domain exist in the target domain. This closed-set setting prevents the detector from detecting undefined objects. To ease this assumption, we propose Source-Free Unknown Object Detection (SFUOD), a novel scenario which enables the detector to not only recognize known objects but also detect undefined objects as unknown objects. To this end, we propose CollaPAUL (Collaborative tuning and Principal Axis-based Unknown Labeling), a novel framework for SFUOD. Collaborative tuning enhances knowledge adaptation by integrating target-dependent knowledge from the auxiliary encoder with source-dependent knowledge from the pre-trained detector through a cross-domain attention mechanism. Additionally, principal axis-based unknown labeling assigns pseudo-labels to unknown objects by estimating objectness via principal axes projection and confidence scores from model predictions. The proposed CollaPAUL achieves state-of-the-art performances on SFUOD benchmarks, and extensive experiments validate its effectiveness. The code will be released after the review.
|
2507.17373
|
This paper has been accepted by ICCV 2025
| null | null |
[] |
[] |
[] |
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0.007748234085738659,
0.026982737705111504,
-0.001133633661083877
] |
63
|
UniEgoMotion: A Unified Model for Egocentric Motion Reconstruction, Forecasting, and Generation
|
[
"Chaitanya Patel",
"Hiroki Nakamura",
"Yuta Kyuragi",
"Kazuki Kozuka",
"Juan Carlos Niebles",
"Ehsan Adeli"
] |
https://openaccess.thecvf.com/content/ICCV2025/html/Patel_UniEgoMotion_A_Unified_Model_for_Egocentric_Motion_Reconstruction_Forecasting_and_ICCV_2025_paper.html
|
https://openaccess.thecvf.com/content/ICCV2025/papers/Patel_UniEgoMotion_A_Unified_Model_for_Egocentric_Motion_Reconstruction_Forecasting_and_ICCV_2025_paper.pdf
|
https://openaccess.thecvf.com/content/ICCV2025/supplemental/Patel_UniEgoMotion_A_Unified_ICCV_2025_supplemental.zip
|
@InProceedings{Patel_2025_ICCV,
author = {Patel, Chaitanya and Nakamura, Hiroki and Kyuragi, Yuta and Kozuka, Kazuki and Niebles, Juan Carlos and Adeli, Ehsan},
title = {UniEgoMotion: A Unified Model for Egocentric Motion Reconstruction, Forecasting, and Generation},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2025},
pages = {10318-10329}
}
|
Egocentric human motion generation and forecasting with scene-context is crucial for enhancing AR/VR experiences, improving human-robot interaction, advancing assistive technologies, and enabling adaptive healthcare solutions by accurately predicting and simulating movement from a first-person perspective. However, existing methods primarily focus on third-person motion synthesis with structured 3D scene contexts, limiting their effectiveness in real-world egocentric settings where limited field of view, frequent occlusions, and dynamic cameras hinder scene perception. To bridge this gap, we introduce Egocentric Motion Generation and Egocentric Motion Forecasting, two novel tasks that utilize first-person images for scene-aware motion synthesis without relying on explicit 3D scene. We propose UniEgoMotion, a unified conditional motion diffusion model with a novel head-centric motion representation tailored for egocentric devices. UniEgoMotion's simple yet effective design supports egocentric motion reconstruction, forecasting, and generation from first-person visual inputs in a unified framework. Unlike previous works that overlook scene semantics, our model effectively extracts image-based scene context to infer plausible 3D motion. To facilitate training, we introduce EE4D-Motion, a large-scale dataset derived from EgoExo4D, augmented with pseudo-ground-truth 3D motion annotations. UniEgoMotion achieves state-of-the-art performance in egocentric motion reconstruction and is the first to generate motion from a single egocentric image. Extensive evaluations demonstrate the effectiveness of our unified framework, setting a new benchmark for egocentric motion modeling and unlocking new possibilities for egocentric applications.
|
2508.01126
|
ICCV 2025. Project Page:
https://chaitanya100100.github.io/UniEgoMotion/
| null |
https://chaitanya100100.github.io/UniEgoMotion
|
[] |
[] |
[] |
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] |
64
|
ToolVQA: A Dataset for Multi-step Reasoning VQA with External Tools
|
[
"Shaofeng Yin",
"Ting Lei",
"Yang Liu"
] |
https://openaccess.thecvf.com/content/ICCV2025/html/Yin_ToolVQA_A_Dataset_for_Multi-step_Reasoning_VQA_with_External_Tools_ICCV_2025_paper.html
|
https://openaccess.thecvf.com/content/ICCV2025/papers/Yin_ToolVQA_A_Dataset_for_Multi-step_Reasoning_VQA_with_External_Tools_ICCV_2025_paper.pdf
|
https://openaccess.thecvf.com/content/ICCV2025/supplemental/Yin_ToolVQA_A_Dataset_ICCV_2025_supplemental.pdf
|
@InProceedings{Yin_2025_ICCV,
author = {Yin, Shaofeng and Lei, Ting and Liu, Yang},
title = {ToolVQA: A Dataset for Multi-step Reasoning VQA with External Tools},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2025},
pages = {4424-4433}
}
|
Integrating external tools into Large Foundation Models (LFMs) has emerged as a promising approach to enhance their problem-solving capabilities. While existing studies have demonstrated strong performance in tool-augmented Visual Question Answering (VQA), recent benchmarks re- veal significant gaps in real-world tool-use proficiency, particularly in functionally diverse multimodal settings re- quiring multi-step reasoning. In this work, we intro- duce ToolVQA, a large-scale multimodal dataset compris- ing 23K samples, designed to bridge this gap. Unlike pre- vious datasets that rely on synthetic scenarios and sim- plified queries, ToolVQA features real-world visual con- texts and challenging implicit multi-step reasoning tasks, better aligning with real user interactions. To construct this dataset, we propose ToolEngine, a novel data genera- tion pipeline that employs image-guided Depth-First Search (DFS) with a Longest Common Subsequence (LCS)-based example matching mechanism to simulate human-like tool- use reasoning. ToolVQA encompasses 10 multimodal tools across 7 diverse domains, with an average inference length of 2.78 reasoning steps per sample. The LLaVA-7B model fine-tuned on ToolVQA not only achieves impressive per- formance on the ToolVQA test set, but also surpasses the large closed-source model GPT-3.5-turbo on five out-of- distribution (OOD) datasets, showing strong generalizabil- ity in real-world tool-use scenarios. Code is available at https://github.com/Fugtemypt123/ToolVQA-release.
|
2508.03284
| null | null | null |
[] |
[] |
[
"DietCoke4671/ToolVQA"
] |
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] |
65
|
Spherical Epipolar Rectification for Deep Two-View Absolute Depth Estimation
|
[
"Pierre-André Brousseau",
"Sébastien Roy"
] |
https://openaccess.thecvf.com/content/ICCV2025/html/Brousseau_Spherical_Epipolar_Rectification_for_Deep_Two-View_Absolute_Depth_Estimation_ICCV_2025_paper.html
|
https://openaccess.thecvf.com/content/ICCV2025/papers/Brousseau_Spherical_Epipolar_Rectification_for_Deep_Two-View_Absolute_Depth_Estimation_ICCV_2025_paper.pdf
|
https://openaccess.thecvf.com/content/ICCV2025/supplemental/Brousseau_Spherical_Epipolar_Rectification_ICCV_2025_supplemental.pdf
|
@InProceedings{Brousseau_2025_ICCV,
author = {Brousseau, Pierre-Andr\'e and Roy, S\'ebastien},
title = {Spherical Epipolar Rectification for Deep Two-View Absolute Depth Estimation},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2025},
pages = {28925-28934}
}
|
Absolute depth estimation from single camera sequence of images is a relevant task given that mobile machines increasingly rely on vision to navigate. Deep learning for stereo matching has been demonstrated to improve performance for stereo rectified depth estimation but these methods require straightforward left-right camera setups. This work proposes to introduce deep stereo matching to two views of a monocular image sequence obtained from a camera in motion in a static scene. This paper introduces a novel and principled spherical epipolar rectification model, which handles all camera motions. This rectification model is differentiable and allows self-supervised deep stereo matching algorithms to compute disparity and recover depth, given known camera pose. This paper also introduces a spherical crop operation which limits rectified image size and allows for competitive absolute depth estimation performance. This results in a spherical rectification model that is demonstrated to provide metric depth and compete favorably with current state-of-the-art monocular depth estimators. The code is available at https://gitlab.com/labv3d/spherical-stereo.git.
| null | null | null |
https://sphericalstereo.github.io/
|
[] |
[] |
[] |
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] |
66
|
ScenePainter: Semantically Consistent Perpetual 3D Scene Generation with Concept Relation Alignment
|
[
"Chong Xia",
"Shengjun Zhang",
"Fangfu Liu",
"Chang Liu",
"Khodchaphun Hirunyaratsameewong",
"Yueqi Duan"
] |
https://openaccess.thecvf.com/content/ICCV2025/html/Xia_ScenePainter_Semantically_Consistent_Perpetual_3D_Scene_Generation_with_Concept_Relation_ICCV_2025_paper.html
|
https://openaccess.thecvf.com/content/ICCV2025/papers/Xia_ScenePainter_Semantically_Consistent_Perpetual_3D_Scene_Generation_with_Concept_Relation_ICCV_2025_paper.pdf
|
https://openaccess.thecvf.com/content/ICCV2025/supplemental/Xia_ScenePainter_Semantically_Consistent_ICCV_2025_supplemental.zip
|
@InProceedings{Xia_2025_ICCV,
author = {Xia, Chong and Zhang, Shengjun and Liu, Fangfu and Liu, Chang and Hirunyaratsameewong, Khodchaphun and Duan, Yueqi},
title = {ScenePainter: Semantically Consistent Perpetual 3D Scene Generation with Concept Relation Alignment},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2025},
pages = {28808-28817}
}
|
Perpetual 3D scene generation aims to produce long-range and coherent 3D view sequences, which is applicable for long-term video synthesis and 3D scene reconstruction. Existing methods follow a "navigate-and-imagine" fashion and rely on outpainting for successive view expansion. However, the generated view sequences suffer from semantic drift issue derived from the accumulated deviation of the outpainting module. To tackle this challenge, we propose ScenePainter, a new framework for semantically consistent 3D scene generation, which aligns the outpainter's scene-specific prior with the comprehension of the current scene. To be specific, we introduce a hierarchical graph structure dubbed SceneConceptGraph to construct relations among multi-level scene concepts, which directs the outpainter for consistent novel views and can be dynamically refined to enhance diversity. Extensive experiments demonstrate that our framework overcomes the semantic drift issue and generates more consistent and immersive 3D view sequences.
