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Wang_kh_Symmetry_Understanding_of_3D_Shapes_via_Chirality_Disentanglement_ICCV_2025_paper | kh: Symmetry Understanding of 3D Shapes via Chirality Disentanglement | [
"Weikang Wang",
"Tobias Weißberg",
"Nafie El Amrani",
"Florian Bernard"
] | https://openaccess.thecvf.com/content/ICCV2025/html/Wang_kh_Symmetry_Understanding_of_3D_Shapes_via_Chirality_Disentanglement_ICCV_2025_paper.html | https://openaccess.thecvf.com/content/ICCV2025/papers/Wang_kh_Symmetry_Understanding_of_3D_Shapes_via_Chirality_Disentanglement_ICCV_2025_paper.pdf | https://openaccess.thecvf.com/content/ICCV2025/supplemental/Wang_kh_Symmetry_Understanding_ICCV_2025_supplemental.pdf | 2508.05505 | title_judge | @InProceedings{Wang_2025_ICCV,
author = {Wang, Weikang and Wei{\ss}berg, Tobias and El Amrani, Nafie and Bernard, Florian},
title = {kh: Symmetry Understanding of 3D Shapes via Chirality Disentanglement},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
... | Chirality information (i.e. information that allows distinguishing left from right) is ubiquitous for various data modes in computer vision, including images, videos, point clouds, and meshes. While chirality has been extensively studied in the image domain, its exploration in shape analysis (such as point clouds and m... |
Yang_Efficient_Adaptation_of_Pre-trained_Vision_Transformer_underpinned_by_Approximately_Orthogonal_ICCV_2025_paper | Efficient Adaptation of Pre-trained Vision Transformer underpinned by Approximately Orthogonal Fine-Tuning Strategy | [
"Yiting Yang",
"Hao Luo",
"Yuan Sun",
"Qingsen Yan",
"Haokui Zhang",
"Wei Dong",
"Guoqing Wang",
"Peng Wang",
"Yang Yang",
"Hengtao Shen"
] | https://openaccess.thecvf.com/content/ICCV2025/html/Yang_Efficient_Adaptation_of_Pre-trained_Vision_Transformer_underpinned_by_Approximately_Orthogonal_ICCV_2025_paper.html | https://openaccess.thecvf.com/content/ICCV2025/papers/Yang_Efficient_Adaptation_of_Pre-trained_Vision_Transformer_underpinned_by_Approximately_Orthogonal_ICCV_2025_paper.pdf | https://openaccess.thecvf.com/content/ICCV2025/supplemental/Yang_Efficient_Adaptation_of_ICCV_2025_supplemental.zip | 2507.13260 | cvf | @InProceedings{Yang_2025_ICCV,
author = {Yang, Yiting and Luo, Hao and Sun, Yuan and Yan, Qingsen and Zhang, Haokui and Dong, Wei and Wang, Guoqing and Wang, Peng and Yang, Yang and Shen, Hengtao},
title = {Efficient Adaptation of Pre-trained Vision Transformer underpinned by Approximately Orthogonal Fin... | A prevalent approach in Parameter-Efficient Fine-Tuning (PEFT) of pre-trained Vision Transformers (ViT) involves freezing the majority of the backbone parameters and solely learning low-rank adaptation weight matrices to accommodate downstream tasks. These low-rank matrices are commonly derived through the multiplicati... |
Ding_MM-IFEngine_Towards_Multimodal_Instruction_Following_ICCV_2025_paper | MM-IFEngine: Towards Multimodal Instruction Following | [
"Shengyuan Ding",
"Shenxi Wu",
"Xiangyu Zhao",
"Yuhang Zang",
"Haodong Duan",
"Xiaoyi Dong",
"Pan Zhang",
"Yuhang Cao",
"Dahua Lin",
"Jiaqi Wang"
] | https://openaccess.thecvf.com/content/ICCV2025/html/Ding_MM-IFEngine_Towards_Multimodal_Instruction_Following_ICCV_2025_paper.html | https://openaccess.thecvf.com/content/ICCV2025/papers/Ding_MM-IFEngine_Towards_Multimodal_Instruction_Following_ICCV_2025_paper.pdf | https://openaccess.thecvf.com/content/ICCV2025/supplemental/Ding_MM-IFEngine_Towards_Multimodal_ICCV_2025_supplemental.pdf | 2504.07957 | title_snapshot | @InProceedings{Ding_2025_ICCV,
author = {Ding, Shengyuan and Wu, Shenxi and Zhao, Xiangyu and Zang, Yuhang and Duan, Haodong and Dong, Xiaoyi and Zhang, Pan and Cao, Yuhang and Lin, Dahua and Wang, Jiaqi},
title = {MM-IFEngine: Towards Multimodal Instruction Following},
booktitle = {Proceedings of th... | The Instruction Following (IF) ability measures how well Multi-modal Large Language Models (MLLMs) understand exactly what users are telling them and doing it right.Existing multimodal instruction following training data is scarce, the benchmarks are simple with atomic instructions, and the evaluation strategies are im... |
Zhou_Who_is_a_Better_Talker_Subjective_and_Objective_Quality_Assessment_ICCV_2025_paper | Who is a Better Talker: Subjective and Objective Quality Assessment for AI-Generated Talking Heads | [
"Yingjie Zhou",
"Jiezhang Cao",
"Zicheng Zhang",
"Farong Wen",
"Yanwei Jiang",
"Jun Jia",
"Xiaohong Liu",
"Xiongkuo Min",
"Guangtao Zhai"
] | https://openaccess.thecvf.com/content/ICCV2025/html/Zhou_Who_is_a_Better_Talker_Subjective_and_Objective_Quality_Assessment_ICCV_2025_paper.html | https://openaccess.thecvf.com/content/ICCV2025/papers/Zhou_Who_is_a_Better_Talker_Subjective_and_Objective_Quality_Assessment_ICCV_2025_paper.pdf | https://openaccess.thecvf.com/content/ICCV2025/supplemental/Zhou_Who_is_a_ICCV_2025_supplemental.pdf | 2507.23343 | cvf | @InProceedings{Zhou_2025_ICCV,
author = {Zhou, Yingjie and Cao, Jiezhang and Zhang, Zicheng and Wen, Farong and Jiang, Yanwei and Jia, Jun and Liu, Xiaohong and Min, Xiongkuo and Zhai, Guangtao},
title = {Who is a Better Talker: Subjective and Objective Quality Assessment for AI-Generated Talking Heads},... | Speech-driven methods for portraits are figuratively known as "Talkers" because of their capability to synthesize speaking mouth shapes and facial movements. Especially with the rapid development of the Text-to-Image (T2I) models, AI-Generated Talking Heads (AGTHs) have gradually become an emerging digital human media.... |
Yang_LayerAnimate_Layer-level_Control_for_Animation_ICCV_2025_paper | LayerAnimate: Layer-level Control for Animation | [
"Yuxue Yang",
"Lue Fan",
"Zuzeng Lin",
"Feng Wang",
"Zhaoxiang Zhang"
] | https://openaccess.thecvf.com/content/ICCV2025/html/Yang_LayerAnimate_Layer-level_Control_for_Animation_ICCV_2025_paper.html | https://openaccess.thecvf.com/content/ICCV2025/papers/Yang_LayerAnimate_Layer-level_Control_for_Animation_ICCV_2025_paper.pdf | https://openaccess.thecvf.com/content/ICCV2025/supplemental/Yang_LayerAnimate_Layer-level_Control_ICCV_2025_supplemental.pdf | 2501.08295 | cvf | @InProceedings{Yang_2025_ICCV,
author = {Yang, Yuxue and Fan, Lue and Lin, Zuzeng and Wang, Feng and Zhang, Zhaoxiang},
title = {LayerAnimate: Layer-level Control for Animation},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
... | Traditional animation production decomposes visual elements into discrete layers to enable independent processing for sketching, refining, coloring, and in-betweening. Existing anime generation video methods typically treat animation as a distinct data domain different from real-world videos, lacking fine-grained contr... |
Wang_Towards_a_Unified_Copernicus_Foundation_Model_for_Earth_Vision_ICCV_2025_paper | Towards a Unified Copernicus Foundation Model for Earth Vision | [
"Yi Wang",
"Zhitong Xiong",
"Chenying Liu",
"Adam J. Stewart",
"Thomas Dujardin",
"Nikolaos Ioannis Bountos",
"Angelos Zavras",
"Franziska Gerken",
"Ioannis Papoutsis",
"Laura Leal-Taixé",
"Xiao Xiang Zhu"
] | https://openaccess.thecvf.com/content/ICCV2025/html/Wang_Towards_a_Unified_Copernicus_Foundation_Model_for_Earth_Vision_ICCV_2025_paper.html | https://openaccess.thecvf.com/content/ICCV2025/papers/Wang_Towards_a_Unified_Copernicus_Foundation_Model_for_Earth_Vision_ICCV_2025_paper.pdf | https://openaccess.thecvf.com/content/ICCV2025/supplemental/Wang_Towards_a_Unified_ICCV_2025_supplemental.pdf | 2503.11849 | cvf | @InProceedings{Wang_2025_ICCV,
author = {Wang, Yi and Xiong, Zhitong and Liu, Chenying and Stewart, Adam J. and Dujardin, Thomas and Bountos, Nikolaos Ioannis and Zavras, Angelos and Gerken, Franziska and Papoutsis, Ioannis and Leal-Taix\'e, Laura and Zhu, Xiao Xiang},
title = {Towards a Unified Copernic... | Advances in Earth observation (EO) foundation models have unlocked the potential of big satellite data to learn generic representations from space, benefiting a wide range of downstream applications crucial to our planet. However, most existing efforts remain limited to fixed spectral sensors, focus solely on the Earth... |
Ghosh_ROADWork_A_Dataset_and_Benchmark_for_Learning_to_Recognize_Observe_ICCV_2025_paper | ROADWork: A Dataset and Benchmark for Learning to Recognize, Observe, Analyze and Drive Through Work Zones | [
"Anurag Ghosh",
"Shen Zheng",
"Robert Tamburo",
"Khiem Vuong",
"Juan Alvarez-Padilla",
"Hailiang Zhu",
"Michael Cardei",
"Nicholas Dunn",
"Christoph Mertz",
"Srinivasa G. Narasimhan"
