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 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
200
|
Omni-scene Perception-oriented Point Cloud Geometry Enhancement for Coordinate Quantization
|
[
"Wang Liu",
"Wei Gao"
] |
https://openaccess.thecvf.com/content/ICCV2025/html/Liu_Omni-scene_Perception-oriented_Point_Cloud_Geometry_Enhancement_for_Coordinate_Quantization_ICCV_2025_paper.html
|
https://openaccess.thecvf.com/content/ICCV2025/papers/Liu_Omni-scene_Perception-oriented_Point_Cloud_Geometry_Enhancement_for_Coordinate_Quantization_ICCV_2025_paper.pdf
| null |
@InProceedings{Liu_2025_ICCV,
author = {Liu, Wang and Gao, Wei},
title = {Omni-scene Perception-oriented Point Cloud Geometry Enhancement for Coordinate Quantization},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2025},
pages = {26055-26064}
}
|
Information quantization has been widely adopted in multimedia content, such as images, videos, and point clouds. The goal of information quantization is to achieve efficient storage and transmission by reducing data precision or redundancy. However, the information distortion caused by quantization will lead to the degradation of signal fidelity and the performance of downstream tasks. This paper focuses on the geometry quantization distortion of point clouds and proposes a unified learning-based quality enhancement framework for omni-scene point clouds. Based on the characteristics of geometry quantization distortion, we analyze and find that existing upsampling methods are not competitive in dealing with point reduction and geometry displacement simultaneously caused by coordinate quantization. Therefore, we design a general rooting-growing-pruning paradigm to efficiently perceive the geometry feature of quantized point clouds and improve the quality significantly. In addition, a novel loss constraint term related to the quantization step parameter is proposed to further improve quality and accelerate model convergence. To the best of our knowledge, this is the first unified quality enhancement framework for object and scene point clouds with coordinate quantization. Extensive experiments verify the superiority of the proposed method on multi-scale point clouds with different levels of quantization distortion, including object (ModelNet40, 8iVFB) and scene (KITTI). In particular, the enhanced point clouds improve the performance of downstream analysis tasks, including classification and 3D object detection.
| null | null | null | null |
[] |
[] |
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] |
201
|
B-VLLM: A Vision Large Language Model with Balanced Spatio-Temporal Tokens
|
[
"Zhuqiang Lu",
"Zhenfei Yin",
"Mengwei He",
"Zhihui Wang",
"Zicheng Liu",
"Zhiyong Wang",
"Kun Hu"
] |
https://openaccess.thecvf.com/content/ICCV2025/html/Lu_B-VLLM_A_Vision_Large_Language_Model_with_Balanced_Spatio-Temporal_Tokens_ICCV_2025_paper.html
|
https://openaccess.thecvf.com/content/ICCV2025/papers/Lu_B-VLLM_A_Vision_Large_Language_Model_with_Balanced_Spatio-Temporal_Tokens_ICCV_2025_paper.pdf
|
https://openaccess.thecvf.com/content/ICCV2025/supplemental/Lu_B-VLLM_A_Vision_ICCV_2025_supplemental.pdf
|
@InProceedings{Lu_2025_ICCV,
author = {Lu, Zhuqiang and Yin, Zhenfei and He, Mengwei and Wang, Zhihui and Liu, Zicheng and Wang, Zhiyong and Hu, Kun},
title = {B-VLLM: A Vision Large Language Model with Balanced Spatio-Temporal Tokens},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2025},
pages = {24549-24558}
}
|
Recently, Vision Large Language Models (VLLMs) with integrated vision encoders have shown promising performance in vision understanding. They encode visual content into sequences of visual tokens, enabling joint processing of visual and textual data. However, understanding videos, especially long videos, remains a challenge as the rapid growth of visual tokens during video encoding risks exceeding VLLMs' context window length and significantly escalates computational cost. To restrict the number of visual tokens, existing VLLMs either: (1) uniformly downsample videos into a fixed number of frames or (2) reducing the number of visual tokens encoded from each frame. We argue that the former neglects temporal dynamics in videos, while the latter fails to preserve spatial details within individual frame. In this work, we propose Balanced-VLLM (B-VLLM), a novel VLLM framework designed to model task relevant spatio-temporal cues, while restricting the number of visual tokens within the VLLM's context window length. Central to our framework is a text-conditioned adaptive frame selection module that dynamically identifies task-relevant frames, which are further de-duplicated with a temporal frame token merging strategy.The visual tokens of these frames then undergo spatial token sampling and an optional spatial token merging strategy for granular control against the token budget. Experiments demonstrate the effectiveness of B-VLLM in balancing the number of frames and visual tokens, moreover, our proposed method introduce 10% performance gain on MVBench. Our code will be publicly available.
| null | null | null | null |
[] |
[] |
[] |
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] |
202
|
ImHead: A Large-scale Implicit Morphable Model for Localized Head Modeling
|
[
"Rolandos Alexandros Potamias",
"Stathis Galanakis",
"Jiankang Deng",
"Athanasios Papaioannou",
"Stefanos Zafeiriou"
] |
https://openaccess.thecvf.com/content/ICCV2025/html/Potamias_ImHead_A_Large-scale_Implicit_Morphable_Model_for_Localized_Head_Modeling_ICCV_2025_paper.html
|
https://openaccess.thecvf.com/content/ICCV2025/papers/Potamias_ImHead_A_Large-scale_Implicit_Morphable_Model_for_Localized_Head_Modeling_ICCV_2025_paper.pdf
|
https://openaccess.thecvf.com/content/ICCV2025/supplemental/Potamias_ImHead_A_Large-scale_ICCV_2025_supplemental.pdf
|
@InProceedings{Potamias_2025_ICCV,
author = {Potamias, Rolandos Alexandros and Galanakis, Stathis and Deng, Jiankang and Papaioannou, Athanasios and Zafeiriou, Stefanos},
title = {ImHead: A Large-scale Implicit Morphable Model for Localized Head Modeling},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2025},
pages = {10196-10206}
}
|
Over the last years, 3D morphable models (3DMMs) have emerged as a state-of-the-art methodology for modeling and generating expressive 3D avatars. However, given their reliance on a strict topology, along with their linear nature, they struggle to represent complex full-head shapes. Following the advent of deep implicit functions, we propose imHead, a novel implicit 3DMM that not only models expressive 3D head avatars but also facilitates localized editing of the facial features. Previous methods directly divided the latent space into local components accompanied by an identity encoding to capture the global shape variations, leading to expensive latent sizes. In contrast, we retain a single compact identity space and introduce an intermediate region-specific latent representation to enable local edits. To train imHead, we curate a large-scale dataset of 4K distinct identities, making a step-towards large scale 3D head modeling. Under a series of experiments we demonstrate the expressive power of the proposed model to represent diverse identities and expressions outperforming previous approaches. Additionally, the proposed approach provides an interpretable solution for 3D face manipulation, allowing the user to make localized edits.
|
2510.10793
| null | null | null |
[] |
[] |
[] |
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] |
203
|
SL2A-INR: Single-Layer Learnable Activation for Implicit Neural Representation
|
[
"Reza Rezaeian",
"Moein Heidari",
"Reza Azad",
"Dorit Merhof",
"Hamid Soltanian-Zadeh",
"Ilker Hacihaliloglu"
] |
https://openaccess.thecvf.com/content/ICCV2025/html/Rezaeian_SL2A-INR_Single-Layer_Learnable_Activation_for_Implicit_Neural_Representation_ICCV_2025_paper.html
|
https://openaccess.thecvf.com/content/ICCV2025/papers/Rezaeian_SL2A-INR_Single-Layer_Learnable_Activation_for_Implicit_Neural_Representation_ICCV_2025_paper.pdf
|
https://openaccess.thecvf.com/content/ICCV2025/supplemental/Rezaeian_SL2A-INR_Single-Layer_Learnable_ICCV_2025_supplemental.pdf
|
@InProceedings{Rezaeian_2025_ICCV,
author = {Rezaeian, Reza and Heidari, Moein and Azad, Reza and Merhof, Dorit and Soltanian-Zadeh, Hamid and Hacihaliloglu, Ilker},
title = {SL2A-INR: Single-Layer Learnable Activation for Implicit Neural Representation},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2025},
pages = {26065-26074}
}
|
Implicit Neural Representation (INR), leveraging a neural network to transform coordinate input into corresponding attributes, has recently driven significant advances in several vision-related domains. However, the performance of INR is heavily influenced by the choice of the nonlinear activation function used in its multilayer perceptron (MLP) architecture. To date, multiple nonlinearities have been investigated, but current INRs still face limitations in capturing high-frequency components and diverse signal types. We show that these challenges can be alleviated by introducing a novel approach in INR architecture. Specifically, we propose SL^ 2 A-INR, a hybrid network that combines a single-layer learnable activation function with an MLP that uses traditional ReLU activations. Our method performs superior across diverse tasks, including image representation, 3D shape reconstruction, and novel view synthesis. Through comprehensive experiments, SL^ 2 A-INR sets new benchmarks in accuracy, quality, and robustness for INR.
| null | null | null | null |
[] |
[] |
[] |
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] |
204
|
Authentic 4D Driving Simulation with a Video Generation Model
|
[
"Lening Wang",
"Wenzhao Zheng",
"Dalong Du",
"Yunpeng Zhang",
"Yilong Ren",
"Han Jiang",
"Zhiyong Cui",
"Haiyang Yu",
"Jie Zhou",
"Shanghang Zhang"
] |
https://openaccess.thecvf.com/content/ICCV2025/html/Wang_Authentic_4D_Driving_Simulation_with_a_Video_Generation_Model_ICCV_2025_paper.html
|
https://openaccess.thecvf.com/content/ICCV2025/papers/Wang_Authentic_4D_Driving_Simulation_with_a_Video_Generation_Model_ICCV_2025_paper.pdf
| null |
@InProceedings{Wang_2025_ICCV,
author = {Wang, Lening and Zheng, Wenzhao and Du, Dalong and Zhang, Yunpeng and Ren, Yilong and Jiang, Han and Cui, Zhiyong and Yu, Haiyang and Zhou, Jie and Zhang, Shanghang},
title = {Authentic 4D Driving Simulation with a Video Generation Model},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2025},
pages = {28892-28902}
}
|
Simulating driving environments in 4D is crucial for developing accurate and immersive autonomous driving systems. Despite progress in generating driving scenes, challenges in transforming views and modeling the dynamics of space and time remain. To tackle these issues, we propose a fresh methodology that reconstructs real-world driving environments and utilizes a generative network to enable 4D simulation. This approach builds continuous 4D point cloud scenes by leveraging surround-view data from autonomous vehicles. By separating the spatial and temporal elements, it creates smooth keyframe sequences. Furthermore, video generation techniques are employed to produce lifelike 4D simulation videos from any given perspective. To extend the range of possible viewpoints, we incorporate training using decomposed camera poses, which allows for enhanced modeling of distant scenes. Additionally, we merge camera trajectory data to synchronize 3D points across consecutive frames, fostering a richer understanding of the evolving scene. With training across multiple scene levels, our method is capable of simulating scenes from any viewpoint and offers deep insight into the evolution of scenes over time in a consistent spatial-temporal framework. In comparison with current methods, this approach excels in maintaining consistency across views, background coherence, and overall accuracy, significantly contributing to the development of more realistic autonomous driving simulations.
| null | null | null | null |
[] |
[] |
[] |
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] |
205
|
Partial Forward Blocking: A Novel Data Pruning Paradigm for Lossless Training Acceleration
|
[
"Dongyue Wu",
"Zilin Guo",
"Jialong Zuo",
"Nong Sang",
"Changxin Gao"
] |
https://openaccess.thecvf.com/content/ICCV2025/html/Wu_Partial_Forward_Blocking_A_Novel_Data_Pruning_Paradigm_for_Lossless_ICCV_2025_paper.html
|
https://openaccess.thecvf.com/content/ICCV2025/papers/Wu_Partial_Forward_Blocking_A_Novel_Data_Pruning_Paradigm_for_Lossless_ICCV_2025_paper.pdf
|
https://openaccess.thecvf.com/content/ICCV2025/supplemental/Wu_Partial_Forward_Blocking_ICCV_2025_supplemental.pdf
|
@InProceedings{Wu_2025_ICCV,
author = {Wu, Dongyue and Guo, Zilin and Zuo, Jialong and Sang, Nong and Gao, Changxin},
title = {Partial Forward Blocking: A Novel Data Pruning Paradigm for Lossless Training Acceleration},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2025},
pages = {319-328}
}
|
The ever-growing size of training datasets enhances the generalization capability of machine learning models but also incurs exorbitant computational costs. Existing data pruning approaches aim to accelerate training by removing those less important samples. However, they often rely on gradients or proxy models, leading to prohibitive additional costs of gradient back-propagation and proxy model training. In this paper, we propose Partial Forward Blocking (PFB), a novel framework for lossless training acceleration. The efficiency of PFB stems from its unique adaptive pruning pipeline: sample importance is assessed based on features extracted from the shallow layers of the target model. Less important samples are then pruned, allowing only the retained ones to proceed with the subsequent forward pass and loss back-propagation. This mechanism significantly reduces the computational overhead of deep-layer forward passes and back-propagation for pruned samples, while also eliminating the need for auxiliary backward computations and proxy model training. Moreover, PFB introduces probability density as an indicator of sample importance. Combined with an adaptive distribution estimation module, our method dynamically prioritizes relatively rare samples, aligning with the constantly evolving training state. Extensive experiments demonstrate the significant superiority of PFB in performance and speed. On ImageNet, PFB achieves a 0.5% accuracy improvement and 33% training time reduction with 40% data pruned.
|
2506.23674
|
Accepted by ICCV2025
| null | null |
[] |
[] |
[] |
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] |
206
|
FedPall: Prototype-based Adversarial and Collaborative Learning for Federated Learning with Feature Drift
|
[
"Yong Zhang",
"Feng Liang",
"Guanghu Yuan",
"Min Yang",
"Chengming Li",
"Xiping Hu"
] |
https://openaccess.thecvf.com/content/ICCV2025/html/Zhang_FedPall_Prototype-based_Adversarial_and_Collaborative_Learning_for_Federated_Learning_with_ICCV_2025_paper.html
|
https://openaccess.thecvf.com/content/ICCV2025/papers/Zhang_FedPall_Prototype-based_Adversarial_and_Collaborative_Learning_for_Federated_Learning_with_ICCV_2025_paper.pdf
| null |
@InProceedings{Zhang_2025_ICCV,
author = {Zhang, Yong and Liang, Feng and Yuan, Guanghu and Yang, Min and Li, Chengming and Hu, Xiping},
title = {FedPall: Prototype-based Adversarial and Collaborative Learning for Federated Learning with Feature Drift},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2025},
pages = {3111-3120}
}
|
Federated learning (FL) enables collaborative training of a global model in the centralized server with data from multiple parties while preserving privacy. However, data heterogeneity can significantly degrade the performance of the global model when each party uses datasets from different sources to train a local model, thereby affecting personalized local models. Among various cases of data heterogeneity, feature drift, feature space difference among parties, is prevalent in real-life data but remains largely unexplored. Feature drift can distract feature extraction learning in clients and thus lead to poor feature extraction and classification performance. To tackle the problem of feature drift in FL, we propose FedPall, an FL framework that utilizes prototype-based adversarial learning to unify feature spaces and collaborative learning to reinforce class information within the features. Moreover, FedPall leverages mixed features generated from global prototypes and local features to enhance the global classifier with classification-relevant information from a global perspective. Evaluation results on three representative feature-drifted datasets demonstrate FedPall's consistently superior performance in classification with feature-drifted data in the FL scenario.
|
2507.04781
|
10 pages, 6 figures, and 1 table
| null | null |
[] |
[] |
[] |
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] |
207
|
From Easy to Hard: Progressive Active Learning Framework for Infrared Small Target Detection with Single Point Supervision
|
[
"Chuang Yu",
"Jinmiao Zhao",
"Yunpeng Liu",
"Sicheng Zhao",
"Yimian Dai",
"Xiangyu Yue"
] |
https://openaccess.thecvf.com/content/ICCV2025/html/Yu_From_Easy_to_Hard_Progressive_Active_Learning_Framework_for_Infrared_ICCV_2025_paper.html
|
https://openaccess.thecvf.com/content/ICCV2025/papers/Yu_From_Easy_to_Hard_Progressive_Active_Learning_Framework_for_Infrared_ICCV_2025_paper.pdf
|
https://openaccess.thecvf.com/content/ICCV2025/supplemental/Yu_From_Easy_to_ICCV_2025_supplemental.pdf
|
@InProceedings{Yu_2025_ICCV,
author = {Yu, Chuang and Zhao, Jinmiao and Liu, Yunpeng and Zhao, Sicheng and Dai, Yimian and Yue, Xiangyu},
title = {From Easy to Hard: Progressive Active Learning Framework for Infrared Small Target Detection with Single Point Supervision},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2025},
pages = {2588-2598}
}
|
Recently, single-frame infrared small target (SIRST) detection with single point supervision has drawn wide-spread attention. However, the latest label evolution with single point supervision (LESPS) framework suffers from instability, excessive label evolution, and difficulty in exerting embedded network performance. Inspired by organisms gradually adapting to their environment and continuously accumulating knowledge, we construct an innovative Progressive Active Learning (PAL) framework, which drives the existing SIRST detection networks progressively and actively recognizes and learns harder samples. Specifically, to avoid the early low-performance model leading to the wrong selection of hard samples, we propose a model pre-start concept, which focuses on automatically selecting a portion of easy samples and helping the model have basic task-specific learning capabilities. Meanwhile, we propose a refined dual-update strategy, which can promote reasonable learning of harder samples and continuous refinement of pseudo-labels. In addition, to alleviate the risk of excessive label evolution, a decay factor is reasonably introduced, which helps to achieve a dynamic balance between the expansion and contraction of target annotations. Extensive experiments show that existing SIRST detection networks equipped with our PAL framework have achieved state-of-the-art (SOTA) results on multiple public datasets. Furthermore, our PAL framework can build an efficient and stable bridge between full supervision and single point supervision tasks. Our code is available at https://github.com/YuChuang1205/PAL
|
2412.11154
| null |
https://github.com/YuChuang1205/PAL
| null |
[] |
[] |
[] |
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] |
208
|
Asynchronous Event Error-Minimizing Noise for Safeguarding Event Dataset
|
[
"Ruofei Wang",
"Peiqi Duan",
"Boxin Shi",
"Renjie Wan"
] |
https://openaccess.thecvf.com/content/ICCV2025/html/Wang_Asynchronous_Event_Error-Minimizing_Noise_for_Safeguarding_Event_Dataset_ICCV_2025_paper.html
|
https://openaccess.thecvf.com/content/ICCV2025/papers/Wang_Asynchronous_Event_Error-Minimizing_Noise_for_Safeguarding_Event_Dataset_ICCV_2025_paper.pdf
|
https://openaccess.thecvf.com/content/ICCV2025/supplemental/Wang_Asynchronous_Event_Error-Minimizing_ICCV_2025_supplemental.pdf
|
@InProceedings{Wang_2025_ICCV,
author = {Wang, Ruofei and Duan, Peiqi and Shi, Boxin and Wan, Renjie},
title = {Asynchronous Event Error-Minimizing Noise for Safeguarding Event Dataset},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2025},
pages = {10141-10150}
}
|
With more event datasets being released online, safeguarding the event dataset against unauthorized usage has become a serious concern for data owners. Unlearnable Examples are proposed to prevent the unauthorized exploitation of image datasets. However, it's unclear how to create unlearnable asynchronous event streams to prevent event misuse. In this work, we propose the first unlearnable event stream generation method to prevent unauthorized training from event datasets. A new form of asynchronous event error-minimizing noise is proposed to perturb event streams, tricking the unauthorized model into learning embedded noise instead of realistic features. To be compatible with the sparse event, a projection strategy is presented to sparsify the noise to render our unlearnable event streams (UEvs). Extensive experiments demonstrate that our method effectively protects event data from unauthorized exploitation, while preserving their utility for legitimate use. We hope our UEvs contribute to the advancement of secure and trustworthy event dataset sharing. Code is available at: https://github.com/rfww/uevs.
|
2507.05728
|
Accepted by ICCV2025
|
https://github.com/rfww/uevs
| null |
[] |
[] |
[] |
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] |
209
|
A Constrained Optimization Approach for Gaussian Splatting from Coarsely-posed Images and Noisy Lidar Point Clouds
|
[
"Jizong Peng",
"Tze Ho Elden Tse",
"Kai Xu",
"Wenchao Gao",
"Angela Yao"
] |
https://openaccess.thecvf.com/content/ICCV2025/html/Peng_A_Constrained_Optimization_Approach_for_Gaussian_Splatting_from_Coarsely-posed_Images_ICCV_2025_paper.html
|
https://openaccess.thecvf.com/content/ICCV2025/papers/Peng_A_Constrained_Optimization_Approach_for_Gaussian_Splatting_from_Coarsely-posed_Images_ICCV_2025_paper.pdf
|
https://openaccess.thecvf.com/content/ICCV2025/supplemental/Peng_A_Constrained_Optimization_ICCV_2025_supplemental.pdf
|
@InProceedings{Peng_2025_ICCV,
author = {Peng, Jizong and Tse, Tze Ho Elden and Xu, Kai and Gao, Wenchao and Yao, Angela},
title = {A Constrained Optimization Approach for Gaussian Splatting from Coarsely-posed Images and Noisy Lidar Point Clouds},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2025},
pages = {2961-2970}
}
|
3D Gaussian Splatting (3DGS) is a powerful reconstruction technique; however, it requires initialization from accurate camera poses and high-fidelity point clouds. Typically, the initialization is taken from Structure-from-Motion (SfM) algorithms; however, SfM is time-consuming and restricts the application of 3DGS in real-world scenarios and large-scale scene reconstruction. We introduce a constrained optimization method for simultaneous camera pose estimation and 3D reconstruction that does not require SfM support. Core to our approach is decomposing a camera pose into a sequence of camera-to-(device-)center and (device-)center-to-world optimizations. To facilitate, we propose two optimization constraints conditioned to the sensitivity of each parameter group and restricts each parameter's search space. In addition, as we learn the scene geometry directly from the noisy point clouds, we propose geometric constraints to improve the reconstruction quality. Experiments demonstrate that the proposed method significantly outperforms the existing (multi-modal) 3DGS baseline and methods supplemented by COLMAP on both our collected dataset and two public benchmarks.
|
2504.09129
| null | null | null |
[] |
[] |
[] |
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] |
210
|
BabyVLM: Data-Efficient Pretraining of VLMs Inspired by Infant Learning
|
[
"Shengao Wang",
"Arjun Chandra",
"Aoming Liu",
"Venkatesh Saligrama",
"Boqing Gong"
] |
https://openaccess.thecvf.com/content/ICCV2025/html/Wang_BabyVLM_Data-Efficient_Pretraining_of_VLMs_Inspired_by_Infant_Learning_ICCV_2025_paper.html
|
https://openaccess.thecvf.com/content/ICCV2025/papers/Wang_BabyVLM_Data-Efficient_Pretraining_of_VLMs_Inspired_by_Infant_Learning_ICCV_2025_paper.pdf
|
https://openaccess.thecvf.com/content/ICCV2025/supplemental/Wang_BabyVLM_Data-Efficient_Pretraining_ICCV_2025_supplemental.pdf
|
@InProceedings{Wang_2025_ICCV,
author = {Wang, Shengao and Chandra, Arjun and Liu, Aoming and Saligrama, Venkatesh and Gong, Boqing},
title = {BabyVLM: Data-Efficient Pretraining of VLMs Inspired by Infant Learning},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2025},
pages = {1380-1390}
}
|
Human infants rapidly develop visual reasoning skills from minimal input, suggesting that developmentally inspired pretraining could significantly enhance the efficiency of vision-language models (VLMs). Although recent efforts have leveraged infant-inspired datasets like SAYCam, existing evaluation benchmarks remain misaligned--they are either too simplistic, narrowly scoped, or tailored for large-scale pretrained models. Additionally, training exclusively on SAYCam overlooks the broader, diverse input from which infants naturally learn. To address these limitations, we propose BabyVLM, a novel framework comprising diverse in-domain evaluation benchmarks and a synthetic training dataset created via child-directed transformations of existing datasets. We demonstrate that VLMs trained with our synthetic dataset achieve superior performance on BabyVLM tasks compared to models trained solely on SAYCam or general-purpose data of the SAYCam size. BabyVLM thus provides a robust, developmentally aligned evaluation tool and illustrates how compact models trained on carefully curated data can generalize effectively, opening pathways toward data-efficient vision-language learning paradigms.
|
2504.09426
| null | null | null |
[] |
[] |
[] |
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] |
211
|
Efficient Concertormer for Image Deblurring and Beyond
|
[
"Pin-Hung Kuo",
"Jinshan Pan",
"Shao-Yi Chien",
"Ming-Hsuan Yang"
] |
https://openaccess.thecvf.com/content/ICCV2025/html/Kuo_Efficient_Concertormer_for_Image_Deblurring_and_Beyond_ICCV_2025_paper.html
|
https://openaccess.thecvf.com/content/ICCV2025/papers/Kuo_Efficient_Concertormer_for_Image_Deblurring_and_Beyond_ICCV_2025_paper.pdf
|
https://openaccess.thecvf.com/content/ICCV2025/supplemental/Kuo_Efficient_Concertormer_for_ICCV_2025_supplemental.pdf
|
@InProceedings{Kuo_2025_ICCV,
author = {Kuo, Pin-Hung and Pan, Jinshan and Chien, Shao-Yi and Yang, Ming-Hsuan},
title = {Efficient Concertormer for Image Deblurring and Beyond},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2025},
pages = {14665-14675}
}
|
The Transformer architecture has excelled in NLP and vision tasks, but its self-attention complexity grows quadratically with image size, making high-resolution tasks computationally expensive. We introduce Concertormer, featuring Concerto Self-Attention (CSA) for image deblurring. CSA splits self-attention into global and local components while retaining partial information in additional dimensions, achieving linear complexity. A Cross-Dimensional Communication module enhances expressiveness by linearly combining attention maps. Additionally, our gated-dconv MLP merges the two-staged Transformer design into a single stage. Extensive evaluations show our method performs favorably against state-of-the-art works in deblurring, deraining, and JPEG artifact removal.
|
2404.06135
| null | null | null |
[] |
[] |
[] |
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] |
212
|
Curve-Aware Gaussian Splatting for 3D Parametric Curve Reconstruction
|
[
"Zhirui Gao",
"Renjiao Yi",
"Yaqiao Dai",
"Xuening Zhu",
"Wei Chen",
"Chenyang Zhu",
"Kai Xu"
] |
https://openaccess.thecvf.com/content/ICCV2025/html/Gao_Curve-Aware_Gaussian_Splatting_for_3D_Parametric_Curve_Reconstruction_ICCV_2025_paper.html
|
https://openaccess.thecvf.com/content/ICCV2025/papers/Gao_Curve-Aware_Gaussian_Splatting_for_3D_Parametric_Curve_Reconstruction_ICCV_2025_paper.pdf
|
https://openaccess.thecvf.com/content/ICCV2025/supplemental/Gao_Curve-Aware_Gaussian_Splatting_ICCV_2025_supplemental.pdf
|
@InProceedings{Gao_2025_ICCV,
author = {Gao, Zhirui and Yi, Renjiao and Dai, Yaqiao and Zhu, Xuening and Chen, Wei and Zhu, Chenyang and Xu, Kai},
title = {Curve-Aware Gaussian Splatting for 3D Parametric Curve Reconstruction},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2025},
pages = {27531-27541}
}
|
This paper presents an end-to-end framework for reconstructing 3D parametric curves directly from multi-view edge maps. Contrasting with existing two-stage methods that follow a sequential "edge point cloud reconstruction and parametric curve fitting" pipeline, our one-stage approach optimizes 3D parametric curves directly from 2D edge maps, eliminating error accumulation caused by the inherent optimization gap between disconnected stages. However, parametric curves inherently lack suitability for rendering-based multi-view optimization, necessitating a complementary representation that preserves their geometric properties while enabling differentiable rendering. We propose a novel bi-directional coupling mechanism between parametric curves and edge-oriented Gaussian components. This tight correspondence formulates a curve-aware Gaussian representation, CurveGaussian, that enables differentiable rendering of 3D curves, allowing direct optimization guided by multi-view evidence. Furthermore, we introduce a dynamically adaptive topology optimization framework during training to refine curve structures through linearization, merging, splitting, and pruning operations. Comprehensive evaluations on the ABC dataset and real-world benchmarks demonstrate our one-stage method's superiority over two-stage alternatives, particularly in producing cleaner and more robust reconstructions. Additionally, by directly optimizing parametric curves, our method significantly reduces the parameter count during training, achieving both higher efficiency and superior performance compared to existing approaches.