|
2507.19058
| null | null | null |
[] |
[] |
[] |
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] |
67
|
ESCNet:Edge-Semantic Collaborative Network for Camouflaged Object Detection
|
[
"Sheng Ye",
"Xin Chen",
"Yan Zhang",
"Xianming Lin",
"Liujuan Cao"
] |
https://openaccess.thecvf.com/content/ICCV2025/html/Ye_ESCNetEdge-Semantic_Collaborative_Network_for_Camouflaged_Object_Detection_ICCV_2025_paper.html
|
https://openaccess.thecvf.com/content/ICCV2025/papers/Ye_ESCNetEdge-Semantic_Collaborative_Network_for_Camouflaged_Object_Detection_ICCV_2025_paper.pdf
| null |
@InProceedings{Ye_2025_ICCV,
author = {Ye, Sheng and Chen, Xin and Zhang, Yan and Lin, Xianming and Cao, Liujuan},
title = {ESCNet:Edge-Semantic Collaborative Network for Camouflaged Object Detection},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2025},
pages = {20053-20063}
}
|
Camouflaged object detection (COD) faces unique challenges where target boundaries are intrinsically ambiguous due to their textural similarity to backgrounds. Existing methods relying on single-modality features often produce fragmented predictions due to insufficient boundary constraints.To address this, we propose ESCNet with dynamically coupled edge-texture perception. Our framework introduces three core innovations that work in concert:1) Adaptive Edge-Texture Perceptor (AETP), which creates an edge prediction behaviour where edge and texture information are mutually reinforcing based on the multi-scale features of the image integrated with the global semantic context of the Transformer;2) Dual-Stream Feature Augmentor (DSFA), which dynamically adjusts the kernel sampling position according to the local texture complexity and edge orientation, thus accurately enhancing the feature information at fractal boundaries and amorphous texture locations;3) Multi-Feature Modulation Module (MFMM), which establishes incremental fine-grained improvements for feature calibration and model prediction through enhanced characterisation of edge perception and hierarchical integration of multiple textures. This interconnected system forms a feedback loop where enhanced representations of edge perception enhance model texture prediction and vice versa. Our ESCNet demonstrates significant performance advantages on all three authoritative datasets.
| null | null | null | null |
[] |
[] |
[] |
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] |
68
|
PixelStitch: Structure-Preserving Pixel-Wise Bidirectional Warps for Unsupervised Image Stitching
|
[
"Hengzhe Jin",
"Lang Nie",
"Chunyu Lin",
"Xiaomei Feng",
"Yao Zhao"
] |
https://openaccess.thecvf.com/content/ICCV2025/html/Jin_PixelStitch_Structure-Preserving_Pixel-Wise_Bidirectional_Warps_for_Unsupervised_Image_Stitching_ICCV_2025_paper.html
|
https://openaccess.thecvf.com/content/ICCV2025/papers/Jin_PixelStitch_Structure-Preserving_Pixel-Wise_Bidirectional_Warps_for_Unsupervised_Image_Stitching_ICCV_2025_paper.pdf
|
https://openaccess.thecvf.com/content/ICCV2025/supplemental/Jin_PixelStitch_Structure-Preserving_Pixel-Wise_ICCV_2025_supplemental.zip
|
@InProceedings{Jin_2025_ICCV,
author = {Jin, Hengzhe and Nie, Lang and Lin, Chunyu and Feng, Xiaomei and Zhao, Yao},
title = {PixelStitch: Structure-Preserving Pixel-Wise Bidirectional Warps for Unsupervised Image Stitching},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2025},
pages = {28125-28134}
}
|
We propose PixelStitch, a pixel-wise bidirectional warp that learns to stitch images as well as preserve structure in an unsupervised paradigm. To produce natural stitched images, we first determine the middle plane through homography decomposition and globally project the original images toward the desired plane. Compared with unidirectional homography transformation, it evenly spreads projective distortion across two views and decreases the proportion of invalid pixels. Then, the bidirectional optical flow fields are established to carry out residual pixel-wise deformation with projection-weighted natural coefficients, encouraging pixel motions to be as unnoticeable as possible in non-overlapping regions while smoothly transitioning into overlapping areas. Crucially, this flexible deformation enables PixelStitch to align large-parallax images and preserve the structural integrity of non-overlapping contents. To obtain high-quality stitched images in the absence of labels, a comprehensive unsupervised objective function is proposed to simultaneously encourage content alignment, structure preservation, and bidirectional consistency. Finally, extensive experiments are conducted to show our superiority to existing state-of-the-art (SoTA) methods in the quantitative metric, qualitative appearance, and generalization ability. The code will be available.
| null | null | null | null |
[] |
[] |
[] |
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] |
69
|
SANA-Sprint: One-Step Diffusion with Continuous-Time Consistency Distillation
|
[
"Junsong Chen",
"Shuchen Xue",
"Yuyang Zhao",
"Jincheng Yu",
"Sayak Paul",
"Junyu Chen",
"Han Cai",
"Song Han",
"Enze Xie"
] |
https://openaccess.thecvf.com/content/ICCV2025/html/Chen_SANA-Sprint_One-Step_Diffusion_with_Continuous-Time_Consistency_Distillation_ICCV_2025_paper.html
|
https://openaccess.thecvf.com/content/ICCV2025/papers/Chen_SANA-Sprint_One-Step_Diffusion_with_Continuous-Time_Consistency_Distillation_ICCV_2025_paper.pdf
|
https://openaccess.thecvf.com/content/ICCV2025/supplemental/Chen_SANA-Sprint_One-Step_Diffusion_ICCV_2025_supplemental.pdf
|
@InProceedings{Chen_2025_ICCV,
author = {Chen, Junsong and Xue, Shuchen and Zhao, Yuyang and Yu, Jincheng and Paul, Sayak and Chen, Junyu and Cai, Han and Han, Song and Xie, Enze},
title = {SANA-Sprint: One-Step Diffusion with Continuous-Time Consistency Distillation},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2025},
pages = {16185-16195}
}
|
This paper presents SANA-Sprint, an efficient diffusion model for ultra-fast text-to-image (T2I) generation. SANA-Sprint is built on a pre-trained foundation model and augmented with hybrid distillation, dramatically reducing inference steps from 20 to 1-4.We introduce three key innovations: (1) We propose a training-free approach that transforms a pre-trained flow-matching model for continuous-time consistency distillation (sCM), eliminating costly training from scratch and achieving high training efficiency. Our hybrid distillation strategy combines sCM with latent adversarial distillation (LADD): sCM ensures alignment with the teacher model, while LADD enhances single-step generation fidelity. (2) SANA-Sprint is a unified step-adaptive model that achieves high-quality generation in 1-4 steps, eliminating step-specific training and improving efficiency. (3) We integrate ControlNet with SANA-Sprint for real-time interactive image generation, enabling instant visual feedback for user interaction. SANA-Sprint establishes a new Pareto frontier in speed-quality tradeoffs, achieving state-of-the-art performance with 7.59 FID and 0.74 GenEval in just 1 step -- outperforming FLUX-schnell (7.94 FID / 0.71 GenEval) while being 10x faster (0.1s vs 1.1s on H100). It also achieves 0.1s (T2I) and 0.25s (ControlNet) latency for 1024x1024 images on H100, and 0.31s (T2I) on an RTX 4090, showcasing its exceptional efficiency and potential for AI-powered consumer applications (AIPC). Code and pre-trained models will be open-sourced.
| null | null | null | null |
[] |
[] |
[] |
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] |
70
|
Information-Bottleneck Driven Binary Neural Network for Change Detection
|
[
"Kaijie Yin",
"Zhiyuan Zhang",
"Shu Kong",
"Tian Gao",
"Cheng-Zhong Xu",
"Hui Kong"
] |
https://openaccess.thecvf.com/content/ICCV2025/html/Yin_Information-Bottleneck_Driven_Binary_Neural_Network_for_Change_Detection_ICCV_2025_paper.html
|
https://openaccess.thecvf.com/content/ICCV2025/papers/Yin_Information-Bottleneck_Driven_Binary_Neural_Network_for_Change_Detection_ICCV_2025_paper.pdf
| null |
@InProceedings{Yin_2025_ICCV,
author = {Yin, Kaijie and Zhang, Zhiyuan and Kong, Shu and Gao, Tian and Xu, Cheng-Zhong and Kong, Hui},
title = {Information-Bottleneck Driven Binary Neural Network for Change Detection},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2025},
pages = {7176-7186}
}
|
In this paper, we propose Binarized Change Detection (BiCD), the first binary neural network (BNN) designed specifically for change detection. Conventional network binarization approaches, which directly quantize both weights and activations in change detection models, severely limit the network's ability to represent input data and distinguish between changed and unchanged regions. This results in significantly lower detection accuracy compared to real-valued networks. To overcome these challenges, BiCD enhances both the representational power and feature separability of BNNs, improving detection performance. Specifically, we introduce an auxiliary objective based on the Information Bottleneck (IB) principle, guiding the encoder to retain essential input information while promoting better feature discrimination. Since directly computing mutual information under the IB principle is intractable, we design a compact, learnable auxiliary module as an approximation target, leading to a simple yet effective optimization strategy that minimizes both reconstruction loss and standard change detection loss.Extensive experiments on street-view and remote sensing datasets demonstrate that BiCD establishes a new benchmark for BNN-based change detection, achieving state-of-the-art performance in this domain.
|
2507.03504
|
ICCV 2025 Accepted
| null | null |
[] |
[] |
[] |
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] |
71
|
Erasing More Than Intended? How Concept Erasure Degrades the Generation of Non-Target Concepts
|
[
"Ibtihel Amara",
"Ahmed Imtiaz Humayun",
"Ivana Kajic",
"Zarana Parekh",
"Natalie Harris",
"Sarah Young",
"Chirag Nagpal",
"Najoung Kim",
"Junfeng He",
"Cristina Nader Vasconcelos",
"Deepak Ramachandran",
"Golnoosh Farnadi",
"Katherine Heller",
"Mohammad Havaei",
"Negar Rostamzadeh"
] |
https://openaccess.thecvf.com/content/ICCV2025/html/Amara_Erasing_More_Than_Intended_How_Concept_Erasure_Degrades_the_Generation_ICCV_2025_paper.html
|
https://openaccess.thecvf.com/content/ICCV2025/papers/Amara_Erasing_More_Than_Intended_How_Concept_Erasure_Degrades_the_Generation_ICCV_2025_paper.pdf
|
https://openaccess.thecvf.com/content/ICCV2025/supplemental/Amara_Erasing_More_Than_ICCV_2025_supplemental.pdf
|
@InProceedings{Amara_2025_ICCV,
author = {Amara, Ibtihel and Humayun, Ahmed Imtiaz and Kajic, Ivana and Parekh, Zarana and Harris, Natalie and Young, Sarah and Nagpal, Chirag and Kim, Najoung and He, Junfeng and Vasconcelos, Cristina Nader and Ramachandran, Deepak and Farnadi, Golnoosh and Heller, Katherine and Havaei, Mohammad and Rostamzadeh, Negar},
title = {Erasing More Than Intended? How Concept Erasure Degrades the Generation of Non-Target Concepts},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2025},
pages = {16420-16430}
}
|
Concept erasure techniques have recently gained significant attention for their potential to remove unwanted concepts from text-to-image models. While these methods often demonstrate promising results in controlled settings, their robustness in real-world applications and suitability for deployment remain uncertain. In this work, we (1) identify a critical gap in evaluating sanitized models, particularly in assessing their performance across diverse concept dimensions, and (2) systematically analyze the failure modes of text-to-image models post-erasure. We focus on the unintended consequences of concept removal on non-target concepts across different levels of interconnected relationships including visually similar, binomial, and semantically related concepts. To address this, we introduce EraseBench, a comprehensive benchmark for evaluating post-erasure performance. EraseBench includes over 100 curated concepts, targeted evaluation prompts, and a robust set of metrics to assess both effectiveness and side effects of erasure. Our findings reveal a phenomenon of concept entanglement, where erasure leads to unintended suppression of non-target concepts, causing spillover degradation that manifests as distortions and a decline in generation quality.