] | https://openaccess.thecvf.com/content/ICCV2025/html/Ghosh_ROADWork_A_Dataset_and_Benchmark_for_Learning_to_Recognize_Observe_ICCV_2025_paper.html | https://openaccess.thecvf.com/content/ICCV2025/papers/Ghosh_ROADWork_A_Dataset_and_Benchmark_for_Learning_to_Recognize_Observe_ICCV_2025_paper.pdf | https://openaccess.thecvf.com/content/ICCV2025/supplemental/Ghosh_ROADWork_A_Dataset_ICCV_2025_supplemental.pdf | 2406.07661 | title_snapshot | @InProceedings{Ghosh_2025_ICCV,
author = {Ghosh, Anurag and Zheng, Shen and Tamburo, Robert and Vuong, Khiem and Alvarez-Padilla, Juan and Zhu, Hailiang and Cardei, Michael and Dunn, Nicholas and Mertz, Christoph and Narasimhan, Srinivasa G.},
title = {ROADWork: A Dataset and Benchmark for Learning to Re... | Perceiving and autonomously navigating through work zones is a challenging and under-explored problem. Open datasets for this long-tailed scenario are scarce. We propose the ROADWork dataset to learn to recognize, observe, analyze, and drive through work zones. State-of-the-art foundation models fail when applied to wo... |
Luo_Gradient_Decomposition_and_Alignment_for_Incremental_Object_Detection_ICCV_2025_paper | Gradient Decomposition and Alignment for Incremental Object Detection | [
"Wenlong Luo",
"Shizhou Zhang",
"De Cheng",
"Yinghui Xing",
"Guoqiang Liang",
"Peng Wang",
"Yanning Zhang"
] | https://openaccess.thecvf.com/content/ICCV2025/html/Luo_Gradient_Decomposition_and_Alignment_for_Incremental_Object_Detection_ICCV_2025_paper.html | https://openaccess.thecvf.com/content/ICCV2025/papers/Luo_Gradient_Decomposition_and_Alignment_for_Incremental_Object_Detection_ICCV_2025_paper.pdf | https://openaccess.thecvf.com/content/ICCV2025/supplemental/Luo_Gradient_Decomposition_and_ICCV_2025_supplemental.pdf | null | null | @InProceedings{Luo_2025_ICCV,
author = {Luo, Wenlong and Zhang, Shizhou and Cheng, De and Xing, Yinghui and Liang, Guoqiang and Wang, Peng and Zhang, Yanning},
title = {Gradient Decomposition and Alignment for Incremental Object Detection},
booktitle = {Proceedings of the IEEE/CVF International Confe... | Incremental object detection (IOD) is crucial for enabling AI systems to continuously learn new object classes over time while retaining knowledge of previously learned categories, allowing model to adapt to dynamic environments without forgetting prior information.Existing IOD methods primarily employ knowledge distil... |
Mao_One_Polyp_Identifies_All_One-Shot_Polyp_Segmentation_with_SAM_via_ICCV_2025_paper | One Polyp Identifies All: One-Shot Polyp Segmentation with SAM via Cascaded Priors and Iterative Prompt Evolution | [
"Xinyu Mao",
"Xiaohan Xing",
"Fei Meng",
"Jianbang Liu",
"Fan Bai",
"Qiang Nie",
"Max Meng"
] | https://openaccess.thecvf.com/content/ICCV2025/html/Mao_One_Polyp_Identifies_All_One-Shot_Polyp_Segmentation_with_SAM_via_ICCV_2025_paper.html | https://openaccess.thecvf.com/content/ICCV2025/papers/Mao_One_Polyp_Identifies_All_One-Shot_Polyp_Segmentation_with_SAM_via_ICCV_2025_paper.pdf | https://openaccess.thecvf.com/content/ICCV2025/supplemental/Mao_One_Polyp_Identifies_ICCV_2025_supplemental.pdf | 2507.16337 | cvf | @InProceedings{Mao_2025_ICCV,
author = {Mao, Xinyu and Xing, Xiaohan and Meng, Fei and Liu, Jianbang and Bai, Fan and Nie, Qiang and Meng, Max},
title = {One Polyp Identifies All: One-Shot Polyp Segmentation with SAM via Cascaded Priors and Iterative Prompt Evolution},
booktitle = {Proceedings of the... | Polyp segmentation is vital for early colorectal cancer detection, yet traditional fully supervised methods struggle with morphological variability and domain shifts, requiring frequent retraining. Additionally, reliance on large-scale annotations is a major bottleneck due to the time-consuming and error-prone nature o... |
Asaad_Gradient_Extrapolation_for_Debiased_Representation_Learning_ICCV_2025_paper | Gradient Extrapolation for Debiased Representation Learning | [
"Ihab Asaad",
"Maha Shadaydeh",
"Joachim Denzler"
] | https://openaccess.thecvf.com/content/ICCV2025/html/Asaad_Gradient_Extrapolation_for_Debiased_Representation_Learning_ICCV_2025_paper.html | https://openaccess.thecvf.com/content/ICCV2025/papers/Asaad_Gradient_Extrapolation_for_Debiased_Representation_Learning_ICCV_2025_paper.pdf | https://openaccess.thecvf.com/content/ICCV2025/supplemental/Asaad_Gradient_Extrapolation_for_ICCV_2025_supplemental.pdf | 2503.13236 | cvf | @InProceedings{Asaad_2025_ICCV,
author = {Asaad, Ihab and Shadaydeh, Maha and Denzler, Joachim},
title = {Gradient Extrapolation for Debiased Representation Learning},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year ... | Machine learning classification models trained with empirical risk minimization (ERM) often inadvertently rely on spurious correlations. When absent in the test data, these unintended associations between non-target attributes and target labels lead to poor generalization. This paper addresses this problem from a model... |
Huang_From_Gaze_to_Movement_Predicting_Visual_Attention_for_Autonomous_Driving_ICCV_2025_paper | From Gaze to Movement: Predicting Visual Attention for Autonomous Driving Human-Machine Interaction based on Programmatic Imitation Learning | [
"Yexin Huang",
"Yongbin Lin",
"Lishengsa Yue",
"Zhihong Yao",
"Jie Wang"
] | https://openaccess.thecvf.com/content/ICCV2025/html/Huang_From_Gaze_to_Movement_Predicting_Visual_Attention_for_Autonomous_Driving_ICCV_2025_paper.html | https://openaccess.thecvf.com/content/ICCV2025/papers/Huang_From_Gaze_to_Movement_Predicting_Visual_Attention_for_Autonomous_Driving_ICCV_2025_paper.pdf | https://openaccess.thecvf.com/content/ICCV2025/supplemental/Huang_From_Gaze_to_ICCV_2025_supplemental.pdf | null | null | @InProceedings{Huang_2025_ICCV,
author = {Huang, Yexin and Lin, Yongbin and Yue, Lishengsa and Yao, Zhihong and Wang, Jie},
title = {From Gaze to Movement: Predicting Visual Attention for Autonomous Driving Human-Machine Interaction based on Programmatic Imitation Learning},
booktitle = {Proceedings ... | Human-machine interaction technology requires not only the distribution of human visual attention but also the prediction of the gaze point trajectory. We introduce PILOT, a programmatic imitation learning approach that predicts a driver's eye movements based on a set of rule-based conditions. These conditions--derived... |
Wu_Less-to-More_Generalization_Unlocking_More_Controllability_by_In-Context_Generation_ICCV_2025_paper | Less-to-More Generalization: Unlocking More Controllability by In-Context Generation | [
"Shaojin Wu",
"Mengqi Huang",
"Wenxu Wu",
"Yufeng Cheng",
"Fei Ding",
"Qian He"
] | https://openaccess.thecvf.com/content/ICCV2025/html/Wu_Less-to-More_Generalization_Unlocking_More_Controllability_by_In-Context_Generation_ICCV_2025_paper.html | https://openaccess.thecvf.com/content/ICCV2025/papers/Wu_Less-to-More_Generalization_Unlocking_More_Controllability_by_In-Context_Generation_ICCV_2025_paper.pdf | https://openaccess.thecvf.com/content/ICCV2025/supplemental/Wu_Less-to-More_Generalization_Unlocking_ICCV_2025_supplemental.pdf | 2504.02160 | cvf | @InProceedings{Wu_2025_ICCV,
author = {Wu, Shaojin and Huang, Mengqi and Wu, Wenxu and Cheng, Yufeng and Ding, Fei and He, Qian},
title = {Less-to-More Generalization: Unlocking More Controllability by In-Context Generation},
booktitle = {Proceedings of the IEEE/CVF International Conference on Comput... | Although subject-driven generation has been extensively explored in image generation due to its wide applications, it still has challenges in data scalability and subject expansibility. For the first challenge, moving from curating single-subject datasets to multiple-subject ones and scaling them is particularly diffic... |
Hernandez_Improving_Large_Vision_and_Language_Models_by_Learning_from_a_ICCV_2025_paper | Improving Large Vision and Language Models by Learning from a Panel of Peers | [
"Jefferson Hernandez",
"Jing Shi",
"Simon Jenni",
"Vicente Ordonez",
"Kushal Kafle"
] | https://openaccess.thecvf.com/content/ICCV2025/html/Hernandez_Improving_Large_Vision_and_Language_Models_by_Learning_from_a_ICCV_2025_paper.html | https://openaccess.thecvf.com/content/ICCV2025/papers/Hernandez_Improving_Large_Vision_and_Language_Models_by_Learning_from_a_ICCV_2025_paper.pdf | https://openaccess.thecvf.com/content/ICCV2025/supplemental/Hernandez_Improving_Large_Vision_ICCV_2025_supplemental.pdf | 2509.01610 | cvf | @InProceedings{Hernandez_2025_ICCV,
author = {Hernandez, Jefferson and Shi, Jing and Jenni, Simon and Ordonez, Vicente and Kafle, Kushal},