|
2506.21401
|
Accepted by ICCV 2025, Code:
https://github.com/zhirui-gao/Curve-Gaussian
| null | null |
[] |
[] |
[] |
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] |
213
|
Sim-DETR: Unlock DETR for Temporal Sentence Grounding
|
[
"Jiajin Tang",
"Zhengxuan Wei",
"Yuchen Zhu",
"Cheng Shi",
"Guanbin Li",
"Liang Lin",
"Sibei Yang"
] |
https://openaccess.thecvf.com/content/ICCV2025/html/Tang_Sim-DETR_Unlock_DETR_for_Temporal_Sentence_Grounding_ICCV_2025_paper.html
|
https://openaccess.thecvf.com/content/ICCV2025/papers/Tang_Sim-DETR_Unlock_DETR_for_Temporal_Sentence_Grounding_ICCV_2025_paper.pdf
| null |
@InProceedings{Tang_2025_ICCV,
author = {Tang, Jiajin and Wei, Zhengxuan and Zhu, Yuchen and Shi, Cheng and Li, Guanbin and Lin, Liang and Yang, Sibei},
title = {Sim-DETR: Unlock DETR for Temporal Sentence Grounding},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2025},
pages = {22760-22771}
}
|
Temporal sentence grounding aims to identify exact moments in a video that correspond to a given textual query, typically addressed with detection transformer (DETR) solutions. However, we find that typical strategies designed to enhance DETR do not improve, and may even degrade, its performance in this task. We systematically analyze and identify the root causes of this abnormal behavior: (1) conflicts between queries from similar target moments and (2) internal query conflicts due to the tension between global semantics and local localization. Building on these insights, we propose a simple yet powerful baseline, Sim-DETR, which extends the standard DETR with two minor modifications in the decoder layers: (1) constraining self-attention between queries based on their semantic and positional overlap and (2) adding query-to-frame alignment to bridge the global and local contexts. Experiments demonstrate that Sim-DETR unlocks the full potential of DETR for temporal sentence grounding, offering a strong baseline for future research.
| null | null | null | null |
[] |
[] |
[] |
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] |
214
|
DynamicID: Zero-Shot Multi-ID Image Personalization with Flexible Facial Editability
|
[
"Xirui Hu",
"Jiahao Wang",
"Hao Chen",
"Weizhan Zhang",
"Benqi Wang",
"Yikun Li",
"Haishun Nan"
] |
https://openaccess.thecvf.com/content/ICCV2025/html/Hu_DynamicID_Zero-Shot_Multi-ID_Image_Personalization_with_Flexible_Facial_Editability_ICCV_2025_paper.html
|
https://openaccess.thecvf.com/content/ICCV2025/papers/Hu_DynamicID_Zero-Shot_Multi-ID_Image_Personalization_with_Flexible_Facial_Editability_ICCV_2025_paper.pdf
|
https://openaccess.thecvf.com/content/ICCV2025/supplemental/Hu_DynamicID_Zero-Shot_Multi-ID_ICCV_2025_supplemental.pdf
|
@InProceedings{Hu_2025_ICCV,
author = {Hu, Xirui and Wang, Jiahao and Chen, Hao and Zhang, Weizhan and Wang, Benqi and Li, Yikun and Nan, Haishun},
title = {DynamicID: Zero-Shot Multi-ID Image Personalization with Flexible Facial Editability},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2025},
pages = {10549-10559}
}
|
Recent advances in text-to-image generation have driven interest in generating personalized human images that depict specific identities from reference images. Although existing methods achieve high-fidelity identity preservation, they are generally limited to single-ID scenarios and offer insufficient facial editability. We present DynamicID, a tuning-free framework that inherently facilitates both single-ID and multi-ID personalized generation with high fidelity and flexible facial editability. Our key innovations include: 1) Semantic-Activated Attention (SAA), which employs query-level activation gating to minimize disruption to the base model when injecting ID features and achieve multi-ID personalization without requiring multi-ID samples during training. 2) Identity-Motion Reconfigurator (IMR), which applies feature-space manipulation to effectively disentangle and reconfigure facial motion and identity features, supporting flexible facial editing. 3) a task-decoupled training paradigm that reduces data dependency, together with VariFace-10k, a curated dataset of 10k unique individuals, each represented by 35 distinct facial images. Experimental results demonstrate that DynamicID outperforms state-of-the-art methods in identity fidelity, facial editability, and multi-ID personalization capability.
|
2503.06505
|
ICCV 2025
|
https://github.com/ByteCat-bot/DynamicID
| null |
[] |
[
"meteorite2023/DynamicID"
] |
[] |
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] |
215
|
Wavelet Policy: Lifting Scheme for Policy Learning in Long-Horizon Tasks
|
[
"Hao Huang",
"Shuaihang Yuan",
"Geeta Chandra Raju Bethala",
"Congcong Wen",
"Anthony Tzes",
"Yi Fang"
] |
https://openaccess.thecvf.com/content/ICCV2025/html/Huang_Wavelet_Policy_Lifting_Scheme_for_Policy_Learning_in_Long-Horizon_Tasks_ICCV_2025_paper.html
|
https://openaccess.thecvf.com/content/ICCV2025/papers/Huang_Wavelet_Policy_Lifting_Scheme_for_Policy_Learning_in_Long-Horizon_Tasks_ICCV_2025_paper.pdf
| null |
@InProceedings{Huang_2025_ICCV,
author = {Huang, Hao and Yuan, Shuaihang and Bethala, Geeta Chandra Raju and Wen, Congcong and Tzes, Anthony and Fang, Yi},
title = {Wavelet Policy: Lifting Scheme for Policy Learning in Long-Horizon Tasks},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2025},
pages = {12349-12359}
}
|
Policy learning focuses on devising strategies for agents in embodied artificial intelligence systems to perform optimal actions based on their perceived states. One of the key challenges in policy learning involves handling complex, long-horizon tasks that require managing extensive sequences of actions and observations with multiple modes. Wavelet analysis offers significant advantages in signal processing, notably in decomposing signals at multiple scales to capture both global trends and fine-grained details. In this work, we introduce a novel wavelet policy learning framework that utilizes wavelet transformations to enhance policy learning. Our approach leverages learnable multi-scale wavelet decomposition to facilitate detailed observation analysis and robust action planning over extended sequences. We detail the design and implementation of our wavelet policy, which incorporates lifting schemes for effective multi-resolution analysis and action generation. This framework is evaluated across multiple complex scenarios, including robotic manipulation, self-driving, and multi-robot collaboration, demonstrating the effectiveness of our method in improving the precision and reliability of the learned policy.
|
2507.04331
|
11 pages, 5 figures, 6 tables
| null | null |
[] |
[] |
[] |
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] |
216
|
METEOR: Multi-Encoder Collaborative Token Pruning for Efficient Vision Language Models
|
[
"Yuchen Liu",
"Yaoming Wang",
"Bowen Shi",
"Xiaopeng Zhang",
"Wenrui Dai",
"Chenglin Li",
"Hongkai Xiong",
"Qi Tian"
] |
https://openaccess.thecvf.com/content/ICCV2025/html/Liu_METEOR_Multi-Encoder_Collaborative_Token_Pruning_for_Efficient_Vision_Language_Models_ICCV_2025_paper.html
|
https://openaccess.thecvf.com/content/ICCV2025/papers/Liu_METEOR_Multi-Encoder_Collaborative_Token_Pruning_for_Efficient_Vision_Language_Models_ICCV_2025_paper.pdf
|
https://openaccess.thecvf.com/content/ICCV2025/supplemental/Liu_METEOR_Multi-Encoder_Collaborative_ICCV_2025_supplemental.pdf
|
@InProceedings{Liu_2025_ICCV,
author = {Liu, Yuchen and Wang, Yaoming and Shi, Bowen and Zhang, Xiaopeng and Dai, Wenrui and Li, Chenglin and Xiong, Hongkai and Tian, Qi},
title = {METEOR: Multi-Encoder Collaborative Token Pruning for Efficient Vision Language Models},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2025},
pages = {21492-21504}
}
|
Vision encoders serve as the cornerstone of multimodal understanding. Single-encoder architectures like CLIP exhibit inherent constraints in generalizing across diverse multimodal tasks, while recent multi-encoder fusion methods introduce prohibitive computational overhead to achieve superior performance using complementary visual representations from multiple vision encoders. To address this, we propose a progressive pruning framework, namely Multi-Encoder collaboraTivE tOken pRuning (METEOR), that eliminates redundant visual tokens across the encoding, fusion, and decoding stages for multi-encoder MLLMs. For multi-vision encoding, we discard redundant tokens within each encoder via a rank guided collaborative token assignment strategy. Subsequently, for multi-vision fusion, we combine the visual features from different encoders while reducing cross-encoder redundancy with cooperative pruning. Finally, we propose an adaptive token pruning method in the LLM decoding stage to further discard irrelevant tokens based on the text prompts with dynamically adjusting pruning ratios for specific task demands. To our best knowledge, this is the first successful attempt that achieves an efficient multi-encoder based vision language model with multi-stage pruning strategies. Extensive experiments on 11 benchmarks demonstrate the effectiveness of our proposed approach. Compared with EAGLE, a typical multi-encoder MLLMs, METEOR reduces 76% visual tokens with only 0.3% performance drop in average. The code is available at https://github.com/YuchenLiu98/METEOR.
|
2507.20842
|
Accepted by ICCV 2025
|
https://github.com/YuchenLiu98/METEOR
| null |
[] |
[] |
[] |
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] |
217
|
Contact-Aware Amodal Completion for Human-Object Interaction via Multi-Regional Inpainting
|
[
"Seunggeun Chi",
"Enna Sachdeva",
"Pin-Hao Huang",
"Kwonjoon Lee"
] |
https://openaccess.thecvf.com/content/ICCV2025/html/Chi_Contact-Aware_Amodal_Completion_for_Human-Object_Interaction_via_Multi-Regional_Inpainting_ICCV_2025_paper.html
|
https://openaccess.thecvf.com/content/ICCV2025/papers/Chi_Contact-Aware_Amodal_Completion_for_Human-Object_Interaction_via_Multi-Regional_Inpainting_ICCV_2025_paper.pdf
|
https://openaccess.thecvf.com/content/ICCV2025/supplemental/Chi_Contact-Aware_Amodal_Completion_ICCV_2025_supplemental.zip
|
@InProceedings{Chi_2025_ICCV,
author = {Chi, Seunggeun and Sachdeva, Enna and Huang, Pin-Hao and Lee, Kwonjoon},
title = {Contact-Aware Amodal Completion for Human-Object Interaction via Multi-Regional Inpainting},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2025},
pages = {9487-9496}
}
|
Amodal completion, the task of inferring the complete appearance of objects despite partial occlusions, is crucial for understanding complex human-object interactions (HOI) in computer vision and robotics. Existing methods, including pre-trained diffusion models, often struggle to generate plausible completions in dynamic scenarios due to their limited understanding of HOI. To address this challenge, we propose a novel approach that leverages physical prior knowledge alongside a specialized multi-regional inpainting technique tailored for HOI. By incorporating physical constraints derived from human topology and contact information, we define two distinct regions: the primary region, where occluded object parts are most likely to reside, and the secondary region, where occlusions are less probable. Our multi-regional inpainting method employs customized denoising strategies across these regions within a diffusion model, thereby enhancing the accuracy and realism of generated completions in both shape and visual detail. Experimental results demonstrate that our approach substantially outperforms existing methods in HOI scenarios, advancing machine perception toward a more human-like understanding of dynamic environments. Furthermore, we show that our pipeline remains robust even without ground-truth contact annotations, broadening its applicability to tasks such as 3D reconstruction and novel view/pose synthesis.
|
2508.00427
|
ICCV 2025 (Highlight)
| null | null |
[] |
[] |
[] |
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] |
218
|
SeqGrowGraph: Learning Lane Topology as a Chain of Graph Expansions
|
[
"Mengwei Xie",
"Shuang Zeng",
"Xinyuan Chang",
"Xinran Liu",
"Zheng Pan",
"Mu Xu",
"Xing Wei"
] |
https://openaccess.thecvf.com/content/ICCV2025/html/Xie_SeqGrowGraph_Learning_Lane_Topology_as_a_Chain_of_Graph_Expansions_ICCV_2025_paper.html
|
https://openaccess.thecvf.com/content/ICCV2025/papers/Xie_SeqGrowGraph_Learning_Lane_Topology_as_a_Chain_of_Graph_Expansions_ICCV_2025_paper.pdf
| null |
@InProceedings{Xie_2025_ICCV,
author = {Xie, Mengwei and Zeng, Shuang and Chang, Xinyuan and Liu, Xinran and Pan, Zheng and Xu, Mu and Wei, Xing},
title = {SeqGrowGraph: Learning Lane Topology as a Chain of Graph Expansions},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2025},
pages = {27166-27175}
}
|
Accurate lane topology is essential for autonomous driving, yet traditional methods struggle to model the complex, non-linear structures--such as loops and bidirectional lanes--prevalent in real-world road structure. We present SeqGrowGraph, a novel framework that learns lane topology as a chain of graph expansions, inspired by human map-drawing processes. Representing the lane graph as a directed graph G=(V,E), with intersections (V) and centerlines (E), SeqGrowGraph incrementally constructs this graph by introducing one vertex at a time. At each step, an adjacency matrix (A) expands from nn to (n+1)(n+1) to encode connectivity, while a geometric matrix (M) captures centerline shapes as quadratic Bezier curves. The graph is serialized into sequences, enabling a transformer model to autoregressively predict the chain of expansions, guided by a depth-first search ordering. Evaluated on nuScenes and Argoverse 2 datasets, SeqGrowGraph achieves state-of-the-art performance.
|
2507.04822
| null | null | null |
[] |
[] |
[] |
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] |
219
|
Ultra-Precision 6DoF Pose Estimation Using 2-D Interpolated Discrete Fourier Transform
|
[
"Guowei Shi",
"Zian Mao",
"Peisen Huang"
] |
https://openaccess.thecvf.com/content/ICCV2025/html/Shi_Ultra-Precision_6DoF_Pose_Estimation_Using_2-D_Interpolated_Discrete_Fourier_Transform_ICCV_2025_paper.html
|
https://openaccess.thecvf.com/content/ICCV2025/papers/Shi_Ultra-Precision_6DoF_Pose_Estimation_Using_2-D_Interpolated_Discrete_Fourier_Transform_ICCV_2025_paper.pdf
|
https://openaccess.thecvf.com/content/ICCV2025/supplemental/Shi_Ultra-Precision_6DoF_Pose_ICCV_2025_supplemental.pdf
|
@InProceedings{Shi_2025_ICCV,
author = {Shi, Guowei and Mao, Zian and Huang, Peisen},
title = {Ultra-Precision 6DoF Pose Estimation Using 2-D Interpolated Discrete Fourier Transform},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2025},
pages = {5802-5810}
}
|
Ultra-precision estimation of 6DoF pose is essential in applications such as semiconductor manufacturing and nanoscale manipulation. Conventional vision-based techniques are often hampered by sensitivity to defocus and limited estimation accuracy. In this paper, we propose a novel two-dimensional interpolated Discrete Fourier Transform (2D-IpDFT) method for robust 6DoF pose estimation using periodic patterns. We further develop a mathematical framework that links image parameters--phase and frequency--to 6DoF pose, which is applicable to both orthographic and quasi-orthographic imaging systems. Extensive experiments on a low-cost setup, featuring an industrial camera and an etched checkerboard pattern, demonstrate translation estimation accuracy at the nanometer level and rotation estimation accuracy at the microradian level.
| null | null | null | null |
[] |
[] |
[] |
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] |
220
|
Neural Compression for 3D Geometry Sets
|
[
"Siyu Ren",
"Junhui Hou",
"Weiyao Lin",
"Wenping Wang"
] |
https://openaccess.thecvf.com/content/ICCV2025/html/Ren_Neural_Compression_for_3D_Geometry_Sets_ICCV_2025_paper.html
|
https://openaccess.thecvf.com/content/ICCV2025/papers/Ren_Neural_Compression_for_3D_Geometry_Sets_ICCV_2025_paper.pdf
|
https://openaccess.thecvf.com/content/ICCV2025/supplemental/Ren_Neural_Compression_for_ICCV_2025_supplemental.pdf
|
@InProceedings{Ren_2025_ICCV,
author = {Ren, Siyu and Hou, Junhui and Lin, Weiyao and Wang, Wenping},
title = {Neural Compression for 3D Geometry Sets},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2025},
pages = {25294-25304}
}
|
We present NeCGS, the first neural compression paradigm, which can compress a geometry set encompassing thousands of detailed and diverse 3D mesh models by up to 900 times with high accuracy and preservation of detailed geometric structures. Specifically, we first propose TSDF-Def, a new implicit representation that is capable of accurately representing irregular 3D mesh models with various structures into regular 4D tensors of uniform and compact size, where 3D surfaces can be extracted through the deformable marching cubes. Then we construct a quantization-aware auto-decoder network architecture to regress these 4D tensors to explore the local geometric similarity within each shape and across different shapes for redundancy removal, resulting in more compact representations, including an embedded feature of a smaller size associated with each 3D model and a network parameter shared by all models. We finally encode the resulting features and network parameters into bitstreams through entropy coding. Besides, our NeCGS can handle the dynamic scenario well, where new 3D models are constantly added to a compressed set. Extensive experiments and ablation studies demonstrate the significant advantages of our NeCGS over state-of-the-art methods both quantitatively and qualitatively. The source code is publicly available at https://github.com/rsy6318/NeCGS.
|
2405.15034
|
Accepted by ICCV 2025
|
https://github.com/rsy6318/NeCGS
| null |
[] |
[] |
[] |
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] |
221
|
Lifting the Structural Morphing for Wide-Angle Images Rectification: Unified Content and Boundary Modeling
|
[
"Wenting Luan",
"Siqi Lu",
"Yongbin Zheng",
"Wanying Xu",
"Lang Nie",
"Zongtan Zhou",
"Kang Liao"
] |
https://openaccess.thecvf.com/content/ICCV2025/html/Luan_Lifting_the_Structural_Morphing_for_Wide-Angle_Images_Rectification_Unified_Content_ICCV_2025_paper.html
|
https://openaccess.thecvf.com/content/ICCV2025/papers/Luan_Lifting_the_Structural_Morphing_for_Wide-Angle_Images_Rectification_Unified_Content_ICCV_2025_paper.pdf
|
https://openaccess.thecvf.com/content/ICCV2025/supplemental/Luan_Lifting_the_Structural_ICCV_2025_supplemental.pdf
|
@InProceedings{Luan_2025_ICCV,
author = {Luan, Wenting and Lu, Siqi and Zheng, Yongbin and Xu, Wanying and Nie, Lang and Zhou, Zongtan and Liao, Kang},
title = {Lifting the Structural Morphing for Wide-Angle Images Rectification: Unified Content and Boundary Modeling},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2025},
pages = {25529-25538}
}
|
The mainstream approach for correcting distortions in wide-angle images typically involves a cascading process of rectification followed by rectangling. These tasks address distorted image content and irregular boundaries separately, using two distinct pipelines. However, this independent optimization prevents the two stages from benefiting each other. It also increases susceptibility to error accumulation and misaligned optimization, ultimately degrading the quality of the rectified image and the performance of downstream vision tasks. In this work, we observe and verify that transformations based on motion representations ((e.g.,, Thin-Plate Spline) exhibit structural continuity in both rectification and rectangling tasks. This continuity enables us to establish their relationships through the perspective of structural morphing, allowing for an optimal solution within a single end-to-end framework. To this end, we propose ConBo-Net, a unified Content and Boundary modeling approach for one-stage wide-angle image correction. Our method jointly addresses distortion rectification and boundary rectangling in an end-to-end manner. To further enhance the model's structural recovery capability, we incorporate physical priors based on the wide-angle camera model during training and introduce an ordinal geometric loss to enforce curvature monotonicity. Extensive experiments demonstrate that ConBo-Net outperforms state-of-the-art two-stage solutions. The code and dataset are available at https://github.com/lwttttt/ConBo-Net
| null | null | null | null |
[] |
[] |
[] |
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222
|
ASGS: Single-Domain Generalizable Open-Set Object Detection via Adaptive Subgraph Searching
|
[
"Yuxuan Yuan",
"Luyao Tang",
"Yixin Chen",
"Chaoqi Chen",
"Yue Huang",
"Xinghao Ding"
] |
https://openaccess.thecvf.com/content/ICCV2025/html/Yuan_ASGS_Single-Domain_Generalizable_Open-Set_Object_Detection_via_Adaptive_Subgraph_Searching_ICCV_2025_paper.html
|
https://openaccess.thecvf.com/content/ICCV2025/papers/Yuan_ASGS_Single-Domain_Generalizable_Open-Set_Object_Detection_via_Adaptive_Subgraph_Searching_ICCV_2025_paper.pdf
|
https://openaccess.thecvf.com/content/ICCV2025/supplemental/Yuan_ASGS_Single-Domain_Generalizable_ICCV_2025_supplemental.pdf
|
@InProceedings{Yuan_2025_ICCV,
author = {Yuan, Yuxuan and Tang, Luyao and Chen, Yixin and Chen, Chaoqi and Huang, Yue and Ding, Xinghao},
title = {ASGS: Single-Domain Generalizable Open-Set Object Detection via Adaptive Subgraph Searching},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2025},
pages = {20911-20921}
}
|
Albeit existing Single-Domain Generalized Object Detection (Single-DGOD) methods enable models to generalize to unseen domains, most assume that the training and testing data share the same label space. In real-world scenarios, unseen domains often introduce previously unknown objects, a challenge that has been largely overlooked. In this paper, we tackle the practical problem of Single-domain Generalizable Open-Set Object Detection (SG-OSOD), which addresses both unseen domains and unknown classes. We identify two key challenges: (1) detecting unknown classes with only known-class data, and (2) learning robust features to mitigate domain shift. To address these challenges, we propose the framework termed \texttt ASGS , which leverages adaptive subgraph structures to enhance the understanding of unknown scenes and classes. \texttt ASGS consists of Subgraph-wise Unknown-class Learning (SUL) and Class-wise Embedding Compaction (CEC). SUL employs non-parametric methods to detect unknown samples and performs Adaptive Subgraph Searching (ASS) for high-order structural feature extraction, enabling domain-robust unknown class learning. Moreover, the CEC module enhances class discrimination robustness through contrastive learning, which results in more compact class clusters in unknown scenarios. Experimental results demonstrate the effectiveness of the proposed \texttt ASGS .
| null | null | null | null |
[] |
[] |
[] |
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] |
223
|
Dual-Temporal Exemplar Representation Network for Video Semantic Segmentation
|
[
"Xiaolong Xu",
"Lei Zhang",
"Jiayi Li",
"Lituan Wang",
"Yifan Guan",
"Yu Yan",
"Leyi Zhang",
"Hao Song"
] |
https://openaccess.thecvf.com/content/ICCV2025/html/Xu_Dual-Temporal_Exemplar_Representation_Network_for_Video_Semantic_Segmentation_ICCV_2025_paper.html
|
https://openaccess.thecvf.com/content/ICCV2025/papers/Xu_Dual-Temporal_Exemplar_Representation_Network_for_Video_Semantic_Segmentation_ICCV_2025_paper.pdf
| null |
@InProceedings{Xu_2025_ICCV,
author = {Xu, Xiaolong and Zhang, Lei and Li, Jiayi and Wang, Lituan and Guan, Yifan and Yan, Yu and Zhang, Leyi and Song, Hao},
title = {Dual-Temporal Exemplar Representation Network for Video Semantic Segmentation},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2025},
pages = {10775-10785}
}
|
Video semantic segmentation aims to assign a class label for each pixel in every video frame. Existing methods predominantly follow the reference-target interaction paradigm, focusing on extracting local temporal contexts while neglecting the integration of global temporal information. Moreover, complex dynamics and varying lighting conditions introduce inter-frame intra-class discrepancies in feature representations, leading to unstable predictions. In this paper, we propose a novel framework, the Dual-Temporal Exemplar Representation Network (DTERN), which utilizes the strong representational capability of cluster centers, i.e., exemplars, to effectively model both local and global temporal information. DTERN consists of two core modules: 1) the Local Temporal Exemplar Module (LTEM), which constructs local exemplars to capture local temporal contexts, ensuring stable and reliable predictions. 2) the Global Temporal Exemplar Module (GTEM), which introduces learnable global exemplars to dynamically model global temporal information, thereby improving the effective consistency of segmentation. Furthermore, we observe that the existing Video Consistency (VC) metric fails to evaluate segmentation accuracy and lacks sensitivity to small-object segmentation. To this end, we propose Video Effective Consistency (VEC) to comprehensively evaluate temporal consistency and segmentation effectiveness. Experiments on VSPW and Cityscape demonstrate that DTERN outperforms state-of-the-art methods. The code is available at https://github.com/zlxilo/DTERN.
| null | null | null | null |
[] |
[] |
[] |
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] |
224
|
UniConvNet: Expanding Effective Receptive Field while Maintaining Asymptotically Gaussian Distribution for ConvNets of Any Scale
|
[
"Yuhao Wang",
"Wei Xi"
] |
https://openaccess.thecvf.com/content/ICCV2025/html/Wang_UniConvNet_Expanding_Effective_Receptive_Field_while_Maintaining_Asymptotically_Gaussian_Distribution_ICCV_2025_paper.html
|
https://openaccess.thecvf.com/content/ICCV2025/papers/Wang_UniConvNet_Expanding_Effective_Receptive_Field_while_Maintaining_Asymptotically_Gaussian_Distribution_ICCV_2025_paper.pdf
|
https://openaccess.thecvf.com/content/ICCV2025/supplemental/Wang_UniConvNet_Expanding_Effective_ICCV_2025_supplemental.pdf
|
@InProceedings{Wang_2025_ICCV,
author = {Wang, Yuhao and Xi, Wei},
title = {UniConvNet: Expanding Effective Receptive Field while Maintaining Asymptotically Gaussian Distribution for ConvNets of Any Scale},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2025},
pages = {20922-20933}
}
|
Convolutional neural networks (ConvNets) with large effective receptive field (ERF), still in their early stages, have demonstrated promising effectiveness while constrained by high parameters and FLOPs costs and disrupted asymptotically Gaussian distribution (AGD) of ERF. This paper proposes an alternative paradigm: rather than merely employing extremely large ERF, it is more effective and effcient to expand the ERF while maintaining AGD of ERF by proper combination of smaller kernels, such as 7x 7 , 9x 9 , 11x 11 . This paper introduces a Three-layer Receptive Field Aggregator and designs a Layer Operator as the fundamental operator from the perspective of receptive field. The ERF can be expanded to the level of existing large-kernel ConvNets through the stack of proposed modules while maintaining AGD of ERF. Using these designs, we propose a universal ConvNet, termed UniConvNet. Extensive experiments on ImageNet-1K, COCO2017, and ADE20K demonstrate that UniConvNet outperforms state-of-the-art CNNs and ViTs across various vision recognition tasks for both lightweight and large-scale models with comparable throughput. Surprisingly, UniConvNet-T achieves 84.2% ImageNet top-1 accuracy with 30M parameters and 5.1G FLOPs. UniConvNet-XL also shows competitive scalability to big data and large models, acquiring 88.4% top-1 accuracy on ImageNet.
|
2508.09000
|
ICCV 2025
|
https://github.com/ai-paperwithcode/UniConvNet
| null |
[] |
[] |
[] |
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] |
225
|
Denoising Token Prediction in Masked Autoregressive Models
|
[
"Ting Yao",
"Yehao Li",
"Yingwei Pan",
"Zhaofan Qiu",
"Tao Mei"
] |
https://openaccess.thecvf.com/content/ICCV2025/html/Yao_Denoising_Token_Prediction_in_Masked_Autoregressive_Models_ICCV_2025_paper.html
|
https://openaccess.thecvf.com/content/ICCV2025/papers/Yao_Denoising_Token_Prediction_in_Masked_Autoregressive_Models_ICCV_2025_paper.pdf
| null |
@InProceedings{Yao_2025_ICCV,
author = {Yao, Ting and Li, Yehao and Pan, Yingwei and Qiu, Zhaofan and Mei, Tao},
title = {Denoising Token Prediction in Masked Autoregressive Models},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2025},
pages = {18024-18033}
}
|
Autoregressive models are just at a tipping point where they could really take off for visual generation. In this paper, we propose to model token prediction using diffusion procedure particularly in masked autoregressive models for image generation. We look into the problem from two critical perspectives: progressively refining the unmasked tokens prediction via a denoising head with the autoregressive model, and representing masked tokens probability distribution by capitalizing on the interdependency across masked and unmasked tokens through a diffusion head. Our proposal harbors an innate agency that remains advantageous in the speed of sequence prediction, and strongly favors high capability in generating quality samples by leveraging the principles of denoising diffusion process. Extensive experiments on both class-conditional and text-to-image tasks demonstrate its superiority, achieving the state-of-the-art FID score of 1.47 and 5.27 on ImageNet and MSCOCO datasets, respectively. More remarkably, our approach leads to 45% speedup in the inference time of image generation against the diffusion models such as DiT-XL/2.