|
2501.09833
| null | null | null |
[] |
[] |
[] |
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] |
72
|
Global and Local Entailment Learning for Natural World Imagery
|
[
"Srikumar Sastry",
"Aayush Dhakal",
"Eric Xing",
"Subash Khanal",
"Nathan Jacobs"
] |
https://openaccess.thecvf.com/content/ICCV2025/html/Sastry_Global_and_Local_Entailment_Learning_for_Natural_World_Imagery_ICCV_2025_paper.html
|
https://openaccess.thecvf.com/content/ICCV2025/papers/Sastry_Global_and_Local_Entailment_Learning_for_Natural_World_Imagery_ICCV_2025_paper.pdf
|
https://openaccess.thecvf.com/content/ICCV2025/supplemental/Sastry_Global_and_Local_ICCV_2025_supplemental.pdf
|
@InProceedings{Sastry_2025_ICCV,
author = {Sastry, Srikumar and Dhakal, Aayush and Xing, Eric and Khanal, Subash and Jacobs, Nathan},
title = {Global and Local Entailment Learning for Natural World Imagery},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2025},
pages = {15770-15780}
}
|
Learning the hierarchical structure of data in vision-language models is a significant challenge. Previous works have attempted to address this challenge by employing entailment learning. However, these approaches fail to model the transitive nature of entailment explicitly, which establishes the relationship between order and semantics within a representation space. In this work, we introduce Radial Cross-Modal Embeddings (RCME), a framework that enables the explicit modeling of transitivity-enforced entailment. Our proposed framework optimizes for the partial order of concepts within vision-language models. By leveraging our framework, we develop a hierarchical vision-language foundation model capable of representing the hierarchy in the Tree of Life. Our experiments on hierarchical species classification and hierarchical retrieval tasks demonstrate the enhanced performance of our models compared to the existing state-of-the-art models. Our code and models are open-sourced at https://vishu26.github.io/RCME.
|
2506.21476
|
Accepted at ICCV 2025
| null |
https://vishu26.github.io/RCME/index.html
|
[] |
[
"MVRL/rcme-tol-vit-base-patch16"
] |
[] |
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-0.018553977832198143
] |
73
|
Ross3D: Reconstructive Visual Instruction Tuning with 3D-Awareness
|
[
"Haochen Wang",
"Yucheng Zhao",
"Tiancai Wang",
"Haoqiang Fan",
"Xiangyu Zhang",
"Zhaoxiang Zhang"
] |
https://openaccess.thecvf.com/content/ICCV2025/html/Wang_Ross3D_Reconstructive_Visual_Instruction_Tuning_with_3D-Awareness_ICCV_2025_paper.html
|
https://openaccess.thecvf.com/content/ICCV2025/papers/Wang_Ross3D_Reconstructive_Visual_Instruction_Tuning_with_3D-Awareness_ICCV_2025_paper.pdf
|
https://openaccess.thecvf.com/content/ICCV2025/supplemental/Wang_Ross3D_Reconstructive_Visual_ICCV_2025_supplemental.pdf
|
@InProceedings{Wang_2025_ICCV,
author = {Wang, Haochen and Zhao, Yucheng and Wang, Tiancai and Fan, Haoqiang and Zhang, Xiangyu and Zhang, Zhaoxiang},
title = {Ross3D: Reconstructive Visual Instruction Tuning with 3D-Awareness},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2025},
pages = {9275-9286}
}
|
The rapid development of Large Multimodal Models (LMMs) for 2D images and videos has spurred efforts to adapt these models for interpreting 3D scenes. However, the absence of large-scale 3D vision-language datasets has posed a significant obstacle. To address this issue, typical approaches focus on injecting 3D awareness into 2D LMMs by designing 3D input-level scene representations. This work provides a new perspective. We introduce reconstructive visual instruction tuning with 3D-awareness (ROSS3D), which integrates 3D-aware visual supervision into the training procedure. Specifically, it incorporates cross-view and global-view reconstruction. The former requires reconstructing masked views by aggregating overlapping information from other views. The latter aims to aggregate information from all available views to recover Bird's-Eye-View images, contributing to a comprehensive overview of the entire scene. Empirically, ROSS3D achieves state-of-the-art performance across various 3D scene understanding benchmarks. More importantly, our semi-supervised experiments demonstrate significant potential in leveraging large amounts of unlabeled 3D vision-only data.
|
2504.01901
| null | null | null |
[] |
[] |
[] |
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] |
74
|
LVFace: Progressive Cluster Optimization for Large Vision Models in Face Recognition
|
[
"Jinghan You",
"Shanglin Li",
"Yuanrui Sun",
"Jiangchuan Wei",
"Mingyu Guo",
"Chao Feng",
"Jiao Ran"
] |
https://openaccess.thecvf.com/content/ICCV2025/html/You_LVFace_Progressive_Cluster_Optimization_for_Large_Vision_Models_in_Face_ICCV_2025_paper.html
|
https://openaccess.thecvf.com/content/ICCV2025/papers/You_LVFace_Progressive_Cluster_Optimization_for_Large_Vision_Models_in_Face_ICCV_2025_paper.pdf
| null |
@InProceedings{You_2025_ICCV,
author = {You, Jinghan and Li, Shanglin and Sun, Yuanrui and Wei, Jiangchuan and Guo, Mingyu and Feng, Chao and Ran, Jiao},
title = {LVFace: Progressive Cluster Optimization for Large Vision Models in Face Recognition},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2025},
pages = {11840-11849}
}
|
Vision Transformers (ViTs) have revolutionized large-scale visual modeling, yet remain underexplored in face recognition (FR) where CNNs still dominate. We identify a critical bottleneck: CNN-inspired training paradigms fail to unlock ViT's potential, leading to suboptimal performance and convergence instability.To address this challenge, we propose LVFace, a ViT-based FR model that integrates Progressive Cluster Optimization (PCO) to achieve superior results. Specifically, PCO sequentially applies negative class sub-sampling (NCS) for robust and fast feature alignment from random initialization, feature expectation penalties for centroid stabilization, performing cluster boundary refinement through full-batch training without NCS constraints. LVFace establishes a new state-of-the-art face recognition baseline, surpassing leading approaches such as UniFace and TopoFR across multiple benchmarks. Extensive experiments demonstrate that LVFace delivers consistent performance gains, while exhibiting scalability to large-scale datasets and compatibility with mainstream VLMs and LLMs. Notably, LVFace secured 1st place in the ICCV 2021 Masked Face Recognition (MFR)-Ongoing Challenge (March 2025), proving its efficacy in real-world scenarios. Project is available at https://github.com/bytedance/LVFace.
|
2501.13420
|
Accepted at ICCV25 as highlight paper, code released at
https://github.com/bytedance/LVFace
|
https://github.com/bytedance/LVFace
| null |
[] |
[
"bytedance-research/LVFace"
] |
[] |
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] |
75
|
Dataset Ownership Verification for Pre-trained Masked Models
|
[
"Yuechen Xie",
"Jie Song",
"Yicheng Shan",
"Xiaoyan Zhang",
"Yuanyu Wan",
"Shengxuming Zhang",
"Jiarui Duan",
"Mingli Song"
] |
https://openaccess.thecvf.com/content/ICCV2025/html/Xie_Dataset_Ownership_Verification_for_Pre-trained_Masked_Models_ICCV_2025_paper.html
|
https://openaccess.thecvf.com/content/ICCV2025/papers/Xie_Dataset_Ownership_Verification_for_Pre-trained_Masked_Models_ICCV_2025_paper.pdf
|
https://openaccess.thecvf.com/content/ICCV2025/supplemental/Xie_Dataset_Ownership_Verification_ICCV_2025_supplemental.zip
|
@InProceedings{Xie_2025_ICCV,
author = {Xie, Yuechen and Song, Jie and Shan, Yicheng and Zhang, Xiaoyan and Wan, Yuanyu and Zhang, Shengxuming and Duan, Jiarui and Song, Mingli},
title = {Dataset Ownership Verification for Pre-trained Masked Models},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2025},
pages = {3132-3142}
}
|
High-quality open-source datasets have emerged as a pivotal catalyst driving the swift advancement of deep learning, while facing the looming threat of potential exploitation. Protecting these datasets is of paramount importance for the interests of their owners. The verification of dataset ownership has evolved into a crucial approach in this domain; however, existing verification techniques are predominantly tailored to supervised models and contrastive pre-trained models, rendering them ill-suited for direct application to the increasingly prevalent masked models. In this work, we introduce the inaugural methodology addressing this critical, yet unresolved challenge, termed Dataset Ownership Verification for Masked Modeling (DOV4MM). The central objective is to ascertain whether a suspicious black-box model has been pre-trained on a particular unlabeled dataset, thereby assisting dataset owners in safeguarding their rights. DOV4MM is grounded in our empirical observation that when a model is pre-trained on the target dataset, the difficulty of reconstructing masked information within the embedding space exhibits a marked contrast to models not pre-trained on that dataset. We validated the efficacy of DOV4MM through ten masked image models on ImageNet-1K and four masked language models on WikiText-103. The results demonstrate that DOV4MM rejects the null hypothesis, with a p-value considerably below 0.05, surpassing all prior approaches. Code is available at https://github.com/xieyc99/DOV4MM.