title = {Improving Large Vision and Language Models by Learning from a Panel of Peers},
booktitle = {Proceedings of the IEEE/CVF International Conference on Compu... | Traditional alignment methods for Large Vision and Language Models (LVLMs) primarily rely on human-curated preference data. Human-generated preference data is costly; machine-generated preference data is limited in quality; and self-supervised preference data often introduces hallucinations. To overcome these limitatio... |
Yi_Federated_Representation_Angle_Learning_ICCV_2025_paper | Federated Representation Angle Learning | [
"Liping Yi",
"Han Yu",
"Gang Wang",
"Xiaoguang Liu",
"Xiaoxiao Li"
] | https://openaccess.thecvf.com/content/ICCV2025/html/Yi_Federated_Representation_Angle_Learning_ICCV_2025_paper.html | https://openaccess.thecvf.com/content/ICCV2025/papers/Yi_Federated_Representation_Angle_Learning_ICCV_2025_paper.pdf | null | null | null | @InProceedings{Yi_2025_ICCV,
author = {Yi, Liping and Yu, Han and Wang, Gang and Liu, Xiaoguang and Li, Xiaoxiao},
title = {Federated Representation Angle Learning},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year ... | Model-heterogeneous federated learning (MHFL) is a challenging FL paradigm designed to allow FL clients to train structurally heterogeneous models under the coordination of an FL server. Existing MHFL methods face significant limitations when it comes to transferring global knowledge to clients as a result of sharing o... |
Zheng_Why_LVLMs_Are_More_Prone_to_Hallucinations_in_Longer_Responses_ICCV_2025_paper | Why LVLMs Are More Prone to Hallucinations in Longer Responses: The Role of Context | [
"Ge Zheng",
"Jiaye Qian",
"Jiajin Tang",
"Sibei Yang"
] | https://openaccess.thecvf.com/content/ICCV2025/html/Zheng_Why_LVLMs_Are_More_Prone_to_Hallucinations_in_Longer_Responses_ICCV_2025_paper.html | https://openaccess.thecvf.com/content/ICCV2025/papers/Zheng_Why_LVLMs_Are_More_Prone_to_Hallucinations_in_Longer_Responses_ICCV_2025_paper.pdf | https://openaccess.thecvf.com/content/ICCV2025/supplemental/Zheng_Why_LVLMs_Are_ICCV_2025_supplemental.pdf | 2510.20229 | title_snapshot | @InProceedings{Zheng_2025_ICCV,
author = {Zheng, Ge and Qian, Jiaye and Tang, Jiajin and Yang, Sibei},
title = {Why LVLMs Are More Prone to Hallucinations in Longer Responses: The Role of Context},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month... | Large Vision-Language Models (LVLMs) have made significant progress in recent years but are also prone to hallucination issues. They exhibit more hallucinations in longer, free-form responses, often attributed to accumulated uncertainties. In this paper, we ask: Does increased hallucination result solely from length-in... |
Das_Training-Free_Personalization_via_Retrieval_and_Reasoning_on_Fingerprints_ICCV_2025_paper | Training-Free Personalization via Retrieval and Reasoning on Fingerprints | [
"Deepayan Das",
"Davide Talon",
"Yiming Wang",
"Massimiliano Mancini",
"Elisa Ricci"
] | https://openaccess.thecvf.com/content/ICCV2025/html/Das_Training-Free_Personalization_via_Retrieval_and_Reasoning_on_Fingerprints_ICCV_2025_paper.html | https://openaccess.thecvf.com/content/ICCV2025/papers/Das_Training-Free_Personalization_via_Retrieval_and_Reasoning_on_Fingerprints_ICCV_2025_paper.pdf | https://openaccess.thecvf.com/content/ICCV2025/supplemental/Das_Training-Free_Personalization_via_ICCV_2025_supplemental.pdf | 2503.18623 | cvf | @InProceedings{Das_2025_ICCV,
author = {Das, Deepayan and Talon, Davide and Wang, Yiming and Mancini, Massimiliano and Ricci, Elisa},
title = {Training-Free Personalization via Retrieval and Reasoning on Fingerprints},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Visi... | Vision Language Models (VLMs) have lead to major improvements in multimodal reasoning, yet they still struggle to understand user-specific concepts. Existing personalization methods address this limitation butheavily rely on training procedures, that can be either costly or unpleasant to individual users.We depart from... |
Chang_How_Far_are_AI-generated_Videos_from_Simulating_the_3D_Visual_ICCV_2025_paper | How Far are AI-generated Videos from Simulating the 3D Visual World: A Learned 3D Evaluation Approach | [
"Chirui Chang",
"Jiahui Liu",
"Zhengzhe Liu",
"Xiaoyang Lyu",
"Yi-Hua Huang",
"Xin Tao",
"Pengfei Wan",
"Di Zhang",
"Xiaojuan Qi"
] | https://openaccess.thecvf.com/content/ICCV2025/html/Chang_How_Far_are_AI-generated_Videos_from_Simulating_the_3D_Visual_ICCV_2025_paper.html | https://openaccess.thecvf.com/content/ICCV2025/papers/Chang_How_Far_are_AI-generated_Videos_from_Simulating_the_3D_Visual_ICCV_2025_paper.pdf | https://openaccess.thecvf.com/content/ICCV2025/supplemental/Chang_How_Far_are_ICCV_2025_supplemental.pdf | 2406.19568 | cvf | @InProceedings{Chang_2025_ICCV,
author = {Chang, Chirui and Liu, Jiahui and Liu, Zhengzhe and Lyu, Xiaoyang and Huang, Yi-Hua and Tao, Xin and Wan, Pengfei and Zhang, Di and Qi, Xiaojuan},
title = {How Far are AI-generated Videos from Simulating the 3D Visual World: A Learned 3D Evaluation Approach},
... | Recent advancements in video diffusion models enable the generation of photorealistic videos with impressive 3D consistency and temporal coherence. However, the extent to which these AI-generated videos simulate the 3D visual world remains underexplored. In this paper, we introduce Learned 3D Evaluation (L3DE), an obje... |
Zhou_Rethinking_Detecting_Salient_and_Camouflaged_Objects_in_Unconstrained_Scenes_ICCV_2025_paper | Rethinking Detecting Salient and Camouflaged Objects in Unconstrained Scenes | [
"Zhangjun Zhou",
"Yiping Li",
"Chunlin Zhong",
"Jianuo Huang",
"Jialun Pei",
"Hua Li",
"He Tang"
] | https://openaccess.thecvf.com/content/ICCV2025/html/Zhou_Rethinking_Detecting_Salient_and_Camouflaged_Objects_in_Unconstrained_Scenes_ICCV_2025_paper.html | https://openaccess.thecvf.com/content/ICCV2025/papers/Zhou_Rethinking_Detecting_Salient_and_Camouflaged_Objects_in_Unconstrained_Scenes_ICCV_2025_paper.pdf | https://openaccess.thecvf.com/content/ICCV2025/supplemental/Zhou_Rethinking_Detecting_Salient_ICCV_2025_supplemental.pdf | 2412.10943 | cvf | @InProceedings{Zhou_2025_ICCV,
author = {Zhou, Zhangjun and Li, Yiping and Zhong, Chunlin and Huang, Jianuo and Pei, Jialun and Li, Hua and Tang, He},
title = {Rethinking Detecting Salient and Camouflaged Objects in Unconstrained Scenes},
booktitle = {Proceedings of the IEEE/CVF International Confere... | While the human visual system employs distinct mechanisms to perceive salient and camouflaged objects, existing models struggle to disentangle these tasks. Specifically, salient object detection (SOD) models frequently misclassify camouflaged objects as salient, while camouflaged object detection (COD) models conversel... |
Liu_OccluGaussian_Occlusion-Aware_Gaussian_Splatting_for_Large_Scene_Reconstruction_and_Rendering_ICCV_2025_paper | OccluGaussian: Occlusion-Aware Gaussian Splatting for Large Scene Reconstruction and Rendering | [
"Shiyong Liu",
"Xiao Tang",
"Zhihao Li",
"Yingfan He",
"Chongjie Ye",
"Jianzhuang Liu",
"Binxiao Huang",
"Shunbo Zhou",
"Xiaofei Wu"
] | https://openaccess.thecvf.com/content/ICCV2025/html/Liu_OccluGaussian_Occlusion-Aware_Gaussian_Splatting_for_Large_Scene_Reconstruction_and_Rendering_ICCV_2025_paper.html | https://openaccess.thecvf.com/content/ICCV2025/papers/Liu_OccluGaussian_Occlusion-Aware_Gaussian_Splatting_for_Large_Scene_Reconstruction_and_Rendering_ICCV_2025_paper.pdf | https://openaccess.thecvf.com/content/ICCV2025/supplemental/Liu_OccluGaussian_Occlusion-Aware_Gaussian_ICCV_2025_supplemental.pdf | 2503.16177 | cvf | @InProceedings{Liu_2025_ICCV,
author = {Liu, Shiyong and Tang, Xiao and Li, Zhihao and He, Yingfan and Ye, Chongjie and Liu, Jianzhuang and Huang, Binxiao and Zhou, Shunbo and Wu, Xiaofei},
title = {OccluGaussian: Occlusion-Aware Gaussian Splatting for Large Scene Reconstruction and Rendering},
bookt... | In large-scale scene reconstruction using 3D Gaussian splatting, it is common to partition the scene into multiple smaller regions and reconstruct them individually. However, existing division methods are occlusion-agnostic, meaning that each region may contain areas with severe occlusions. As a result, the cameras wit... |
Ma_VisionMath_Vision-Form_Mathematical_Problem-Solving_ICCV_2025_paper | VisionMath: Vision-Form Mathematical Problem-Solving | [
"Zongyang Ma",
"Yuxin Chen",
"Ziqi Zhang",
"Zhongang Qi",