| null | null | null | null |
[] |
[] |
[] |
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] |
226
|
Uncertainty-Aware Gradient Stabilization for Small Object Detection
|
[
"Huixin Sun",
"Yanjing Li",
"Linlin Yang",
"Xianbin Cao",
"Baochang Zhang"
] |
https://openaccess.thecvf.com/content/ICCV2025/html/Sun_Uncertainty-Aware_Gradient_Stabilization_for_Small_Object_Detection_ICCV_2025_paper.html
|
https://openaccess.thecvf.com/content/ICCV2025/papers/Sun_Uncertainty-Aware_Gradient_Stabilization_for_Small_Object_Detection_ICCV_2025_paper.pdf
|
https://openaccess.thecvf.com/content/ICCV2025/supplemental/Sun_Uncertainty-Aware_Gradient_Stabilization_ICCV_2025_supplemental.pdf
|
@InProceedings{Sun_2025_ICCV,
author = {Sun, Huixin and Li, Yanjing and Yang, Linlin and Cao, Xianbin and Zhang, Baochang},
title = {Uncertainty-Aware Gradient Stabilization for Small Object Detection},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2025},
pages = {8407-8417}
}
|
Despite advances in generic object detection, there remains a performance gap in detecting small objects compared to normal-scale objects. We reveal that conventional object localization methods suffer from gradient instability in small objects due to sharper loss curvature, leading to a convergence challenge. To address the issue, we propose Uncertainty-Aware Gradient Stabilization (UGS), a framework that reformulates object localization as a classification task to stabilize gradients. UGS quantizes continuous labels into interval non-uniform discrete representations. Under a classification-based objective, the localization branch generates bounded and confidence-driven gradients, mitigating instability. Furthermore, UGS integrates an uncertainty minimization (UM) loss that reduces prediction variance and an uncertainty-guided refinement (UR) module that identifies and refines high-uncertainty regions via perturbations. Evaluated on four benchmarks, UGS consistently improves anchor-based, anchor-free, and leading small object detectors. Notably, UGS enhances DINO-5scale by 2.6 AP on VisDrone, surpassing prior state-of-the-art performance.
|
2303.01803
| null | null | null |
[] |
[] |
[] |
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0.044054292142391205,
0.004535661078989506,
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] |
227
|
Radiant Foam: Real-Time Differentiable Ray Tracing
|
[
"Shrisudhan Govindarajan",
"Daniel Rebain",
"Kwang Moo Yi",
"Andrea Tagliasacchi"
] |
https://openaccess.thecvf.com/content/ICCV2025/html/Govindarajan_Radiant_Foam_Real-Time_Differentiable_Ray_Tracing_ICCV_2025_paper.html
|
https://openaccess.thecvf.com/content/ICCV2025/papers/Govindarajan_Radiant_Foam_Real-Time_Differentiable_Ray_Tracing_ICCV_2025_paper.pdf
|
https://openaccess.thecvf.com/content/ICCV2025/supplemental/Govindarajan_Radiant_Foam_Real-Time_ICCV_2025_supplemental.pdf
|
@InProceedings{Govindarajan_2025_ICCV,
author = {Govindarajan, Shrisudhan and Rebain, Daniel and Yi, Kwang Moo and Tagliasacchi, Andrea},
title = {Radiant Foam: Real-Time Differentiable Ray Tracing},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2025},
pages = {4135-4145}
}
|
Research on differentiable scene representations is consistently moving towards more efficient, real-time models. Recently, this has led to the popularization of splatting methods, which eschew the traditional ray-based rendering of radiance fields in favor of rasterization. This has yielded a significant improvement in rendering speeds due to the efficiency of rasterization algorithms and hardware, but has come at a cost: the approximations that make rasterization efficient also make implementation of light transport phenomena like reflection and refraction much more difficult. We propose a novel scene representation which avoids these approximations, but keeps the efficiency and reconstruction quality of splatting by leveraging a decades-old efficient volumetric mesh ray tracing algorithm which has been largely overlooked in recent computer vision research. The resulting model, which we name Radiant Foam, achieves rendering speed and quality comparable to Gaussian Splatting, without the constraints of rasterization. Unlike ray traced Gaussian models that use hardware ray tracing acceleration, our method requires no special hardware or APIs beyond the standard features of a programmable GPU.
|
2502.01157
| null | null | null |
[] |
[] |
[] |
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] |
228
|
Attention to the Burstiness in Visual Prompt Tuning!
|
[
"Yuzhu Wang",
"Manni Duan",
"Shu Kong"
] |
https://openaccess.thecvf.com/content/ICCV2025/html/Wang_Attention_to_the_Burstiness_in_Visual_Prompt_Tuning_ICCV_2025_paper.html
|
https://openaccess.thecvf.com/content/ICCV2025/papers/Wang_Attention_to_the_Burstiness_in_Visual_Prompt_Tuning_ICCV_2025_paper.pdf
|
https://openaccess.thecvf.com/content/ICCV2025/supplemental/Wang_Attention_to_the_ICCV_2025_supplemental.pdf
|
@InProceedings{Wang_2025_ICCV,
author = {Wang, Yuzhu and Duan, Manni and Kong, Shu},
title = {Attention to the Burstiness in Visual Prompt Tuning!},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2025},
pages = {4253-4263}
}
|
Visual Prompt Tuning (VPT) is a parameter-efficient finetuning technique that adapts a pre-trained vision Transformer (ViT) by learning a small set of parameters in the input space, known as prompts. In VPT, we uncover "burstiness" in the values arising from the interaction of image patch embeddings, and the key and query projectors within Transformer's self-attention module. Interestingly, the values of patch embeddings and the key and query projectors exhibit Laplacian and hyper-Laplacian distribution, respectively. Intuitively, these non-Gaussian distributions pose challenges for learning prompts. To address this, we propose whitening these data, de-correlating them and equalizing their variance towards more Gaussian before learning prompts. We derive the whitening matrix over random image patch embeddings and ViT's key and query projectors, and multiply it with the prompt to be learned in a bilinear manner.Surprisingly, this method significantly accelerates prompt tuning and boosts accuracy, e.g., >25 points on the CUB dataset; interestingly, it learns "bursty prompts".As bilinear models are known to introduce burstiness, we present a compact method by learning two small sets of parameters whose multiplication yields the final prompts. We call the proposed methods Bilinear Prompt Tuning (BPT). Extensive experiments demonstrate that BPT methods not only outperform various VPT methods across multiple benchmark datasets but also reduce parameter count and computation overhead.
|
2506.22908
| null | null | null |
[] |
[] |
[] |
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] |
229
|
BadVideo: Stealthy Backdoor Attack against Text-to-Video Generation
|
[
"Ruotong Wang",
"Mingli Zhu",
"Jiarong Ou",
"Rui Chen",
"Xin Tao",
"Pengfei Wan",
"Baoyuan Wu"
] |
https://openaccess.thecvf.com/content/ICCV2025/html/Wang_BadVideo_Stealthy_Backdoor_Attack_against_Text-to-Video_Generation_ICCV_2025_paper.html
|
https://openaccess.thecvf.com/content/ICCV2025/papers/Wang_BadVideo_Stealthy_Backdoor_Attack_against_Text-to-Video_Generation_ICCV_2025_paper.pdf
|
https://openaccess.thecvf.com/content/ICCV2025/supplemental/Wang_BadVideo_Stealthy_Backdoor_ICCV_2025_supplemental.pdf
|
@InProceedings{Wang_2025_ICCV,
author = {Wang, Ruotong and Zhu, Mingli and Ou, Jiarong and Chen, Rui and Tao, Xin and Wan, Pengfei and Wu, Baoyuan},
title = {BadVideo: Stealthy Backdoor Attack against Text-to-Video Generation},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2025},
pages = {19075-19084}
}
|
Text-to-video (T2V) generative models have rapidly advanced and found widespread applications across fields like entertainment, education, and marketing. However, the adversarial vulnerabilities of these models remain rarely explored. We observe that in T2V generation tasks, the generated videos often contain substantial redundant information not explicitly specified in the text prompts, such as environmental elements, secondary objects, and additional details, providing opportunities for malicious attackers to embed hidden harmful content. Exploiting this inherent redundancy, we introduce BadVideo, the first backdoor attack framework tailored for T2V generation. Our attack focuses on designing target adversarial outputs through two key strategies: (1) Spatio-Temporal Composition, which combines different spatiotemporal features to encode malicious information; (2) Dynamic Element Transformation, which introduces transformations in redundant elements over time to convey malicious information. Based on these strategies, the attacker's malicious target seamlessly integrates with the user's textual instructions, providing high stealthiness. Moreover, by exploiting the temporal dimension of videos, our attack successfully evades traditional content moderation systems that primarily analyze spatial information within individual frames. Extensive experiments demonstrate that BadVideo achieves high attack success rates while preserving original semantics and maintaining excellent performance on clean inputs. Overall, our work reveals the adversarial vulnerability of T2V models, calling attention to potential risks and misuse. Our project page is at https://wrt2000.github.io/BadVideo2025/.
|
2504.16907
|
Accepted by ICCV 2025
| null | null |
[] |
[] |
[] |
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] |
230
|
Cassic: Towards Content-Adaptive State-Space Models for Learned Image Compression
|
[
"Shiyu Qin",
"Jinpeng Wang",
"Yimin Zhou",
"Bin Chen",
"Tianci Luo",
"Baoyi An",
"Tao Dai",
"Shu-Tao Xia",
"Yaowei Wang"
] |
https://openaccess.thecvf.com/content/ICCV2025/html/Qin_Cassic_Towards_Content-Adaptive_State-Space_Models_for_Learned_Image_Compression_ICCV_2025_paper.html
|
https://openaccess.thecvf.com/content/ICCV2025/papers/Qin_Cassic_Towards_Content-Adaptive_State-Space_Models_for_Learned_Image_Compression_ICCV_2025_paper.pdf
| null |
@InProceedings{Qin_2025_ICCV,
author = {Qin, Shiyu and Wang, Jinpeng and Zhou, Yimin and Chen, Bin and Luo, Tianci and An, Baoyi and Dai, Tao and Xia, Shu-Tao and Wang, Yaowei},
title = {Cassic: Towards Content-Adaptive State-Space Models for Learned Image Compression},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2025},
pages = {15727-15736}
}
|
Learned image compression (LIC) demonstrates superior rate-distortion (RD) performance compared to traditional methods. Recent method MambaVC attempts to introduce Mamba, a variant of state space models, into this field aim to establish a new paradigm beyond convolutional neural networks and transformers. However, this approach relies on predefined four-directional scanning, which prioritizes spatial proximity over content and semantic relationships, resulting in suboptimal redundancy elimination. Additionally, it focuses solely on nonlinear transformations, neglecting entropy model improvements crucial for accurate probability estimation in entropy coding. To address these limitations, we propose a novel framework based on content-adaptive visual state space model, Cassic, through dual innovation.First, we design a content-adaptive selective scan based on weighted activation maps and bit allocation maps, subsequently developing a content-adaptive visual state space block. Second, we present a mamba-based channel-wise auto-regressive entropy model to fully leverage inter-slice bit allocation consistency for enhanced probability estimation. Extensive experimental results demonstrate that our method achieves state-of-the-art performance across three datasets while maintaining faster processing speeds than existing MambaVC approach.
| null | null | null | null |
[] |
[] |
[] |
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] |
231
|
AIGI-Holmes: Towards Explainable and Generalizable AI-Generated Image Detection via Multimodal Large Language Models
|
[
"Ziyin Zhou",
"Yunpeng Luo",
"Yuanchen Wu",
"Ke Sun",
"Jiayi Ji",
"Ke Yan",
"Shouhong Ding",
"Xiaoshuai Sun",
"Yunsheng Wu",
"Rongrong Ji"
] |
https://openaccess.thecvf.com/content/ICCV2025/html/Zhou_AIGI-Holmes_Towards_Explainable_and_Generalizable_AI-Generated_Image_Detection_via_Multimodal_ICCV_2025_paper.html
|
https://openaccess.thecvf.com/content/ICCV2025/papers/Zhou_AIGI-Holmes_Towards_Explainable_and_Generalizable_AI-Generated_Image_Detection_via_Multimodal_ICCV_2025_paper.pdf
|
https://openaccess.thecvf.com/content/ICCV2025/supplemental/Zhou_AIGI-Holmes_Towards_Explainable_ICCV_2025_supplemental.pdf
|
@InProceedings{Zhou_2025_ICCV,
author = {Zhou, Ziyin and Luo, Yunpeng and Wu, Yuanchen and Sun, Ke and Ji, Jiayi and Yan, Ke and Ding, Shouhong and Sun, Xiaoshuai and Wu, Yunsheng and Ji, Rongrong},
title = {AIGI-Holmes: Towards Explainable and Generalizable AI-Generated Image Detection via Multimodal Large Language Models},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2025},
pages = {18746-18758}
}
|
The rapid development of AI-generated content (AIGC) technology has led to the misuse of highly realistic AI-generated images (AIGI) in spreading misinformation, posing a threat to public information security. Although existing AIGI detection techniques are generally effective, they face two issues: 1) a lack of human-verifiable explanations, and 2) a lack of generalization in the latest generation technology. To address these issues, we introduce a large-scale and comprehensive dataset, Holmes-Set, which includes the Holmes-SFTSet, an instruction-tuning dataset with explanations on whether images are AI-generated, and the Holmes-DPOSet, a human-aligned preference dataset. Our work introduces an efficient data annotation method called the Multi-Expert Jury, enhancing data generation through structured MLLM explanations and quality control via cross-model evaluation, expert defect filtering, and human preference modification. In addition, we propose Holmes Pipeline, a meticulously designed three-stage training framework comprising visual expert pre-training, supervised fine-tuning, and direct preference optimization. Holmes Pipeline adapts multimodal large language models (MLLMs) for AIGI detection while generating human-verifiable and human-aligned explanations, ultimately yielding our model AIGI-Holmes. During the inference stage, we introduce a collaborative decoding strategy that integrates the model perception of the visual expert with the semantic reasoning of MLLMs, further enhancing the generalization capabilities. Extensive experiments on three benchmarks validate the effectiveness of our AIGI-Holmes.
| null | null | null | null |
[] |
[] |
[] |
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] |
232
|
TokensGen: Harnessing Condensed Tokens for Long Video Generation
|
[
"Wenqi Ouyang",
"Zeqi Xiao",
"Danni Yang",
"Yifan Zhou",
"Shuai Yang",
"Lei Yang",
"Jianlou Si",
"Xingang Pan"
] |
https://openaccess.thecvf.com/content/ICCV2025/html/Ouyang_TokensGen_Harnessing_Condensed_Tokens_for_Long_Video_Generation_ICCV_2025_paper.html
|
https://openaccess.thecvf.com/content/ICCV2025/papers/Ouyang_TokensGen_Harnessing_Condensed_Tokens_for_Long_Video_Generation_ICCV_2025_paper.pdf
|
https://openaccess.thecvf.com/content/ICCV2025/supplemental/Ouyang_TokensGen_Harnessing_Condensed_ICCV_2025_supplemental.zip
|
@InProceedings{Ouyang_2025_ICCV,
author = {Ouyang, Wenqi and Xiao, Zeqi and Yang, Danni and Zhou, Yifan and Yang, Shuai and Yang, Lei and Si, Jianlou and Pan, Xingang},
title = {TokensGen: Harnessing Condensed Tokens for Long Video Generation},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2025},
pages = {18197-18206}
}
|
Generating consistent long videos is a complex challenge: while diffusion-based generative models generate visually impressive short clips, extending them to longer durations often leads to memory bottlenecks and long-term inconsistency. In this paper, we propose TokensGen, a novel two-stage framework that leverages condensed tokens to address these issues. Our method decomposes long video generation into three core tasks: (1) inner-clip semantic control, (2) long-term consistency control, and (3) inter-clip smooth transition. First, we train To2V (Token-to-Video), a short video diffusion model guided by text and video tokens, with a Video Tokenizer that condenses short clips into semantically rich tokens. Second, we introduce T2To (Text-to-Token), a video token diffusion transformer that generates all tokens at once, ensuring global consistency across clips. Finally, during inference, an adaptive FIFO-Diffusion strategy seamlessly connects adjacent clips, reducing boundary artifacts and enhancing smooth transitions.Experimental results demonstrate that our approach significantly enhances long-term temporal and content coherence without incurring prohibitive computational overhead. By leveraging condensed tokens and pre-trained short video models, our method provides a scalable, modular solution for long video generation, opening new possibilities for storytelling, cinematic production, and immersive simulations.
|
2507.15728
|
Project page: https://vicky0522.github.io/tokensgen-webpage/
| null | null |
[] |
[] |
[] |
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] |
233
|
Cross-View Isolated Sign Language Recognition via View Synthesis and Feature Disentanglement
|
[
"Xin Shen",
"Xinyu Wang",
"Lei Shen",
"Kaihao Zhang",
"Xin Yu"
] |
https://openaccess.thecvf.com/content/ICCV2025/html/Shen_Cross-View_Isolated_Sign_Language_Recognition_via_View_Synthesis_and_Feature_ICCV_2025_paper.html
|
https://openaccess.thecvf.com/content/ICCV2025/papers/Shen_Cross-View_Isolated_Sign_Language_Recognition_via_View_Synthesis_and_Feature_ICCV_2025_paper.pdf
|
https://openaccess.thecvf.com/content/ICCV2025/supplemental/Shen_Cross-View_Isolated_Sign_ICCV_2025_supplemental.pdf
|
@InProceedings{Shen_2025_ICCV,
author = {Shen, Xin and Wang, Xinyu and Shen, Lei and Zhang, Kaihao and Yu, Xin},
title = {Cross-View Isolated Sign Language Recognition via View Synthesis and Feature Disentanglement},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2025},
pages = {20647-20657}
}
|
Cross-view isolated sign language recognition (CV-ISLR) addresses the challenge of identifying isolated signs from viewpoints unseen during training, a problem aggravated by the scarcity of multi-view data in existing benchmarks. To bridge this gap, we introduce a novel two-stage framework comprising View Synthesis and Contrastive Multi-task View-Semantics Recognition. In the View Synthesis stage, we simulate unseen viewpoints by extracting 3D keypoints from the front-view training dataset and synthesizing common-view 2D skeleton sequences with virtual camera rotation, which enriches view diversity without the cost of multi-camera setups. However, direct training on these synthetic samples leads to limited improvement, as viewpoint-specific and semantics-specific features remain entangled. To overcome this drawback, we present a Contrastive Multi-task View-Semantics Recognition (CMVSR) module that disentangles viewpoint-dependent features from sign semantics. In this way, CMVSR obtains view-invariant representations of the sign video, leading to robust recognition performance against various camera viewpoints. We evaluate our approach on the MM-WLAuslan dataset, the first benchmark for CV-ISLR, and on our extended protocol MTV-Test that includes additional multi-view data captured in the wild. Experimental results demonstrate that our method not only improves the accuracy of front-view skeleton-based isolated sign language recognition, but also exhibits superior generalization to novel viewpoints.
| null | null | null | null |
[] |
[] |
[] |
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] |
234
|
R1-Onevision: Advancing Generalized Multimodal Reasoning through Cross-Modal Formalization
|
[
"Yi Yang",
"Xiaoxuan He",
"Hongkun Pan",
"Xiyan Jiang",
"Yan Deng",
"Xingtao Yang",
"Haoyu Lu",
"Dacheng Yin",
"Fengyun Rao",
"Minfeng Zhu",
"Bo Zhang",
"Wei Chen"
] |
https://openaccess.thecvf.com/content/ICCV2025/html/Yang_R1-Onevision_Advancing_Generalized_Multimodal_Reasoning_through_Cross-Modal_Formalization_ICCV_2025_paper.html
|
https://openaccess.thecvf.com/content/ICCV2025/papers/Yang_R1-Onevision_Advancing_Generalized_Multimodal_Reasoning_through_Cross-Modal_Formalization_ICCV_2025_paper.pdf
|
https://openaccess.thecvf.com/content/ICCV2025/supplemental/Yang_R1-Onevision_Advancing_Generalized_ICCV_2025_supplemental.pdf
|
@InProceedings{Yang_2025_ICCV,
author = {Yang, Yi and He, Xiaoxuan and Pan, Hongkun and Jiang, Xiyan and Deng, Yan and Yang, Xingtao and Lu, Haoyu and Yin, Dacheng and Rao, Fengyun and Zhu, Minfeng and Zhang, Bo and Chen, Wei},
title = {R1-Onevision: Advancing Generalized Multimodal Reasoning through Cross-Modal Formalization},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2025},
pages = {2376-2385}
}
|
Large Language Models have demonstrated remarkable reasoning capability in complex textual tasks. However, multimodal reasoning, which requires integrating visual and textual information, remains a significant challenge. Existing visual-language models often struggle to effectively analyze and reason visual content, resulting in suboptimal performance on complex reasoning tasks. Moreover, the absence of comprehensive benchmarks hinders the accurate assessment of multimodal reasoning capabilities. In this paper, we introduce R1-Onevision, a multimodal reasoning model designed to bridge the gap between visual perception and deep reasoning. To achieve this, we propose a cross-modal reasoning pipeline that transforms images into formal textual representations, enabling precise language-based reasoning. Leveraging this pipeline, we construct the R1-Onevision dataset which provides detailed, step-by-step multimodal reasoning annotations across diverse domains. We further develop the R1-Onevision model through supervised fine-tuning and reinforcement learning to cultivate advanced reasoning and robust generalization abilities. To comprehensively evaluate multimodal reasoning performance across different grades, we introduce R1-Onevision-Bench, a benchmark aligned with human educational stages, covering exams from junior high school to university and beyond. Experimental results show that R1-Onevision achieves state-of-the-art performance, outperforming models such as GPT-4o and Qwen2.5-VL on multiple challenging multimodal reasoning benchmarks. Code, dataset and benchmark are available at https://github.com/Fancy-MLLM/R1-Onevision
| null | null | null | null |
[] |
[] |
[] |
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] |
235
|
HIS-GPT: Towards 3D Human-In-Scene Multimodal Understanding
|
[
"Jiahe Zhao",
"Ruibing Hou",
"Zejie Tian",
"Hong Chang",
"Shiguang Shan"
] |
https://openaccess.thecvf.com/content/ICCV2025/html/Zhao_HIS-GPT_Towards_3D_Human-In-Scene_Multimodal_Understanding_ICCV_2025_paper.html
|
https://openaccess.thecvf.com/content/ICCV2025/papers/Zhao_HIS-GPT_Towards_3D_Human-In-Scene_Multimodal_Understanding_ICCV_2025_paper.pdf
|
https://openaccess.thecvf.com/content/ICCV2025/supplemental/Zhao_HIS-GPT_Towards_3D_ICCV_2025_supplemental.pdf
|
@InProceedings{Zhao_2025_ICCV,
author = {Zhao, Jiahe and Hou, Ruibing and Tian, Zejie and Chang, Hong and Shan, Shiguang},
title = {HIS-GPT: Towards 3D Human-In-Scene Multimodal Understanding},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2025},
pages = {4317-4327}
}
|
We propose a new task to benchmark human-in-scene understanding for embodied agents: Human-In-Scene Question Answering (HIS-QA). Given a human motion within a 3D scene, HIS-QA requires the agent to comprehend human states and behaviors, reason about its surrounding environment, and answer human-related questions within the scene. To support this new task, we present HIS-Bench, a multimodal benchmark that systematically evaluates HIS understanding across a broad spectrum, from basic perception to commonsense reasoning and planning. Our evaluation of various vision-language models on HIS-Bench reveals significant limitations in their ability to handle HIS-QA tasks. To this end, we propose HIS-GPT, the first foundation model for HIS understanding. HIS-GPT integrates 3D scene context and human motion dynamics into large language models while incorporating specialized mechanisms to capture human-scene interactions. Extensive experiments demonstrate that HIS-GPT sets a new state-of-the-art on HIS-QA tasks. We hope this work inspires future research of human behavior analysis in 3D scenes, advancing embodied AI and world models.
| null | null | null | null |
[] |
[] |
[] |
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] |
236
|
Learnable Logit Adjustment for Imbalanced Semi-Supervised Learning under Class Distribution Mismatch
|
[
"Hyuck Lee",
"Taemin Park",
"Heeyoung Kim"
] |
https://openaccess.thecvf.com/content/ICCV2025/html/Lee_Learnable_Logit_Adjustment_for_Imbalanced_Semi-Supervised_Learning_under_Class_Distribution_ICCV_2025_paper.html
|
https://openaccess.thecvf.com/content/ICCV2025/papers/Lee_Learnable_Logit_Adjustment_for_Imbalanced_Semi-Supervised_Learning_under_Class_Distribution_ICCV_2025_paper.pdf
|
https://openaccess.thecvf.com/content/ICCV2025/supplemental/Lee_Learnable_Logit_Adjustment_ICCV_2025_supplemental.pdf
|
@InProceedings{Lee_2025_ICCV,
author = {Lee, Hyuck and Park, Taemin and Kim, Heeyoung},
title = {Learnable Logit Adjustment for Imbalanced Semi-Supervised Learning under Class Distribution Mismatch},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2025},
pages = {2664-2674}
}
|
In class-imbalanced learning (CIL), post-hoc logit adjustment (LA) effectively mitigates class imbalance by adjusting biased logits according to label frequencies. Given the success of LA in CIL, recent class-imbalanced semi-supervised learning (CISSL) algorithms incorporated LA, leading to improved performance when labeled and unlabeled datasets share the same class distribution. However, a common real-world scenario involves the unknown class distribution of the unlabeled set, which may mismatch that of the labeled set. In this case, LA may result in an inappropriate degree of logit adjustments, potentially degrading classification performance due to its inability to incorporate the unknown class distribution of the unlabeled set. To address this problem, we propose a novel CISSL algorithm named learnable logit adjustment (LLA). Unlike the original LA, LLA learns the appropriate degree of logit adjustment by minimizing the class-averaged loss computed for both the labeled and unlabeled sets. Based on the learned degree, LLA refines the biased pseudo-labels of base semi-supervised learning algorithms and adjusts the biased class predictions on the test set by adjusting the logits. Experimental results on benchmark datasets demonstrate that LLA achieves state-of-the-art performance in CISSL.
| null | null | null | null |
[] |
[] |
[] |
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] |
237
|
Subjective Camera 1.0: Bridging Human Cognition and Visual Reconstruction through Sequence-Aware Sketch-Guided Diffusion
|
[
"Haoyang Chen",
"Dongfang Sun",
"Caoyuan Ma",
"Shiqin Wang",
"Kewei Zhang",
"Zheng Wang",
"Zhixiang Wang"
] |
https://openaccess.thecvf.com/content/ICCV2025/html/Chen_Subjective_Camera_1.0_Bridging_Human_Cognition_and_Visual_Reconstruction_through_ICCV_2025_paper.html
|
https://openaccess.thecvf.com/content/ICCV2025/papers/Chen_Subjective_Camera_1.0_Bridging_Human_Cognition_and_Visual_Reconstruction_through_ICCV_2025_paper.pdf
| null |
@InProceedings{Chen_2025_ICCV,
author = {Chen, Haoyang and Sun, Dongfang and Ma, Caoyuan and Wang, Shiqin and Zhang, Kewei and Wang, Zheng and Wang, Zhixiang},
title = {Subjective Camera 1.0: Bridging Human Cognition and Visual Reconstruction through Sequence-Aware Sketch-Guided Diffusion},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2025},
pages = {17838-17847}
}
|
We introduce the concept of a subjective camera to reconstruct meaningful moments that physical cameras fail to capture. We propose Subjective Camera 1.0, a framework for reconstructing real-world scenes from readily accessible subjective readouts, i.e., textual descriptions and progressively drawn rough sketches. Built on optimization-based alignment of diffusion models, our approach avoids large-scale paired training data and mitigates generalization issues. To address the challenge of integrating multiple abstract concepts in real-world scenarios, we design a Sequence-Aware Sketch-Guided Diffusion framework with three loss terms for concept-wise sequential optimization, following the natural order of subjective readouts. Experiments on two datasets demonstrate that our method achieves state-of-the-art performance in image quality as well as spatial and semantic alignment with target scenes. User studies with 40 participants further confirm that our approach is consistently preferred. Our project page is at: subjective-camera.github.io
|
2506.23711
| null | null | null |
[] |
[] |
[] |
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] |
238
|
Learning Robust Image Watermarking with Lossless Cover Recovery
|
[
"Jiale Chen",
"Wei Wang",
"Chongyang Shi",
"Li Dong",
"Xiping Hu"
] |
https://openaccess.thecvf.com/content/ICCV2025/html/Chen_Learning_Robust_Image_Watermarking_with_Lossless_Cover_Recovery_ICCV_2025_paper.html
|
https://openaccess.thecvf.com/content/ICCV2025/papers/Chen_Learning_Robust_Image_Watermarking_with_Lossless_Cover_Recovery_ICCV_2025_paper.pdf
|
https://openaccess.thecvf.com/content/ICCV2025/supplemental/Chen_Learning_Robust_Image_ICCV_2025_supplemental.pdf
|
@InProceedings{Chen_2025_ICCV,
author = {Chen, Jiale and Wang, Wei and Shi, Chongyang and Dong, Li and Hu, Xiping},
title = {Learning Robust Image Watermarking with Lossless Cover Recovery},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2025},
pages = {15056-15065}
}
|
Watermarking as a traceable authentication technology has been widely applied in image copyright protection. However, most existing watermarking methods embed watermarks by adding irremovable perturbations to the cover image, causing permanent distortion. To address this issue, we propose a novel watermarking approach termed Cover-Recoverable Watermark (CRMark). CRMark can losslessly recover the cover image and watermark in lossless channels and enables robust watermark extraction in lossy channels. CRMark leverages an integer Invertible Watermarking Network (iIWN) to achieve a lossless invertible mapping between the cover-image-watermark pair and the stego image. During the training phase, CRMark employs an encoder-noise-layer-decoder architecture to enhance its robustness against distortions. In the inference phase, CRMark first maps the cover-image-watermark pair into an overflowed stego image and a latent variable. Subsequently, the overflowed pixels and the latent variable are losslessly compressed into an auxiliary bitstream, which is then embedded into the clipped stego image using reversible data hiding. During extraction, in lossy channels, the noised stego image can directly undergo inverse mapping via iIWN to extract the watermark. In lossless channels, the latent variable and overflowed stego image are first recovered using reversible data hiding, followed by watermark extraction through iIWN. Extensive experimental results demonstrate that CRMark can be perfectly recovered in lossless channels while remaining robust to common distortions. Code is available at https://github.com/chenoly/CRMark.