|
2507.12022
|
Accepted by ICCV 2025
|
https://github.com/xieyc99/DOV4MM
| null |
[] |
[] |
[] |
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] |
76
|
VLR-Driver: Large Vision-Language-Reasoning Models for Embodied Autonomous Driving
|
[
"Fanjie Kong",
"Yitong Li",
"Weihuang Chen",
"Chen Min",
"Yizhe Li",
"Zhiqiang Gao",
"Haoyang Li",
"Zhongyu Guo",
"Hongbin Sun"
] |
https://openaccess.thecvf.com/content/ICCV2025/html/Kong_VLR-Driver_Large_Vision-Language-Reasoning_Models_for_Embodied_Autonomous_Driving_ICCV_2025_paper.html
|
https://openaccess.thecvf.com/content/ICCV2025/papers/Kong_VLR-Driver_Large_Vision-Language-Reasoning_Models_for_Embodied_Autonomous_Driving_ICCV_2025_paper.pdf
| null |
@InProceedings{Kong_2025_ICCV,
author = {Kong, Fanjie and Li, Yitong and Chen, Weihuang and Min, Chen and Li, Yizhe and Gao, Zhiqiang and Li, Haoyang and Guo, Zhongyu and Sun, Hongbin},
title = {VLR-Driver: Large Vision-Language-Reasoning Models for Embodied Autonomous Driving},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2025},
pages = {26966-26976}
}
|
The rise of embodied intelligence and multi-modal large language models has led to exciting advancements in the field of autonomous driving, establishing it as a prominent research focus in both academia and industry. However, when confronted with intricate and ambiguous traffic scenarios, the lack of logical reasoning and cognitive decision-making capabilities remains the primary challenge impeding the realization of embodied autonomous driving. Although Vision Language Models (VLMs) have enhanced the deep semantic understanding of autonomous driving systems, they exhibit notable limitations in decision explainability when handling rare and long-tail traffic scenarios. In this paper, we propose VLR-Driver, a novel multi-modal Vision-Language-Reasoning (VLR) framework based on Chain of Thought (CoT) for embodied autonomous driving. The framework employs a spatiotemporal CoT reasoning approach to recursively analyze potential safety risks and driving intentions of other agents, thereby delivering an efficient and transparent decision-making process. Furthermore, we construct a multi-modal reasoning-decision dataset to support the advancement of hierarchical reasoning of VLMs in autonomous driving. Closed-loop experiments conducted in CARLA demonstrate that the VLR-Driver significantly outperforms state-of-the-art end-to-end methods. Notably, key metrics such as driving score improved by 17.5%, while the success rate improved by 22.2%, offering a more transparent, reliable, and secure solution for autonomous driving systems.
| null | null | null | null |
[] |
[] |
[] |
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] |
77
|
ResGS: Residual Densification of 3D Gaussian for Efficient Detail Recovery
|
[
"Yanzhe Lyu",
"Kai Cheng",
"Xin Kang",
"Xuejin Chen"
] |
https://openaccess.thecvf.com/content/ICCV2025/html/Lyu_ResGS_Residual_Densification_of_3D_Gaussian_for_Efficient_Detail_Recovery_ICCV_2025_paper.html
|
https://openaccess.thecvf.com/content/ICCV2025/papers/Lyu_ResGS_Residual_Densification_of_3D_Gaussian_for_Efficient_Detail_Recovery_ICCV_2025_paper.pdf
|
https://openaccess.thecvf.com/content/ICCV2025/supplemental/Lyu_ResGS_Residual_Densification_ICCV_2025_supplemental.zip
|
@InProceedings{Lyu_2025_ICCV,
author = {Lyu, Yanzhe and Cheng, Kai and Kang, Xin and Chen, Xuejin},
title = {ResGS: Residual Densification of 3D Gaussian for Efficient Detail Recovery},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2025},
pages = {28093-28102}
}
|
Recently, 3D Gaussian Splatting (3D-GS) has prevailed in novel view synthesis, achieving high fidelity and efficiency. However, it often struggles to capture rich details and complete geometry. Our analysis reveals that the 3D-GS densification operation lacks adaptiveness and faces a dilemma between geometry coverage and detail recovery. To address this, we introduce a novel densification operation, residual split, which adds a downscaled Gaussian as a residual. Our approach is capable of adaptively retrieving details and complementing missing geometry. To further support this method, we propose a pipeline named ResGS. Specifically, we integrate a Gaussian image pyramid for progressive supervision and implement a selection scheme that prioritizes the densification of coarse Gaussians over time. Extensive experiments demonstrate that our method achieves SOTA rendering quality. Consistent performance improvements can be achieved by applying our residual split on various 3D-GS variants, underscoring its versatility and potential for broader application in 3D-GS-based applications.
|
2412.07494
| null | null | null |
[] |
[] |
[] |
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] |
78
|
Language Driven Occupancy Prediction
|
[
"Zhu Yu",
"Bowen Pang",
"Lizhe Liu",
"Runmin Zhang",
"Qiang Li",
"Si-Yuan Cao",
"Maochun Luo",
"Mingxia Chen",
"Sheng Yang",
"Hui-Liang Shen"
] |
https://openaccess.thecvf.com/content/ICCV2025/html/Yu_Language_Driven_Occupancy_Prediction_ICCV_2025_paper.html
|
https://openaccess.thecvf.com/content/ICCV2025/papers/Yu_Language_Driven_Occupancy_Prediction_ICCV_2025_paper.pdf
|
https://openaccess.thecvf.com/content/ICCV2025/supplemental/Yu_Language_Driven_Occupancy_ICCV_2025_supplemental.pdf
|
@InProceedings{Yu_2025_ICCV,
author = {Yu, Zhu and Pang, Bowen and Liu, Lizhe and Zhang, Runmin and Li, Qiang and Cao, Si-Yuan and Luo, Maochun and Chen, Mingxia and Yang, Sheng and Shen, Hui-Liang},
title = {Language Driven Occupancy Prediction},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2025},
pages = {7548-7558}
}
|
We introduce LOcc, an effective and generalizable framework for open-vocabulary occupancy (OVO) prediction. Previous approaches typically supervise the networks through coarse voxel-to-text correspondences via image features as intermediates or noisy and sparse correspondences from voxel-based model-view projections. To alleviate the inaccurate supervision, we propose a semantic transitive labeling pipeline to generate dense and fine-grained 3D language occupancy ground truth. Our pipeline presents a feasible way to dig into the valuable semantic information of images, transferring text labels from images to LiDAR point clouds and ultimately to voxels, to establish precise voxel-to-text correspondences. By replacing the original prediction head of supervised occupancy models with a geometry head for binary occupancy states and a language head for language features, LOcc effectively uses the generated language ground truth to guide the learning of 3D language volume. Through extensive experiments, we demonstrate that our transitive semantic labeling pipeline can produce more accurate pseudo-labeled ground truth, diminishing labor-intensive human annotations. Additionally, we validate LOcc across various architectures, where all models consistently outperform state-of-the-art zero-shot occupancy prediction approaches on the Occ3D-nuScenes dataset.
|
2411.16072
|
ICCV 2025; Project Page: https://github.com/pkqbajng/LOcc
| null | null |
[] |
[] |
[] |
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] |
79
|
Prior2Former - Evidential Modeling of Mask Transformers for Assumption-Free Open-World Panoptic Segmentation
|
[
"Sebastian Schmidt",
"Julius Koerner",
"Dominik Fuchsgruber",
"Stefano Gasperini",
"Federico Tombari",
"Stephan Günnemann"
] |
https://openaccess.thecvf.com/content/ICCV2025/html/Schmidt_Prior2Former_-_Evidential_Modeling_of_Mask_Transformers_for_Assumption-Free_Open-World_ICCV_2025_paper.html
|
https://openaccess.thecvf.com/content/ICCV2025/papers/Schmidt_Prior2Former_-_Evidential_Modeling_of_Mask_Transformers_for_Assumption-Free_Open-World_ICCV_2025_paper.pdf
|
https://openaccess.thecvf.com/content/ICCV2025/supplemental/Schmidt_Prior2Former_-_Evidential_ICCV_2025_supplemental.pdf
|
@InProceedings{Schmidt_2025_ICCV,
author = {Schmidt, Sebastian and Koerner, Julius and Fuchsgruber, Dominik and Gasperini, Stefano and Tombari, Federico and G\"unnemann, Stephan},
title = {Prior2Former - Evidential Modeling of Mask Transformers for Assumption-Free Open-World Panoptic Segmentation},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2025},
pages = {23646-23656}
}
|
In panoptic segmentation, individual instances must be separated within semantic classes. As state-of-the-art methods rely on a pre-defined set of classes, they struggle with novel categories and out-of-distribution (OOD) data. This is particularly problematic in safety-critical applications, such as autonomous driving, where reliability in unseen scenarios is essential. We address the gap between outstanding benchmark performance and reliability by proposing Prior2Former (P2F), the first approach for segmentation vision transformers rooted in evidential learning. P2F extends the mask vision transformer architecture by incorporating a Beta prior for computing model uncertainty in pixel-wise binary mask assignments. This design enables high-quality uncertainty estimation that effectively detects novel and OOD objects, enabling state-of-the-art anomaly instance segmentation and open-world panoptic segmentation. Unlike most segmentation models addressing unknown classes, P2F operates without access to OOD data samples or contrastive training on void (i.e., unlabeled) classes, making it highly applicable in real-world scenarios where such prior information is unavailable. Additionally, P2F can be flexibly applied to anomaly instance and panoptic segmentation. Through comprehensive experiments on the Cityscapes, COCO, SegmentMeIfYouCan, and OoDIS datasets, P2F demonstrates state-of-the-art performance. Especially in OoDIS, P2F ranks first in its category.
|
2504.04841
| null | null | null |
[] |
[] |
[] |
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80
|
E-NeMF: Event-based Neural Motion Field for Novel Space-time View Synthesis of Dynamic Scenes
|
[
"Yan Liu",
"Zehao Chen",
"Haojie Yan",
"De Ma",
"Huajin Tang",
"Qian Zheng",
"Gang Pan"
] |
https://openaccess.thecvf.com/content/ICCV2025/html/Liu_E-NeMF_Event-based_Neural_Motion_Field_for_Novel_Space-time_View_Synthesis_ICCV_2025_paper.html
|
https://openaccess.thecvf.com/content/ICCV2025/papers/Liu_E-NeMF_Event-based_Neural_Motion_Field_for_Novel_Space-time_View_Synthesis_ICCV_2025_paper.pdf
| null |
@InProceedings{Liu_2025_ICCV,
author = {Liu, Yan and Chen, Zehao and Yan, Haojie and Ma, De and Tang, Huajin and Zheng, Qian and Pan, Gang},
title = {E-NeMF: Event-based Neural Motion Field for Novel Space-time View Synthesis of Dynamic Scenes},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2025},
pages = {10854-10864}
}
|
Synthesizing novel space-time views from a monocular video is a highly ill-posed problem, and its effectiveness relies on accurately reconstructing motion and appearance of the dynamic scene.Frame-based methods for novel space-time view synthesis in dynamic scenes rely on simplistic motion assumptions due to the absence of inter-frame cues, which makes them fall in complex motion. Event camera captures inter-frame cues with high temporal resolution, which makes it hold the promising potential to handle high speed and complex motion. However, it is still difficult due to the event noise and sparsity. To mitigate the impact caused by event noise and sparsity, we propose E-NeMF, which alleviates the impact of event noise with Parametric Motion Representation and mitigates the event sparsity with Flow Prediction Module. Experiments on multiple real-world datasets demonstrate our superior performance in handling high-speed and complex motion.