"Chunfeng Yuan",
"Shaojie Zhu",
"Chengxiang Zhuo",
"Bing Li",
"Ye Liu",
"Zang Li",
"Ying Shan",
"Weiming Hu"
] | https://openaccess.thecvf.com/content/ICCV2025/html/Ma_VisionMath_Vision-Form_Mathematical_Problem-Solving_ICCV_2025_paper.html | https://openaccess.thecvf.com/content/ICCV2025/papers/Ma_VisionMath_Vision-Form_Mathematical_Problem-Solving_ICCV_2025_paper.pdf | https://openaccess.thecvf.com/content/ICCV2025/supplemental/Ma_VisionMath_Vision-Form_Mathematical_ICCV_2025_supplemental.pdf | null | null | @InProceedings{Ma_2025_ICCV,
author = {Ma, Zongyang and Chen, Yuxin and Zhang, Ziqi and Qi, Zhongang and Yuan, Chunfeng and Zhu, Shaojie and Zhuo, Chengxiang and Li, Bing and Liu, Ye and Li, Zang and Shan, Ying and Hu, Weiming},
title = {VisionMath: Vision-Form Mathematical Problem-Solving},
booktitl... | Mathematical problems in real-world scenarios are often presented in a purely vision-form, where textual problem statement and accompanying math figures, e.g., geometry figures and functional graphs, are integrated into a single image. This vision-form problem-solving task requires precise comprehension and reasoning o... |
Shou_Unsupervised_RGB-D_Point_Cloud_Registration_for_Scenes_with_Low_Overlap_ICCV_2025_paper | Unsupervised RGB-D Point Cloud Registration for Scenes with Low Overlap and Photometric Inconsistency | [
"Yejun Shou",
"Haocheng Wang",
"Lingfeng Shen",
"Qian Zheng",
"Gang Pan",
"Yanlong Cao"
] | https://openaccess.thecvf.com/content/ICCV2025/html/Shou_Unsupervised_RGB-D_Point_Cloud_Registration_for_Scenes_with_Low_Overlap_ICCV_2025_paper.html | https://openaccess.thecvf.com/content/ICCV2025/papers/Shou_Unsupervised_RGB-D_Point_Cloud_Registration_for_Scenes_with_Low_Overlap_ICCV_2025_paper.pdf | https://openaccess.thecvf.com/content/ICCV2025/supplemental/Shou_Unsupervised_RGB-D_Point_ICCV_2025_supplemental.pdf | null | null | @InProceedings{Shou_2025_ICCV,
author = {Shou, Yejun and Wang, Haocheng and Shen, Lingfeng and Zheng, Qian and Pan, Gang and Cao, Yanlong},
title = {Unsupervised RGB-D Point Cloud Registration for Scenes with Low Overlap and Photometric Inconsistency},
booktitle = {Proceedings of the IEEE/CVF Interna... | Point cloud registration is a fundamental task in 3D vision, playing a crucial role in various fields. With the rapid advancement of RGB-D sensors, unsupervised point cloud registration methods based on RGB-D sequences have demonstrated excellent performance. However, existing methods struggle in scenes with low overla... |
Zhang_CWNet_Causal_Wavelet_Network_for_Low-Light_Image_Enhancement_ICCV_2025_paper | CWNet: Causal Wavelet Network for Low-Light Image Enhancement | [
"Tongshun Zhang",
"Pingping Liu",
"Yubing Lu",
"Mengen Cai",
"Zijian Zhang",
"Zhe Zhang",
"Qiuzhan Zhou"
] | https://openaccess.thecvf.com/content/ICCV2025/html/Zhang_CWNet_Causal_Wavelet_Network_for_Low-Light_Image_Enhancement_ICCV_2025_paper.html | https://openaccess.thecvf.com/content/ICCV2025/papers/Zhang_CWNet_Causal_Wavelet_Network_for_Low-Light_Image_Enhancement_ICCV_2025_paper.pdf | https://openaccess.thecvf.com/content/ICCV2025/supplemental/Zhang_CWNet_Causal_Wavelet_ICCV_2025_supplemental.pdf | 2507.10689 | cvf | @InProceedings{Zhang_2025_ICCV,
author = {Zhang, Tongshun and Liu, Pingping and Lu, Yubing and Cai, Mengen and Zhang, Zijian and Zhang, Zhe and Zhou, Qiuzhan},
title = {CWNet: Causal Wavelet Network for Low-Light Image Enhancement},
booktitle = {Proceedings of the IEEE/CVF International Conference on... | Traditional Low-Light Image Enhancement (LLIE) methods primarily focus on uniform brightness adjustment, often neglecting instance-level semantic information and the inherent characteristics of different features. To address these limitations, we propose CWNet (Causal Wavelet Network), a novel architecture that leverag... |
Cheng_Demeter_A_Parametric_Model_of_Crop_Plant_Morphology_from_the_ICCV_2025_paper | Demeter: A Parametric Model of Crop Plant Morphology from the Real World | [
"Tianhang Cheng",
"Albert J. Zhai",
"Evan Z. Chen",
"Rui Zhou",
"Yawen Deng",
"Zitong Li",
"Kejie Zhao",
"Janice Shiu",
"Qianyu Zhao",
"Yide Xu",
"Xinlei Wang",
"Yuan Shen",
"Sheng Wang",
"Lisa Ainsworth",
"Kaiyu Guan",
"Shenlong Wang"
] | https://openaccess.thecvf.com/content/ICCV2025/html/Cheng_Demeter_A_Parametric_Model_of_Crop_Plant_Morphology_from_the_ICCV_2025_paper.html | https://openaccess.thecvf.com/content/ICCV2025/papers/Cheng_Demeter_A_Parametric_Model_of_Crop_Plant_Morphology_from_the_ICCV_2025_paper.pdf | https://openaccess.thecvf.com/content/ICCV2025/supplemental/Cheng_Demeter_A_Parametric_ICCV_2025_supplemental.pdf | 2510.16377 | title_snapshot | @InProceedings{Cheng_2025_ICCV,
author = {Cheng, Tianhang and Zhai, Albert J. and Chen, Evan Z. and Zhou, Rui and Deng, Yawen and Li, Zitong and Zhao, Kejie and Shiu, Janice and Zhao, Qianyu and Xu, Yide and Wang, Xinlei and Shen, Yuan and Wang, Sheng and Ainsworth, Lisa and Guan, Kaiyu and Wang, Shenlong},
... | Learning 3D parametric shape models of objects has gained popularity in vision and graphics and has showed broad utility in 3D reconstruction, generation, understanding, and simulation. While powerful models exist for humans and animals, equally expressive approaches for modeling plants are lacking. In this work, we pr... |
Wang_VideoLLaMB_Long_Streaming_Video_Understanding_with_Recurrent_Memory_Bridges_ICCV_2025_paper | VideoLLaMB: Long Streaming Video Understanding with Recurrent Memory Bridges | [
"Yuxuan Wang",
"Yiqi Song",
"Cihang Xie",
"Yang Liu",
"Zilong Zheng"
] | https://openaccess.thecvf.com/content/ICCV2025/html/Wang_VideoLLaMB_Long_Streaming_Video_Understanding_with_Recurrent_Memory_Bridges_ICCV_2025_paper.html | https://openaccess.thecvf.com/content/ICCV2025/papers/Wang_VideoLLaMB_Long_Streaming_Video_Understanding_with_Recurrent_Memory_Bridges_ICCV_2025_paper.pdf | https://openaccess.thecvf.com/content/ICCV2025/supplemental/Wang_VideoLLaMB_Long_Streaming_ICCV_2025_supplemental.pdf | 2409.01071 | cvf | @InProceedings{Wang_2025_ICCV,
author = {Wang, Yuxuan and Song, Yiqi and Xie, Cihang and Liu, Yang and Zheng, Zilong},
title = {VideoLLaMB: Long Streaming Video Understanding with Recurrent Memory Bridges},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
... | Recent advancements in large-scale video-language models have shown significant potential for real-time planning and detailed interactions. However, their high computational demands and the scarcity of annotated datasets limit their practicality for academic researchers. In this work, we introduce VideoLLaMB, a novel a... |
Xu_Automated_Red_Teaming_for_Text-to-Image_Models_through_Feedback-Guided_Prompt_Iteration_ICCV_2025_paper | Automated Red Teaming for Text-to-Image Models through Feedback-Guided Prompt Iteration with Vision-Language Models | [
"Wei Xu",
"Kangjie Chen",
"Jiawei Qiu",
"Yuyang Zhang",
"Run Wang",
"Jin Mao",
"Tianwei Zhang",
"Lina Wang"
] | https://openaccess.thecvf.com/content/ICCV2025/html/Xu_Automated_Red_Teaming_for_Text-to-Image_Models_through_Feedback-Guided_Prompt_Iteration_ICCV_2025_paper.html | https://openaccess.thecvf.com/content/ICCV2025/papers/Xu_Automated_Red_Teaming_for_Text-to-Image_Models_through_Feedback-Guided_Prompt_Iteration_ICCV_2025_paper.pdf | https://openaccess.thecvf.com/content/ICCV2025/supplemental/Xu_Automated_Red_Teaming_ICCV_2025_supplemental.pdf | null | null | @InProceedings{Xu_2025_ICCV,
author = {Xu, Wei and Chen, Kangjie and Qiu, Jiawei and Zhang, Yuyang and Wang, Run and Mao, Jin and Zhang, Tianwei and Wang, Lina},
title = {Automated Red Teaming for Text-to-Image Models through Feedback-Guided Prompt Iteration with Vision-Language Models},
booktitle = ... | Text-to-image models have achieved remarkable progress in generating high-quality images from textual prompts, yet their potential for misuse like generating unsafe content remains a critical concern. Existing safety mechanisms, such as filtering and fine-tuning, remain insufficient in preventing vulnerabilities expose... |
Li_CoA-VLA_Improving_Vision-Language-Action_Models_via_Visual-Text_Chain-of-Affordance_ICCV_2025_paper | CoA-VLA: Improving Vision-Language-Action Models via Visual-Text Chain-of-Affordance | [
"Jinming Li",
"Yichen Zhu",
"Zhibin Tang",
"Junjie Wen",
"Minjie Zhu",
"Xiaoyu Liu",
"Chengmeng Li",
"Ran Cheng",
"Yaxin Peng",
"Yan Peng",
"Feifei Feng"