| null | null | null | null |
[] |
[] |
[] |
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] |
239
|
Enhancing Transformers Through Conditioned Embedded Tokens
|
[
"Hemanth Saratchandran",
"Simon Lucey"
] |
https://openaccess.thecvf.com/content/ICCV2025/html/Saratchandran_Enhancing_Transformers_Through_Conditioned_Embedded_Tokens_ICCV_2025_paper.html
|
https://openaccess.thecvf.com/content/ICCV2025/papers/Saratchandran_Enhancing_Transformers_Through_Conditioned_Embedded_Tokens_ICCV_2025_paper.pdf
|
https://openaccess.thecvf.com/content/ICCV2025/supplemental/Saratchandran_Enhancing_Transformers_Through_ICCV_2025_supplemental.pdf
|
@InProceedings{Saratchandran_2025_ICCV,
author = {Saratchandran, Hemanth and Lucey, Simon},
title = {Enhancing Transformers Through Conditioned Embedded Tokens},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2025},
pages = {4786-4795}
}
|
Transformers have transformed modern machine learning, driving breakthroughs in computer vision, natural language processing, and robotics. At the core of their success lies the attention mechanism, which enables the modeling of global dependencies among input tokens. However, we reveal that the attention block in transformers suffers from inherent ill-conditioning, which hampers gradient-based optimization and leads to inefficient training. To address this, we develop a theoretical framework that establishes a direct relationship between the conditioning of the attention block and that of the embedded tokenized data. Building on this insight, we introduce conditioned embedded tokens, a method that systematically modifies the embedded tokens to improve the conditioning of the attention mechanism. Our analysis demonstrates that this approach significantly mitigates ill-conditioning, leading to more stable and efficient training. We validate our methodology across various transformer architectures, achieving consistent improvements in image classification, object detection, instance segmentation, and natural language processing, highlighting its broad applicability and effectiveness.
|
2505.12789
| null | null | null |
[] |
[] |
[] |
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] |
240
|
Drawing Developmental Trajectory from Cortical Surface Reconstruction
|
[
"Wenxuan Wu",
"Ruowen Qu",
"Zhongliang Liu",
"Zhuoyan Dai",
"Dongzi Shi",
"Sijin Yu",
"Tong Xiong",
"Shiping Liu",
"Xiangmin Xu",
"Xiaofen Xing",
"Xin Zhang"
] |
https://openaccess.thecvf.com/content/ICCV2025/html/Wu_Drawing_Developmental_Trajectory_from_Cortical_Surface_Reconstruction_ICCV_2025_paper.html
|
https://openaccess.thecvf.com/content/ICCV2025/papers/Wu_Drawing_Developmental_Trajectory_from_Cortical_Surface_Reconstruction_ICCV_2025_paper.pdf
|
https://openaccess.thecvf.com/content/ICCV2025/supplemental/Wu_Drawing_Developmental_Trajectory_ICCV_2025_supplemental.pdf
|
@InProceedings{Wu_2025_ICCV,
author = {Wu, Wenxuan and Qu, Ruowen and Liu, Zhongliang and Dai, Zhuoyan and Shi, Dongzi and Yu, Sijin and Xiong, Tong and Liu, Shiping and Xu, Xiangmin and Xing, Xiaofen and Zhang, Xin},
title = {Drawing Developmental Trajectory from Cortical Surface Reconstruction},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2025},
pages = {11026-11035}
}
|
Diffeomorphic-based cortical surface reconstruction typically involves a series of deformation processes to extract the cerebral cortex from brain magnetic resonance images (MRI). While most methods are designed for adult brains using Neural Ordinary Differential Equations (NODE) with fixed step sizes, the neonatal brain, which exhibits dramatic changes in cortical folding patterns early in life, requires a more adaptive approach. To address this, we develop a dual-task framework to directly characterize the brain development trajectory through processes of cortical surface reconstruction. For white matter (inner surfaces), we employ an Age-Conditioned ODE with adaptive step sizes. It is initially trained on a limited set of longitudinal paired data to establish a coarse trajectory, which is then refined through sample training of single-point data and knowledge distillation. For the pial surfaces (outer surfaces), we position the midthickness surfaces as intermediates and employ a cycle-consistent semi-supervised training strategy to depict a coherent brain development trajectory between the inner and outer surfaces. Our approach is the first to achieve precise developmental prediction directly on triangular meshes. Furthermore, by enhancing interpretability at each stage of the deformation process, this approach improves the applicability of diffeomorphic-based methods. The proposed method has demonstrated state-of-the-art performance in modeling developmental trajectories and cortical surface reconstruction within the developing Human Connectome Project dataset (dHCP).
| null | null | null | null |
[] |
[] |
[] |
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] |
241
|
CryoFastAR: Fast Cryo-EM Ab initio Reconstruction Made Easy
|
[
"Jiakai Zhang",
"Shouchen Zhou",
"Haizhao Dai",
"Xinhang Liu",
"Peihao Wang",
"Zhiwen Fan",
"Yuan Pei",
"Jingyi Yu"
] |
https://openaccess.thecvf.com/content/ICCV2025/html/Zhang_CryoFastAR_Fast_Cryo-EM_Ab_initio_Reconstruction_Made_Easy_ICCV_2025_paper.html
|
https://openaccess.thecvf.com/content/ICCV2025/papers/Zhang_CryoFastAR_Fast_Cryo-EM_Ab_initio_Reconstruction_Made_Easy_ICCV_2025_paper.pdf
|
https://openaccess.thecvf.com/content/ICCV2025/supplemental/Zhang_CryoFastAR_Fast_Cryo-EM_ICCV_2025_supplemental.pdf
|
@InProceedings{Zhang_2025_ICCV,
author = {Zhang, Jiakai and Zhou, Shouchen and Dai, Haizhao and Liu, Xinhang and Wang, Peihao and Fan, Zhiwen and Pei, Yuan and Yu, Jingyi},
title = {CryoFastAR: Fast Cryo-EM Ab initio Reconstruction Made Easy},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2025},
pages = {8462-8471}
}
|
Pose estimation from unordered images is fundamental for 3D reconstruction, robotics, and scientific imaging. Recent geometric foundation models, such as DUSt3R, enable end-to-end dense 3D reconstruction but remain underexplored in scientific imaging fields like cryo-electron microscopy (cryo-EM) for near-atomic protein reconstruction. In cryo-EM, pose estimation and 3D reconstruction from unordered particle images still depend on time-consuming iterative optimization, primarily due to challenges such as low signal-to-noise ratios (SNR) and distortions from the contrast transfer function (CTF). We introduce CryoFastAR, the first geometric foundation model that can directly predict poses from Cryo-EM noisy images for Fast ab initio Reconstruction. By integrating multi-view features and training on large-scale simulated cryo-EM data with realistic noise and CTF modulations, CryoFastAR enhances pose estimation accuracy and generalization. To enhance training stability, we propose a progressive training strategy that first allows the model to extract essential features under simpler conditions before gradually increasing difficulty to improve robustness. Experiments show that CryoFastAR achieves comparable quality while significantly accelerating inference over traditional iterative approaches on both synthetic and real datasets.
|
2506.05864
| null | null | null |
[] |
[] |
[] |
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] |
242
|
3DGS-LM: Faster Gaussian-Splatting Optimization with Levenberg-Marquardt
|
[
"Lukas Höllein",
"Aljaž Božič",
"Michael Zollhöfer",
"Matthias Nießner"
] |
https://openaccess.thecvf.com/content/ICCV2025/html/Hollein_3DGS-LM_Faster_Gaussian-Splatting_Optimization_with_Levenberg-Marquardt_ICCV_2025_paper.html
|
https://openaccess.thecvf.com/content/ICCV2025/papers/Hollein_3DGS-LM_Faster_Gaussian-Splatting_Optimization_with_Levenberg-Marquardt_ICCV_2025_paper.pdf
|
https://openaccess.thecvf.com/content/ICCV2025/supplemental/Hollein_3DGS-LM_Faster_Gaussian-Splatting_ICCV_2025_supplemental.pdf
|
@InProceedings{Hollein_2025_ICCV,
author = {H\"ollein, Lukas and Bo\v{z}i\v{c}, Alja\v{z} and Zollh\"ofer, Michael and Nie{\ss}ner, Matthias},
title = {3DGS-LM: Faster Gaussian-Splatting Optimization with Levenberg-Marquardt},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2025},
pages = {26740-26750}
}
|
We present 3DGS-LM, a new method that accelerates the reconstruction of 3D Gaussian Splatting (3DGS) by replacing its ADAM optimizer with a tailored Levenberg-Marquardt (LM). Existing methods reduce the optimization time by decreasing the number of Gaussians or by improving the implementation of the differentiable rasterizer. However, they still rely on the ADAM optimizer to fit Gaussian parameters of a scene in thousands of iterations, which can take up to an hour. To this end, we change the optimizer to LM that runs in conjunction with the 3DGS differentiable rasterizer. For efficient GPU parallelization, we propose a caching data structure for intermediate gradients that allows us to efficiently calculate Jacobian-vector products in custom CUDA kernels. In every LM iteration, we calculate update directions from multiple image subsets using these kernels and combine them in a weighted mean. Overall, our method is 20% faster than the original 3DGS while obtaining the same reconstruction quality. Our optimization is also agnostic to other methods that accelerate 3DGS, thus enabling even faster speedups compared to vanilla 3DGS.
| null | null | null |
https://lukashoel.github.io/3DGS-LM/
|
[] |
[] |
[] |
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] |
243
|
GauUpdate: New Object Insertion in 3D Gaussian Fields with Consistent Global Illumination
|
[
"Chengwei Ren",
"Fan Zhang",
"Liangchao Xu",
"Liang Pan",
"Ziwei Liu",
"Wenping Wang",
"Xiao-Ping Zhang",
"Yuan Liu"
] |
https://openaccess.thecvf.com/content/ICCV2025/html/Ren_GauUpdate_New_Object_Insertion_in_3D_Gaussian_Fields_with_Consistent_ICCV_2025_paper.html
|
https://openaccess.thecvf.com/content/ICCV2025/papers/Ren_GauUpdate_New_Object_Insertion_in_3D_Gaussian_Fields_with_Consistent_ICCV_2025_paper.pdf
|
https://openaccess.thecvf.com/content/ICCV2025/supplemental/Ren_GauUpdate_New_Object_ICCV_2025_supplemental.zip
|
@InProceedings{Ren_2025_ICCV,
author = {Ren, Chengwei and Zhang, Fan and Xu, Liangchao and Pan, Liang and Liu, Ziwei and Wang, Wenping and Zhang, Xiao-Ping and Liu, Yuan},
title = {GauUpdate: New Object Insertion in 3D Gaussian Fields with Consistent Global Illumination},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2025},
pages = {28653-28663}
}
|
3D Gaussian Splatting (3DGS) is a prevailing technique to reconstruct large-scale 3D scenes from multiview images for novel view synthesis, like a room, a block, and even a city. Such large-scale scenes are not static with changes constantly happening in these scenes, like a new building being built or a new decoration being set up. To keep the reconstructed 3D Gaussian fields up-to-date, a naive way is to reconstruct the whole scene after changing, which is extremely costly and inefficient. In this paper, we propose a new method called GauUpdate that allows partially updating an old 3D Gaussian field with new objects from a new 3D Gaussian field. However, simply inserting the new objects leads to inconsistent appearances because the old and new Gaussian fields may have different lighting environments from each other. GauUpdate addresses this problem by applying inverse rendering techniques in the 3DGS to recover both the materials and environmental lights. Based on the materials and lighting, we relight the new objects in the old 3D Gaussian field for consistent global illumination. For an accurate estimation of the materials and lighting, we put additional constraints on the materials and lighting conditions, that these two fields share the same materials but different environment lights, to improve their qualities. We conduct experiments on both synthetic scenes and real-world scenes to evaluate GauUpdate, which demonstrate that GauUpdate achieves realistic object insertion in 3D Gaussian fields with consistent appearances.
| null | null | null | null |
[] |
[] |
[] |
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] |
244
|
OphCLIP: Hierarchical Retrieval-Augmented Learning for Ophthalmic Surgical Video-Language Pretraining
|
[
"Ming Hu",
"Kun Yuan",
"Yaling Shen",
"Feilong Tang",
"Xiaohao Xu",
"Lin Zhou",
"Wei Li",
"Ying Chen",
"Zhongxing Xu",
"Zelin Peng",
"Siyuan Yan",
"Vinkle Srivastav",
"Diping Song",
"Tianbin Li",
"Danli Shi",
"Jin Ye",
"Nicolas Padoy",
"Nassir Navab",
"Junjun He",
"Zongyuan Ge"
] |
https://openaccess.thecvf.com/content/ICCV2025/html/Hu_OphCLIP_Hierarchical_Retrieval-Augmented_Learning_for_Ophthalmic_Surgical_Video-Language_Pretraining_ICCV_2025_paper.html
|
https://openaccess.thecvf.com/content/ICCV2025/papers/Hu_OphCLIP_Hierarchical_Retrieval-Augmented_Learning_for_Ophthalmic_Surgical_Video-Language_Pretraining_ICCV_2025_paper.pdf
|
https://openaccess.thecvf.com/content/ICCV2025/supplemental/Hu_OphCLIP_Hierarchical_Retrieval-Augmented_ICCV_2025_supplemental.pdf
|
@InProceedings{Hu_2025_ICCV,
author = {Hu, Ming and Yuan, Kun and Shen, Yaling and Tang, Feilong and Xu, Xiaohao and Zhou, Lin and Li, Wei and Chen, Ying and Xu, Zhongxing and Peng, Zelin and Yan, Siyuan and Srivastav, Vinkle and Song, Diping and Li, Tianbin and Shi, Danli and Ye, Jin and Padoy, Nicolas and Navab, Nassir and He, Junjun and Ge, Zongyuan},
title = {OphCLIP: Hierarchical Retrieval-Augmented Learning for Ophthalmic Surgical Video-Language Pretraining},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2025},
pages = {19838-19849}
}
|
Vision-language pretraining (VLP) enables open-world generalization beyond predefined labels, a critical capability in surgery due to the diversity of procedures, instruments, and patient anatomies. However, applying VLP to ophthalmic surgery presents unique challenges, including limited vision-language data, intricate procedural workflows, and the need for hierarchical understanding, ranging from fine-grained surgical actions to global clinical reasoning. To address these, we introduce OphVL, a large-scale, hierarchically structured dataset containing over 375K video-text pairs, making it 15x larger than existing surgical VLP datasets. OphVL captures a diverse range of ophthalmic surgical attributes, including surgical phases, operations, actions, instruments, medications, disease causes, surgical objectives, and postoperative care recommendations. By aligning short clips with detailed narratives and full-length videos with structured titles, OphVL provides both fine-grained surgical details and high-level procedural context. Building on OphVL, we propose OphCLIP, a hierarchical retrieval-augmented VLP framework. OphCLIP leverages silent surgical videos as a knowledge base, retrieving semantically relevant content to enhance narrated procedure learning. This enables OphCLIP to integrate explicit linguistic supervision with implicit visual knowledge, improving ophthalmic workflow modeling. Evaluations across 11 benchmark datasets for surgical phase recognition and multi-instrument identification demonstrate OphCLIP's robust generalization and superior performance, establishing it as a foundation model for ophthalmic surgery.
|
2411.15421
| null | null | null |
[] |
[] |
[] |
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] |
245
|
Hints of Prompt: Enhancing Visual Representation for Multimodal LLMs in Autonomous Driving
|
[
"Hao Zhou",
"Zhanning Gao",
"Zhili Chen",
"Maosheng Ye",
"Qifeng Chen",
"Tongyi Cao",
"Honggang Qi"
] |
https://openaccess.thecvf.com/content/ICCV2025/html/Zhou_Hints_of_Prompt_Enhancing_Visual_Representation_for_Multimodal_LLMs_in_ICCV_2025_paper.html
|
https://openaccess.thecvf.com/content/ICCV2025/papers/Zhou_Hints_of_Prompt_Enhancing_Visual_Representation_for_Multimodal_LLMs_in_ICCV_2025_paper.pdf
|
https://openaccess.thecvf.com/content/ICCV2025/supplemental/Zhou_Hints_of_Prompt_ICCV_2025_supplemental.pdf
|
@InProceedings{Zhou_2025_ICCV,
author = {Zhou, Hao and Gao, Zhanning and Chen, Zhili and Ye, Maosheng and Chen, Qifeng and Cao, Tongyi and Qi, Honggang},
title = {Hints of Prompt: Enhancing Visual Representation for Multimodal LLMs in Autonomous Driving},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2025},
pages = {6165-6175}
}
|
In light of the dynamic nature of autonomous driving environments and stringent safety requirements, general MLLMs combined with CLIP alone often struggle to accurately represent driving-specific scenarios, particularly in complex interactions and long-tail cases. To address this, we propose the Hints of Prompt (HoP) framework, which introduces three key enhancements: Affinity hint to emphasize instance-level structure by strengthening token-wise connections, Semantic hint to incorporate high-level information relevant to driving-specific cases, such as complex interactions among vehicles and traffic signs, and Question hint to align visual features with the query context, focusing on question-relevant regions. These hints are fused through a Hint Fusion module, enriching visual representations by capturing driving-related representations with limited domain data, ensuring faster adaptation to driving scenarios. Extensive experiments confirm the effectiveness of the HoP framework, showing that it significantly outperforms previous state-of-the-art methods in all key metrics.
|
2411.13076
| null | null | null |
[] |
[] |
[] |
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] |
246
|
IM360: Large-scale Indoor Mapping with 360 Cameras
|
[
"Dongki Jung",
"Jaehoon Choi",
"Yonghan Lee",
"Dinesh Manocha"
] |
https://openaccess.thecvf.com/content/ICCV2025/html/Jung_IM360_Large-scale_Indoor_Mapping_with_360_Cameras_ICCV_2025_paper.html
|
https://openaccess.thecvf.com/content/ICCV2025/papers/Jung_IM360_Large-scale_Indoor_Mapping_with_360_Cameras_ICCV_2025_paper.pdf
|
https://openaccess.thecvf.com/content/ICCV2025/supplemental/Jung_IM360_Large-scale_Indoor_ICCV_2025_supplemental.pdf
|
@InProceedings{Jung_2025_ICCV,
author = {Jung, Dongki and Choi, Jaehoon and Lee, Yonghan and Manocha, Dinesh},
title = {IM360: Large-scale Indoor Mapping with 360 Cameras},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2025},
pages = {29040-29050}
}
|
We present a novel 3D mapping pipeline for large-scale indoor environments. To address the significant challenges in large-scale indoor scenes, such as prevalent occlusions and textureless regions, we propose IM360, a novel approach that leverages the wide field of view of omnidirectional images and integrates the spherical camera model into the Structure-from-Motion (SfM) pipeline. Our SfM utilizes dense matching features specifically designed for 360 images, demonstrating superior capability in image registration. Furthermore, with the aid of mesh-based neural rendering techniques, we introduce a texture optimization method that refines texture maps and accurately captures view-dependent properties by combining diffuse and specular components. We evaluate our pipeline on large-scale indoor scenes, demonstrating its effectiveness in real-world scenarios. In practice, IM360 demonstrates superior performance, achieving a 3.5 PSNR increase in textured mesh reconstruction. We attain state-of-the-art performance in terms of camera localization and registration on Matterport3D and Stanford2D3D.
|
2502.12545
| null | null | null |
[] |
[] |
[] |
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-0.021172307431697845,
0.0720011368393898,
0.018894871696829796
] |
247
|
RegGS: Unposed Sparse Views Gaussian Splatting with 3DGS Registration
|
[
"Chong Cheng",
"Yu Hu",
"Sicheng Yu",
"Beizhen Zhao",
"Zijian Wang",
"Hao Wang"
] |
https://openaccess.thecvf.com/content/ICCV2025/html/Cheng_RegGS_Unposed_Sparse_Views_Gaussian_Splatting_with_3DGS_Registration_ICCV_2025_paper.html
|
https://openaccess.thecvf.com/content/ICCV2025/papers/Cheng_RegGS_Unposed_Sparse_Views_Gaussian_Splatting_with_3DGS_Registration_ICCV_2025_paper.pdf
|
https://openaccess.thecvf.com/content/ICCV2025/supplemental/Cheng_RegGS_Unposed_Sparse_ICCV_2025_supplemental.pdf
|
@InProceedings{Cheng_2025_ICCV,
author = {Cheng, Chong and Hu, Yu and Yu, Sicheng and Zhao, Beizhen and Wang, Zijian and Wang, Hao},
title = {RegGS: Unposed Sparse Views Gaussian Splatting with 3DGS Registration},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2025},
pages = {8100-8109}
}
|
3D Gaussian Splatting (3DGS) has demonstrated its potential in reconstructing scenes from unposed images. However, optimization-based 3DGS methods struggle with sparse views due to limited prior knowledge. Meanwhile, feed-forward Gaussian approaches are constrained by input formats, making it challenging to incorporate more input views. To address these challenges, we propose RegGS, a 3D Gaussian registration-based framework for reconstructing unposed sparse views. RegGS aligns local 3D Gaussians generated by a feed-forward network into a globally consistent 3D Gaussian representation. Technically, we implement an entropy-regularized Sinkhorn algorithm to efficiently solve the optimal transport Mixture 2-Wasserstein MW_2 distance, which serves as an alignment metric for Gaussian mixture models (GMMs) in Sim(3) space. Furthermore, we design a joint 3DGS registration module that integrates the MW_2 distance, photometric consistency, and depth geometry. This enables a coarse-to-fine registration process while accurately estimating camera poses and aligning the scene. Experiments on the RE10K and ACID datasets demonstrate that RegGS effectively registers local Gaussians with high fidelity, achieving precise pose estimation and high-quality novel view synthesis.
|
2507.08136
|
Accepted to ICCV 2025
| null | null |
[] |
[] |
[] |
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] |
248
|
SU-RGS: Relightable 3D Gaussian Splatting from Sparse Views under Unconstrained Illuminations
|
[
"Qi Zhang",
"Chi Huang",
"Qian Zhang",
"Nan Li",
"Wei Feng"
] |
https://openaccess.thecvf.com/content/ICCV2025/html/Zhang_SU-RGS_Relightable_3D_Gaussian_Splatting_from_Sparse_Views_under_Unconstrained_ICCV_2025_paper.html
|
https://openaccess.thecvf.com/content/ICCV2025/papers/Zhang_SU-RGS_Relightable_3D_Gaussian_Splatting_from_Sparse_Views_under_Unconstrained_ICCV_2025_paper.pdf
| null |
@InProceedings{Zhang_2025_ICCV,
author = {Zhang, Qi and Huang, Chi and Zhang, Qian and Li, Nan and Feng, Wei},
title = {SU-RGS: Relightable 3D Gaussian Splatting from Sparse Views under Unconstrained Illuminations},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2025},
pages = {26859-26868}
}
|
The latest advancements in scene relighting have been predominantly driven by inverse rendering with 3D Gaussian Splatting (3DGS). However, existing methods remain overly reliant on densely sampled images under static illumination conditions, which is prohibitively expensive and even impractical in real-world scenarios. In this paper, we propose a novel learning from Sparse views under Unconstrained illuminations Relightable 3D Gaussian Splatting (dubbed SU-RGS), to address this challenge by jointly optimizing 3DGS representations, surface materials, and environment illuminations (i.e., unknown and various lighting conditions in training) using only sparse input views. Firstly, SU-RGS presents a varying appearance rendering strategy, enabling each 3D Gaussian can perform inconsistent color under various lightings. Next, SU-RGS establishes the multi-view semantic consistency by constructing hierarchical semantics pseudo-labels across inter-views, to compensate for extra supervisions and facilitate sparse inverse rendering for confronting unconstrained illuminations. Additionally, we introduce an adaptive transient object perception component that integrates the scene geometry and semantics in a fine-grained manner, to quantify and eliminate the uncertainty of the foreground. Extensive experiments on both synthetic and real-world challenging datasets demonstrate the effectiveness of SU-RGS, achieving the state-of-the-art performance for scene inverse rendering by learning 3DGS from only sparse views under unconstrained illuminations.
| null | null | null | null |
[] |
[] |
[] |
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] |
249
|
Representation Shift: Unifying Token Compression with FlashAttention
|
[
"Joonmyung Choi",
"Sanghyeok Lee",
"Byungoh Ko",
"Eunseo Kim",
"Jihyung Kil",
"Hyunwoo J. Kim"
] |
https://openaccess.thecvf.com/content/ICCV2025/html/Choi_Representation_Shift_Unifying_Token_Compression_with_FlashAttention_ICCV_2025_paper.html
|
https://openaccess.thecvf.com/content/ICCV2025/papers/Choi_Representation_Shift_Unifying_Token_Compression_with_FlashAttention_ICCV_2025_paper.pdf
| null |
@InProceedings{Choi_2025_ICCV,
author = {Choi, Joonmyung and Lee, Sanghyeok and Ko, Byungoh and Kim, Eunseo and Kil, Jihyung and Kim, Hyunwoo J.},
title = {Representation Shift: Unifying Token Compression with FlashAttention},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2025},
pages = {20456-20466}
}
|
Transformers have demonstrated remarkable success across vision, language, and video. Yet, increasing task complexity has led to larger models and more tokens, raising the quadratic cost of self-attention and the overhead of GPU memory access. To reduce the computation cost of self-attention, prior work has proposed token compression techniques that drop redundant or less informative tokens. Meanwhile, fused attention kernels such as FlashAttention have been developed to alleviate memory overhead by avoiding attention map construction and its associated I/O to HBM. This, however, makes it incompatible with most training-free token compression methods, which rely on attention maps to determine token importance. Here, we propose Representation Shift, a training-free, model-agnostic metric that measures the degree of change in each token's representation. This seamlessly integrates token compression with FlashAttention, without attention maps or retraining. Our method further generalizes beyond Transformers to CNNs and state space models. Extensive experiments show that Representation Shift enables effective token compression compatible with FlashAttention, yielding significant speedups of up to 5.5x and 4.4x in video-text retrieval and video QA, respectively. Code is available at https://github.com/mlvlab/Representation-Shift.
|
2508.00367
|
International Conference on Computer Vision (ICCV), 2025
|
https://github.com/mlvlab/Representation-Shift
| null |
[] |
[] |
[] |
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] |
250
|
DGTalker: Disentangled Generative Latent Space Learning for Audio-Driven Gaussian Talking Heads
|
[
"Xiaoxi Liang",
"Yanbo Fan",
"Qiya Yang",
"Xuan Wang",
"Wei Gao",
"Ge Li"
] |
https://openaccess.thecvf.com/content/ICCV2025/html/Liang_DGTalker_Disentangled_Generative_Latent_Space_Learning_for_Audio-Driven_Gaussian_Talking_ICCV_2025_paper.html
|
https://openaccess.thecvf.com/content/ICCV2025/papers/Liang_DGTalker_Disentangled_Generative_Latent_Space_Learning_for_Audio-Driven_Gaussian_Talking_ICCV_2025_paper.pdf
|
https://openaccess.thecvf.com/content/ICCV2025/supplemental/Liang_DGTalker_Disentangled_Generative_ICCV_2025_supplemental.zip
|
@InProceedings{Liang_2025_ICCV,
author = {Liang, Xiaoxi and Fan, Yanbo and Yang, Qiya and Wang, Xuan and Gao, Wei and Li, Ge},
title = {DGTalker: Disentangled Generative Latent Space Learning for Audio-Driven Gaussian Talking Heads},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2025},
pages = {11079-11088}
}
|
In this work, we investigate the generation of high-fidelity, audio-driven 3D Gaussian talking heads from monocular videos. We present DGTalker, an innovative framework designed for real-time, high-fidelity, and 3D-aware talking head synthesis. By leveraging Gaussian generative priors and treating the task as a latent space navigation problem, our method effectively alleviates the lack of 3D information and the low-quality detail reconstruction caused by the absence of structure priors in monocular videos, which is a longstanding challenge in existing 3DGS-based approaches. To ensure precise lip synchronization and nuanced expression control, we propose a disentangled latent space navigation method that independently models lip motion and talking expressions. Additionally, we introduce an effective masked cross-view supervision strategy to enable robust learning within the disentangled framework. We conduct extensive experiments and demonstrate that DGTalker surpasses current state-of-the-art methods in visual quality, motion accuracy, and controllability.