| null | null | null | null |
[] |
[] |
[] |
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] |
81
|
Event-based Tiny Object Detection: A Benchmark Dataset and Baseline
|
[
"Nuo Chen",
"Chao Xiao",
"Yimian Dai",
"Shiman He",
"Miao Li",
"Wei An"
] |
https://openaccess.thecvf.com/content/ICCV2025/html/Chen_Event-based_Tiny_Object_Detection_A_Benchmark_Dataset_and_Baseline_ICCV_2025_paper.html
|
https://openaccess.thecvf.com/content/ICCV2025/papers/Chen_Event-based_Tiny_Object_Detection_A_Benchmark_Dataset_and_Baseline_ICCV_2025_paper.pdf
|
https://openaccess.thecvf.com/content/ICCV2025/supplemental/Chen_Event-based_Tiny_Object_ICCV_2025_supplemental.pdf
|
@InProceedings{Chen_2025_ICCV,
author = {Chen, Nuo and Xiao, Chao and Dai, Yimian and He, Shiman and Li, Miao and An, Wei},
title = {Event-based Tiny Object Detection: A Benchmark Dataset and Baseline},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2025},
pages = {7209-7218}
}
|
Small object detection (SOD) in anti-UAV task is a challenging problem due to the small size of UAVs and complex backgrounds. Traditional frame-based cameras struggle to detect small objects in complex environments due to their low frame rates, limited dynamic range, and data redundancy. Event cameras, with microsecond temporal resolution and high dynamic range, provide a more effective solution for SOD. However, existing event-based object detection datasets are limited in scale, feature large targets size, and lack diverse backgrounds, making them unsuitable for SOD benchmarks. In this paper, we introduce a Event-based Small object detection (EVSOD) dataset (namely EV-UAV), the first large-scale, highly diverse benchmark for anti-UAV tasks. It includes 147 sequences with over 2.3 million event-level annotations, featuring extremely small targets (averaging 6.8 x 5.4 pixels) and diverse scenarios such as urban clutter and extreme lighting conditions. Furthermore, based on the observation that small moving targets form continuous curves in spatiotemporal event point clouds, we propose Event based Sparse Segmentation Network (EV-SpSegNet), a novel baseline for event segmentation in point cloud space, along with a Spatiotemporal Correlation (STC) loss that leverages motion continuity to guide the network in retaining target events. Extensive experiments on the EV-UAV dataset demonstrate the superiority of our method and provide a benchmark for future research in EVSOD.
|
2506.23575
| null |
https://github.com/ChenYichen9527/Ev-UAV
| null |
[] |
[] |
[] |
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] |
82
|
Optimal Transport for Brain-Image Alignment: Unveiling Redundancy and Synergy in Neural Information Processing
|
[
"Yang Xiao",
"Wang Lu",
"Jie Ji",
"Ruimeng Ye",
"Gen Li",
"Xiaolong Ma",
"Bo Hui"
] |
https://openaccess.thecvf.com/content/ICCV2025/html/Xiao_Optimal_Transport_for_Brain-Image_Alignment_Unveiling_Redundancy_and_Synergy_in_ICCV_2025_paper.html
|
https://openaccess.thecvf.com/content/ICCV2025/papers/Xiao_Optimal_Transport_for_Brain-Image_Alignment_Unveiling_Redundancy_and_Synergy_in_ICCV_2025_paper.pdf
| null |
@InProceedings{Xiao_2025_ICCV,
author = {Xiao, Yang and Lu, Wang and Ji, Jie and Ye, Ruimeng and Li, Gen and Ma, Xiaolong and Hui, Bo},
title = {Optimal Transport for Brain-Image Alignment: Unveiling Redundancy and Synergy in Neural Information Processing},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2025},
pages = {20445-20455}
}
|
The design of artificial neural networks (ANNs) is inspired by the structure of the human brain, and in turn, ANNs offer a potential means to interpret and understand brain signals. Existing methods primarily align brain signals with stimulus signals using Mean Squared Error (MSE), which focuses only on local point-wise alignment and ignores global matching, leading to coarse interpretations and inaccuracies in brain signal decoding. In this paper, we address these issues through optimal transport (OT) and theoretically demonstrate why OT provides a more effective alignment strategy than MSE. Specifically, we construct a transport plan between brain voxel embeddings and image embeddings, enabling more precise matching. By controlling the amount of transport, we mitigate the influence of redundant information. We apply our alignment model directly to the Brain Captioning task by feeding brain signals into a large language model (LLM) instead of images. Our approach achieves state-of-the-art performance across ten evaluation metrics, surpassing the previous best method by an average of 6.11% in singlesubject training and 3.81% in cross-subject training. Additionally, we have uncovered several insightful conclusions that align with existing brain research. We unveil the redundancy and synergy of brain information processing through region masking and data dimensionality reduction visualization experiments. We believe our approach paves the way for a more precise understanding of brain signals in the future. The code is available at https://github.com/NKUShaw/OT-Alignment4brain-to-image
|
2503.10663
|
14pages
| null | null |
[] |
[] |
[] |
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] |
83
|
Adversarial Distribution Matching for Diffusion Distillation Towards Efficient Image and Video Synthesis
|
[
"Yanzuo Lu",
"Yuxi Ren",
"Xin Xia",
"Shanchuan Lin",
"Xing Wang",
"Xuefeng Xiao",
"Andy J. Ma",
"Xiaohua Xie",
"Jian-Huang Lai"
] |
https://openaccess.thecvf.com/content/ICCV2025/html/Lu_Adversarial_Distribution_Matching_for_Diffusion_Distillation_Towards_Efficient_Image_and_ICCV_2025_paper.html
|
https://openaccess.thecvf.com/content/ICCV2025/papers/Lu_Adversarial_Distribution_Matching_for_Diffusion_Distillation_Towards_Efficient_Image_and_ICCV_2025_paper.pdf
|
https://openaccess.thecvf.com/content/ICCV2025/supplemental/Lu_Adversarial_Distribution_Matching_ICCV_2025_supplemental.pdf
|
@InProceedings{Lu_2025_ICCV,
author = {Lu, Yanzuo and Ren, Yuxi and Xia, Xin and Lin, Shanchuan and Wang, Xing and Xiao, Xuefeng and Ma, Andy J. and Xie, Xiaohua and Lai, Jian-Huang},
title = {Adversarial Distribution Matching for Diffusion Distillation Towards Efficient Image and Video Synthesis},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2025},
pages = {16818-16829}
}
|
Distribution Matching Distillation (DMD) is a promising score distillation technique that compresses pre-trained teacher diffusion models into efficient one-step or multi-step student generators.Nevertheless, its reliance on the reverse Kullback-Leibler (KL) divergence minimization potentially induces mode collapse (or mode-seeking) in certain applications.To circumvent this inherent drawback, we propose Adversarial Distribution Matching (ADM), a novel framework that leverages diffusion-based discriminators to align the latent predictions between real and fake score estimators for score distillation in an adversarial manner.In the context of extremely challenging one-step distillation, we further improve the pre-trained generator by adversarial distillation with hybrid discriminators in both latent and pixel spaces.Different from the mean squared error used in DMD2 pre-training, our method incorporates the distributional loss on ODE pairs collected from the teacher model, and thus providing a better initialization for score distillation fine-tuning in the next stage.By combining the adversarial distillation pre-training with ADM fine-tuning into a unified pipeline termed DMDX, our proposed method achieves superior one-step performance on SDXL compared to DMD2 while consuming less GPU time.Additional experiments that apply multi-step ADM distillation on SD3-Medium, SD3.5-Large, and CogVideoX set a new benchmark towards efficient image and video synthesis.
|
2507.18569
|
Accepted by ICCV 2025 (Highlight)
| null | null |
[] |
[] |
[] |
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] |
84
|
Switch-a-View: View Selection Learned from Unlabeled In-the-wild Videos
|
[
"Sagnik Majumder",
"Tushar Nagarajan",
"Ziad Al-Halah",
"Kristen Grauman"
] |
https://openaccess.thecvf.com/content/ICCV2025/html/Majumder_Switch-a-View_View_Selection_Learned_from_Unlabeled_In-the-wild_Videos_ICCV_2025_paper.html
|
https://openaccess.thecvf.com/content/ICCV2025/papers/Majumder_Switch-a-View_View_Selection_Learned_from_Unlabeled_In-the-wild_Videos_ICCV_2025_paper.pdf
|
https://openaccess.thecvf.com/content/ICCV2025/supplemental/Majumder_Switch-a-View_View_Selection_ICCV_2025_supplemental.pdf
|
@InProceedings{Majumder_2025_ICCV,
author = {Majumder, Sagnik and Nagarajan, Tushar and Al-Halah, Ziad and Grauman, Kristen},
title = {Switch-a-View: View Selection Learned from Unlabeled In-the-wild Videos},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2025},
pages = {11969-11979}
}
|
We introduce Switch-a-View, a model that learns to automatically select the viewpoint to display at each timepoint when creating a how-to video. The key insight of our approach is how to train such a model from unlabeled--but human-edited--video samples. We pose a pretext task that pseudo-labels segments in the training videos for their primary viewpoint (egocentric or exocentric), and then discovers the patterns between the visual and spoken content in a how-to video on the one hand and its view-switch moments on the other hand. Armed with this predictor, our model can be applied to new multi-view videos to orchestrate which viewpoint should be displayed when. We demonstrate our idea on a variety of real-world videos from HowTo100M and Ego-Exo4D, and rigorously validate its advantages.
| null | null | null | null |
[] |
[] |
[] |
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] |
85
|
E-SAM: Training-Free Segment Every Entity Model
|
[
"Weiming Zhang",
"Dingwen Xiao",
"Lei Chen",
"Lin Wang"
] |
https://openaccess.thecvf.com/content/ICCV2025/html/Zhang_E-SAM_Training-Free_Segment_Every_Entity_Model_ICCV_2025_paper.html
|
https://openaccess.thecvf.com/content/ICCV2025/papers/Zhang_E-SAM_Training-Free_Segment_Every_Entity_Model_ICCV_2025_paper.pdf
|
https://openaccess.thecvf.com/content/ICCV2025/supplemental/Zhang_E-SAM_Training-Free_Segment_ICCV_2025_supplemental.pdf
|
@InProceedings{Zhang_2025_ICCV,
author = {Zhang, Weiming and Xiao, Dingwen and Chen, Lei and Wang, Lin},
title = {E-SAM: Training-Free Segment Every Entity Model},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2025},
pages = {24688-24697}
}
|
Entity Segmentation (ES) aims at identifying and segmenting distinct entities within an image without the need for predefined class labels. This characteristic makes ES well-suited to open-world applications with adaptation to diverse and dynamically changing environments, where new and previously unseen entities may appear frequently. Existing ES methods either require large annotated datasets or high training costs, limiting their scalability and adaptability. Recently, the Segment Anything Model (SAM), especially in its Automatic Mask Generation (AMG) mode, has shown potential for holistic image segmentation. However, it struggles with over-segmentation and under-segmentation, making it less effective for ES. In this paper, we introduce E-SAM, a novel training-free framework that exhibits exceptional ES capability. Specifically, we first propose Multi-level Mask Generation (MMG) that hierarchically processes SAM's AMG outputs to generate reliable object-level masks while preserving fine details at other levels. Entity-level Mask Refinement (EMR) then refines these object-level masks into accurate entity-level masks. That is, it separates overlapping masks to address the redundancy issues inherent in SAM's outputs and merges similar masks by evaluating entity-level consistency. Lastly, Under-Segmentation Refinement (USR) addresses under-segmentation by generating additional high-confidence masks fused with EMR outputs to produce the final ES map. These three modules are seamlessly optimized to achieve the best ES without additional training overhead. Extensive experiments demonstrate that E-SAM achieves state-of-the-art performance compared to prior ES methods, demonstrating a significant improvement by +30.1 on benchmark metrics.