] | https://openaccess.thecvf.com/content/ICCV2025/html/Li_CoA-VLA_Improving_Vision-Language-Action_Models_via_Visual-Text_Chain-of-Affordance_ICCV_2025_paper.html | https://openaccess.thecvf.com/content/ICCV2025/papers/Li_CoA-VLA_Improving_Vision-Language-Action_Models_via_Visual-Text_Chain-of-Affordance_ICCV_2025_paper.pdf | https://openaccess.thecvf.com/content/ICCV2025/supplemental/Li_CoA-VLA_Improving_Vision-Language-Action_ICCV_2025_supplemental.pdf | 2412.20451 | title_judge | @InProceedings{Li_2025_ICCV,
author = {Li, Jinming and Zhu, Yichen and Tang, Zhibin and Wen, Junjie and Zhu, Minjie and Liu, Xiaoyu and Li, Chengmeng and Cheng, Ran and Peng, Yaxin and Peng, Yan and Feng, Feifei},
title = {CoA-VLA: Improving Vision-Language-Action Models via Visual-Text Chain-of-Affordan... | Robot foundation models, particularly Vision-Language-Action (VLA) models, have garnered significant attention for their ability to enhance robot policy learning, greatly improving robot's generalization and robustness. OpenAI's recent model, O1, showcased impressive capabilities in solving complex problems by utilizin... |
Peirone_HiERO_Understanding_the_Hierarchy_of_Human_Behavior_Enhances_Reasoning_on_ICCV_2025_paper | HiERO: Understanding the Hierarchy of Human Behavior Enhances Reasoning on Egocentric Videos | [
"Simone Alberto Peirone",
"Francesca Pistilli",
"Giuseppe Averta"
] | https://openaccess.thecvf.com/content/ICCV2025/html/Peirone_HiERO_Understanding_the_Hierarchy_of_Human_Behavior_Enhances_Reasoning_on_ICCV_2025_paper.html | https://openaccess.thecvf.com/content/ICCV2025/papers/Peirone_HiERO_Understanding_the_Hierarchy_of_Human_Behavior_Enhances_Reasoning_on_ICCV_2025_paper.pdf | https://openaccess.thecvf.com/content/ICCV2025/supplemental/Peirone_HiERO_Understanding_the_ICCV_2025_supplemental.pdf | 2505.12911 | cvf | @InProceedings{Peirone_2025_ICCV,
author = {Peirone, Simone Alberto and Pistilli, Francesca and Averta, Giuseppe},
title = {HiERO: Understanding the Hierarchy of Human Behavior Enhances Reasoning on Egocentric Videos},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Visi... | Human activities are particularly complex and variable, and this makes challenging for deep learning models to reason about them. However, we note that such variability does have an underlying structure, composed of a hierarchy of patterns of related actions. We argue that such structure can emerge naturally from unscr... |
Teng_FVGen_Accelerating_Novel-View_Synthesis_with_Adversarial_Video_Diffusion_Distillation_ICCV_2025_paper | FVGen: Accelerating Novel-View Synthesis with Adversarial Video Diffusion Distillation | [
"Wenbin Teng",
"Gonglin Chen",
"Haiwei Chen",
"Yajie Zhao"
] | https://openaccess.thecvf.com/content/ICCV2025/html/Teng_FVGen_Accelerating_Novel-View_Synthesis_with_Adversarial_Video_Diffusion_Distillation_ICCV_2025_paper.html | https://openaccess.thecvf.com/content/ICCV2025/papers/Teng_FVGen_Accelerating_Novel-View_Synthesis_with_Adversarial_Video_Diffusion_Distillation_ICCV_2025_paper.pdf | https://openaccess.thecvf.com/content/ICCV2025/supplemental/Teng_FVGen_Accelerating_Novel-View_ICCV_2025_supplemental.pdf | 2508.06392 | cvf | @InProceedings{Teng_2025_ICCV,
author = {Teng, Wenbin and Chen, Gonglin and Chen, Haiwei and Zhao, Yajie},
title = {FVGen: Accelerating Novel-View Synthesis with Adversarial Video Diffusion Distillation},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
... | Recent progress in 3D reconstruction has enabled realistic 3D models from dense image captures, yet challenges persist with sparse views, often leading to artifacts in unseen areas. Recent works leverage Video Diffusion Models (VDMs) to generate dense observations, filling the gaps when only sparse views are available ... |
Esmaeilzehi_ZFusion_Efficient_Deep_Compositional_Zero-shot_Learning_for_Blind_Image_Super-Resolution_ICCV_2025_paper | ZFusion: Efficient Deep Compositional Zero-shot Learning for Blind Image Super-Resolution with Generative Diffusion Prior | [
"Alireza Esmaeilzehi",
"Hossein Zaredar",
"Yapeng Tian",
"Laleh Seyyed-Kalantari"
] | https://openaccess.thecvf.com/content/ICCV2025/html/Esmaeilzehi_ZFusion_Efficient_Deep_Compositional_Zero-shot_Learning_for_Blind_Image_Super-Resolution_ICCV_2025_paper.html | https://openaccess.thecvf.com/content/ICCV2025/papers/Esmaeilzehi_ZFusion_Efficient_Deep_Compositional_Zero-shot_Learning_for_Blind_Image_Super-Resolution_ICCV_2025_paper.pdf | https://openaccess.thecvf.com/content/ICCV2025/supplemental/Esmaeilzehi_ZFusion_Efficient_Deep_ICCV_2025_supplemental.pdf | null | null | @InProceedings{Esmaeilzehi_2025_ICCV,
author = {Esmaeilzehi, Alireza and Zaredar, Hossein and Tian, Yapeng and Seyyed-Kalantari, Laleh},
title = {ZFusion: Efficient Deep Compositional Zero-shot Learning for Blind Image Super-Resolution with Generative Diffusion Prior},
booktitle = {Proceedings of the... | Deep blind image super resolution (Blind SR) schemes strive to provide high performances under various image degradation processes. Despite the significant advancement in the area of Blind SR, the performances of these methods still may not be as high as one would desire in the case of real-world degradation operations... |
Maity_Doodle_Your_Keypoints_Sketch-Based_Few-Shot_Keypoint_Detection_ICCV_2025_paper | Doodle Your Keypoints: Sketch-Based Few-Shot Keypoint Detection | [
"Subhajit Maity",
"Ayan Kumar Bhunia",
"Subhadeep Koley",
"Pinaki Nath Chowdhury",
"Aneeshan Sain",
"Yi-Zhe Song"
] | https://openaccess.thecvf.com/content/ICCV2025/html/Maity_Doodle_Your_Keypoints_Sketch-Based_Few-Shot_Keypoint_Detection_ICCV_2025_paper.html | https://openaccess.thecvf.com/content/ICCV2025/papers/Maity_Doodle_Your_Keypoints_Sketch-Based_Few-Shot_Keypoint_Detection_ICCV_2025_paper.pdf | https://openaccess.thecvf.com/content/ICCV2025/supplemental/Maity_Doodle_Your_Keypoints_ICCV_2025_supplemental.pdf | 2507.07994 | cvf | @InProceedings{Maity_2025_ICCV,
author = {Maity, Subhajit and Bhunia, Ayan Kumar and Koley, Subhadeep and Chowdhury, Pinaki Nath and Sain, Aneeshan and Song, Yi-Zhe},
title = {Doodle Your Keypoints: Sketch-Based Few-Shot Keypoint Detection},
booktitle = {Proceedings of the IEEE/CVF International Conf... | Keypoint detection, integral to modern machine perception, faces challenges in few-shot learning, particularly when source data from the same distribution as the query is unavailable. This gap is addressed by leveraging sketches, a popular form of human expression, providing a source-free alternative. However, challeng... |
Wang_Open-Vocabulary_Octree-Graph_for_3D_Scene_Understanding_ICCV_2025_paper | Open-Vocabulary Octree-Graph for 3D Scene Understanding | [
"Zhigang Wang",
"Yifei Su",
"Chenhui Li",
"Dong Wang",
"Yan Huang",
"Xuelong Li",
"Bin Zhao"
] | https://openaccess.thecvf.com/content/ICCV2025/html/Wang_Open-Vocabulary_Octree-Graph_for_3D_Scene_Understanding_ICCV_2025_paper.html | https://openaccess.thecvf.com/content/ICCV2025/papers/Wang_Open-Vocabulary_Octree-Graph_for_3D_Scene_Understanding_ICCV_2025_paper.pdf | null | 2411.16253 | cvf | @InProceedings{Wang_2025_ICCV,
author = {Wang, Zhigang and Su, Yifei and Li, Chenhui and Wang, Dong and Huang, Yan and Li, Xuelong and Zhao, Bin},
title = {Open-Vocabulary Octree-Graph for 3D Scene Understanding},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (I... | Open-vocabulary 3D scene understanding is indispensable for embodied agents. Recent works leverage pretrained vision-language models (VLMs) for object segmentation and project them to point clouds to build 3D maps. Despite progress, a point cloud is a set of unordered coordinates that requires substantial storage space... |
Xu_FlexGen_Flexible_Multi-View_Generation_from_Text_and_Image_Inputs_ICCV_2025_paper | FlexGen: Flexible Multi-View Generation from Text and Image Inputs | [
"Xinli Xu",
"Wenhang Ge",
"Jiantao Lin",
"Jiawei Feng",
"Lie Xu",
"Hanfeng Zhao",
"Shunsi Zhang",
"Ying-Cong Chen"
] | https://openaccess.thecvf.com/content/ICCV2025/html/Xu_FlexGen_Flexible_Multi-View_Generation_from_Text_and_Image_Inputs_ICCV_2025_paper.html | https://openaccess.thecvf.com/content/ICCV2025/papers/Xu_FlexGen_Flexible_Multi-View_Generation_from_Text_and_Image_Inputs_ICCV_2025_paper.pdf | https://openaccess.thecvf.com/content/ICCV2025/supplemental/Xu_FlexGen_Flexible_Multi-View_ICCV_2025_supplemental.pdf | 2410.10745 | cvf | @InProceedings{Xu_2025_ICCV,
author = {Xu, Xinli and Ge, Wenhang and Lin, Jiantao and Feng, Jiawei and Xu, Lie and Zhao, Hanfeng and Zhang, Shunsi and Chen, Ying-Cong},
title = {FlexGen: Flexible Multi-View Generation from Text and Image Inputs},