| null | null | null | null |
[] |
[] |
[] |
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] |
251
|
RALoc: Enhancing Outdoor LiDAR Localization via Rotation Awareness
|
[
"Yuyang Yang",
"Wen Li",
"Sheng Ao",
"Qingshan Xu",
"Shangshu Yu",
"Yu Guo",
"Yin Zhou",
"Siqi Shen",
"Cheng Wang"
] |
https://openaccess.thecvf.com/content/ICCV2025/html/Yang_RALoc_Enhancing_Outdoor_LiDAR_Localization_via_Rotation_Awareness_ICCV_2025_paper.html
|
https://openaccess.thecvf.com/content/ICCV2025/papers/Yang_RALoc_Enhancing_Outdoor_LiDAR_Localization_via_Rotation_Awareness_ICCV_2025_paper.pdf
|
https://openaccess.thecvf.com/content/ICCV2025/supplemental/Yang_RALoc_Enhancing_Outdoor_ICCV_2025_supplemental.pdf
|
@InProceedings{Yang_2025_ICCV,
author = {Yang, Yuyang and Li, Wen and Ao, Sheng and Xu, Qingshan and Yu, Shangshu and Guo, Yu and Zhou, Yin and Shen, Siqi and Wang, Cheng},
title = {RALoc: Enhancing Outdoor LiDAR Localization via Rotation Awareness},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2025},
pages = {3304-3313}
}
|
LiDAR localization is a fundamental task in autonomous driving and robotics. Scene Coordinate Regression (SCR) exhibits leading pose accuracy, achieving impressive results in learning-based localization. We observe that the real-world LiDAR scans captured from different viewpoints usually result in the catastrophic collapse of SCR. However, existing LiDAR localization methods have largely overlooked the issue of rotation sensitivity in SCR. In this paper, we present RALoc, an outdoor LiDAR localization method with rotation awareness to achieve accurate localization. The key to our approach is to design a Point Cloud Canonicalization module, which leverages a powerful equivariant key feature aggregation to transform the input LiDAR scan towards a consistent orientation, effectively eliminating the adverse effects of rotation. This proposed module has promising scalability and can be seamlessly integrated with the existing LiDAR localization network. Moreover, we propose the Bidirectional LiDAR Localization (BiLiLo) dataset as a benchmark to evaluate the performance of various methods in large outdoor scenes with significant rotation changes. Extensive experiments show that RALoc significantly improves localization performance in scenarios with large rotation changes, and also achieves competitive performance in the Oxford Radar RobotCar dataset. Our code and dataset will be released upon acceptance.
| null | null | null | null |
[] |
[] |
[] |
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] |
252
|
EDiT: Efficient Diffusion Transformers with Linear Compressed Attention
|
[
"Philipp Becker",
"Abhinav Mehrotra",
"Ruchika Chavhan",
"Malcolm Chadwick",
"Luca Morreale",
"Mehdi Noroozi",
"Alberto Gil C. P. Ramos",
"Sourav Bhattacharya"
] |
https://openaccess.thecvf.com/content/ICCV2025/html/Becker_EDiT_Efficient_Diffusion_Transformers_with_Linear_Compressed_Attention_ICCV_2025_paper.html
|
https://openaccess.thecvf.com/content/ICCV2025/papers/Becker_EDiT_Efficient_Diffusion_Transformers_with_Linear_Compressed_Attention_ICCV_2025_paper.pdf
|
https://openaccess.thecvf.com/content/ICCV2025/supplemental/Becker_EDiT_Efficient_Diffusion_ICCV_2025_supplemental.pdf
|
@InProceedings{Becker_2025_ICCV,
author = {Becker, Philipp and Mehrotra, Abhinav and Chavhan, Ruchika and Chadwick, Malcolm and Morreale, Luca and Noroozi, Mehdi and Gil C. P. Ramos, Alberto and Bhattacharya, Sourav},
title = {EDiT: Efficient Diffusion Transformers with Linear Compressed Attention},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2025},
pages = {19608-19616}
}
|
Diffusion Transformers (DiTs) have emerged as a leading architecture for text-to-image synthesis, producing high-quality and photorealistic images. However, the quadratic scaling properties of the attention in DiTs hinder image generation with higher resolution or devices with limited resources. This work introduces an efficient diffusion transformer (EDiT) to alleviate these efficiency bottlenecks in conventional DiTs and Multimodal DiTs (MM-DiTs). First, we present a novel linear compressed attention method that uses a multi-layer convolutional network to modulate queries with local information while keys and values are spatially aggregated. Second, we formulate a hybrid attention scheme for multi-modal inputs that combines linear attention for image-to-image interactions and standard scaled dot-product attention for interactions involving prompts. Merging these two approaches leads to an expressive, linear-time Multimodal Efficient Diffusion Transformer (MM-EDiT). We demonstrate the effectiveness of the EDiT and MM-EDiT architectures by integrating them into PixArt-Sigma (conventional DiT) and Stable Diffusion 3.5-Medium (MM-DiT), achieving up to 2.2x speedup with comparable image quality after distillation.
|
2503.16726
| null | null | null |
[] |
[] |
[] |
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] |
253
|
GenHancer: Imperfect Generative Models are Secretly Strong Vision-Centric Enhancers
|
[
"Shijie Ma",
"Yuying Ge",
"Teng Wang",
"Yuxin Guo",
"Yixiao Ge",
"Ying Shan"
] |
https://openaccess.thecvf.com/content/ICCV2025/html/Ma_GenHancer_Imperfect_Generative_Models_are_Secretly_Strong_Vision-Centric_Enhancers_ICCV_2025_paper.html
|
https://openaccess.thecvf.com/content/ICCV2025/papers/Ma_GenHancer_Imperfect_Generative_Models_are_Secretly_Strong_Vision-Centric_Enhancers_ICCV_2025_paper.pdf
|
https://openaccess.thecvf.com/content/ICCV2025/supplemental/Ma_GenHancer_Imperfect_Generative_ICCV_2025_supplemental.pdf
|
@InProceedings{Ma_2025_ICCV,
author = {Ma, Shijie and Ge, Yuying and Wang, Teng and Guo, Yuxin and Ge, Yixiao and Shan, Ying},
title = {GenHancer: Imperfect Generative Models are Secretly Strong Vision-Centric Enhancers},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2025},
pages = {24402-24412}
}
|
The synergy between generative and discriminative models receives growing attention. While discriminative Contrastive Language-Image Pre-Training (CLIP) excels in high-level semantics, it struggles with perceiving fine-grained visual details. Generally, to enhance representations, generative models take CLIP's visual features as conditions for reconstruction. However, the underlying principle remains underexplored. In this work, we empirically found that visually perfect generations are not always optimal for representation enhancement. The essence lies in effectively extracting fine-grained knowledge from generative models while mitigating irrelevant information. To explore critical factors, we delve into three aspects: (1) Conditioning mechanisms: We found that even a small number of local tokens can drastically reduce the difficulty of reconstruction, leading to collapsed training. We thus conclude that utilizing only global visual tokens as conditions is the most effective strategy. (2) Denoising configurations: We observed that end-to-end training introduces extraneous information. To address this, we propose a two-stage training strategy to prioritize learning useful visual knowledge. Additionally, we demonstrate that lightweight denoisers can yield remarkable improvements. (3) Generation paradigms: We explore both continuous and discrete denoisers with desirable outcomes, validating the versatility of our method. Through our in-depth explorations, we have finally arrived at an effective method, namely GenHancer, which consistently outperforms prior arts on the MMVP-VLM benchmark, e.g., 6.0% on OpenAICLIP. The enhanced CLIP can be further plugged into multimodal large language models for better vision-centric performance. All the models and codes are made publicly available.
|
2503.19480
|
ICCV 2025. Project released at:
https://mashijie1028.github.io/GenHancer/
| null |
https://mashijie1028.github.io/GenHancer/
|
[] |
[
"msj9817/GenHancer"
] |
[] |
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] |
254
|
LLaVA-3D: A Simple yet Effective Pathway to Empowering LMMs with 3D Capabilities
|
[
"Chenming Zhu",
"Tai Wang",
"Wenwei Zhang",
"Jiangmiao Pang",
"Xihui Liu"
] |
https://openaccess.thecvf.com/content/ICCV2025/html/Zhu_LLaVA-3D_A_Simple_yet_Effective_Pathway_to_Empowering_LMMs_with_ICCV_2025_paper.html
|
https://openaccess.thecvf.com/content/ICCV2025/papers/Zhu_LLaVA-3D_A_Simple_yet_Effective_Pathway_to_Empowering_LMMs_with_ICCV_2025_paper.pdf
|
https://openaccess.thecvf.com/content/ICCV2025/supplemental/Zhu_LLaVA-3D_A_Simple_ICCV_2025_supplemental.pdf
|
@InProceedings{Zhu_2025_ICCV,
author = {Zhu, Chenming and Wang, Tai and Zhang, Wenwei and Pang, Jiangmiao and Liu, Xihui},
title = {LLaVA-3D: A Simple yet Effective Pathway to Empowering LMMs with 3D Capabilities},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2025},
pages = {4295-4305}
}
|
Recent advancements in Large Multimodal Models (LMMs) have greatly enhanced their proficiency in 2D visual understanding tasks, enabling them to effectively process and understand images and videos. However, the development of LMMs with 3D scene understanding capabilities has been hindered by the lack of large-scale 3D vision-language datasets and powerful 3D encoders. In this paper, we introduce a simple yet effective framework called LLaVA-3D. Leveraging the strong 2D visual understanding priors from LLaVA, our LLaVA-3D efficiently adapts LLaVA for 3D scene understanding without compromising 2D understanding capabilities. To achieve this, we utilize the 3D position embeddings to enhance the 2D CLIP Patches with 3D spatial context information and construct 3D patches. By integrating the 3D position embeddings into 2D LMMs and employing joint 2D and 3D vision-language instruction tuning, we establish a unified architecture for both 2D visual understanding and 3D scene understanding. In contrast to previous 3D LMMs, LLaVA-3D supports decoding accurate 3D spatial perception outputs, e.g., 3D bounding boxes, directly from these 3D patches, without relying on the time-consuming off-the-shelf 3D segmentors. Experimental results show that LLaVA-3D converges 3.5x faster than existing 3D LMMs when trained on 3D vision-language datasets. Moreover, LLaVA-3D not only achieves state-of-the-art performance across various 3D tasks but also maintains comparable 2D visual understanding and vision-language conversation capabilities with LLaVA.
| null | null | null | null |
[] |
[] |
[] |
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] |
255
|
Understanding Museum Exhibits using Vision-Language Reasoning
|
[
"Ada-Astrid Balauca",
"Sanjana Garai",
"Stefan Balauca",
"Rasesh Udayakumar Shetty",
"Naitik Agrawal",
"Dhwanil Subhashbhai Shah",
"Yuqian Fu",
"Xi Wang",
"Kristina Toutanova",
"Danda Pani Paudel",
"Luc Van Gool"
] |
https://openaccess.thecvf.com/content/ICCV2025/html/Balauca_Understanding_Museum_Exhibits_using_Vision-Language_Reasoning_ICCV_2025_paper.html
|
https://openaccess.thecvf.com/content/ICCV2025/papers/Balauca_Understanding_Museum_Exhibits_using_Vision-Language_Reasoning_ICCV_2025_paper.pdf
|
https://openaccess.thecvf.com/content/ICCV2025/supplemental/Balauca_Understanding_Museum_Exhibits_ICCV_2025_supplemental.pdf
|
@InProceedings{Balauca_2025_ICCV,
author = {Balauca, Ada-Astrid and Garai, Sanjana and Balauca, Stefan and Shetty, Rasesh Udayakumar and Agrawal, Naitik and Shah, Dhwanil Subhashbhai and Fu, Yuqian and Wang, Xi and Toutanova, Kristina and Paudel, Danda Pani and Van Gool, Luc},
title = {Understanding Museum Exhibits using Vision-Language Reasoning},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2025},
pages = {2227-2238}
}
|
Museums serve as repositories of cultural heritage and historical artifacts from diverse epochs, civilizations, and regions, preserving well-documented collections that encapsulate vast knowledge, which, when systematically structured into large-scale datasets, can train specialized models. Visitors engage with exhibits through curiosity and questions, making expert domain-specific models essential for interactive query resolution and gaining historical insights. Understanding exhibits from images requires analyzing visual features and linking them to historical knowledge to derive meaningful correlations. We facilitate such reasoning by (a) collecting and curating a large-scale dataset of 65M images and 200M question-answer pairs for exhibits from all around the world; (b) training large vision-language models (VLMs) on the collected dataset; (c) benchmarking their ability on five visual question answering tasks, specifically designed to reflect real-world inquiries and challenges observed in museum settings.The complete dataset is labeled by museum experts, ensuring the quality and the practical significance of the labels. We train two VLMs from different categories: BLIP with vision-language aligned embeddings, but lacking the expressive power of large language models, and the LLaVA model, a powerful instruction-tuned LLM enriched with vision-language reasoning capabilities. Through extensive experiments, we find that while both model types effectively answer visually grounded questions, large vision-language models excel in queries requiring deeper historical context and reasoning. We further demonstrate the necessity of fine-tuning models on large-scale domain-specific datasets by showing that our fine-tuned models significantly outperform current SOTA VLMs in answering questions related to specific attributes, highlighting their limitations in handling complex, nuanced queries. Our dataset, benchmarks, and source code will be made publicly available.
|
2412.01370
| null | null | null |
[] |
[] |
[] |
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] |
256
|
MINERVA: Evaluating Complex Video Reasoning
|
[
"Arsha Nagrani",
"Sachit Menon",
"Ahmet Iscen",
"Shyamal Buch",
"Ramin Mehran",
"Nilpa Jha",
"Anja Hauth",
"Yukun Zhu",
"Carl Vondrick",
"Mikhail Sirotenko",
"Cordelia Schmid",
"Tobias Weyand"
] |
https://openaccess.thecvf.com/content/ICCV2025/html/Nagrani_MINERVA_Evaluating_Complex_Video_Reasoning_ICCV_2025_paper.html
|
https://openaccess.thecvf.com/content/ICCV2025/papers/Nagrani_MINERVA_Evaluating_Complex_Video_Reasoning_ICCV_2025_paper.pdf
|
https://openaccess.thecvf.com/content/ICCV2025/supplemental/Nagrani_MINERVA_Evaluating_Complex_ICCV_2025_supplemental.pdf
|
@InProceedings{Nagrani_2025_ICCV,
author = {Nagrani, Arsha and Menon, Sachit and Iscen, Ahmet and Buch, Shyamal and Mehran, Ramin and Jha, Nilpa and Hauth, Anja and Zhu, Yukun and Vondrick, Carl and Sirotenko, Mikhail and Schmid, Cordelia and Weyand, Tobias},
title = {MINERVA: Evaluating Complex Video Reasoning},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2025},
pages = {23968-23978}
}
|
Multimodal LLMs are turning their focus to video benchmarks, however most video benchmarks only provide outcome supervision, with no intermediate or interpretable reasoning steps. This makes it challenging to assess if models are truly able to combine perceptual and temporal information to reason about videos, or simply get the correct answer by chance or by exploiting linguistic biases. To remedy this, we provide a new video reasoning dataset called MINERVA for modern multimodal models. Each question in the dataset comes with 5 answer choices, as well as detailed, hand-crafted reasoning traces. Our dataset is multimodal, diverse in terms of video domain and length, and consists of complex multi-step questions. Extensive benchmarking shows that our dataset provides a challenge for frontier open-source and proprietary models. We perform fine-grained error analysis to identify common failure modes across various models, and create a taxonomy of reasoning errors. We use this to explore both human and LLM-as-a-judge methods for scoring video reasoning traces, and find that failure modes are primarily related to temporal localization, followed by visual perception errors, as opposed to logical or completeness errors. The dataset, along with questions, answer candidates and reasoning traces is publicly available under https://github.com/google-deepmind/neptune?tab=readme-ov-file#minerva https://github.com/google-deepmind/neptune?tab=readme-ov-file\#minerva.
|
2505.00681
| null |
https://github.com/google-deepmind/neptune
| null |
[] |
[] |
[] |
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] |
257
|
Active Membership Inference Test (aMINT): Enhancing Model Auditability with Multi-Task Learning.
|
[
"Daniel DeAlcala",
"Aythami Morales",
"Julian Fierrez",
"Gonzalo Mancera",
"Ruben Tolosana",
"Javier Ortega-Garcia"
] |
https://openaccess.thecvf.com/content/ICCV2025/html/DeAlcala_Active_Membership_Inference_Test_aMINT_Enhancing_Model_Auditability_with_Multi-Task_ICCV_2025_paper.html
|
https://openaccess.thecvf.com/content/ICCV2025/papers/DeAlcala_Active_Membership_Inference_Test_aMINT_Enhancing_Model_Auditability_with_Multi-Task_ICCV_2025_paper.pdf
| null |
@InProceedings{DeAlcala_2025_ICCV,
author = {DeAlcala, Daniel and Morales, Aythami and Fierrez, Julian and Mancera, Gonzalo and Tolosana, Ruben and Ortega-Garcia, Javier},
title = {Active Membership Inference Test (aMINT): Enhancing Model Auditability with Multi-Task Learning.},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2025},
pages = {647-656}
}
|
Active Membership Inference Test (aMINT) is a method designed to detect whether given data were used during the training of machine learning models. In Active MINT, we propose a novel multitask learning process that involves training simultaneously two models: the original or Audited Model, and a secondary model, referred to as the MINT Model, responsible for identifying the data used for training the Audited Model. This novel multi-task learning approach has been designed to incorporate the auditability of the model as an optimization objective during the training process of neural networks. The proposed approach incorporates intermediate activation maps as inputs to the MINT layers, which are trained to enhance the detection of training data. We present results using a wide range of neural networks, from lighter architectures such as MobileNet to more complex ones such as Vision Transformers, evaluated in 5 public benchmarks. Our proposed Active MINT achieves over 80% accuracy in detecting if given data was used for training, significantly outperforming previous approaches in the literature. Our aMINT and related methodological developments contribute to increasing transparency in AI models, facilitating stronger safeguards in AI deployments to achieve proper security, privacy, and copyright protection (Code available in https://github.com/DanieldeAlcala/Membership-Inference-Test.git)
|
2509.07879
| null | null | null |
[] |
[] |
[] |
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] |
258
|
One Perturbation is Enough: On Generating Universal Adversarial Perturbations against Vision-Language Pre-training Models
|
[
"Hao Fang",
"Jiawei Kong",
"Wenbo Yu",
"Bin Chen",
"Jiawei Li",
"Hao Wu",
"Shu-Tao Xia",
"Ke Xu"
] |
https://openaccess.thecvf.com/content/ICCV2025/html/Fang_One_Perturbation_is_Enough_On_Generating_Universal_Adversarial_Perturbations_against_ICCV_2025_paper.html
|
https://openaccess.thecvf.com/content/ICCV2025/papers/Fang_One_Perturbation_is_Enough_On_Generating_Universal_Adversarial_Perturbations_against_ICCV_2025_paper.pdf
|
https://openaccess.thecvf.com/content/ICCV2025/supplemental/Fang_One_Perturbation_is_ICCV_2025_supplemental.pdf
|
@InProceedings{Fang_2025_ICCV,
author = {Fang, Hao and Kong, Jiawei and Yu, Wenbo and Chen, Bin and Li, Jiawei and Wu, Hao and Xia, Shu-Tao and Xu, Ke},
title = {One Perturbation is Enough: On Generating Universal Adversarial Perturbations against Vision-Language Pre-training Models},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2025},
pages = {4090-4100}
}
|
Vision-Language Pre-training (VLP) models have exhibited unprecedented capability in many applications by taking full advantage of the learned multimodal alignment. However, previous studies have shown they are vulnerable to maliciously crafted adversarial samples. Despite recent success, these attacks are generally instance-specific and require generating perturbations for each input sample. In this paper, we reveal that VLP models are also susceptible to the instance-agnostic universal adversarial perturbation (UAP). Specifically, we design a novel Contrastive-training Perturbation Generator with Cross-modal conditions (C-PGC). In light that the pivotal multimodal alignment in VLP models is achieved via contrastive learning, we devise to turn this powerful weapon against VLP models themselves. I.e., we employ a malicious version of contrastive learning to train the proposed generator using our carefully crafted positive and negative image-text pairs. Once training is complete, the generator is able to produce universal perturbations that can essentially destroy the established alignment relationship in VLP models. Besides, C-PGC fully utilizes the characteristics of Vision-and-Language (V+L) scenarios by incorporating both unimodal and cross-modal information as effective guidance. Extensive experiments show that C-PGC successfully forces adversarial samples to move away from their original area in the VLP model's feature space, thus fundamentally enhancing attack performance across various victim models and V+L tasks.
|
2406.05491
| null |
https://github.com/ffhibnese/CPGC_VLP_Universal_Attacks
| null |
[] |
[] |
[] |
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] |
259
|
When Lighting Deceives: Exposing Vision-Language Models' Illumination Vulnerability Through Illumination Transformation Attack
|
[
"Hanqing Liu",
"Shouwei Ruan",
"Yao Huang",
"Shiji Zhao",
"Xingxing Wei"
] |
https://openaccess.thecvf.com/content/ICCV2025/html/Liu_When_Lighting_Deceives_Exposing_Vision-Language_Models_Illumination_Vulnerability_Through_Illumination_ICCV_2025_paper.html
|
https://openaccess.thecvf.com/content/ICCV2025/papers/Liu_When_Lighting_Deceives_Exposing_Vision-Language_Models_Illumination_Vulnerability_Through_Illumination_ICCV_2025_paper.pdf
|
https://openaccess.thecvf.com/content/ICCV2025/supplemental/Liu_When_Lighting_Deceives_ICCV_2025_supplemental.pdf
|
@InProceedings{Liu_2025_ICCV,
author = {Liu, Hanqing and Ruan, Shouwei and Huang, Yao and Zhao, Shiji and Wei, Xingxing},
title = {When Lighting Deceives: Exposing Vision-Language Models' Illumination Vulnerability Through Illumination Transformation Attack},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2025},
pages = {10485-10495}
}
|
Vision-Language Models (VLMs) have achieved remarkable success in various tasks, yet their robustness to real-world illumination variations remains largely unexplored. To bridge this gap, we propose Illumination Transformation Attack (ITA), the first framework to systematically assess VLMs' robustness against illumination changes. However, there still exist two key challenges: (1) how to model global illumination with fine-grained control to achieve diverse lighting conditions and (2) how to ensure adversarial effectiveness while maintaining naturalness. To address the first challenge, we innovatively decompose global illumination into multiple parameterized point light sources based on the illumination rendering equation. This design enables us to model more diverse lighting variations that previous methods could not capture. Then, by integrating these parameterized lighting variations with physics-based lighting reconstruction techniques, we could precisely render such light interactions in the original scenes, finally meeting the goal of fine-grained lighting control. For the second challenge, by controlling illumination through the lighting reconstrution model's latent space rather than direct pixel manipulation, we inherently preserve physical lighting priors. Furthermore, to prevent potential reconstruction artifacts, we design additional perceptual constraints for maintaining visual consistency with original images and diversity constraints for avoiding light source convergence. Extensive experiments demonstrate that our ITA could significantly reduce the performance of advanced VLMs, e.g., LLaVA-1.6, while possessing competitive naturalness, exposing VLMS' critical illuminiation vulnerabilities.
|
2503.06903
| null | null | null |
[] |
[] |
[] |
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] |
260
|
Exploring View Consistency for Scene-Adaptive Low-Light Light Field Image Enhancement
|
[
"Shuo Zhang",
"Chen Gao",
"Youfang Lin"
] |
https://openaccess.thecvf.com/content/ICCV2025/html/Zhang_Exploring_View_Consistency_for_Scene-Adaptive_Low-Light_Light_Field_Image_Enhancement_ICCV_2025_paper.html
|
https://openaccess.thecvf.com/content/ICCV2025/papers/Zhang_Exploring_View_Consistency_for_Scene-Adaptive_Low-Light_Light_Field_Image_Enhancement_ICCV_2025_paper.pdf
|
https://openaccess.thecvf.com/content/ICCV2025/supplemental/Zhang_Exploring_View_Consistency_ICCV_2025_supplemental.pdf
|
@InProceedings{Zhang_2025_ICCV,
author = {Zhang, Shuo and Gao, Chen and Lin, Youfang},
title = {Exploring View Consistency for Scene-Adaptive Low-Light Light Field Image Enhancement},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2025},
pages = {7340-7349}
}
|
Light Field (LF) images captured under low illumination conditions typically exhibit low quality. Recent learning-based methods for low-light LF enhancement are generally tailored to specific illumination inputs, limiting their performance in real-world scenes. Moreover, how to maintain the inherent view-consistency in the enhanced images also remain as a difficult problem. In this paper, we propose to explore the view consistency for scene-adaptive low-light LF enhancement. We first analyze the view consistency for LF illumination maps and design a self-supervised view-consistent loss to keep the consistency between the illumination maps of different views in LFs. To enhance the model's perception of illumination, we combine both global and local information to estimate the illumination map, which is easily plugged into other models. Subsequently, we use the illumination maps to light up the low-light LF images and restore the corruption to produce the final enhanced image. Extensive experiments demonstrate that our View-Consistenct Network (VCNet) outperforms state-of-the-art methods on real-world low-light LF datasets in both fixed lighting conditions and dynamic lighting conditions. Our proposed illumination adjustment is also demonstrated that can comprehensively improve the performance of existing methods in terms of both image quality and view consistency.