| null | null | null | null |
[] |
[] |
[] |
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] |
86
|
ViewSRD: 3D Visual Grounding via Structured Multi-View Decomposition
|
[
"Ronggang Huang",
"Haoxin Yang",
"Yan Cai",
"Xuemiao Xu",
"Huaidong Zhang",
"Shengfeng He"
] |
https://openaccess.thecvf.com/content/ICCV2025/html/Huang_ViewSRD_3D_Visual_Grounding_via_Structured_Multi-View_Decomposition_ICCV_2025_paper.html
|
https://openaccess.thecvf.com/content/ICCV2025/papers/Huang_ViewSRD_3D_Visual_Grounding_via_Structured_Multi-View_Decomposition_ICCV_2025_paper.pdf
|
https://openaccess.thecvf.com/content/ICCV2025/supplemental/Huang_ViewSRD_3D_Visual_ICCV_2025_supplemental.pdf
|
@InProceedings{Huang_2025_ICCV,
author = {Huang, Ronggang and Yang, Haoxin and Cai, Yan and Xu, Xuemiao and Zhang, Huaidong and He, Shengfeng},
title = {ViewSRD: 3D Visual Grounding via Structured Multi-View Decomposition},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2025},
pages = {9726-9736}
}
|
3D visual grounding aims to identify and localize objects in a 3D space based on textual descriptions. However, existing methods struggle with disentangling targets from anchors in complex multi-anchor queries and resolving inconsistencies in spatial descriptions caused by perspective variations.To tackle these challenges, we propose ViewSRD, a framework that formulates 3D visual grounding as a structured multi-view decomposition process. First, the Simple Relation Decoupling (SRD) module restructures complex multi-anchor queries into a set of targeted single-anchor statements, generating a structured set of perspective-aware descriptions that clarify positional relationships. These decomposed representations serve as the foundation for the Multi-view Textual-Scene Interaction (Multi-TSI) module, which integrates textual and scene features across multiple viewpoints using shared, Cross-modal Consistent View Tokens (CCVTs) to preserve spatial correlations. Finally, a Textual-Scene Reasoning module synthesizes multi-view predictions into a unified and robust 3D visual grounding.Experiments on 3D visual grounding datasets show that ViewSRD significantly outperforms state-of-the-art methods, particularly in complex queries requiring precise spatial differentiation.Code is available at https://github.com/visualjason/ViewSRD
|
2507.11261
|
Accepted by ICCV 2025
|
https://github.com/visualjason/ViewSRD
| null |
[] |
[] |
[] |
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] |
87
|
UniPortrait: A Unified Framework for Identity-Preserving Single- and Multi-Human Image Personalization
|
[
"Junjie He",
"Yifeng Geng",
"Liefeng Bo"
] |
https://openaccess.thecvf.com/content/ICCV2025/html/He_UniPortrait_A_Unified_Framework_for_Identity-Preserving_Single-_and_Multi-Human_Image_ICCV_2025_paper.html
|
https://openaccess.thecvf.com/content/ICCV2025/papers/He_UniPortrait_A_Unified_Framework_for_Identity-Preserving_Single-_and_Multi-Human_Image_ICCV_2025_paper.pdf
|
https://openaccess.thecvf.com/content/ICCV2025/supplemental/He_UniPortrait_A_Unified_ICCV_2025_supplemental.pdf
|
@InProceedings{He_2025_ICCV,
author = {He, Junjie and Geng, Yifeng and Bo, Liefeng},
title = {UniPortrait: A Unified Framework for Identity-Preserving Single- and Multi-Human Image Personalization},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2025},
pages = {14399-14408}
}
|
This paper presents UniPortrait, an innovative human image personalization framework that unifies single- and multi-ID customization with high face fidelity, extensive facial editability, free-form input description, and diverse layout generation. UniPortrait consists of only two plug-and-play modules: an ID embedding module and an ID routing module. The ID embedding module extracts versatile editable facial features with a decoupling strategy for each ID and embeds them into the context space of diffusion models. The ID routing module then combines and distributes these embeddings adaptively to their respective regions within the synthesized image, achieving the customization of single and multiple IDs. With a carefully designed two-stage training scheme, UniPortrait achieves superior performance in both single- and multi-ID customization. Quantitative and qualitative experiments demonstrate the advantages of our method over existing approaches as well as its good scalability, e.g., the universal compatibility with existing generative control tools.
| null | null | null | null |
[] |
[] |
[] |
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] |
88
|
FDPT: Federated Discrete Prompt Tuning for Black-Box Visual-Language Models
|
[
"Jiaqi Wu",
"Simin Chen",
"Jing Tang",
"Yuzhe Yang",
"Yiming Chen",
"Lixu Wang",
"Song Lin",
"Zehua Wang",
"Wei Chen",
"Zijian Tian"
] |
https://openaccess.thecvf.com/content/ICCV2025/html/Wu_FDPT_Federated_Discrete_Prompt_Tuning_for_Black-Box_Visual-Language_Models_ICCV_2025_paper.html
|
https://openaccess.thecvf.com/content/ICCV2025/papers/Wu_FDPT_Federated_Discrete_Prompt_Tuning_for_Black-Box_Visual-Language_Models_ICCV_2025_paper.pdf
|
https://openaccess.thecvf.com/content/ICCV2025/supplemental/Wu_FDPT_Federated_Discrete_ICCV_2025_supplemental.pdf
|
@InProceedings{Wu_2025_ICCV,
author = {Wu, Jiaqi and Chen, Simin and Tang, Jing and Yang, Yuzhe and Chen, Yiming and Wang, Lixu and Lin, Song and Wang, Zehua and Chen, Wei and Tian, Zijian},
title = {FDPT: Federated Discrete Prompt Tuning for Black-Box Visual-Language Models},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2025},
pages = {2461-2470}
}
|
General-purpose Vision-Language Models (VLMs) have driven major advancements in multimodal AI. Fine-tuning these models with task-specific data enhances adaptability to various downstream tasks but suffers from privacy risks. While potential solutions like federated learning can address user data privacy concerns, model protection is also essential. Other methods that rely on black-box VLM APIs usually require the access of prediction logits, leaving them open to inversion attacks. Moreover, addressing the challenges of tuning complexity and data transmission efficiency in federated VLM scenarios is also crucial. To address these challenges, we propose FDPT--a federated discrete prompt tuning method utilizing black-box VLMs. During client optimization stage, FDPT employs an agent-driven framework leveraging large language models (LLMs) with enhanced reasoning capacities to systematically optimize discrete prompt representations, and also utilizes feedback mechanisms and chain of thought to enhance prediction accuracy. Importantly, it performs optimization by relying not on the predicted logic vectors output by LLMs but on textual results, avoiding reverse attack risks. During global aggregation stage, We mimic human electoral activities by employing evolutionary computation methods underpinned by semantic similarity computation to implement enhanced zero-order optimization for acquiring representative global tokens, thereby achieving knowledge aggregation. FDPT significantly outperforms nine state-of-the-art methods in image classification and visual question-answering, reducing communication overhead while generating highly transferable optimized prompts. Additionally, it exhibits improved robustness to data heterogeneity.
| null | null | null | null |
[] |
[] |
[] |
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] |
89
|
Exploiting Diffusion Prior for Task-driven Image Restoration
|
[
"Jaeha Kim",
"Junghun Oh",
"Kyoung Mu Lee"
] |
https://openaccess.thecvf.com/content/ICCV2025/html/Kim_Exploiting_Diffusion_Prior_for_Task-driven_Image_Restoration_ICCV_2025_paper.html
|
https://openaccess.thecvf.com/content/ICCV2025/papers/Kim_Exploiting_Diffusion_Prior_for_Task-driven_Image_Restoration_ICCV_2025_paper.pdf
|
https://openaccess.thecvf.com/content/ICCV2025/supplemental/Kim_Exploiting_Diffusion_Prior_ICCV_2025_supplemental.pdf
|
@InProceedings{Kim_2025_ICCV,
author = {Kim, Jaeha and Oh, Junghun and Lee, Kyoung Mu},
title = {Exploiting Diffusion Prior for Task-driven Image Restoration},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2025},
pages = {10151-10161}
}
|
Task-driven image restoration (TDIR) has recently emerged to address performance drops in high-level vision tasks caused by low-quality (LQ) inputs. Previous TDIR methods struggle to handle practical scenarios in which images are degraded by multiple complex factors, leaving minimal clues for restoration. This motivates us to leverage the diffusion prior, one of the most powerful natural image priors. However, while the diffusion prior can help generate visually plausible results, using it to restore task-relevant details remains challenging, even when combined with recent TDIR methods. To address this, we propose EDTR, which effectively harnesses the power of diffusion prior to restore task-relevant details. Specifically, we propose directly leveraging useful clues from LQ images in the diffusion process by generating from pixel-error-based pre-restored LQ images with mild noise added. Moreover, we employ a small number of denoising steps to prevent the generation of redundant details that dilute crucial task-related information. We demonstrate that our method effectively utilizes diffusion prior for TDIR, significantly enhancing task performance and visual quality across diverse tasks with multiple complex degradations.