booktitle = {Proceedings of the IEEE/CVF International... | In this work, we introduce FlexGen, a flexible framework designed to generate controllable and consistent multi-view images, conditioned on a single-view image, or a text prompt, or both. FlexGen tackles the challenges of controllable multi-view synthesis through additional conditioning on 3D-aware text annotations. We... |
Kim_SummDiff_Generative_Modeling_of_Video_Summarization_with_Diffusion_ICCV_2025_paper | SummDiff: Generative Modeling of Video Summarization with Diffusion | [
"Kwanseok Kim",
"Jaehoon Hahm",
"Sumin Kim",
"Jinhwan Sul",
"Byunghak Kim",
"Joonseok Lee"
] | https://openaccess.thecvf.com/content/ICCV2025/html/Kim_SummDiff_Generative_Modeling_of_Video_Summarization_with_Diffusion_ICCV_2025_paper.html | https://openaccess.thecvf.com/content/ICCV2025/papers/Kim_SummDiff_Generative_Modeling_of_Video_Summarization_with_Diffusion_ICCV_2025_paper.pdf | https://openaccess.thecvf.com/content/ICCV2025/supplemental/Kim_SummDiff_Generative_Modeling_ICCV_2025_supplemental.pdf | 2510.08458 | cvf | @InProceedings{Kim_2025_ICCV,
author = {Kim, Kwanseok and Hahm, Jaehoon and Kim, Sumin and Sul, Jinhwan and Kim, Byunghak and Lee, Joonseok},
title = {SummDiff: Generative Modeling of Video Summarization with Diffusion},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vi... | Video summarization is a task of shortening a video by choosing a subset of frames while preserving its essential moments. Despite the innate subjectivity of the task, previous works have deterministically regressed to an averaged frame score over multiple raters, ignoring the inherent subjectivity of what constitutes ... |
Kim_FlowDPS__Flow-Driven_Posterior_Sampling_for_Inverse_Problems_ICCV_2025_paper | FlowDPS : Flow-Driven Posterior Sampling for Inverse Problems | [
"Jeongsol Kim",
"Bryan Sangwoo Kim",
"Jong Chul Ye"
] | https://openaccess.thecvf.com/content/ICCV2025/html/Kim_FlowDPS__Flow-Driven_Posterior_Sampling_for_Inverse_Problems_ICCV_2025_paper.html | https://openaccess.thecvf.com/content/ICCV2025/papers/Kim_FlowDPS__Flow-Driven_Posterior_Sampling_for_Inverse_Problems_ICCV_2025_paper.pdf | https://openaccess.thecvf.com/content/ICCV2025/supplemental/Kim_FlowDPS__Flow-Driven_ICCV_2025_supplemental.pdf | 2503.08136 | cvf | @InProceedings{Kim_2025_ICCV,
author = {Kim, Jeongsol and Kim, Bryan Sangwoo and Ye, Jong Chul},
title = {FlowDPS : Flow-Driven Posterior Sampling for Inverse Problems},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year ... | Flow matching is a recent state-of-the-art framework for generative modeling based on ordinary differential equations (ODEs). While closely related to diffusion models, it provides a more general perspective on generative modeling.Although inverse problem solving has been extensively explored using diffusion models, it... |
Tran_Head2Body_Body_Pose_Generation_from_Multi-sensory_Head-mounted_Inputs_ICCV_2025_paper | Head2Body: Body Pose Generation from Multi-sensory Head-mounted Inputs | [
"Minh Tran",
"Hongda Mao",
"Qingshuang Chen",
"Yelin Kim"
] | https://openaccess.thecvf.com/content/ICCV2025/html/Tran_Head2Body_Body_Pose_Generation_from_Multi-sensory_Head-mounted_Inputs_ICCV_2025_paper.html | https://openaccess.thecvf.com/content/ICCV2025/papers/Tran_Head2Body_Body_Pose_Generation_from_Multi-sensory_Head-mounted_Inputs_ICCV_2025_paper.pdf | https://openaccess.thecvf.com/content/ICCV2025/supplemental/Tran_Head2Body_Body_Pose_ICCV_2025_supplemental.pdf | null | null | @InProceedings{Tran_2025_ICCV,
author = {Tran, Minh and Mao, Hongda and Chen, Qingshuang and Kim, Yelin},
title = {Head2Body: Body Pose Generation from Multi-sensory Head-mounted Inputs},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {Oc... | Generating body pose from head-mounted, egocentric inputs is essential for immersive VR/AR and assistive technologies, as it supports more natural interactions. However, the task is challenging due to limited visibility of body parts in first-person views and the sparseness of sensory data, with only a single device pl... |
Tang_Closed-Loop_Transfer_for_Weakly-supervised_Affordance_Grounding_ICCV_2025_paper | Closed-Loop Transfer for Weakly-supervised Affordance Grounding | [
"Jiajin Tang",
"Zhengxuan Wei",
"Ge Zheng",
"Sibei Yang"
] | https://openaccess.thecvf.com/content/ICCV2025/html/Tang_Closed-Loop_Transfer_for_Weakly-supervised_Affordance_Grounding_ICCV_2025_paper.html | https://openaccess.thecvf.com/content/ICCV2025/papers/Tang_Closed-Loop_Transfer_for_Weakly-supervised_Affordance_Grounding_ICCV_2025_paper.pdf | null | 2510.17384 | title_snapshot | @InProceedings{Tang_2025_ICCV,
author = {Tang, Jiajin and Wei, Zhengxuan and Zheng, Ge and Yang, Sibei},
title = {Closed-Loop Transfer for Weakly-supervised Affordance Grounding},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
... | Humans can perform previously unexperienced interactions with novel objects simply by observing others engage with them. Weakly-supervised affordance grounding mimics this process by learning to locate object regions that enable actions on egocentric images, using exocentric interaction images with image-level annotati... |
Tan_OminiControl_Minimal_and_Universal_Control_for_Diffusion_Transformer_ICCV_2025_paper | OminiControl: Minimal and Universal Control for Diffusion Transformer | [
"Zhenxiong Tan",
"Songhua Liu",
"Xingyi Yang",
"Qiaochu Xue",
"Xinchao Wang"
] | https://openaccess.thecvf.com/content/ICCV2025/html/Tan_OminiControl_Minimal_and_Universal_Control_for_Diffusion_Transformer_ICCV_2025_paper.html | https://openaccess.thecvf.com/content/ICCV2025/papers/Tan_OminiControl_Minimal_and_Universal_Control_for_Diffusion_Transformer_ICCV_2025_paper.pdf | https://openaccess.thecvf.com/content/ICCV2025/supplemental/Tan_OminiControl_Minimal_and_ICCV_2025_supplemental.pdf | 2411.15098 | cvf | @InProceedings{Tan_2025_ICCV,
author = {Tan, Zhenxiong and Liu, Songhua and Yang, Xingyi and Xue, Qiaochu and Wang, Xinchao},
title = {OminiControl: Minimal and Universal Control for Diffusion Transformer},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
... | We present OminiControl, a novel approach that rethinks how image conditions are integrated into Diffusion Transformer (DiT) architectures. Current image conditioning methods either introduce substantial parameter overhead or handle only specific control tasks effectively, limiting their practical versatility. OminiCon... |
Yu_Zeroth-Order_Fine-Tuning_of_LLMs_in_Random_Subspaces_ICCV_2025_paper | Zeroth-Order Fine-Tuning of LLMs in Random Subspaces | [
"Ziming Yu",
"Pan Zhou",
"Sike Wang",
"Jia Li",
"Mi Tian",
"Hua Huang"
] | https://openaccess.thecvf.com/content/ICCV2025/html/Yu_Zeroth-Order_Fine-Tuning_of_LLMs_in_Random_Subspaces_ICCV_2025_paper.html | https://openaccess.thecvf.com/content/ICCV2025/papers/Yu_Zeroth-Order_Fine-Tuning_of_LLMs_in_Random_Subspaces_ICCV_2025_paper.pdf | https://openaccess.thecvf.com/content/ICCV2025/supplemental/Yu_Zeroth-Order_Fine-Tuning_of_ICCV_2025_supplemental.pdf | 2410.08989 | cvf | @InProceedings{Yu_2025_ICCV,
author = {Yu, Ziming and Zhou, Pan and Wang, Sike and Li, Jia and Tian, Mi and Huang, Hua},
title = {Zeroth-Order Fine-Tuning of LLMs in Random Subspaces},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {Octob... | Fine-tuning Large Language Models (LLMs) has proven effective for a variety of downstream tasks. However, as LLMs grow in size, the memory demands for backpropagation become increasingly prohibitive. Zeroth-order (ZO) optimization methods offer a memory-efficient alternative by using forward passes to estimate gradient... |
Rakib_G2D_Boosting_Multimodal_Learning_with_Gradient-Guided_Distillation_ICCV_2025_paper | G2D: Boosting Multimodal Learning with Gradient-Guided Distillation | [
"Mohammed Rakib",
"Arunkumar Bagavathi"
] | https://openaccess.thecvf.com/content/ICCV2025/html/Rakib_G2D_Boosting_Multimodal_Learning_with_Gradient-Guided_Distillation_ICCV_2025_paper.html | https://openaccess.thecvf.com/content/ICCV2025/papers/Rakib_G2D_Boosting_Multimodal_Learning_with_Gradient-Guided_Distillation_ICCV_2025_paper.pdf | https://openaccess.thecvf.com/content/ICCV2025/supplemental/Rakib_G2D_Boosting_Multimodal_ICCV_2025_supplemental.pdf | 2506.21514 | title_judge | @InProceedings{Rakib_2025_ICCV,
author = {Rakib, Mohammed and Bagavathi, Arunkumar},