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[] |
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] |
261
|
DIA: The Adversarial Exposure of Deterministic Inversion in Diffusion Models
|
[
"Seunghoo Hong",
"Geonho Son",
"Juhun Lee",
"Simon S. Woo"
] |
https://openaccess.thecvf.com/content/ICCV2025/html/Hong_DIA_The_Adversarial_Exposure_of_Deterministic_Inversion_in_Diffusion_Models_ICCV_2025_paper.html
|
https://openaccess.thecvf.com/content/ICCV2025/papers/Hong_DIA_The_Adversarial_Exposure_of_Deterministic_Inversion_in_Diffusion_Models_ICCV_2025_paper.pdf
|
https://openaccess.thecvf.com/content/ICCV2025/supplemental/Hong_DIA_The_Adversarial_ICCV_2025_supplemental.pdf
|
@InProceedings{Hong_2025_ICCV,
author = {Hong, Seunghoo and Son, Geonho and Lee, Juhun and Woo, Simon S.},
title = {DIA: The Adversarial Exposure of Deterministic Inversion in Diffusion Models},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2025},
pages = {17994-18003}
}
|
Diffusion models have shown to be strong representation learners, showcasing state-of-the-art performance across multiple domains. Aside from accelerated sampling, DDIM also enables the inversion of real images back to their latent codes. A direct inheriting application of this inversion operation is real image editing, where the inversion yields latent trajectories to be utilized during the synthesis of the edited image. Unfortunately, this practical tool has enabled malicious users to freely synthesize misinformative or deepfake contents with greater ease, which promotes the spread of unethical and abusive, as well as privacy-, and copyright-infringing contents. While defensive algorithms such as AdvDM and Photoguard have been shown to disrupt the diffusion process on these images, the misalignment between their objectives and the iterative denoising trajectory at test time results in weak disruptive performance. In this work, we present the DDIM Inversion Attack (DIA) that attacks the integrated DDIM trajectory path. Our results support the effective disruption, surpassing previous defensive methods across various editing methods. We believe that our frameworks and results can provide practical defense methods against the malicious use of AI for both the industry and the research community. Our code is available here: https://anonymous.4open.science/r/DIA-13419/.
|
2510.00778
| null | null | null |
[] |
[] |
[] |
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] |
262
|
Rectifying Magnitude Neglect in Linear Attention
|
[
"Qihang Fan",
"Huaibo Huang",
"Yuang Ai",
"Ran He"
] |
https://openaccess.thecvf.com/content/ICCV2025/html/Fan_Rectifying_Magnitude_Neglect_in_Linear_Attention_ICCV_2025_paper.html
|
https://openaccess.thecvf.com/content/ICCV2025/papers/Fan_Rectifying_Magnitude_Neglect_in_Linear_Attention_ICCV_2025_paper.pdf
|
https://openaccess.thecvf.com/content/ICCV2025/supplemental/Fan_Rectifying_Magnitude_Neglect_ICCV_2025_supplemental.pdf
|
@InProceedings{Fan_2025_ICCV,
author = {Fan, Qihang and Huang, Huaibo and Ai, Yuang and He, Ran},
title = {Rectifying Magnitude Neglect in Linear Attention},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2025},
pages = {21505-21514}
}
|
As the core operator of Transformers, Softmax Attention exhibits excellent global modeling capabilities. However, its quadratic complexity limits its applicability to vision tasks. In contrast, Linear Attention shares a similar formulation with Softmax Attention while achieving linear complexity, enabling efficient global information modeling. Nevertheless, Linear Attention suffers from a significant performance degradation compared to standard Softmax Attention. In this paper, we analyze the underlying causes of this issue based on the formulation of Linear Attention. We find that, unlike Softmax Attention, Linear Attention entirely disregards the magnitude information of the Query(Q or \phi(Q)). The absence of magnitude information prevents the attention score distribution from dynamically adapting as the Query scales. As a result, despite its structural similarity to Softmax Attention, Linear Attention exhibits a significantly different attention score distribution. Based on this observation, we propose **Magnitude-Aware Linear Attention** (MALA), which modifies the computation of Linear Attention to fully incorporate the Query's magnitude. This adjustment allows MALA to generate an attention score distribution that closely resembles Softmax Attention while exhibiting a more well-balanced structure. As a result, MALA surpasses Softmax Attention in performance while maintaining only linear complexity. We build Magnitude-Aware Vision Transformer (MAViT) based on MALA, achieving **84.7%** accuracy on ImageNet-1K with only **27M** parameters and **4.6G** flops, without using any additional data or labels. It also exhibits excellent inference efficiency. This result highlights the strong potential of MALA.
|
2507.00698
|
Accepted by ICCV2025, highlight paper
|
https://github.com/qhfan/MALA
| null |
[] |
[] |
[] |
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] |
263
|
GEMeX: A Large-Scale, Groundable, and Explainable Medical VQA Benchmark for Chest X-ray Diagnosis
|
[
"Bo Liu",
"Ke Zou",
"Li-Ming Zhan",
"Zexin Lu",
"Xiaoyu Dong",
"Yidi Chen",
"Chengqiang Xie",
"Jiannong Cao",
"Xiao-Ming Wu",
"Huazhu Fu"
] |
https://openaccess.thecvf.com/content/ICCV2025/html/Liu_GEMeX_A_Large-Scale_Groundable_and_Explainable_Medical_VQA_Benchmark_for_ICCV_2025_paper.html
|
https://openaccess.thecvf.com/content/ICCV2025/papers/Liu_GEMeX_A_Large-Scale_Groundable_and_Explainable_Medical_VQA_Benchmark_for_ICCV_2025_paper.pdf
|
https://openaccess.thecvf.com/content/ICCV2025/supplemental/Liu_GEMeX_A_Large-Scale_ICCV_2025_supplemental.pdf
|
@InProceedings{Liu_2025_ICCV,
author = {Liu, Bo and Zou, Ke and Zhan, Li-Ming and Lu, Zexin and Dong, Xiaoyu and Chen, Yidi and Xie, Chengqiang and Cao, Jiannong and Wu, Xiao-Ming and Fu, Huazhu},
title = {GEMeX: A Large-Scale, Groundable, and Explainable Medical VQA Benchmark for Chest X-ray Diagnosis},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2025},
pages = {21310-21320}
}
|
Medical Visual Question Answering (Med-VQA) combines computer vision and natural language processing to automatically answer clinical inquiries about medical images. However, current Med-VQA datasets exhibit two significant limitations: (1) they often lack visual and textual explanations for answers, hindering comprehension for patients and junior doctors; (2) they typically offer a narrow range of question formats, inadequately reflecting the diverse requirements in practical scenarios. These limitations pose significant challenges to the development of a reliable and user-friendly Med-VQA system. To address these challenges, we introduce a large-scale, Groundable, and Explainable Medical VQA benchmark for chest X-ray diagnosis (GEMeX), featuring several innovative components: (1) a multi-modal explainability mechanism that offers detailed visual and textual explanations for each question-answer pair, thereby enhancing answer comprehensibility; (2) four question types--open-ended, closed-ended, single-choice, and multiple-choice--to better reflect practical needs. With 151,025 images and 1,605,575 questions, GEMeX is the currently largest chest X-ray VQA dataset. Evaluation of 12 representative large vision language models (LVLMs) on GEMeX reveals suboptimal performance, underscoring the dataset's complexity. Meanwhile, we propose a strong model by fine-tuning an existing LVLM on the GEMeX training set. The substantial performance improvement showcases the dataset's effectiveness. The benchmark is available at www.med-vqa.com/GEMeX.
| null | null | null |
https://www.med-vqa.com/GEMeX/
|
[] |
[] |
[] |
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] |
264
|
Learning to See Inside Opaque Liquid Containers using Speckle Vibrometry
|
[
"Matan Kichler",
"Shai Bagon",
"Mark Sheinin"
] |
https://openaccess.thecvf.com/content/ICCV2025/html/Kichler_Learning_to_See_Inside_Opaque_Liquid_Containers_using_Speckle_Vibrometry_ICCV_2025_paper.html
|
https://openaccess.thecvf.com/content/ICCV2025/papers/Kichler_Learning_to_See_Inside_Opaque_Liquid_Containers_using_Speckle_Vibrometry_ICCV_2025_paper.pdf
|
https://openaccess.thecvf.com/content/ICCV2025/supplemental/Kichler_Learning_to_See_ICCV_2025_supplemental.pdf
|
@InProceedings{Kichler_2025_ICCV,
author = {Kichler, Matan and Bagon, Shai and Sheinin, Mark},
title = {Learning to See Inside Opaque Liquid Containers using Speckle Vibrometry},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2025},
pages = {9466-9476}
}
|
Computer vision seeks to infer a wide range of information about objects and events. However, vision systems based on conventional imaging are limited to extracting information only from the visible surfaces of scene objects. For instance, a vision system can detect and identify a Coke can in the scene, but it cannot determine whether the can is full or empty. In this paper, we aim to expand the scope of computer vision to include the novel task of inferring the hidden liquid levels of opaque containers by sensing the tiny vibrations on their surfaces. Our method provides a first-of-a-kind way to inspect the fill level of multiple sealed containers remotely, at once, without needing physical manipulation and manual weighing. First, we propose a novel speckle-based vibration sensing system for simultaneously capturing scene vibrations on a 2D grid of points. We use our system to efficiently and remotely capture a dataset of vibration responses for a variety of everyday liquid containers. Then, we develop a transformer-based approach for analyzing the captured vibrations and classifying the container type and its hidden liquid level at the time of measurement. Our architecture is invariant to the vibration source, yielding correct liquid level estimates for controlled and ambient scene sound sources. Moreover, our model generalizes to unseen container instances within known classes (e.g., training on five Coke cans of a six-pack, testing on a sixth) and fluid levels. We demonstrate our method by recovering liquid levels from various everyday containers.
|
2507.20757
|
ICCV 2025
| null | null |
[] |
[] |
[] |
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] |
265
|
HouseTour: A Virtual Real Estate A(I)gent
|
[
"Ata Çelen",
"Marc Pollefeys",
"Daniel Barath",
"Iro Armeni"
] |
https://openaccess.thecvf.com/content/ICCV2025/html/Celen_HouseTour_A_Virtual_Real_Estate_AIgent_ICCV_2025_paper.html
|
https://openaccess.thecvf.com/content/ICCV2025/papers/Celen_HouseTour_A_Virtual_Real_Estate_AIgent_ICCV_2025_paper.pdf
|
https://openaccess.thecvf.com/content/ICCV2025/supplemental/Celen_HouseTour_A_Virtual_ICCV_2025_supplemental.zip
|
@InProceedings{Celen_2025_ICCV,
author = {\c{C}elen, Ata and Pollefeys, Marc and Barath, Daniel and Armeni, Iro},
title = {HouseTour: A Virtual Real Estate A(I)gent},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2025},
pages = {17761-17771}
}
|
We introduce HouseTour, a method for spatially-aware 3D camera trajectory and natural language summary generation from a collection of images depicting an existing 3D space. Unlike existing vision-language models (VLMs), which struggle with geometric reasoning, our approach generates smooth video trajectories via a diffusion process constrained by known camera poses and integrates this information into the VLM for 3D-grounded descriptions. We synthesize the final video using 3D Gaussian splatting to render novel views along the trajectory. To support this task, we present the HouseTour dataset, which includes over 1,200 house-tour videos with camera poses, 3D reconstructions, and real estate descriptions. Experiments demonstrate that incorporating 3D camera trajectories into the text generation process improves performance over methods handling each task independently. We evaluate both individual and end-to-end performance, introducing a new joint metric. Our work enables automated, professional-quality video creation for real estate and touristic applications without requiring specialized expertise or equipment.
| null | null | null | null |
[] |
[] |
[] |
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] |
266
|
Debiased Teacher for Day-to-Night Domain Adaptive Object Detection
|
[
"Yiming Cui",
"Liang Li",
"Haibing Yin",
"Yuhan Gao",
"Yaoqi Sun",
"Chenggang Yan"
] |
https://openaccess.thecvf.com/content/ICCV2025/html/Cui_Debiased_Teacher_for_Day-to-Night_Domain_Adaptive_Object_Detection_ICCV_2025_paper.html
|
https://openaccess.thecvf.com/content/ICCV2025/papers/Cui_Debiased_Teacher_for_Day-to-Night_Domain_Adaptive_Object_Detection_ICCV_2025_paper.pdf
|
https://openaccess.thecvf.com/content/ICCV2025/supplemental/Cui_Debiased_Teacher_for_ICCV_2025_supplemental.pdf
|
@InProceedings{Cui_2025_ICCV,
author = {Cui, Yiming and Li, Liang and Yin, Haibing and Gao, Yuhan and Sun, Yaoqi and Yan, Chenggang},
title = {Debiased Teacher for Day-to-Night Domain Adaptive Object Detection},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2025},
pages = {2577-2587}
}
|
Day-to-Night Domain Adaptive Object Detection (DN-DAOD) is a significant challenge due to the low visibility and signal-to-noise ratio at night. Although recent self-training approaches achieve promising results, they fail to address three critical biases: distribution bias, training bias, and confirmation bias. Therefore, we propose a Debiased Teacher to address the above biases from three aspects: domain transforming, representation compensating, and pseudo label calibrating. Concretely, the day-to-night domain transforming module (DNDT) leverages physical priors to model some key day-night domain differences, thus transforming daytime images into night-like images. Then, the cross-domain representation compensating module (CDRC) selectively mixes objects from nighttime and night-like images to compensate for the model's general representation of nighttime objects. Further, to correct confirmation bias caused by learning from inaccurate pseudo labels, the pseudo label confirmation calibrating module (ConCal) is designed to obtain accurate pseudo labels for better nighttime knowledge learning. Experimental results on three benchmarks demonstrate that our method outperforms current SOTA methods by a large margin.
| null | null | null | null |
[] |
[] |
[] |
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] |
267
|
Frequency-Aligned Knowledge Distillation for Lightweight Spatiotemporal Forecasting
|
[
"Yuqi Li",
"Chuanguang Yang",
"Hansheng Zeng",
"Zeyu Dong",
"Zhulin An",
"Yongjun Xu",
"Yingli Tian",
"Hao Wu"
] |
https://openaccess.thecvf.com/content/ICCV2025/html/Li_Frequency-Aligned_Knowledge_Distillation_for_Lightweight_Spatiotemporal_Forecasting_ICCV_2025_paper.html
|
https://openaccess.thecvf.com/content/ICCV2025/papers/Li_Frequency-Aligned_Knowledge_Distillation_for_Lightweight_Spatiotemporal_Forecasting_ICCV_2025_paper.pdf
| null |
@InProceedings{Li_2025_ICCV,
author = {Li, Yuqi and Yang, Chuanguang and Zeng, Hansheng and Dong, Zeyu and An, Zhulin and Xu, Yongjun and Tian, Yingli and Wu, Hao},
title = {Frequency-Aligned Knowledge Distillation for Lightweight Spatiotemporal Forecasting},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2025},
pages = {7262-7272}
}
|
Spatiotemporal forecasting tasks, such as traffic flow, combustion dynamics, and weather forecasting, often require complex models that suffer from low training efficiency and high memory consumption. This paper proposes a lightweight framework, Spectral Decoupled Knowledge Distillation, which transfers the multi-scale spatiotemporal representations from a complex teacher model to a more efficient lightweight student network. The teacher model follows an encoder-latent evolution-decoder architecture, where its latent evolution module decouples high-frequency details (e.g., instant traffic fluctuations) and low-frequency trends (e.g. long-term weather evolution) using convolution (local high-frequency extractor) and Transformer (global low-frequency modeler). However, the multi-layer convolution and deconvolution structures result in slow training and high memory usage. To address these issues, we propose a frequency-aligned knowledge distillation strategy, which extracts multi-scale spectral features from the teacher's latent space, including high and low frequency components, to guide the lightweight student model (e.g., ResNet, U-Net) in capturing both local fine-grained variations and global evolution patterns. Experiments show that the student model achieves over 95% of the teacher's forecasting accuracy while using only 20%-30% of its memory, with training speed improved by more than 50%. Our theoretical analysis reveals that the frequency-domain decoupling enables the student model to capture long-range dependencies without the need for complex structures. The frequency-aligned distillation mechanism further mitigates the inherent bias of lightweight models in cross-scale spatiotemporal dynamics modeling. This framework offers an effective and general solution for high-accuracy spatiotemporal forecasting in resource-constrained scenarios.
|
2507.02939
|
Accepted by ICCV-2025, 11 pages
|
https://github.com/itsnotacie/SDKD
| null |
[] |
[] |
[] |
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] |
268
|
Colors See Colors Ignore: Clothes Changing ReID with Color Disentanglement
|
[
"Priyank Pathak",
"Yogesh S. Rawat"
] |
https://openaccess.thecvf.com/content/ICCV2025/html/Pathak_Colors_See_Colors_Ignore_Clothes_Changing_ReID_with_Color_Disentanglement_ICCV_2025_paper.html
|
https://openaccess.thecvf.com/content/ICCV2025/papers/Pathak_Colors_See_Colors_Ignore_Clothes_Changing_ReID_with_Color_Disentanglement_ICCV_2025_paper.pdf
|
https://openaccess.thecvf.com/content/ICCV2025/supplemental/Pathak_Colors_See_Colors_ICCV_2025_supplemental.pdf
|
@InProceedings{Pathak_2025_ICCV,
author = {Pathak, Priyank and Rawat, Yogesh S.},
title = {Colors See Colors Ignore: Clothes Changing ReID with Color Disentanglement},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2025},
pages = {16797-16807}
}
|
Clothes-Changing Re-Identification (CC-ReID) aims to recognize individuals across different locations and times, irrespective of clothing. Existing methods often rely on additional models or annotations to learn robust, clothing-invariant features, making them resource-intensive. In contrast, we explore the use of color --specifically foreground and background colors--as a lightweight, annotation-free proxy for mitigating appearance bias in ReID models. We propose Colors See, Colors Ignore (CSCI), an RGB-only method that leverages color information directly from raw images or video frames. CSCI efficiently captures color-related appearance bias ('Color See') while disentangling it from identity-relevant ReID features ('Color Ignore'). To achieve this, we introduce S2A self-attention, a novel self-attention to prevent information leak between color and identity cues within the feature space. Our analysis shows a strong correspondence between learned color embeddings and clothing attributes, validating color as an effective proxy when explicit clothing labels are unavailable. We demonstrate the effectiveness of CSCI on both image and video ReID with extensive experiments on four CC-ReID datasets. We improve the baseline by Top-1 2.9% on LTCC and 5.0% on PRCC for image-based ReID, and 1.0% on CCVID and 2.5% on MeVID for video-based ReID without relying on additional supervision. Our results highlight the potential of color as a cost-effective solution for addressing appearance bias in CC-ReID.
|
2507.07230
|
ICCV'25 paper
|
https://github.com/ppriyank/ICCV-CSCI-Person-ReID
|
https://ucf-crcv.github.io/ReID/CSCI/
|
[] |
[
"ppriyank/CSCI-CC-ReID"
] |
[] |
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] |
269
|
Image as an IMU: Estimating Camera Motion from a Single Motion-Blurred Image
|
[
"Jerred Chen",
"Ronald Clark"
] |
https://openaccess.thecvf.com/content/ICCV2025/html/Chen_Image_as_an_IMU_Estimating_Camera_Motion_from_a_Single_ICCV_2025_paper.html
|
https://openaccess.thecvf.com/content/ICCV2025/papers/Chen_Image_as_an_IMU_Estimating_Camera_Motion_from_a_Single_ICCV_2025_paper.pdf
|
https://openaccess.thecvf.com/content/ICCV2025/supplemental/Chen_Image_as_an_ICCV_2025_supplemental.zip
|
@InProceedings{Chen_2025_ICCV,
author = {Chen, Jerred and Clark, Ronald},
title = {Image as an IMU: Estimating Camera Motion from a Single Motion-Blurred Image},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2025},
pages = {90-99}
}
|
In many robotics and VR/AR applications, fast camera motions lead to a high level of motion blur, causing existing camera pose estimation methods to fail. In this work, we propose a novel framework that leverages motion blur as a rich cue for motion estimation rather than treating it as an unwanted artifact. Our approach works by predicting a dense motion flow field and a monocular depth map directly from a single motion-blurred image. We then recover the instantaneous camera velocity by solving a linear least squares problem under the small motion assumption. In essence, our method produces an IMU-like measurement that robustly captures fast and aggressive camera movements. To train our model, we construct a large-scale dataset with realistic synthetic motion blur derived from ScanNet++v2 and further refine our model by training end-to-end on real data using our fully differentiable pipeline. Extensive evaluations on real-world benchmarks demonstrate that our method achieves state-of-the-art angular and translational velocity estimates, outperforming current methods like MASt3R and COLMAP.
|
2503.17358
|
Project page: https://jerredchen.github.io/image-as-imu/
| null | null |
[] |
[] |
[] |
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] |
270
|
AnyI2V: Animating Any Conditional Image with Motion Control
|
[
"Ziye Li",
"Hao Luo",
"Xincheng Shuai",
"Henghui Ding"
] |
https://openaccess.thecvf.com/content/ICCV2025/html/Li_AnyI2V_Animating_Any_Conditional_Image_with_Motion_Control_ICCV_2025_paper.html
|
https://openaccess.thecvf.com/content/ICCV2025/papers/Li_AnyI2V_Animating_Any_Conditional_Image_with_Motion_Control_ICCV_2025_paper.pdf
|
https://openaccess.thecvf.com/content/ICCV2025/supplemental/Li_AnyI2V_Animating_Any_ICCV_2025_supplemental.pdf
|
@InProceedings{Li_2025_ICCV,
author = {Li, Ziye and Luo, Hao and Shuai, Xincheng and Ding, Henghui},
title = {AnyI2V: Animating Any Conditional Image with Motion Control},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2025},
pages = {17302-17311}
}
|
Recent advancements in video generation, particularly in diffusion models, have driven notable progress in text-to-video (T2V) and image-to-video (I2V) synthesis. However, challenges remain in effectively integrating dynamic motion signals and flexible spatial constraints. Existing T2V methods typically rely on text prompts, which inherently lack precise control over the spatial layout of generated content. In contrast, I2V methods are limited by their dependence on real images, which restricts the editability of the synthesized content. Although some methods incorporate ControlNet to introduce image-based conditioning, they often lack explicit motion control and require computationally expensive training. To address these limitations, we propose AnyI2V, a training-free framework that animates any conditional images with user-defined motion trajectories. AnyI2V supports a broader range of modalities as the conditional image, including data types such as meshes and point clouds that are not supported by ControlNet, enabling more flexible and versatile video generation. Additionally, it supports mixed conditional inputs and enables style transfer and editing via LoRA and text prompts. Extensive experiments demonstrate that the proposed AnyI2V achieves superior performance and provides a new perspective in spatial- and motion-controlled video generation.
|
2507.02857
|
ICCV 2025, Project Page: https://henghuiding.com/AnyI2V/
| null |
https://henghuiding.com/AnyI2V/
|
[] |
[] |
[] |
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] |
271
|
GENMO: A GENeralist Model for Human MOtion
|
[
"Jiefeng Li",
"Jinkun Cao",
"Haotian Zhang",
"Davis Rempe",
"Jan Kautz",
"Umar Iqbal",
"Ye Yuan"
] |
https://openaccess.thecvf.com/content/ICCV2025/html/Li_GENMO_A_GENeralist_Model_for_Human_MOtion_ICCV_2025_paper.html
|
https://openaccess.thecvf.com/content/ICCV2025/papers/Li_GENMO_A_GENeralist_Model_for_Human_MOtion_ICCV_2025_paper.pdf
|
https://openaccess.thecvf.com/content/ICCV2025/supplemental/Li_GENMO_A_GENeralist_ICCV_2025_supplemental.pdf
|
@InProceedings{Li_2025_ICCV,
author = {Li, Jiefeng and Cao, Jinkun and Zhang, Haotian and Rempe, Davis and Kautz, Jan and Iqbal, Umar and Yuan, Ye},
title = {GENMO: A GENeralist Model for Human MOtion},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2025},
pages = {11766-11776}
}
|
Human motion modeling traditionally separates motion generation and estimation into distinct tasks with specialized models. Motion generation models focus on creating diverse, realistic motions from inputs like text, audio, or keyframes, while motion estimation models aim to reconstruct accurate motion trajectories from observations like videos. Despite sharing underlying representations of temporal dynamics and kinematics, this separation limits knowledge transfer between tasks and requires maintaining separate models. We present GENMO, a unified Generalist Model for Human Motion that bridges motion estimation and generation in a single framework. Our key insight is to reformulate motion estimation as constrained motion generation, where the output motion must precisely satisfy observed conditioning signals. Leveraging the synergy between regression and diffusion, GENMO achieves accurate global motion estimation while enabling diverse motion generation. We also introduce an estimation-guided training objective that exploits in-the-wild videos with 2D annotations and text descriptions to enhance generative diversity. Furthermore, our novel architecture handles variable-length motions and mixed multimodal conditions (text, audio, video) at different time intervals, offering flexible control. This unified approach creates synergistic benefits: generative priors improve estimated motions under challenging conditions like occlusions, while diverse video data enhances generation capabilities. Extensive experiments demonstrate GENMO's effectiveness as a generalist framework that successfully handles multiple human motion tasks within a single model.
|
2505.01425
|
Project page: https://research.nvidia.com/labs/dair/genmo/
| null | null |
[] |
[] |
[] |
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] |
272
|
M-Net: MRI Brain Tumor Sequential Segmentation Network via Mesh-Cast
|
[
"Jiacheng Lu",
"Hui Ding",
"Shiyu Zhang",
"Guoping Huo"
] |
https://openaccess.thecvf.com/content/ICCV2025/html/Lu_M-Net_MRI_Brain_Tumor_Sequential_Segmentation_Network_via_Mesh-Cast_ICCV_2025_paper.html
|
https://openaccess.thecvf.com/content/ICCV2025/papers/Lu_M-Net_MRI_Brain_Tumor_Sequential_Segmentation_Network_via_Mesh-Cast_ICCV_2025_paper.pdf
| null |
@InProceedings{Lu_2025_ICCV,
author = {Lu, Jiacheng and Ding, Hui and Zhang, Shiyu and Huo, Guoping},
title = {M-Net: MRI Brain Tumor Sequential Segmentation Network via Mesh-Cast},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2025},
pages = {20116-20125}
}
|
MRI tumor segmentation remains a critical challenge in medical imaging, where volumetric analysis faces unique computational demands due to the complexity of 3D data. The spatially sequential arrangement of adjacent MRI slices provides valuable information that enhances segmentation continuity and accuracy, yet this characteristic remains underutilized in many existing models. The spatial correlations between adjacent MRI slices can be regarded as "temporal-like" data, similar to frame sequences in video segmentation tasks. To bridge this gap, we propose M-Net, a flexible framework specifically designed for sequential image segmentation. M-Net introduces the novel Mesh-Cast mechanism, which seamlessly integrates arbitrary sequential models into the processing of both channel and temporal information, thereby systematically capturing the inherent "temporal-like" spatial correlations between MRI slices and ensuring consistent segmentation across sequences. Additionally, we define an MRI sequential input pattern and design a Two-Phase Sequential (TPS) training strategy, which first focuses on learning common patterns across sequences before refining slice-specific feature extraction. This approach leverages temporal modeling techniques to preserve volumetric contextual information while avoiding the high computational cost of full 3D convolutions, thereby enhancing the generalizability and robustness of M-Net in sequential segmentation tasks. Experiments on the BraTS2019 and BraTS2023 datasets demonstrate that M-Net outperforms existing methods across all key metrics, establishing itself as a robust solution for temporally-aware MRI tumor segmentation.
| null | null | null | null |
[] |
[] |
[] |
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] |
273
|
Weakly Supervised Visible-Infrared Person Re-Identification via Heterogeneous Expert Collaborative Consistency Learning
|
[
"Yafei Zhang",
"Lingqi Kong",
"Huafeng Li",
"Jie Wen"
] |
https://openaccess.thecvf.com/content/ICCV2025/html/Zhang_Weakly_Supervised_Visible-Infrared_Person_Re-Identification_via_Heterogeneous_Expert_Collaborative_Consistency_ICCV_2025_paper.html
|
https://openaccess.thecvf.com/content/ICCV2025/papers/Zhang_Weakly_Supervised_Visible-Infrared_Person_Re-Identification_via_Heterogeneous_Expert_Collaborative_Consistency_ICCV_2025_paper.pdf
|
https://openaccess.thecvf.com/content/ICCV2025/supplemental/Zhang_Weakly_Supervised_Visible-Infrared_ICCV_2025_supplemental.pdf
|
@InProceedings{Zhang_2025_ICCV,
author = {Zhang, Yafei and Kong, Lingqi and Li, Huafeng and Wen, Jie},
title = {Weakly Supervised Visible-Infrared Person Re-Identification via Heterogeneous Expert Collaborative Consistency Learning},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2025},
pages = {12659-12669}
}
|
To reduce the reliance of visible-infrared person re-identification (ReID) models on labeled cross-modal samples, this paper explores a weakly supervised cross-modal person ReID method that uses only single-modal sample identity labels, addressing scenarios where cross-modal identity labels are unavailable. To mitigate the impact of missing cross-modal labels on model performance, we propose a heterogeneous expert collaborative consistency learning framework, designed to establish robust cross-modal identity correspondences in a weakly supervised manner. This framework leverages labeled data from each modality to independently train dedicated classification experts. To associate cross-modal samples, these classification experts act as heterogeneous predictors, predicting the identities of samples from the other modality. To improve prediction accuracy, we design a cross-modal relationship fusion mechanism that effectively integrates predictions from different experts. Under the implicit supervision provided by cross-modal identity correspondences, collaborative and consistent learning among the experts is encouraged, significantly enhancing the model's ability to extract modality-invariant features and improve cross-modal identity recognition. Experimental results on two challenging datasets validate the effectiveness of the proposed method. Code is available at https://github.com/KongLingqi2333/WSL-VIReID.
|
2507.12942
|
Accepted by ICCV 2025
| null | null |
[] |
[] |
[] |
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] |
274
|
Prompt-A-Video: Prompt Your Video Diffusion Model via Preference-Aligned LLM
|
[
"Yatai Ji",
"Jiacheng Zhang",
"Jie Wu",
"Shilong Zhang",
"Shoufa Chen",
"Chongjian Ge",
"Peize Sun",
"Weifeng Chen",
"Wenqi Shao",
"Xuefeng Xiao",
"Weilin Huang",
"Ping Luo"
] |
https://openaccess.thecvf.com/content/ICCV2025/html/Ji_Prompt-A-Video_Prompt_Your_Video_Diffusion_Model_via_Preference-Aligned_LLM_ICCV_2025_paper.html
|
https://openaccess.thecvf.com/content/ICCV2025/papers/Ji_Prompt-A-Video_Prompt_Your_Video_Diffusion_Model_via_Preference-Aligned_LLM_ICCV_2025_paper.pdf
|
https://openaccess.thecvf.com/content/ICCV2025/supplemental/Ji_Prompt-A-Video_Prompt_Your_ICCV_2025_supplemental.pdf
|
@InProceedings{Ji_2025_ICCV,
author = {Ji, Yatai and Zhang, Jiacheng and Wu, Jie and Zhang, Shilong and Chen, Shoufa and Ge, Chongjian and Sun, Peize and Chen, Weifeng and Shao, Wenqi and Xiao, Xuefeng and Huang, Weilin and Luo, Ping},
title = {Prompt-A-Video: Prompt Your Video Diffusion Model via Preference-Aligned LLM},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2025},
pages = {18725-18735}
}
|
Text-to-video models have made remarkable advancements through optimization on high-quality text-video pairs, where the textual prompts play a pivotal role in determining quality of output videos. However, achieving the desired output often entails multiple revisions and iterative inference to refine user-provided prompts. Current automatic methods for refining prompts encounter challenges such as Modality-Inconsistency, Cost-Discrepancy, and Model-Unaware when applied to text-to-video diffusion models. To address these problem, we introduce an LLM-based prompt adaptation framework, termed as Prompt-A-Video, which excels in crafting Video-Centric, Labor-Free and Preference-Aligned prompts tailored to specific video diffusion model. Our approach involves a meticulously crafted two-stage optimization and alignment system. Initially, we conduct a reward-guided prompt evolution pipeline to automatically create optimal prompts pool and leverage them for supervised fine-tuning (SFT) of the LLM. Then multi-dimensional rewards are employed to generate pairwise data for the SFT model, followed by the direct preference optimization (DPO) algorithm to further facilitate preference alignment. Through extensive experimentation and comparative analyses, we validate the effectiveness of Prompt-A-Video across diverse generation models, highlighting its potential to push the boundaries of video generation.