|
2507.22459
|
Accepted to ICCV 2025. Code is available at
https://github.com/JaehaKim97/EDTR
| null | null |
[] |
[] |
[] |
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90
|
CE-FAM: Concept-Based Explanation via Fusion of Activation Maps
|
[
"Michihiro Kuroki",
"Toshihiko Yamasaki"
] |
https://openaccess.thecvf.com/content/ICCV2025/html/Kuroki_CE-FAM_Concept-Based_Explanation_via_Fusion_of_Activation_Maps_ICCV_2025_paper.html
|
https://openaccess.thecvf.com/content/ICCV2025/papers/Kuroki_CE-FAM_Concept-Based_Explanation_via_Fusion_of_Activation_Maps_ICCV_2025_paper.pdf
| null |
@InProceedings{Kuroki_2025_ICCV,
author = {Kuroki, Michihiro and Yamasaki, Toshihiko},
title = {CE-FAM: Concept-Based Explanation via Fusion of Activation Maps},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2025},
pages = {1413-1422}
}
|
Although saliency maps can highlight important regions to explain the reasoning behind image classification in artificial intelligence (AI), the meaning of these regions is left to the user's interpretation. In contrast, concept-based explanations decompose AI predictions into human-understandable concepts, clarifying their contributions. However, few methods can simultaneously reveal what concepts an image classifier learns, which regions are associated with them, and how they contribute to predictions.We propose a novel concept-based explanation method, Concept-based Explanation via Fusion of Activation Maps (CE-FAM). It employs a branched network that shares activation maps with an image classifier and learns to mimic the embeddings of a Vision and Language Model (VLM). The branch network predicts concepts in an image, and their corresponding regions are represented by a weighted sum of activation maps, with weights given by the gradients of the concept prediction scores. Their contributions are quantified based on their impact on the image classification score. Our method provides a general framework for identifying the concept regions and their contributions while leveraging VLM knowledge to handle arbitrary concepts without requiring an annotated dataset. Furthermore, we introduce a novel evaluation metric to assess the accuracy of the concept regions. Our qualitative and quantitative evaluations demonstrate our method outperforms existing approaches and excels in zero-shot inference for unseen concepts.
| null | null | null | null |
[] |
[] |
[] |
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] |
91
|
Dual-level Prototype Learning for Composite Degraded Image Restoration
|
[
"Zhongze Wang",
"Haitao Zhao",
"Lujian Yao",
"Jingchao Peng",
"Kaijie Zhao"
] |
https://openaccess.thecvf.com/content/ICCV2025/html/Wang_Dual-level_Prototype_Learning_for_Composite_Degraded_Image_Restoration_ICCV_2025_paper.html
|
https://openaccess.thecvf.com/content/ICCV2025/papers/Wang_Dual-level_Prototype_Learning_for_Composite_Degraded_Image_Restoration_ICCV_2025_paper.pdf
|
https://openaccess.thecvf.com/content/ICCV2025/supplemental/Wang_Dual-level_Prototype_Learning_ICCV_2025_supplemental.pdf
|
@InProceedings{Wang_2025_ICCV,
author = {Wang, Zhongze and Zhao, Haitao and Yao, Lujian and Peng, Jingchao and Zhao, Kaijie},
title = {Dual-level Prototype Learning for Composite Degraded Image Restoration},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2025},
pages = {14006-14016}
}
|
Images captured under severe weather conditions often suffer from complex, composite degradations, varying in intensity. In this paper, we introduce a novel method, Dual-Level Prototype Learning (DPL), to tackle the challenging task of composite degraded image restoration. Unlike previous methods that rely on fixed embeddings to characterize degradation types, DPL maintains a number of degradation-level prototypes to represent the specific degradation scenes dynamically. Furthermore, considering the diverse factors influencing each degradation type, factor-level prototypes are incorporated to capture variations in individual degradation factors. Image features are matched with both degradation-level and factor-level prototypes, producing detailed scene embeddings that enhance the network's understanding of composite degradations. These scene embeddings are then processed through Dual Scene Embedding Transformer Blocks to guide the restoration process. To further refine the prototype distribution, we propose a Prototype Scatter Learning Loss, which enables prototypes within the same degradation to learn more information and push prototypes between different degradations to be separate. Additionally, we introduce a new dataset named Variable Composite Degradation (VCD) dataset which contains images with different intensities of each type of composite degradation to validate the efficacy of our method. Extensive experiments demonstrate that DPL significantly outperforms existing methods in restoring images with composite degradations.
| null | null | null | null |
[] |
[] |
[] |
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] |
92
|
Forensic-MoE: Exploring Comprehensive Synthetic Image Detection Traces with Mixture of Experts
|
[
"Mingqi Fang",
"Ziguang Li",
"Lingyun Yu",
"Quanwei Yang",
"Hongtao Xie",
"Yongdong Zhang"
] |
https://openaccess.thecvf.com/content/ICCV2025/html/Fang_Forensic-MoE_Exploring_Comprehensive_Synthetic_Image_Detection_Traces_with_Mixture_of_ICCV_2025_paper.html
|
https://openaccess.thecvf.com/content/ICCV2025/papers/Fang_Forensic-MoE_Exploring_Comprehensive_Synthetic_Image_Detection_Traces_with_Mixture_of_ICCV_2025_paper.pdf
|
https://openaccess.thecvf.com/content/ICCV2025/supplemental/Fang_Forensic-MoE_Exploring_Comprehensive_ICCV_2025_supplemental.pdf
|
@InProceedings{Fang_2025_ICCV,
author = {Fang, Mingqi and Li, Ziguang and Yu, Lingyun and Yang, Quanwei and Xie, Hongtao and Zhang, Yongdong},
title = {Forensic-MoE: Exploring Comprehensive Synthetic Image Detection Traces with Mixture of Experts},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2025},
pages = {17772-17782}
}
|
Recently, synthetic images have evolved incredibly realistic with the development of generative techniques. To avoid the spread of misinformation and identify synthetic content, research on synthetic image detection becomes urgent. Unfortunately, limited to the singular forensic perspective, existing methods struggle to explore sufficient traces encountered with diverse synthetic techniques. In response to this, we argue that different synthetic images encompass a variety of forensic traces, and utilizing multiple experts to explore traces from diverse perspectives will be beneficial. Accordingly, a novel detector with the Mixture of multiple forensic Experts is proposed, named Forensic-MoE. To integrate multiple experts and enhance the knowledge interaction, Forensic-MoE follows an adapter-backbone architecture. Specifically, multiple adapters trained on different synthetic images serve as the trace exploration experts, and they are uniformly integrated into a pretrained backbone model to learn the detection prior and encourage the expert interaction. By guiding multiple experts to align with each other and collaborate, Forensic-MoE can integrate comprehensive and discriminative detection traces from multiple perspectives. Moreover, for the discrimination improvement of each expert, a multi-stage structure is proposed for efficient trace perception, and a patch decentralization strategy is applied to encourage the model's attention on every local region. Code will be available in https://github.com/fangmq77/Forensic-MoE
| null | null | null | null |
[] |
[] |
[] |
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] |
93
|
Robust Adverse Weather Removal via Spectral-based Spatial Grouping
|
[
"Yuhwan Jeong",
"Yunseo Yang",
"Youngho Yoon",
"Kuk-Jin Yoon"
] |
https://openaccess.thecvf.com/content/ICCV2025/html/Jeong_Robust_Adverse_Weather_Removal_via_Spectral-based_Spatial_Grouping_ICCV_2025_paper.html
|
https://openaccess.thecvf.com/content/ICCV2025/papers/Jeong_Robust_Adverse_Weather_Removal_via_Spectral-based_Spatial_Grouping_ICCV_2025_paper.pdf
|
https://openaccess.thecvf.com/content/ICCV2025/supplemental/Jeong_Robust_Adverse_Weather_ICCV_2025_supplemental.pdf
|
@InProceedings{Jeong_2025_ICCV,
author = {Jeong, Yuhwan and Yang, Yunseo and Yoon, Youngho and Yoon, Kuk-Jin},
title = {Robust Adverse Weather Removal via Spectral-based Spatial Grouping},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2025},
pages = {11872-11883}
}
|
Adverse weather conditions cause diverse and complex degradation patterns, driving the development of All-in-One (AiO) models. However, recent AiO solutions still struggle to capture diverse degradations, since global filtering methods like direct operations on the frequency domain fail to handle highly variable and localized distortions. To address these issue, we propose Spectral-based Spatial Grouping Transformer (SSGformer), a novel approach that leverages spectral decomposition and group-wise attention for multi-weather image restoration. SSGformer decomposes images into high-frequency edge features using conventional edge detection and low-frequency information via Singular Value Decomposition. We utilize multi-head linear attention to effectively model the relationship between these features. The fused features are integrated with the input to generate a grouping-mask that clusters regions based on the spatial similarity and image texture. To fully leverage this mask, we introduce a group-wise attention mechanism, enabling robust adverse weather removal and ensuring consistent performance across diverse weather conditions. We also propose a Spatial Grouping Transformer Block that uses both channel attention and spatial attention, effectively balancing feature-wise relationships and spatial dependencies. Extensive experiments show the superiority of our approach, validating its effectiveness in handling the varied and intricate adverse weather degradations.
|
2507.22498
|
accepted by ICCV25
| null | null |
[] |
[] |
[] |
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] |
94
|
PLAN: Proactive Low-Rank Allocation for Continual Learning
|
[
"Xiequn Wang",
"Zhan Zhuang",
"Yu Zhang"
] |
https://openaccess.thecvf.com/content/ICCV2025/html/Wang_PLAN_Proactive_Low-Rank_Allocation_for_Continual_Learning_ICCV_2025_paper.html
|
https://openaccess.thecvf.com/content/ICCV2025/papers/Wang_PLAN_Proactive_Low-Rank_Allocation_for_Continual_Learning_ICCV_2025_paper.pdf
|
https://openaccess.thecvf.com/content/ICCV2025/supplemental/Wang_PLAN_Proactive_Low-Rank_ICCV_2025_supplemental.pdf
|
@InProceedings{Wang_2025_ICCV,
author = {Wang, Xiequn and Zhuang, Zhan and Zhang, Yu},
title = {PLAN: Proactive Low-Rank Allocation for Continual Learning},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2025},
pages = {2909-2918}
}
|
Continual learning (CL) requires models to continuously adapt to new tasks without forgetting past knowledge. In this work, we propose \underline P roactive \underline L ow-rank \underline A llocatio\underline N (PLAN), a framework that extends Low-Rank Adaptation (LoRA) to enable efficient and interference-aware fine-tuning of large pre-trained models in CL settings. PLAN proactively manages the allocation of task-specific subspaces by introducing orthogonal basis vectors for each task and optimizing them through a perturbation-based strategy that minimizes conflicts with previously learned parameters. Furthermore, PLAN incorporates a novel selection mechanism that identifies and assigns basis vectors with minimal sensitivity to interference, reducing the risk of degrading past knowledge while maintaining efficient adaptation to new tasks. Empirical results on standard CL benchmarks demonstrate that PLAN consistently outperforms existing methods, establishing a new state-of-the-art for continual learning with foundation models.