title = {G2D: Boosting Multimodal Learning with Gradient-Guided Distillation},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = ... | Multimodal learning aims to leverage information from diverse data modalities to achieve more comprehensive performance. However, conventional multimodal models often suffer from modality imbalance, where one or a few modalities dominate model optimization, leading to suboptimal feature representation and underutilizat... |
Li_AIComposer_Any_Style_and_Content_Image_Composition_via_Feature_Integration_ICCV_2025_paper | AIComposer: Any Style and Content Image Composition via Feature Integration | [
"Haowen Li",
"Zhenfeng Fan",
"Zhang Wen",
"Zhengzhou Zhu",
"Yunjin Li"
] | https://openaccess.thecvf.com/content/ICCV2025/html/Li_AIComposer_Any_Style_and_Content_Image_Composition_via_Feature_Integration_ICCV_2025_paper.html | https://openaccess.thecvf.com/content/ICCV2025/papers/Li_AIComposer_Any_Style_and_Content_Image_Composition_via_Feature_Integration_ICCV_2025_paper.pdf | https://openaccess.thecvf.com/content/ICCV2025/supplemental/Li_AIComposer_Any_Style_ICCV_2025_supplemental.zip | 2507.20721 | cvf | @InProceedings{Li_2025_ICCV,
author = {Li, Haowen and Fan, Zhenfeng and Wen, Zhang and Zhu, Zhengzhou and Li, Yunjin},
title = {AIComposer: Any Style and Content Image Composition via Feature Integration},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
... | Image composition has advanced significantly with large-scale pre-trained T2I diffusion models. Despite progress in same-domain composition, cross-domain composition remains under-explored. The main challenges are the stochastic nature of diffusion models and the style gap between input images, leading to failures and ... |
Do_PAN-Crafter_Learning_Modality-Consistent_Alignment_for_PAN-Sharpening_ICCV_2025_paper | PAN-Crafter: Learning Modality-Consistent Alignment for PAN-Sharpening | [
"Jeonghyeok Do",
"Sungpyo Kim",
"Geunhyuk Youk",
"Jaehyup Lee",
"Munchurl Kim"
] | https://openaccess.thecvf.com/content/ICCV2025/html/Do_PAN-Crafter_Learning_Modality-Consistent_Alignment_for_PAN-Sharpening_ICCV_2025_paper.html | https://openaccess.thecvf.com/content/ICCV2025/papers/Do_PAN-Crafter_Learning_Modality-Consistent_Alignment_for_PAN-Sharpening_ICCV_2025_paper.pdf | https://openaccess.thecvf.com/content/ICCV2025/supplemental/Do_PAN-Crafter_Learning_Modality-Consistent_ICCV_2025_supplemental.pdf | 2505.23367 | title_snapshot | @InProceedings{Do_2025_ICCV,
author = {Do, Jeonghyeok and Kim, Sungpyo and Youk, Geunhyuk and Lee, Jaehyup and Kim, Munchurl},
title = {PAN-Crafter: Learning Modality-Consistent Alignment for PAN-Sharpening},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)}... | PAN-sharpening aims to fuse high-resolution panchromatic (PAN) images with low-resolution multi-spectral (MS) images to generate high-resolution multi-spectral (HRMS) outputs. However, cross-modality misalignment---caused by sensor placement, acquisition timing, and resolution disparity---induces a fundamental challeng... |
Nam_M2SFormer_Multi-Spectral_and_Multi-Scale_Attention_with_Edge-Aware_Difficulty_Guidance_for_ICCV_2025_paper | M2SFormer: Multi-Spectral and Multi-Scale Attention with Edge-Aware Difficulty Guidance for Image Forgery Localization | [
"Ju-Hyeon Nam",
"Dong-Hyun Moon",
"Sang-Chul Lee"
] | https://openaccess.thecvf.com/content/ICCV2025/html/Nam_M2SFormer_Multi-Spectral_and_Multi-Scale_Attention_with_Edge-Aware_Difficulty_Guidance_for_ICCV_2025_paper.html | https://openaccess.thecvf.com/content/ICCV2025/papers/Nam_M2SFormer_Multi-Spectral_and_Multi-Scale_Attention_with_Edge-Aware_Difficulty_Guidance_for_ICCV_2025_paper.pdf | https://openaccess.thecvf.com/content/ICCV2025/supplemental/Nam_M2SFormer_Multi-Spectral_and_ICCV_2025_supplemental.pdf | 2506.20922 | cvf | @InProceedings{Nam_2025_ICCV,
author = {Nam, Ju-Hyeon and Moon, Dong-Hyun and Lee, Sang-Chul},
title = {M2SFormer: Multi-Spectral and Multi-Scale Attention with Edge-Aware Difficulty Guidance for Image Forgery Localization},
booktitle = {Proceedings of the IEEE/CVF International Conference on Compute... | Image editing techniques have rapidly advanced, facilitating both innovative use cases and malicious manipulation of digital images. Deep learning-based methods have recently achieved high accuracy in pixel-level forgery localization, yet they frequently struggle with computational overhead and limited representation p... |
Lu_Pinco_Position-induced_Consistent_Adapter_for_Diffusion_Transformer_in_Foreground-conditioned_Inpainting_ICCV_2025_paper | Pinco: Position-induced Consistent Adapter for Diffusion Transformer in Foreground-conditioned Inpainting | [
"Guangben Lu",
"Yuzhen Du",
"Yizhe Tang",
"Zhimin Sun",
"Ran Yi",
"Yifan Qi",
"Tianyi Wang",
"Lizhuang Ma",
"Fangyuan Zou"
] | https://openaccess.thecvf.com/content/ICCV2025/html/Lu_Pinco_Position-induced_Consistent_Adapter_for_Diffusion_Transformer_in_Foreground-conditioned_Inpainting_ICCV_2025_paper.html | https://openaccess.thecvf.com/content/ICCV2025/papers/Lu_Pinco_Position-induced_Consistent_Adapter_for_Diffusion_Transformer_in_Foreground-conditioned_Inpainting_ICCV_2025_paper.pdf | https://openaccess.thecvf.com/content/ICCV2025/supplemental/Lu_Pinco_Position-induced_Consistent_ICCV_2025_supplemental.pdf | 2412.03812 | cvf | @InProceedings{Lu_2025_ICCV,
author = {Lu, Guangben and Du, Yuzhen and Tang, Yizhe and Sun, Zhimin and Yi, Ran and Qi, Yifan and Wang, Tianyi and Ma, Lizhuang and Zou, Fangyuan},
title = {Pinco: Position-induced Consistent Adapter for Diffusion Transformer in Foreground-conditioned Inpainting},
bookt... | Foreground-conditioned inpainting aims to seamlessly fill the background region of an image by utilizing the provided foreground subject and a text description. While existing T2I-based image inpainting methods can be applied to this task, they suffer from issues of subject shape expansion, distortion, or impaired abil... |
Zhao_ReconDreamer_Harmonizing_Generative_and_Reconstructive_Models_for_Driving_Scene_Representation_ICCV_2025_paper | ReconDreamer++: Harmonizing Generative and Reconstructive Models for Driving Scene Representation | [
"Guosheng Zhao",
"Xiaofeng Wang",
"Chaojun Ni",
"Zheng Zhu",
"Wenkang Qin",
"Guan Huang",
"Xingang Wang"
] | https://openaccess.thecvf.com/content/ICCV2025/html/Zhao_ReconDreamer_Harmonizing_Generative_and_Reconstructive_Models_for_Driving_Scene_Representation_ICCV_2025_paper.html | https://openaccess.thecvf.com/content/ICCV2025/papers/Zhao_ReconDreamer_Harmonizing_Generative_and_Reconstructive_Models_for_Driving_Scene_Representation_ICCV_2025_paper.pdf | https://openaccess.thecvf.com/content/ICCV2025/supplemental/Zhao_ReconDreamer_Harmonizing_Generative_ICCV_2025_supplemental.zip | 2503.18438 | title_snapshot | @InProceedings{Zhao_2025_ICCV,
author = {Zhao, Guosheng and Wang, Xiaofeng and Ni, Chaojun and Zhu, Zheng and Qin, Wenkang and Huang, Guan and Wang, Xingang},
title = {ReconDreamer++: Harmonizing Generative and Reconstructive Models for Driving Scene Representation},
booktitle = {Proceedings of the I... | Combining reconstruction models with generative models has emerged as a promising paradigm for closed-loop simulation in autonomous driving. For example, ReconDreamer has demonstrated remarkable success in rendering large-scale maneuvers. However, a significant gap remains between the generated data and real-world sens... |
He_SyncDiff_Synchronized_Motion_Diffusion_for_Multi-Body_Human-Object_Interaction_Synthesis_ICCV_2025_paper | SyncDiff: Synchronized Motion Diffusion for Multi-Body Human-Object Interaction Synthesis | [
"Wenkun He",
"Yun Liu",
"Ruitao Liu",
"Li Yi"
] | https://openaccess.thecvf.com/content/ICCV2025/html/He_SyncDiff_Synchronized_Motion_Diffusion_for_Multi-Body_Human-Object_Interaction_Synthesis_ICCV_2025_paper.html | https://openaccess.thecvf.com/content/ICCV2025/papers/He_SyncDiff_Synchronized_Motion_Diffusion_for_Multi-Body_Human-Object_Interaction_Synthesis_ICCV_2025_paper.pdf | https://openaccess.thecvf.com/content/ICCV2025/supplemental/He_SyncDiff_Synchronized_Motion_ICCV_2025_supplemental.zip | 2412.20104 | cvf | @InProceedings{He_2025_ICCV,
author = {He, Wenkun and Liu, Yun and Liu, Ruitao and Yi, Li},
title = {SyncDiff: Synchronized Motion Diffusion for Multi-Body Human-Object Interaction Synthesis},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month ... | Synthesizing realistic human-object interaction motions is a critical problem in VR/AR and human animation. Unlike the commonly studied scenarios involving a single human or hand interacting with one object, we address a more generic multi-body setting with arbitrary numbers of humans, hands, and objects. The high corr... |