| null | null | null | null |
[] |
[] |
[] |
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] |
275
|
Diffusion Image Prior
|
[
"Hamadi Chihaoui",
"Paolo Favaro"
] |
https://openaccess.thecvf.com/content/ICCV2025/html/Chihaoui_Diffusion_Image_Prior_ICCV_2025_paper.html
|
https://openaccess.thecvf.com/content/ICCV2025/papers/Chihaoui_Diffusion_Image_Prior_ICCV_2025_paper.pdf
|
https://openaccess.thecvf.com/content/ICCV2025/supplemental/Chihaoui_Diffusion_Image_Prior_ICCV_2025_supplemental.pdf
|
@InProceedings{Chihaoui_2025_ICCV,
author = {Chihaoui, Hamadi and Favaro, Paolo},
title = {Diffusion Image Prior},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2025},
pages = {24636-24644}
}
|
Zero-shot image restoration (IR) methods based on pretrained diffusion models have recently achieved significant success. These methods typically require at least a parametric form of the degradation model. However, in real-world scenarios, the degradation may be too complex to define explicitly without relying on crude approximations. To handle this general case, we introduce the DIffusion Image Prior (DIIP). We take inspiration from the Deep Image Prior (DIP), since it can be used to remove artifacts without the need for an explicit degradation model. However, in contrast to DIP, we find that pretrained diffusion models offer a much stronger prior, despite being trained without knowledge from corrupted data. We show that, the optimization process in DIIP first reconstructs a clean version of the image before eventually overfitting to the degraded input, but it does so for a broader range of degradations than DIP. In light of this result, we propose a blind image restoration (IR) method based on early stopping, which does not require prior knowledge of the degradation model. We validate DIIP on various degradation-blind IR tasks, including JPEG artifact removal, waterdrop removal, denoising and super-resolution with state-of-the-art results.
|
2503.21410
| null | null | null |
[] |
[] |
[] |
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] |
276
|
Constructing Ophthalmic MLLM for Positioning-diagnosis Collaboration Through Clinical Cognitive Chain Reasoning
|
[
"Xinyao Liu",
"Diping Song"
] |
https://openaccess.thecvf.com/content/ICCV2025/html/Liu_Constructing_Ophthalmic_MLLM_for_Positioning-diagnosis_Collaboration_Through_Clinical_Cognitive_Chain_ICCV_2025_paper.html
|
https://openaccess.thecvf.com/content/ICCV2025/papers/Liu_Constructing_Ophthalmic_MLLM_for_Positioning-diagnosis_Collaboration_Through_Clinical_Cognitive_Chain_ICCV_2025_paper.pdf
|
https://openaccess.thecvf.com/content/ICCV2025/supplemental/Liu_Constructing_Ophthalmic_MLLM_ICCV_2025_supplemental.pdf
|
@InProceedings{Liu_2025_ICCV,
author = {Liu, Xinyao and Song, Diping},
title = {Constructing Ophthalmic MLLM for Positioning-diagnosis Collaboration Through Clinical Cognitive Chain Reasoning},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2025},
pages = {21547-21556}
}
|
Multimodal large language models (MLLMs) demonstrate significant potential in the field of medical diagnosis. However, they face critical challenges in specialized domains such as ophthalmology, particularly the fragmentation of annotation granularity and inconsistencies in clinical reasoning logic, which hinder precise cross-modal understanding. This paper introduces FundusExpert, an ophthalmology-specific MLLM with integrated positioning-diagnosis reasoning capabilities, along with FundusGen, a dataset constructed through the intelligent Fundus-Engine system. Fundus-Engine automates localization and leverages MLLM-based semantic expansion to integrate global disease classification, local object detection, and fine-grained feature analysis within a single fundus image. Additionally, by constructing a clinically aligned cognitive chain, it guides the model to generate interpretable reasoning paths. FundusExpert, fine-tuned with instruction data from FundusGen, achieves the best performance in ophthalmic question-answering tasks, surpassing the average accuracy of the 40B MedRegA by 26.6%. It also excels in zero-shot report generation tasks, achieving a clinical consistency of 77.0%, significantly outperforming GPT-4o's 47.6%. Furthermore, we reveal a scaling law between data quality and model capability (L \propto N^ 0.068 ), demonstrating that the cognitive alignment annotations in FundusGen enhance data utilization efficiency. By integrating region-level localization with diagnostic reasoning chains, our work develops a scalable, clinically-aligned MLLM and explores a pathway toward bridging the visual-language gap in specific MLLMs. Our project can be found at https://github.com/MeteorElf/FundusExpert.
|
2507.17539
| null |
https://github.com/MeteorElf/FundusExpert
| null |
[] |
[
"MeteorElf/FundusExpert"
] |
[
"MeteorElf/Fundus-MMBench"
] |
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] |
277
|
MemDistill: Distilling LiDAR Knowledge into Memory for Camera-Only 3D Object Detection
|
[
"Donghyeon Kwon",
"Youngseok Yoon",
"Hyeongseok Son",
"Suha Kwak"
] |
https://openaccess.thecvf.com/content/ICCV2025/html/Kwon_MemDistill_Distilling_LiDAR_Knowledge_into_Memory_for_Camera-Only_3D_Object_ICCV_2025_paper.html
|
https://openaccess.thecvf.com/content/ICCV2025/papers/Kwon_MemDistill_Distilling_LiDAR_Knowledge_into_Memory_for_Camera-Only_3D_Object_ICCV_2025_paper.pdf
| null |
@InProceedings{Kwon_2025_ICCV,
author = {Kwon, Donghyeon and Yoon, Youngseok and Son, Hyeongseok and Kwak, Suha},
title = {MemDistill: Distilling LiDAR Knowledge into Memory for Camera-Only 3D Object Detection},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2025},
pages = {6828-6838}
}
|
Camera-based 3D object detection has gained attention for its cost-effectiveness, but it in general lags behind LiDAR-based approaches due to its lack of explicit 3D spatial cues. To take the best of both camera- and LiDAR-based detectors, we propose MemDistill, a novel cross-modal knowledge distillation framework for 3D object detection.MemDistill transfers rich 3D knowledge from a LiDAR-based teacher model to a camera-based student model through a dedicated memory unit and a scene-dependent memory retrieval module.To be specific, our framework distills the teacher's 3D knowledge, optimizes the memory to store that knowledge compactly, and learns the retriever that searches the memory to produce 3D features relevant to the input scene, compensating for the missing LiDAR modality.Experiments on the nuScenes dataset demonstrate that MemDistill significantly improves performance of its camera-only baseline, achieving the state of the art in camera-based 3D object detection.
| null | null | null | null |
[] |
[] |
[] |
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] |
278
|
OpenRSD: Towards Open-prompts for Object Detection in Remote Sensing Images
|
[
"Ziyue Huang",
"Yongchao Feng",
"Ziqi Liu",
"Shuai Yang",
"Qingjie Liu",
"Yunhong Wang"
] |
https://openaccess.thecvf.com/content/ICCV2025/html/Huang_OpenRSD_Towards_Open-prompts_for_Object_Detection_in_Remote_Sensing_Images_ICCV_2025_paper.html
|
https://openaccess.thecvf.com/content/ICCV2025/papers/Huang_OpenRSD_Towards_Open-prompts_for_Object_Detection_in_Remote_Sensing_Images_ICCV_2025_paper.pdf
|
https://openaccess.thecvf.com/content/ICCV2025/supplemental/Huang_OpenRSD_Towards_Open-prompts_ICCV_2025_supplemental.pdf
|
@InProceedings{Huang_2025_ICCV,
author = {Huang, Ziyue and Feng, Yongchao and Liu, Ziqi and Yang, Shuai and Liu, Qingjie and Wang, Yunhong},
title = {OpenRSD: Towards Open-prompts for Object Detection in Remote Sensing Images},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2025},
pages = {8384-8394}
}
|
Remote sensing object detection has made significant progress, but most studies still focus on closed-set detection, limiting generalization across diverse datasets. Open-vocabulary object detection (OVD) provides a solution by leveraging multimodal associations between text prompts and visual features. However, existing OVD methods for remote sensing (RS) images are constrained by small-scale datasets and fail to address the unique challenges of remote sensing interpretation, include oriented object detection and the need for both high precision and real-time performance in diverse scenarios. To tackle these challenges, we propose OpenRSD, a universal open-prompt RS object detection framework. OpenRSD supports multimodal prompts and integrates multi-task detection heads to balance accuracy and real-time requirements. Additionally, we design a multi-stage training pipeline to enhance the generalization of model. Evaluated on seven public datasets, OpenRSD demonstrates superior performance in oriented and horizontal bounding box detection, with real-time inference capabilities suitable for large-scale RS image analysis. Compared to YOLO-World, OpenRSD exhibits an 8.7% higher average precision and achieves an inference speed of 20.8 FPS. Codes and models will be released.
|
2503.06146
|
11 pages, 4 figures
| null | null |
[] |
[] |
[] |
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] |
279
|
Privacy-centric Deep Motion Retargeting for Anonymization of Skeleton-Based Motion Visualization
|
[
"Thomas Carr",
"Depeng Xu",
"Shuhan Yuan",
"Aidong Lu"
] |
https://openaccess.thecvf.com/content/ICCV2025/html/Carr_Privacy-centric_Deep_Motion_Retargeting_for_Anonymization_of_Skeleton-Based_Motion_Visualization_ICCV_2025_paper.html
|
https://openaccess.thecvf.com/content/ICCV2025/papers/Carr_Privacy-centric_Deep_Motion_Retargeting_for_Anonymization_of_Skeleton-Based_Motion_Visualization_ICCV_2025_paper.pdf
|
https://openaccess.thecvf.com/content/ICCV2025/supplemental/Carr_Privacy-centric_Deep_Motion_ICCV_2025_supplemental.pdf
|
@InProceedings{Carr_2025_ICCV,
author = {Carr, Thomas and Xu, Depeng and Yuan, Shuhan and Lu, Aidong},
title = {Privacy-centric Deep Motion Retargeting for Anonymization of Skeleton-Based Motion Visualization},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2025},
pages = {13162-13170}
}
|
Capturing and visualizing motion using skeleton-based techniques is a key aspect of computer vision, particularly in virtual reality (VR) settings. Its popularity has surged, driven by the simplicity of obtaining skeleton data and the growing appetite for virtual interaction. Although this skeleton data appears to be non-identifiable, it can be exploited to derive personally identifiable information (PII), posing a risk of inadvertent privacy breaches. In this paper, we explore the application of motion retargeting and its ability to mitigate privacy leakages. Motion retargeting can effectively transfer the motion from an initial user onto a dummy skeleton with the purpose of hiding PII. We propose a Privacy-centric Deep Motion Retargeting model (PMR), which mitigates the PII through adversarial learning. In our evaluation, our proposed model achieves motion retargeting performance on par with the current state-of-the-art models. More importantly, it effectively prevents the attackers from identifying the initial user.
| null | null | null | null |
[] |
[] |
[] |
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] |
280
|
FlashDepth: Real-time Streaming Video Depth Estimation at 2K Resolution
|
[
"Gene Chou",
"Wenqi Xian",
"Guandao Yang",
"Mohamed Abdelfattah",
"Bharath Hariharan",
"Noah Snavely",
"Ning Yu",
"Paul Debevec"
] |
https://openaccess.thecvf.com/content/ICCV2025/html/Chou_FlashDepth_Real-time_Streaming_Video_Depth_Estimation_at_2K_Resolution_ICCV_2025_paper.html
|
https://openaccess.thecvf.com/content/ICCV2025/papers/Chou_FlashDepth_Real-time_Streaming_Video_Depth_Estimation_at_2K_Resolution_ICCV_2025_paper.pdf
| null |
@InProceedings{Chou_2025_ICCV,
author = {Chou, Gene and Xian, Wenqi and Yang, Guandao and Abdelfattah, Mohamed and Hariharan, Bharath and Snavely, Noah and Yu, Ning and Debevec, Paul},
title = {FlashDepth: Real-time Streaming Video Depth Estimation at 2K Resolution},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2025},
pages = {9638-9648}
}
|
A versatile video depth estimation model should be consistent and accurate across frames, produce high-resolution depth maps, and support real-time streaming. We propose a method, FlashDepth, that satisfies all three requirements, performing depth estimation for a 2044x1148 streaming video at 24 FPS. We show that, with careful modifications to pretrained single-image depth models, these capabilities are enabled with relatively little data and training. We validate our approach across multiple unseen datasets against state-of-the-art depth models, and find that our method outperforms them in terms of boundary sharpness and speed by a significant margin, while maintaining competitive accuracy. We hope our model will enable various applications that require high-resolution depth, such as visual effects editing, and online decision-making, such as robotics. We release all code and model weights at https://github.com/Eyeline-Research/FlashDepth.
|
2504.07093
| null |
https://github.com/Eyeline-Research/FlashDepth
| null |
[] |
[] |
[] |
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] |
281
|
Towards Performance Consistency in Multi-Level Model Collaboration
|
[
"Qi Li",
"Runpeng Yu",
"Xinchao Wang"
] |
https://openaccess.thecvf.com/content/ICCV2025/html/Li_Towards_Performance_Consistency_in_Multi-Level_Model_Collaboration_ICCV_2025_paper.html
|
https://openaccess.thecvf.com/content/ICCV2025/papers/Li_Towards_Performance_Consistency_in_Multi-Level_Model_Collaboration_ICCV_2025_paper.pdf
|
https://openaccess.thecvf.com/content/ICCV2025/supplemental/Li_Towards_Performance_Consistency_ICCV_2025_supplemental.pdf
|
@InProceedings{Li_2025_ICCV,
author = {Li, Qi and Yu, Runpeng and Wang, Xinchao},
title = {Towards Performance Consistency in Multi-Level Model Collaboration},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2025},
pages = {2567-2576}
}
|
Parameter-level model merging is an emerging paradigm in multi-task learning with significant promise. Previous research has explored its connections with prediction-level model ensembling--commonly viewed as the upper bound for merging--to reveal the potential of achieving performance consistency between the two. However, this observation relies on certain preconditions, such as being limited to two models, using ViT-based models, and all models are fine-tuned from the same pre-trained checkpoint. To further understand the intrinsic connections between these two paradigms, this paper explores an interesting possibility: If these restrictions are removed, can performance consistency still be achieved between merging and ensembling? To answer this question, we first theoretically establish a performance correlation between merging and ensembling. We find that even when previous restrictions are not met, there is still a way for model merging to attain a near-identical and superior performance similar to that of ensembling. To verify whether our findings are practical, we introduce a validation framework termed \underline Neu ral \underline Lig and (NeuLig). The learning process of NeuLig is meticulously designed with a specialized loss function supported by theoretical foundations. Experimental results demonstrate the robust resilience of NeuLig in terms of both model scale and the number of collaborating models. For instance, for the case involving 5 CLIP-ViT-B/32 models, parameter-level merging achieves the same performance as prediction-level ensembling (merging: 95.44% vs. ensembling: 95.46%).
| null | null | null | null |
[] |
[] |
[] |
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] |
282
|
Polarimetric Neural Field via Unified Complex-Valued Wave Representation
|
[
"Chu Zhou",
"Yixin Yang",
"Junda Liao",
"Heng Guo",
"Boxin Shi",
"Imari Sato"
] |
https://openaccess.thecvf.com/content/ICCV2025/html/Zhou_Polarimetric_Neural_Field_via_Unified_Complex-Valued_Wave_Representation_ICCV_2025_paper.html
|
https://openaccess.thecvf.com/content/ICCV2025/papers/Zhou_Polarimetric_Neural_Field_via_Unified_Complex-Valued_Wave_Representation_ICCV_2025_paper.pdf
|
https://openaccess.thecvf.com/content/ICCV2025/supplemental/Zhou_Polarimetric_Neural_Field_ICCV_2025_supplemental.pdf
|
@InProceedings{Zhou_2025_ICCV,
author = {Zhou, Chu and Yang, Yixin and Liao, Junda and Guo, Heng and Shi, Boxin and Sato, Imari},
title = {Polarimetric Neural Field via Unified Complex-Valued Wave Representation},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2025},
pages = {25660-25669}
}
|
Polarization has found applications in various computer vision tasks by providing additional physical cues. However, due to the limitations of current imaging systems, polarimetric parameters are typically stored in discrete form, which is non-differentiable and limits their applicability in polarization-based vision. While current neural field methods have shown promise for continuous signal reconstruction, they struggle to model the intrinsic physical interdependencies among polarimetric parameters. In this work, we propose a physics-grounded representation scheme to represent polarimetric parameters as a unified complex-valued wave. Tailored to this scheme, we propose a tuning-free fitting strategy along with a lightweight complex-valued neural network, enabling property-preserved reconstruction. Experimental results show that our method achieves state-of-the-art performance and facilitates smooth polarized image rendering and flexible resolution adjustments.
| null | null | null | null |
[] |
[] |
[] |
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] |
283
|
CLIP-Adapted Region-to-Text Learning for Generative Open-Vocabulary Semantic Segmentation
|
[
"Jiannan Ge",
"Lingxi Xie",
"Hongtao Xie",
"Pandeng Li",
"Sun-Ao Liu",
"Xiaopeng Zhang",
"Qi Tian",
"Yongdong Zhang"
] |
https://openaccess.thecvf.com/content/ICCV2025/html/Ge_CLIP-Adapted_Region-to-Text_Learning_for_Generative_Open-Vocabulary_Semantic_Segmentation_ICCV_2025_paper.html
|
https://openaccess.thecvf.com/content/ICCV2025/papers/Ge_CLIP-Adapted_Region-to-Text_Learning_for_Generative_Open-Vocabulary_Semantic_Segmentation_ICCV_2025_paper.pdf
|
https://openaccess.thecvf.com/content/ICCV2025/supplemental/Ge_CLIP-Adapted_Region-to-Text_Learning_ICCV_2025_supplemental.pdf
|
@InProceedings{Ge_2025_ICCV,
author = {Ge, Jiannan and Xie, Lingxi and Xie, Hongtao and Li, Pandeng and Liu, Sun-Ao and Zhang, Xiaopeng and Tian, Qi and Zhang, Yongdong},
title = {CLIP-Adapted Region-to-Text Learning for Generative Open-Vocabulary Semantic Segmentation},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2025},
pages = {24034-24044}
}
|
In recent years, Open-Vocabulary Semantic Segmentation (OVSS) has been largely advanced. However, existing methods mostly rely on a pre-trained vision-language model (e.g., CLIP) and require a predefined set of classes to guide the semantic segmentation process during the inference. This not only narrows the application scenario but also constrains comprehension within a finite vocabulary. To overcome this, we reformulate OVSS as a text generation task and propose the CLIP-adapted Region-to-Text Network (CRTNet) that achieves vocabulary-free OVSS by generating category names and descriptions upon segmentation masks. The training process consists of two steps to ensure an accurate and detailed interpretation of the masked regions: (i) the initial step adapts CLIP visual features to mask-level proposal features using binarized masks extracted by a trained mask extractor, and (ii) the subsequent step involves selecting and aggregating these features to become text-aware by integrating CLIP text embeddings, effectively aligning visual data with corresponding linguistic data to facilitate region-to-text learning. Furthermore, we introduce a series of parsing and filtering techniques to integrate multiple sources of training data to improve the generalization ability of our model. Experiments demonstrate that our model not only excels in OVSS but also exhibits scalability and can be adapted to various foundation models (e.g., SAM) without being retrained.
| null | null | null | null |
[] |
[] |
[] |
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] |
284
|
Where, What, Why: Towards Explainable Driver Attention Prediction
|
[
"Yuchen Zhou",
"Jiayu Tang",
"Xiaoyan Xiao",
"Yueyao Lin",
"Linkai Liu",
"Zipeng Guo",
"Hao Fei",
"Xiaobo Xia",
"Chao Gou"
] |
https://openaccess.thecvf.com/content/ICCV2025/html/Zhou_Where_What_Why_Towards_Explainable_Driver_Attention_Prediction_ICCV_2025_paper.html
|
https://openaccess.thecvf.com/content/ICCV2025/papers/Zhou_Where_What_Why_Towards_Explainable_Driver_Attention_Prediction_ICCV_2025_paper.pdf
| null |
@InProceedings{Zhou_2025_ICCV,
author = {Zhou, Yuchen and Tang, Jiayu and Xiao, Xiaoyan and Lin, Yueyao and Liu, Linkai and Guo, Zipeng and Fei, Hao and Xia, Xiaobo and Gou, Chao},
title = {Where, What, Why: Towards Explainable Driver Attention Prediction},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2025},
pages = {2675-2685}
}
|
Modeling task-driven attention in driving is a fundamental challenge for both autonomous vehicles and cognitive science. Existing methods primarily predict where drivers look by generating spatial heatmaps, but fail to capture the cognitive motivations behind attention allocation in specific contexts, which limits deeper understanding of attention mechanisms. To bridge this gap, we introduce Explainable Driver Attention Prediction, a novel task paradigm that jointly predicts spatial attention regions (where), parses attended semantics (what), and provides cognitive reasoning for attention allocation (why). To support this, we present W3DA, the first large-scale explainable driver attention dataset. It enriches existing benchmarks with detailed semantic and causal annotations across diverse driving scenarios, including normal conditions, safety-critical situations, and traffic accidents. We further propose LLada, a Large Language model-driven framework for driver attention prediction, which unifies pixel modeling, semantic parsing, and cognitive reasoning within an end-to-end architecture. Extensive experiments demonstrate the effectiveness of LLada, exhibiting robust generalization across datasets and driving conditions. This work serves as a key step toward a deeper understanding of driver attention mechanisms, with significant implications for autonomous driving, intelligent driver training, and human-computer interaction.
|
2506.23088
|
Accepted by ICCV 2025
| null | null |
[] |
[] |
[] |
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] |
285
|
MeshAnything V2: Artist-Created Mesh Generation with Adjacent Mesh Tokenization
|
[
"Yiwen Chen",
"Yikai Wang",
"Yihao Luo",
"Zhengyi Wang",
"Zilong Chen",
"Jun Zhu",
"Chi Zhang",
"Guosheng Lin"
] |
https://openaccess.thecvf.com/content/ICCV2025/html/Chen_MeshAnything_V2_Artist-Created_Mesh_Generation_with_Adjacent_Mesh_Tokenization_ICCV_2025_paper.html
|
https://openaccess.thecvf.com/content/ICCV2025/papers/Chen_MeshAnything_V2_Artist-Created_Mesh_Generation_with_Adjacent_Mesh_Tokenization_ICCV_2025_paper.pdf
|
https://openaccess.thecvf.com/content/ICCV2025/supplemental/Chen_MeshAnything_V2_Artist-Created_ICCV_2025_supplemental.pdf
|
@InProceedings{Chen_2025_ICCV,
author = {Chen, Yiwen and Wang, Yikai and Luo, Yihao and Wang, Zhengyi and Chen, Zilong and Zhu, Jun and Zhang, Chi and Lin, Guosheng},
title = {MeshAnything V2: Artist-Created Mesh Generation with Adjacent Mesh Tokenization},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2025},
pages = {13922-13931}
}
|
Meshes are the de facto 3D representation in the industry but are labor-intensive to produce. Recently, a line of research has focused on autoregressively generating meshes. This approach processes meshes into a sequence composed of vertices and then generates them vertex by vertex, similar to how a language model generates text. These methods have achieved some success but still struggle to generate complex meshes. One primary reason for this limitation is their inefficient tokenization methods. To address this issue, we introduce MeshAnything V2, an advanced mesh generation model designed to create Artist-Created Meshes that align precisely with specified shapes. A key innovation behind MeshAnything V2 is our novel Adjacent Mesh Tokenization (AMT) method. Unlike traditional approaches that represent each face using three vertices, AMT optimizes this by employing a single vertex wherever feasible, effectively reducing the token sequence length by about half on average. This not only streamlines the tokenization process but also results in more compact and well-structured sequences, enhancing the efficiency of mesh generation. With these improvements, MeshAnything V2 effectively doubles the face limit compared to previous models, delivering superior performance without increasing computational costs. Our extensive experiments across various mesh tokenization methods demonstrate that AMT is pivotal in achieving optimal results in both efficiency and performance.
|
2408.02555
|
Project Page: https://buaacyw.github.io/meshanything-v2/ Github:
https://github.com/buaacyw/MeshAnythingV2
| null | null |
[
"Yiwen-ntu/MeshAnything"
] |
[
"Yiwen-ntu/MeshAnythingV2"
] |
[] |
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] |
286
|
Visual Intention Grounding for Egocentric Assistants
|
[
"Pengzhan Sun",
"Junbin Xiao",
"Tze Ho Elden Tse",
"Yicong Li",
"Arjun Akula",
"Angela Yao"
] |
https://openaccess.thecvf.com/content/ICCV2025/html/Sun_Visual_Intention_Grounding_for_Egocentric_Assistants_ICCV_2025_paper.html
|
https://openaccess.thecvf.com/content/ICCV2025/papers/Sun_Visual_Intention_Grounding_for_Egocentric_Assistants_ICCV_2025_paper.pdf
|
https://openaccess.thecvf.com/content/ICCV2025/supplemental/Sun_Visual_Intention_Grounding_ICCV_2025_supplemental.pdf
|
@InProceedings{Sun_2025_ICCV,
author = {Sun, Pengzhan and Xiao, Junbin and Tse, Tze Ho Elden and Li, Yicong and Akula, Arjun and Yao, Angela},
title = {Visual Intention Grounding for Egocentric Assistants},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2025},
pages = {2512-2522}
}
|
Visual grounding associates textual descriptions with objects in an image. Conventional methods target third-person image inputs and named object queries. In applications such as AI assistants, the perspective shifts -- inputs are egocentric, and objects may be referred to implicitly through needs and intentions. To bridge this gap, we introduce EgoIntention, the first dataset for egocentric visual intention grounding. EgoIntention challenges multimodal LLMs to 1) understand and ignore unintended contextual objects and 2) reason about uncommon object functionalities. Benchmark results show that current models misidentify context objects and lack affordance understanding in egocentric views. We also propose Reason-to-Ground (RoG) instruction tuning; it enables hybrid training with normal descriptions and egocentric intentions with a chained intention reasoning and object grounding mechanism. RoG significantly outperforms naive finetuning and hybrid training on EgoIntention, while maintaining or slightly improving naive description grounding. This advancement enables unified visual grounding for egocentric and exocentric visual inputs while handling explicit object queries and implicit human intentions.
|
2504.13621
| null | null | null |
[] |
[] |
[] |
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] |
287
|
Analyzing Finetuning Representation Shift for Multimodal LLMs Steering
|
[
"Pegah Khayatan",
"Mustafa Shukor",
"Jayneel Parekh",
"Arnaud Dapogny",
"Matthieu Cord"
] |
https://openaccess.thecvf.com/content/ICCV2025/html/Khayatan_Analyzing_Finetuning_Representation_Shift_for_Multimodal_LLMs_Steering_ICCV_2025_paper.html
|
https://openaccess.thecvf.com/content/ICCV2025/papers/Khayatan_Analyzing_Finetuning_Representation_Shift_for_Multimodal_LLMs_Steering_ICCV_2025_paper.pdf
|
https://openaccess.thecvf.com/content/ICCV2025/supplemental/Khayatan_Analyzing_Finetuning_Representation_ICCV_2025_supplemental.pdf
|
@InProceedings{Khayatan_2025_ICCV,
author = {Khayatan, Pegah and Shukor, Mustafa and Parekh, Jayneel and Dapogny, Arnaud and Cord, Matthieu},
title = {Analyzing Finetuning Representation Shift for Multimodal LLMs Steering},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2025},
pages = {2206-2216}
}
|
Multimodal LLMs (MLLMs) have reached remarkable levels of proficiency in understanding multimodal inputs. However, understanding and interpreting the behavior of such complex models is a challenging task, not to mention the dynamic shifts that may occur during fine-tuning, or due to covariate shift between datasets. In this work, we apply concept-level analysis towards MLLM understanding. More specifically, we propose to map hidden states to interpretable visual and textual concepts. This enables us to more efficiently compare certain semantic dynamics, such as the shift from an original and fine-tuned model, revealing concept alteration and potential biases that may occur during fine-tuning. We also demonstrate the use of shift vectors to capture these concepts changes. These shift vectors allow us to recover fine-tuned concepts by applying simple, computationally inexpensive additive concept shifts in the original model. Finally, our findings also have direct applications for MLLM steering, which can be used for model debiasing as well as enforcing safety in MLLM output. All in all, we propose a novel, training-free, ready-to-use framework for MLLM behavior interpretability and control. Our implementation is publicly available.