| null | null | null | null |
[] |
[] |
[] |
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] |
95
|
EMoTive: Event-guided Trajectory Modeling for 3D Motion Estimation
|
[
"Zengyu Wan",
"Wei Zhai",
"Yang Cao",
"Zhengjun Zha"
] |
https://openaccess.thecvf.com/content/ICCV2025/html/Wan_EMoTive_Event-guided_Trajectory_Modeling_for_3D_Motion_Estimation_ICCV_2025_paper.html
|
https://openaccess.thecvf.com/content/ICCV2025/papers/Wan_EMoTive_Event-guided_Trajectory_Modeling_for_3D_Motion_Estimation_ICCV_2025_paper.pdf
|
https://openaccess.thecvf.com/content/ICCV2025/supplemental/Wan_EMoTive_Event-guided_Trajectory_ICCV_2025_supplemental.pdf
|
@InProceedings{Wan_2025_ICCV,
author = {Wan, Zengyu and Zhai, Wei and Cao, Yang and Zha, Zhengjun},
title = {EMoTive: Event-guided Trajectory Modeling for 3D Motion Estimation},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2025},
pages = {9342-9351}
}
|
Visual 3D motion estimation aims to infer the motion of 2D pixels in 3D space based on visual cues. The key challenge arises from depth variation induced spatio-temporal motion inconsistencies, disrupting the assumptions of local spatial or temporal motion smoothness in previous motion estimation frameworks. In contrast, event cameras offer new possibilities for 3D motion estimation through continuous adaptive pixel-level responses to scene changes. This paper presents EMoTive, a novel event-based framework that models spatio-temporal trajectories via event-guided non-uniform parametric curves, effectively characterizing locally heterogeneous spatio-temporal motion. Specifically, we first introduce Event Kymograph - an event projection method that leverages a continuous temporal projection kernel and decouples spatial observations to encode fine-grained temporal evolution explicitly. For motion representation, we introduce a density-aware adaptation mechanism to fuse spatial and temporal features under event guidance, coupled with a non-uniform rational curve parameterization framework to adaptively model heterogeneous trajectories. The final 3D motion estimation is achieved through multi-temporal sampling of parametric trajectories, yielding optical flow and depth motion fields. To facilitate evaluation, we introduce CarlaEvent3D, a multi-dynamic synthetic dataset for comprehensive validation. Extensive experiments on both this dataset and a real-world benchmark demonstrate the effectiveness of the proposed method.
|
2503.11371
| null | null | null |
[] |
[] |
[] |
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] |
96
|
RobuSTereo: Robust Zero-Shot Stereo Matching under Adverse Weather
|
[
"Yuran Wang",
"Yingping Liang",
"Yutao Hu",
"Ying Fu"
] |
https://openaccess.thecvf.com/content/ICCV2025/html/Wang_RobuSTereo_Robust_Zero-Shot_Stereo_Matching_under_Adverse_Weather_ICCV_2025_paper.html
|
https://openaccess.thecvf.com/content/ICCV2025/papers/Wang_RobuSTereo_Robust_Zero-Shot_Stereo_Matching_under_Adverse_Weather_ICCV_2025_paper.pdf
|
https://openaccess.thecvf.com/content/ICCV2025/supplemental/Wang_RobuSTereo_Robust_Zero-Shot_ICCV_2025_supplemental.zip
|
@InProceedings{Wang_2025_ICCV,
author = {Wang, Yuran and Liang, Yingping and Hu, Yutao and Fu, Ying},
title = {RobuSTereo: Robust Zero-Shot Stereo Matching under Adverse Weather},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2025},
pages = {25134-25144}
}
|
Learning-based stereo matching models struggle in adverse weather conditions due to the scarcity of corresponding training data and the challenges in extracting discriminative features from degraded images. These limitations significantly hinder zero-shot generalization to out-of-distribution weather conditions. In this paper, we propose **RobuSTereo** a novel framework that enhances the zero-shot generalization of stereo matching models under adverse weather by addressing both data scarcity and feature extraction challenges. First, we introduce a diffusion-based simulation pipeline with a stereo consistency module, which generates high-quality stereo data tailored for adverse conditions. By training stereo matching models on our synthetic datasets, we reduce the domain gap between clean and degraded images, significantly improving the models' robustness to unseen weather conditions. The stereo consistency module ensures structural alignment across synthesized image pairs, preserving geometric integrity and enhancing depth estimation accuracy. Second, we design a robust feature encoder that combines a specialized ConvNet with a denoising transformer to extract stable and reliable features from degraded images. The ConvNet captures fine-grained local structures, while the denoising transformer refines global representations, effectively mitigating the impact of noise, low visibility, and weather-induced distortions. This enables more accurate disparity estimation even under challenging visual conditions. Extensive experiments demonstrate that **RobuSTereo** significantly improves the robustness and generalization of stereo matching models across diverse adverse weather scenarios.
|
2507.01653
|
accepted by ICCV25
| null | null |
[] |
[] |
[] |
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] |
97
|
SemGes: Semantics-aware Co-Speech Gesture Generation using Semantic Coherence and Relevance Learning
|
[
"Lanmiao Liu",
"Esam Ghaleb",
"Asli Ozyurek",
"Zerrin Yumak"
] |
https://openaccess.thecvf.com/content/ICCV2025/html/Liu_SemGes_Semantics-aware_Co-Speech_Gesture_Generation_using_Semantic_Coherence_and_Relevance_ICCV_2025_paper.html
|
https://openaccess.thecvf.com/content/ICCV2025/papers/Liu_SemGes_Semantics-aware_Co-Speech_Gesture_Generation_using_Semantic_Coherence_and_Relevance_ICCV_2025_paper.pdf
|
https://openaccess.thecvf.com/content/ICCV2025/supplemental/Liu_SemGes_Semantics-aware_Co-Speech_ICCV_2025_supplemental.pdf
|
@InProceedings{Liu_2025_ICCV,
author = {Liu, Lanmiao and Ghaleb, Esam and Ozyurek, Asli and Yumak, Zerrin},
title = {SemGes: Semantics-aware Co-Speech Gesture Generation using Semantic Coherence and Relevance Learning},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2025},
pages = {13963-13973}
}
|
Creating a virtual avatar with semantically coherent gestures that are aligned with speech is a challenging task. Existing gesture generation research mainly focused on generating rhythmic beat gestures, neglecting the semantic context of the gestures. In this paper, we propose a novel approach for semantic grounding in co-speech gesture generation that integrates semantic information at both fine-grained and global levels. Our approach starts with learning the motion prior through a vector-quantized variational autoencoder. Built on this model, a second-stage module is applied to automatically generate gestures from speech, text-based semantics and speaker identity that ensures consistency between the semantic relevance of generated gestures and co-occurring speech semantics through semantic coherence and relevance modules. Experimental results demonstrate that our approach enhances the realism and coherence of semantic gestures. Extensive experiments and user studies show that our method outperforms state-of-the-art approaches across two benchmarks in co-speech gesture generation in both objective and subjective metrics. The qualitative results of our model can be viewed at https://semgesture.github.io. Our code, dataset and pre-trained models will be shared upon acceptance.
|
2507.19359
|
Accepted to IEEE/CVF International Conference on Computer Vision
(ICCV) 2025
| null |
https://semgesture.github.io.
|
[] |
[] |
[] |
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] |
98
|
From Sharp to Blur: Unsupervised Domain Adaptation for 2D Human Pose Estimation Under Extreme Motion Blur Using Event Cameras
|
[
"Youngho Kim",
"Hoonhee Cho",
"Kuk-Jin Yoon"
] |
https://openaccess.thecvf.com/content/ICCV2025/html/Kim_From_Sharp_to_Blur_Unsupervised_Domain_Adaptation_for_2D_Human_ICCV_2025_paper.html
|
https://openaccess.thecvf.com/content/ICCV2025/papers/Kim_From_Sharp_to_Blur_Unsupervised_Domain_Adaptation_for_2D_Human_ICCV_2025_paper.pdf
| null |
@InProceedings{Kim_2025_ICCV,
author = {Kim, Youngho and Cho, Hoonhee and Yoon, Kuk-Jin},
title = {From Sharp to Blur: Unsupervised Domain Adaptation for 2D Human Pose Estimation Under Extreme Motion Blur Using Event Cameras},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2025},
pages = {9406-9417}
}
|
Human pose estimation is critical for applications such as rehabilitation, sports analytics, and AR/VR systems. However, rapid motion and low-light conditions often introduce motion blur, significantly degrading pose estimation due to the domain gap between sharp and blurred images. Most datasets assume stable conditions, making models trained on sharp images struggle in blurred environments. To address this, we introduce a novel domain adaptation approach that leverages event cameras, which capture high temporal resolution motion data and are inherently robust to motion blur. Using event-based augmentation, we generate motion-aware blurred images, effectively bridging the domain gap between sharp and blurred domains without requiring paired annotations. Additionally, we develop a student-teacher framework that iteratively refines pseudo-labels, leveraging mutual uncertainty masking to eliminate incorrect labels and enable more effective learning. Experimental results demonstrate that our approach outperforms conventional domain-adaptive human pose estimation methods, achieving robust pose estimation under motion blur without requiring annotations in the target domain. Our findings highlight the potential of event cameras as a scalable and effective solution for domain adaptation in real-world motion blur environments. Our project codes are available at https://github.com/kmax2001/EvSharp2Blur.
|
2507.22438
| null |
https://github.com/kmax2001/EvSharp2Blur
| null |
[] |
[] |
[] |
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] |
99
|
HyPiDecoder: Hybrid Pixel Decoder for Efficient Segmentation and Detection
|
[
"Fengzhe Zhou",
"Humphrey Shi"
] |
https://openaccess.thecvf.com/content/ICCV2025/html/Zhou_HyPiDecoder_Hybrid_Pixel_Decoder_for_Efficient_Segmentation_and_Detection_ICCV_2025_paper.html
|
https://openaccess.thecvf.com/content/ICCV2025/papers/Zhou_HyPiDecoder_Hybrid_Pixel_Decoder_for_Efficient_Segmentation_and_Detection_ICCV_2025_paper.pdf
| null |
@InProceedings{Zhou_2025_ICCV,
author = {Zhou, Fengzhe and Shi, Humphrey},
title = {HyPiDecoder: Hybrid Pixel Decoder for Efficient Segmentation and Detection},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2025},
pages = {22100-22109}
}
|
Recently, Mask2Former has achieved significant success as a universal image segmentation framework, with its Multi-Scale Deformable Attention (MSDeformAttn) Pixel Decoder becoming a widely adopted component in current segmentation models. However, the inefficiency of MSDeformAttn has become a performance bottleneck for segmenters. To address this, we propose the Hyper Pixel Decoder (HyPiDecoder), an improved Pixel Decoder design that replaces parts of the MSDeformAttn layers with convolution-based FPN layers, introducing explicit locality information and significantly boosting inference speed. Experimental results show that HyPiDecoder can be applied to both universal segmentation models and unified segmentation and detection models, achieving improvements in both speed and accuracy across object detection, semantic, instance, and panoptic segmentation tasks. The Mask DINO model integrated with HyPiDecoder achieves a new SOTA of 58.8 PQ on COCO panoptic segmentation with SwinL-scale backbone and no extra training data, with a 127% increase in inference speed compared to the original model. Code is available at \href https://github.com/HyPiDecoder/HybridPixelDecoder https://github.com/HyPiDecoder/HybridPixelDecoder .
| null | null | null | null |
[] |
[] |
[] |
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