|
2501.03012
|
ICCV 2025. The first three authors contributed equally. Project page
and code: https://pegah-
kh.github.io/projects/lmm-finetuning-analysis-and-steering/
| null | null |
[] |
[] |
[] |
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288
|
TeethGenerator: A two-stage framework for paired pre- and post-orthodontic 3D dental data generation
|
[
"Changsong Lei",
"Yaqian Liang",
"Shaofeng Wang",
"Jiajia Dai",
"Yong-Jin Liu"
] |
https://openaccess.thecvf.com/content/ICCV2025/html/Lei_TeethGenerator_A_two-stage_framework_for_paired_pre-_and_post-orthodontic_3D_ICCV_2025_paper.html
|
https://openaccess.thecvf.com/content/ICCV2025/papers/Lei_TeethGenerator_A_two-stage_framework_for_paired_pre-_and_post-orthodontic_3D_ICCV_2025_paper.pdf
|
https://openaccess.thecvf.com/content/ICCV2025/supplemental/Lei_TeethGenerator_A_two-stage_ICCV_2025_supplemental.pdf
|
@InProceedings{Lei_2025_ICCV,
author = {Lei, Changsong and Liang, Yaqian and Wang, Shaofeng and Dai, Jiajia and Liu, Yong-Jin},
title = {TeethGenerator: A two-stage framework for paired pre- and post-orthodontic 3D dental data generation},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2025},
pages = {25872-25881}
}
|
Digital orthodontics represents a prominent and critical application of computer vision technology in the medical field. So far, the labor-intensive process of collecting clinical data, particularly in acquiring paired 3D orthodontic teeth models, constitutes a crucial bottleneck for developing tooth arrangement neural networks. Although numerous general 3D shape generation methods have been proposed, most of them focus on single-object generation and are insufficient for generating anatomically structured teeth models, each comprising 24-32 segmented teeth. In this paper, we propose TeethGenerator, a novel two-stage framework designed to synthesize paired 3D teeth models pre- and post-orthodontic, aiming to facilitate the training of downstream tooth arrangement networks. Specifically, our approach consists of two key modules: (1) a teeth shape generation module that leverages a diffusion model to learn the distribution of morphological characteristics of teeth, enabling the generation of diverse post-orthodontic teeth models; and (2) a teeth style generation module that synthesizes corresponding pre-orthodontic teeth models by incorporating desired styles as conditional inputs. Extensive qualitative and quantitative experiments demonstrate that our synthetic dataset aligns closely with the distribution of real orthodontic data, and promotes tooth alignment performance significantly when combined with real data for training. The code and dataset are available at https://github.com/lcshhh/teeth generator.
| null | null | null | null |
[] |
[] |
[] |
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] |
289
|
Enhancing Zero-shot Object Counting via Text-guided Local Ranking and Number-evoked Global Attention
|
[
"Shiwei Zhang",
"Qi Zhou",
"Wei Ke"
] |
https://openaccess.thecvf.com/content/ICCV2025/html/Zhang_Enhancing_Zero-shot_Object_Counting_via_Text-guided_Local_Ranking_and_Number-evoked_ICCV_2025_paper.html
|
https://openaccess.thecvf.com/content/ICCV2025/papers/Zhang_Enhancing_Zero-shot_Object_Counting_via_Text-guided_Local_Ranking_and_Number-evoked_ICCV_2025_paper.pdf
|
https://openaccess.thecvf.com/content/ICCV2025/supplemental/Zhang_Enhancing_Zero-shot_Object_ICCV_2025_supplemental.pdf
|
@InProceedings{Zhang_2025_ICCV,
author = {Zhang, Shiwei and Zhou, Qi and Ke, Wei},
title = {Enhancing Zero-shot Object Counting via Text-guided Local Ranking and Number-evoked Global Attention},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2025},
pages = {21097-21106}
}
|
Text-guided zero-shot object counting leverages vision-language models (VLMs) to count objects of an arbitrary class given by a text prompt. Existing approaches for this challenging task only utilize local patch-level features to fuse with text feature, ignoring the important influence of the global image-level feature. In this paper, we propose a universal strategy that can exploit both local patch-level features and global image-level feature simultaneously. Specifically, to improve the localization ability of VLMs, we propose Text-guided Local Ranking. Depending on the prior knowledge that foreground patches have higher similarity with the text prompt, a new local-text rank loss is designed to increase the differences between the similarity scores of foreground and background patches which push foreground and background patches apart. To enhance the counting ability of VLMs, Number-evoked Global Attention is introduced to first align global image-level feature with multiple number-conditioned text prompts. Then, the one with the highest similarity is selected to compute cross-attention with the global image-level feature. Through extensive experiments on widely used datasets and methods, the proposed approach has demonstrated superior advancements in performance, generalization, and scalability. Furthermore, to better evaluate text-guided zero-shot object counting methods, we propose a dataset named ZSC-8K, which is larger and more challenging, to establish a new benchmark. Codes and ZSC-8K dataset will be available.
| null | null | null | null |
[] |
[] |
[] |
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] |
290
|
Bidirectional Likelihood Estimation with Multi-Modal Large Language Models for Text-Video Retrieval
|
[
"Dohwan Ko",
"Ji Soo Lee",
"Minhyuk Choi",
"Zihang Meng",
"Hyunwoo J. Kim"
] |
https://openaccess.thecvf.com/content/ICCV2025/html/Ko_Bidirectional_Likelihood_Estimation_with_Multi-Modal_Large_Language_Models_for_Text-Video_ICCV_2025_paper.html
|
https://openaccess.thecvf.com/content/ICCV2025/papers/Ko_Bidirectional_Likelihood_Estimation_with_Multi-Modal_Large_Language_Models_for_Text-Video_ICCV_2025_paper.pdf
|
https://openaccess.thecvf.com/content/ICCV2025/supplemental/Ko_Bidirectional_Likelihood_Estimation_ICCV_2025_supplemental.pdf
|
@InProceedings{Ko_2025_ICCV,
author = {Ko, Dohwan and Lee, Ji Soo and Choi, Minhyuk and Meng, Zihang and Kim, Hyunwoo J.},
title = {Bidirectional Likelihood Estimation with Multi-Modal Large Language Models for Text-Video Retrieval},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2025},
pages = {22263-22273}
}
|
Text-Video Retrieval aims to find the most relevant text (or video) candidate given a video (or text) query from large-scale online databases. Recent work leverages multi-modal large language models (MLLMs) to improve retrieval, especially for long or complex query-candidate pairs. However, we observe that the naive application of MLLMs, i.e., retrieval based on candidate likelihood, introduces candidate prior bias, favoring candidates with inherently higher priors over those more relevant to the query. To this end, we propose a novel retrieval framework, Bidirectional Likelihood Estimation with MLLM (BLiM), which leverages both query and candidate likelihoods by training the model to generate text from a given video as well as video features from a given text. Furthermore, we introduce Candidate Prior Normalization (CPN), a simple yet effective training-free score calibration module designed to mitigate candidate prior bias in candidate likelihood. On four Text-Video Retrieval benchmarks, our BLiM equipped with CPN outperforms previous state-of-the-art models by 6.4 R@1 on average, effectively alleviating candidate prior bias and emphasizing query-candidate relevance. Our in-depth analysis across various multi-modal tasks beyond retrieval highlights the broad applicability of CPN which enhances visual understanding by reducing reliance on textual priors. Code is available at \href https://github.com/mlvlab/BLiM https://github.com/mlvlab/BLiM .
|
2507.23284
|
ICCV 2025 Highlight
|
https://github.com/mlvlab/BLiM
| null |
[] |
[] |
[
"ikodoh/BLiM-Data"
] |
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] |
291
|
TinyViM: Frequency Decoupling for Tiny Hybrid Vision Mamba
|
[
"Xiaowen Ma",
"Zhenliang Ni",
"Xinghao Chen"
] |
https://openaccess.thecvf.com/content/ICCV2025/html/Ma_TinyViM_Frequency_Decoupling_for_Tiny_Hybrid_Vision_Mamba_ICCV_2025_paper.html
|
https://openaccess.thecvf.com/content/ICCV2025/papers/Ma_TinyViM_Frequency_Decoupling_for_Tiny_Hybrid_Vision_Mamba_ICCV_2025_paper.pdf
|
https://openaccess.thecvf.com/content/ICCV2025/supplemental/Ma_TinyViM_Frequency_Decoupling_ICCV_2025_supplemental.pdf
|
@InProceedings{Ma_2025_ICCV,
author = {Ma, Xiaowen and Ni, Zhenliang and Chen, Xinghao},
title = {TinyViM: Frequency Decoupling for Tiny Hybrid Vision Mamba},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2025},
pages = {23519-23529}
}
|
Mamba has shown great potential for computer vision due to its linear complexity in modeling the global context with respect to the input length. However, existing lightweight Mamba-based backbones cannot demonstrate performance that matches Convolution or Transformer-based methods. By observing, we find that simply modifying the scanning path in the image domain is not conducive to fully exploiting the potential of vision Mamba. In this paper, we first perform comprehensive spectral and quantitative analyses, and verify that the Mamba block mainly models low-frequency information under Convolution-Mamba hybrid architecture. Based on the analyses, we introduce a novel Laplace mixer to decouple the features in terms of frequency and input only the low-frequency components into the Mamba block. In addition, considering the redundancy of the features and the different requirements for high-frequency details and low-frequency global information at different stages, we introduce a frequency ramp inception, i.e., gradually reduce the input dimensions of the high-frequency branches, so as to efficiently trade-off the high-frequency and low-frequency components at different layers. By integrating mobile-friendly convolution and efficient Laplace mixer, we build a series of tiny hybrid vision Mamba called TinyViM. The proposed TinyViM achieves impressive performance on several downstream tasks including image classification, semantic segmentation, object detection and instance segmentation. In particular, TinyViM outperforms Convolution, Transformer and Mamba-based models with similar scales, and the throughput is about 2-3 times higher than that of other Mamba-based models. The good balance between the efficiency and performance of TinyViM shows that a properly designed and optimized vision mamba can achieve high performance with a small model size. Code is available at https://github.com/xwmaxwma/TinyViM.
|
2411.17473
| null |
https://github.com/xwmaxwma/TinyViM
| null |
[] |
[] |
[] |
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] |
292
|
Harnessing Massive Satellite Imagery with Efficient Masked Image Modeling
|
[
"Fengxiang Wang",
"Hongzhen Wang",
"Di Wang",
"Zonghao Guo",
"Zhenyu Zhong",
"Long Lan",
"Wenjing Yang",
"Jing Zhang"
] |
https://openaccess.thecvf.com/content/ICCV2025/html/Wang_Harnessing_Massive_Satellite_Imagery_with_Efficient_Masked_Image_Modeling_ICCV_2025_paper.html
|
https://openaccess.thecvf.com/content/ICCV2025/papers/Wang_Harnessing_Massive_Satellite_Imagery_with_Efficient_Masked_Image_Modeling_ICCV_2025_paper.pdf
| null |
@InProceedings{Wang_2025_ICCV,
author = {Wang, Fengxiang and Wang, Hongzhen and Wang, Di and Guo, Zonghao and Zhong, Zhenyu and Lan, Long and Yang, Wenjing and Zhang, Jing},
title = {Harnessing Massive Satellite Imagery with Efficient Masked Image Modeling},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2025},
pages = {6935-6947}
}
|
Masked Image Modeling (MIM) has become an essential method for building foundational visual models in remote sensing (RS). However, the limitations in size and diversity of existing RS datasets restrict the ability of MIM methods to learn generalizable representations. Additionally, conventional MIM techniques, which require reconstructing all tokens, introduce unnecessary computational overhead. To address these issues, we present a new pre-training pipeline for RS models, featuring the creation of a large-scale RS dataset and an efficient MIM approach. We curated a high-quality dataset named **OpticalRS-13M** by collecting publicly available RS datasets and processing them through exclusion, slicing, and deduplication. OpticalRS-13M comprises 13 million optical images covering various RS tasks, such as object detection and pixel segmentation. To enhance efficiency, we propose SelectiveMAE, a pre-training method that dynamically encodes and reconstructs semantically rich patch tokens, thereby reducing the inefficiencies of traditional MIM models caused by redundant background pixels in RS images. Extensive experiments show that OpticalRS-13M significantly improves classification, detection, and segmentation performance, while SelectiveMAE increases training efficiency over 2xtimes. This highlights the effectiveness and scalability of our pipeline in developing RS foundational models.
|
2406.11933
|
ICCV 2025
|
https://github.com/MiliLab/SelectiveMAE
| null |
[] |
[
"initiacms/SelectiveMAE"
] |
[
"initiacms/OpticalRS-4M"
] |
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] |
293
|
Visual-Oriented Fine-Grained Knowledge Editing for MultiModal Large Language Models
|
[
"Zhen Zeng",
"Leijiang Gu",
"Xun Yang",
"Zhangling Duan",
"Zenglin Shi",
"Meng Wang"
] |
https://openaccess.thecvf.com/content/ICCV2025/html/Zeng_Visual-Oriented_Fine-Grained_Knowledge_Editing_for_MultiModal_Large_Language_Models_ICCV_2025_paper.html
|
https://openaccess.thecvf.com/content/ICCV2025/papers/Zeng_Visual-Oriented_Fine-Grained_Knowledge_Editing_for_MultiModal_Large_Language_Models_ICCV_2025_paper.pdf
|
https://openaccess.thecvf.com/content/ICCV2025/supplemental/Zeng_Visual-Oriented_Fine-Grained_Knowledge_ICCV_2025_supplemental.pdf
|
@InProceedings{Zeng_2025_ICCV,
author = {Zeng, Zhen and Gu, Leijiang and Yang, Xun and Duan, Zhangling and Shi, Zenglin and Wang, Meng},
title = {Visual-Oriented Fine-Grained Knowledge Editing for MultiModal Large Language Models},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2025},
pages = {2491-2500}
}
|
Existing knowledge editing works for MultiModal Large Language Models primarily focus on text-oriented, coarse-grained scenarios, where modifying textual content alone is sufficient. As a result, they fail to capture the unique challenges of multimodal editing, particularly when visual information is central to knowledge representation. In this paper, we introduce a visual-oriented, fine-grained multimodal knowledge editing task that targets precise modifications in images containing multiple interacting entities. To support this, we propose the Fine-Grained Visual Knowledge Editing (FGVEdit) benchmark, designed to evaluate the accuracy and effectiveness of multimodal editing at a granular level. To address this challenge, we present the Multimodal Scope Classifier-based Knowledge Editor (MSCKE), a new framework that leverages a multimodal scope classifier to integrate both textual and visual information. By accurately identifying and updating knowledge localized within images, MSCKE ensures precise editing while preserving unrelated content. Extensive experiments on the FGVEdit benchmark highlight the complexity of this new task and demonstrate that existing methods struggle with fine-grained multimodal editing. Our results highlight MSCKE as a scalable and promising framework for advancing multimodal knowledge editing. Code is available at https://github.com/zeng-zhen/FGVEdit.
|
2411.12790
| null | null | null |
[] |
[] |
[
"ZhenZeng/FGVEdit"
] |
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] |
294
|
Bridging Domain Generalization to Multimodal Domain Generalization via Unified Representations
|
[
"Hai Huang",
"Yan Xia",
"Sashuai Zhou",
"Hanting Wang",
"Shulei Wang",
"Zhou Zhao"
] |
https://openaccess.thecvf.com/content/ICCV2025/html/Huang_Bridging_Domain_Generalization_to_Multimodal_Domain_Generalization_via_Unified_Representations_ICCV_2025_paper.html
|
https://openaccess.thecvf.com/content/ICCV2025/papers/Huang_Bridging_Domain_Generalization_to_Multimodal_Domain_Generalization_via_Unified_Representations_ICCV_2025_paper.pdf
|
https://openaccess.thecvf.com/content/ICCV2025/supplemental/Huang_Bridging_Domain_Generalization_ICCV_2025_supplemental.pdf
|
@InProceedings{Huang_2025_ICCV,
author = {Huang, Hai and Xia, Yan and Zhou, Sashuai and Wang, Hanting and Wang, Shulei and Zhao, Zhou},
title = {Bridging Domain Generalization to Multimodal Domain Generalization via Unified Representations},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2025},
pages = {22488-22498}
}
|
Domain Generalization (DG) aims to enhance model robustness in unseen or distributionally shifted target domains through training exclusively on source domains. Although existing DG techniques, such as data manipulation, learning strategies, and representation learning, have shown significant progress, they predominantly address single-modal data. With the emergence of numerous multi-modal datasets and increasing demand for multi-modal tasks, a key challenge in Multi-modal Domain Generalization (MMDG) has emerged: enabling models trained on multi-modal sources to generalize to unseen target distributions within the same modality set. Due to the inherent differences between modalities, directly transferring methods from single-modal DG to MMDG typically yields sub-optimal results. These methods often exhibit randomness during generalization due to the invisibility of target domains and fail to consider inter-modal consistency. Applying these methods independently to each modality in the MMDG setting before combining them can lead to divergent generalization directions across different modalities, resulting in degraded generalization capabilities. To address these challenges, we propose a novel approach that leverages Unified Representations to map different paired modalities together, effectively adapting DG methods to MMDG by enabling synchronized multi-modal improvements within the unified space. Additionally, we introduce a supervised disentanglement framework that separates modal-general and modal-specific information, further enhancing the alignment of unified representations. Extensive experiments on benchmark datasets, including EPIC-Kitchens and Human-Animal-Cartoon, demonstrate the effectiveness and superiority of our method in enhancing multi-modal domain generalization.
|
2507.03304
|
Accepted by ICCV 2025
| null | null |
[] |
[] |
[] |
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] |
295
|
A Recipe for Generating 3D Worlds from a Single Image
|
[
"Katja Schwarz",
"Denis Rozumny",
"Samuel Rota Bulò",
"Lorenzo Porzi",
"Peter Kontschieder"
] |
https://openaccess.thecvf.com/content/ICCV2025/html/Schwarz_A_Recipe_for_Generating_3D_Worlds_from_a_Single_Image_ICCV_2025_paper.html
|
https://openaccess.thecvf.com/content/ICCV2025/papers/Schwarz_A_Recipe_for_Generating_3D_Worlds_from_a_Single_Image_ICCV_2025_paper.pdf
|
https://openaccess.thecvf.com/content/ICCV2025/supplemental/Schwarz_A_Recipe_for_ICCV_2025_supplemental.pdf
|
@InProceedings{Schwarz_2025_ICCV,
author = {Schwarz, Katja and Rozumny, Denis and Bul\`o, Samuel Rota and Porzi, Lorenzo and Kontschieder, Peter},
title = {A Recipe for Generating 3D Worlds from a Single Image},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2025},
pages = {3520-3530}
}
|
We introduce a recipe for generating immersive 3D worlds from a single image by framing the task as an in-context learning problem for 2D inpainting models. This approach requires minimal training and uses existing generative models. Our process involves two steps: generating coherent panoramas using a pre-trained diffusion model and lifting these into 3D with a metric depth estimator. We then fill unobserved regions by conditioning the inpainting model on rendered point clouds, requiring minimal fine-tuning. Tested on both synthetic and real images, our method produces high-quality 3D environments suitable for VR display. By explicitly modeling the 3D structure of the generated environment from the start, our approach consistently outperforms state-of-the-art, video synthesis-based methods along multiple quantitative image quality metrics.
|
2503.16611
| null | null | null |
[] |
[] |
[] |
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] |
296
|
MVGBench: a Comprehensive Benchmark for Multi-view Generation Models
|
[
"Xianghui Xie",
"Jan Eric Lessen",
"Gerard Pons-Moll"
] |
https://openaccess.thecvf.com/content/ICCV2025/html/Xie_MVGBench_a_Comprehensive_Benchmark_for_Multi-view_Generation_Models_ICCV_2025_paper.html
|
https://openaccess.thecvf.com/content/ICCV2025/papers/Xie_MVGBench_a_Comprehensive_Benchmark_for_Multi-view_Generation_Models_ICCV_2025_paper.pdf
|
https://openaccess.thecvf.com/content/ICCV2025/supplemental/Xie_MVGBench_a_Comprehensive_ICCV_2025_supplemental.pdf
|
@InProceedings{Xie_2025_ICCV,
author = {Xie, Xianghui and Lessen, Jan Eric and Pons-Moll, Gerard},
title = {MVGBench: a Comprehensive Benchmark for Multi-view Generation Models},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2025},
pages = {8207-8218}
}
|
We propose MVGBench, a comprehensive benchmark for multi-view image generation models (MVGs) that evaluates 3D consistency in geometry and texture, image quality, and semantics (using vision language models). Recently, MVGs have been the main driving force in 3D object creation. However, existing metrics compare generated images against ground truth target views, which is not suitable for generative tasks where multiple solutions exist while differing from ground truth. Furthermore, different MVGs are trained on different view angles, synthetic data and specific lightings robustness to these factors and generalization to real data are rarely evaluated thoroughly. Without a rigorous evaluation protocol, it is also unclear what design choices contribute to the progress of MVGs. MVGBench evaluates three different aspects: best setup performance, generalization to real data and robustness. Instead of comparing against ground truth, we introduce a novel 3D self-consistency metric which compares 3D reconstructions from disjoint generated multi-views. We systematically compare 12 existing MVGs on 4 different curated real and synthetic datasets. With our analysis, we identify important limitations of existing methods specially in terms of robustness and generalization, and we find the most critical design choices. Using the discovered best practices, we propose ViFiGen, a method that outperforms all evaluated MVGs on 3D consistency. Our benchmark suite and pretrained models are released.
| null | null | null | null |
[] |
[] |
[] |
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] |
297
|
Fewer Denoising Steps or Cheaper Per-Step Inference: Towards Compute-Optimal Diffusion Model Deployment
|
[
"Zhenbang Du",
"Yonggan Fu",
"Lifu Wang",
"Jiayi Qian",
"Xiao Luo",
"Yingyan Celine Lin"
] |
https://openaccess.thecvf.com/content/ICCV2025/html/Du_Fewer_Denoising_Steps_or_Cheaper_Per-Step_Inference_Towards_Compute-Optimal_Diffusion_ICCV_2025_paper.html
|
https://openaccess.thecvf.com/content/ICCV2025/papers/Du_Fewer_Denoising_Steps_or_Cheaper_Per-Step_Inference_Towards_Compute-Optimal_Diffusion_ICCV_2025_paper.pdf
|
https://openaccess.thecvf.com/content/ICCV2025/supplemental/Du_Fewer_Denoising_Steps_ICCV_2025_supplemental.pdf
|
@InProceedings{Du_2025_ICCV,
author = {Du, Zhenbang and Fu, Yonggan and Wang, Lifu and Qian, Jiayi and Luo, Xiao and Lin, Yingyan Celine},
title = {Fewer Denoising Steps or Cheaper Per-Step Inference: Towards Compute-Optimal Diffusion Model Deployment},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2025},
pages = {3001-3010}
}
|
Diffusion models have shown remarkable success across generative tasks, yet their high computational demands challenge deployment on resource-limited platforms. This paper investigates a critical question for compute-optimal diffusion model deployment: Under a post-training setting without fine-tuning, is it more effective to reduce the number of denoising steps or to use a cheaper per-step inference? Intuitively, reducing the number of denoising steps increases the variability of the distributions across steps, making the model more sensitive to compression. In contrast, keeping more denoising steps makes the differences smaller, preserving redundancy, and making post-training compression more feasible. To systematically examine this, we propose PostDiff, a training-free framework for accelerating pre-trained diffusion models by reducing redundancy at both the input level and module level in a post-training manner. At the input level, we propose a mixed-resolution denoising scheme based on the insight that reducing generation resolution in early denoising steps can enhance low-frequency components and improve final generation fidelity. At the module level, we employ a hybrid module caching strategy to reuse computations across denoising steps. Extensive experiments and ablation studies demonstrate that (1) PostDiff can significantly improve the fidelity-efficiency trade-off of state-of-the-art diffusion models, and (2) to boost efficiency while maintaining decent generation fidelity, reducing per-step inference cost is often more effective than reducing the number of denoising steps. Our code is available at https://github.com/GATECH-EIC/PostDiff.
|
2508.06160
|
Accepted by ICCV 2025
|
https://github.com/GATECH-EIC/PostDiff
| null |
[] |
[] |
[] |
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] |
298
|
Competitive Distillation: A Simple Learning Strategy for Improving Visual Classification
|
[
"Daqian Shi",
"Xiaolei Diao",
"Xu Chen",
"Cédric M John"
] |
https://openaccess.thecvf.com/content/ICCV2025/html/Shi_Competitive_Distillation_A_Simple_Learning_Strategy_for_Improving_Visual_Classification_ICCV_2025_paper.html
|
https://openaccess.thecvf.com/content/ICCV2025/papers/Shi_Competitive_Distillation_A_Simple_Learning_Strategy_for_Improving_Visual_Classification_ICCV_2025_paper.pdf
| null |
@InProceedings{Shi_2025_ICCV,
author = {Shi, Daqian and Diao, Xiaolei and Chen, Xu and John, C\'edric M},
title = {Competitive Distillation: A Simple Learning Strategy for Improving Visual Classification},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2025},
pages = {2981-2990}
}
|
Deep Neural Networks (DNNs) have significantly advanced the field of computer vision. To improve DNN training process, knowledge distillation methods demonstrate their effectiveness in accelerating network training by introducing a fixed learning direction from the teacher network to student networks. In this context, several distillation-based optimization strategies are proposed, e.g., deep mutual learning and self-distillation, as an attempt to achieve generic training performance enhancement through the cooperative training of multiple networks. However, such strategies achieve limited improvements due to the poor understanding of the impact of learning directions among networks across different iterations. In this paper, we propose a novel competitive distillation strategy that allows each network in a group to potentially act as a teacher based on its performance, enhancing the overall learning performance. Competitive distillation organizes a group of networks to perform a shared task and engage in competition, where competitive optimization is proposed to improve the parameter updating process. We further introduce stochastic perturbation in competitive distillation, aiming to motivate networks to induce mutations to achieve better visual representations and global optimum. The experimental results show that competitive distillation achieves promising performance in diverse tasks and datasets.
|
2506.23285
|
Accepted by ICCV 2025
| null | null |
[] |
[] |
[] |
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] |
299
|
DynFaceRestore: Balancing Fidelity and Quality in Diffusion-Guided Blind Face Restoration with Dynamic Blur-Level Mapping and Guidance
|
[
"Huu-Phu Do",
"Yu-Wei Chen",
"Yi-Cheng Liao",
"Chi-Wei Hsiao",
"Han-Yang Wang",
"Wei-Chen Chiu",
"Ching-Chun Huang"
] |
https://openaccess.thecvf.com/content/ICCV2025/html/Do_DynFaceRestore_Balancing_Fidelity_and_Quality_in_Diffusion-Guided_Blind_Face_Restoration_ICCV_2025_paper.html
|
https://openaccess.thecvf.com/content/ICCV2025/papers/Do_DynFaceRestore_Balancing_Fidelity_and_Quality_in_Diffusion-Guided_Blind_Face_Restoration_ICCV_2025_paper.pdf
|
https://openaccess.thecvf.com/content/ICCV2025/supplemental/Do_DynFaceRestore_Balancing_Fidelity_ICCV_2025_supplemental.pdf
|
@InProceedings{Do_2025_ICCV,
author = {Do, Huu-Phu and Chen, Yu-Wei and Liao, Yi-Cheng and Hsiao, Chi-Wei and Wang, Han-Yang and Chiu, Wei-Chen and Huang, Ching-Chun},
title = {DynFaceRestore: Balancing Fidelity and Quality in Diffusion-Guided Blind Face Restoration with Dynamic Blur-Level Mapping and Guidance},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2025},
pages = {10432-10441}
}
|
Blind Face Restoration aims to recover high-fidelity, detail-rich facial images from unknown degraded inputs, presenting significant challenges in preserving both identity and detail. Pre-trained diffusion models have been increasingly used as image priors to generate fine details. Still, existing methods often use fixed diffusion sampling timesteps and a global guidance scale, assuming uniform degradation. This limitation and potentially imperfect degradation kernel estimation frequently lead to under- or over-diffusion, resulting in an imbalance between fidelity and quality. We propose DynFaceRestore, a novel blind face restoration approach that learns to map any blindly degraded input to Gaussian blurry images. By leveraging these blurry images and their respective Gaussian kernels, we dynamically select the starting timesteps for each blurry image and apply closed-form guidance during the diffusion sampling process to maintain fidelity. Additionally, we introduce a dynamic guidance scaling adjuster that modulates the guidance strength across local regions, enhancing detail generation in complex areas while preserving structural fidelity in contours. This strategy effectively balances the trade-off between fidelity and quality. DynFaceRestore achieves state-of-the-art performance in both quantitative and qualitative evaluations, demonstrating robustness and effectiveness in blind face restoration.
|
2507.13797
|
Accepted by ICCV 2025
| null | null |
[] |
[] |
[] |
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