Kravets_Rethinking_Few_Shot_CLIP_Benchmarks_A_Critical_Analysis_in_the_ICCV_2025_paper | Rethinking Few Shot CLIP Benchmarks: A Critical Analysis in the Inductive Setting | [
"Alexey Kravets",
"Da Chen",
"Vinay P. Namboodiri"
] | https://openaccess.thecvf.com/content/ICCV2025/html/Kravets_Rethinking_Few_Shot_CLIP_Benchmarks_A_Critical_Analysis_in_the_ICCV_2025_paper.html | https://openaccess.thecvf.com/content/ICCV2025/papers/Kravets_Rethinking_Few_Shot_CLIP_Benchmarks_A_Critical_Analysis_in_the_ICCV_2025_paper.pdf | https://openaccess.thecvf.com/content/ICCV2025/supplemental/Kravets_Rethinking_Few_Shot_ICCV_2025_supplemental.pdf | 2507.20834 | cvf | @InProceedings{Kravets_2025_ICCV,
author = {Kravets, Alexey and Chen, Da and Namboodiri, Vinay P.},
title = {Rethinking Few Shot CLIP Benchmarks: A Critical Analysis in the Inductive Setting},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month ... | CLIP is a foundational model with transferable classification performance in the few-shot setting. Several methods have shown improved performance of CLIP using few-shot examples. However, so far all these techniques have been benchmarked using standard few-shot datasets. We argue that this mode of evaluation does not ... |
Liu_Mind_the_Gap_Aligning_Vision_Foundation_Models_to_Image_Feature_ICCV_2025_paper | Mind the Gap: Aligning Vision Foundation Models to Image Feature Matching | [
"Yuhan Liu",
"Jingwen Fu",
"Yang Wu",
"Kangyi Wu",
"Pengna Li",
"Jiayi Wu",
"Sanping Zhou",
"Jingmin Xin"
] | https://openaccess.thecvf.com/content/ICCV2025/html/Liu_Mind_the_Gap_Aligning_Vision_Foundation_Models_to_Image_Feature_ICCV_2025_paper.html | https://openaccess.thecvf.com/content/ICCV2025/papers/Liu_Mind_the_Gap_Aligning_Vision_Foundation_Models_to_Image_Feature_ICCV_2025_paper.pdf | https://openaccess.thecvf.com/content/ICCV2025/supplemental/Liu_Mind_the_Gap_ICCV_2025_supplemental.pdf | 2507.10318 | cvf | @InProceedings{Liu_2025_ICCV,
author = {Liu, Yuhan and Fu, Jingwen and Wu, Yang and Wu, Kangyi and Li, Pengna and Wu, Jiayi and Zhou, Sanping and Xin, Jingmin},
title = {Mind the Gap: Aligning Vision Foundation Models to Image Feature Matching},
booktitle = {Proceedings of the IEEE/CVF International ... | Leveraging the vision foundation models has emerged as a mainstream paradigm that improves the performance of image feature matching. However, previous works have ignored the misalignment when introducing the foundation models into feature matching. The misalignment arises from the discrepancy between the foundation mo... |
Yang_CoStoDet-DDPM_Collaborative_Training_of_Stochastic_and_Deterministic_Models_Improves_Surgical_ICCV_2025_paper | CoStoDet-DDPM: Collaborative Training of Stochastic and Deterministic Models Improves Surgical Workflow Anticipation and Recognition | [
"Kaixiang Yang",
"Xin Li",
"Qiang Li",
"Zhiwei Wang"
] | https://openaccess.thecvf.com/content/ICCV2025/html/Yang_CoStoDet-DDPM_Collaborative_Training_of_Stochastic_and_Deterministic_Models_Improves_Surgical_ICCV_2025_paper.html | https://openaccess.thecvf.com/content/ICCV2025/papers/Yang_CoStoDet-DDPM_Collaborative_Training_of_Stochastic_and_Deterministic_Models_Improves_Surgical_ICCV_2025_paper.pdf | https://openaccess.thecvf.com/content/ICCV2025/supplemental/Yang_CoStoDet-DDPM_Collaborative_Training_ICCV_2025_supplemental.pdf | 2503.10216 | title_snapshot | @InProceedings{Yang_2025_ICCV,
author = {Yang, Kaixiang and Li, Xin and Li, Qiang and Wang, Zhiwei},
title = {CoStoDet-DDPM: Collaborative Training of Stochastic and Deterministic Models Improves Surgical Workflow Anticipation and Recognition},
booktitle = {Proceedings of the IEEE/CVF International C... | Anticipating and recognizing surgical workflows are critical for intelligent surgical assistance systems. However, existing methods rely on deterministic decision-making, struggling to generalize across the large anatomical and procedural variations inherent in real-world surgeries. In this paper, we introduce an innov... |
Jiao_GSOT3D_Towards_Generic_3D_Single_Object_Tracking_in_the_Wild_ICCV_2025_paper | GSOT3D: Towards Generic 3D Single Object Tracking in the Wild | [
"Yifan Jiao",
"Yunhao Li",
"Junhua Ding",
"Qing Yang",
"Song Fu",
"Heng Fan",
"Libo Zhang"
] | https://openaccess.thecvf.com/content/ICCV2025/html/Jiao_GSOT3D_Towards_Generic_3D_Single_Object_Tracking_in_the_Wild_ICCV_2025_paper.html | https://openaccess.thecvf.com/content/ICCV2025/papers/Jiao_GSOT3D_Towards_Generic_3D_Single_Object_Tracking_in_the_Wild_ICCV_2025_paper.pdf | https://openaccess.thecvf.com/content/ICCV2025/supplemental/Jiao_GSOT3D_Towards_Generic_ICCV_2025_supplemental.pdf | 2412.02129 | cvf | @InProceedings{Jiao_2025_ICCV,
author = {Jiao, Yifan and Li, Yunhao and Ding, Junhua and Yang, Qing and Fu, Song and Fan, Heng and Zhang, Libo},
title = {GSOT3D: Towards Generic 3D Single Object Tracking in the Wild},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Visio... | In this paper, we present a novel benchmark, GSOT3D, that aims at facilitating development of generic 3D single object tracking (SOT) in the wild. Specifically, GSOT3D offers 620 sequences with 123K frames, and covers a wide selection of 54 object categories. Each sequence is offered with multiple modalities, including... |
Liu_UnZipLoRA_Separating_Content_and_Style_from_a_Single_Image_ICCV_2025_paper | UnZipLoRA: Separating Content and Style from a Single Image | [
"Chang Liu",
"Viraj Shah",
"Aiyu Cui",
"Svetlana Lazebnik"
] | https://openaccess.thecvf.com/content/ICCV2025/html/Liu_UnZipLoRA_Separating_Content_and_Style_from_a_Single_Image_ICCV_2025_paper.html | https://openaccess.thecvf.com/content/ICCV2025/papers/Liu_UnZipLoRA_Separating_Content_and_Style_from_a_Single_Image_ICCV_2025_paper.pdf | https://openaccess.thecvf.com/content/ICCV2025/supplemental/Liu_UnZipLoRA_Separating_Content_ICCV_2025_supplemental.pdf | 2412.04465 | cvf | @InProceedings{Liu_2025_ICCV,
author = {Liu, Chang and Shah, Viraj and Cui, Aiyu and Lazebnik, Svetlana},
title = {UnZipLoRA: Separating Content and Style from a Single Image},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
... | This paper introduces UnZipLoRA, a method for decomposing an image into its constituent subject and style, represented as two distinct LoRAs (Low-Rank Adaptations). Unlike existing personalization techniques that focus on either subject or style in isolation, or require separate training sets for each, UnZipLoRA disent... |
Tan_What_You_Have_is_What_You_Track_Adaptive_and_Robust_ICCV_2025_paper | What You Have is What You Track: Adaptive and Robust Multimodal Tracking | [
"Yuedong Tan",
"Jiawei Shao",
"Eduard Zamfir",
"Ruanjun Li",
"Zhaochong An",
"Chao Ma",
"Danda Paudel",
"Luc Van Gool",
"Radu Timofte",
"Zongwei Wu"
] | https://openaccess.thecvf.com/content/ICCV2025/html/Tan_What_You_Have_is_What_You_Track_Adaptive_and_Robust_ICCV_2025_paper.html | https://openaccess.thecvf.com/content/ICCV2025/papers/Tan_What_You_Have_is_What_You_Track_Adaptive_and_Robust_ICCV_2025_paper.pdf | https://openaccess.thecvf.com/content/ICCV2025/supplemental/Tan_What_You_Have_ICCV_2025_supplemental.pdf | 2507.05899 | cvf | @InProceedings{Tan_2025_ICCV,
author = {Tan, Yuedong and Shao, Jiawei and Zamfir, Eduard and Li, Ruanjun and An, Zhaochong and Ma, Chao and Paudel, Danda and Van Gool, Luc and Timofte, Radu and Wu, Zongwei},
title = {What You Have is What You Track: Adaptive and Robust Multimodal Tracking},
booktitle... | Multimodal data is known to be helpful for visual tracking by improving robustness to appearance variations. However, sensor synchronization challenges often compromise data availability, particularly in video settings where shortages can be temporal. Despite its importance, this area remains underexplored. In this pap... |
He_RareCLIP_Rarity-aware_Online_Zero-shot_Industrial_Anomaly_Detection_ICCV_2025_paper | RareCLIP: Rarity-aware Online Zero-shot Industrial Anomaly Detection | [
"Jianfang He",
"Min Cao",
"Silong Peng",
"Qiong Xie"
] | https://openaccess.thecvf.com/content/ICCV2025/html/He_RareCLIP_Rarity-aware_Online_Zero-shot_Industrial_Anomaly_Detection_ICCV_2025_paper.html | https://openaccess.thecvf.com/content/ICCV2025/papers/He_RareCLIP_Rarity-aware_Online_Zero-shot_Industrial_Anomaly_Detection_ICCV_2025_paper.pdf | https://openaccess.thecvf.com/content/ICCV2025/supplemental/He_RareCLIP_Rarity-aware_Online_ICCV_2025_supplemental.pdf | null | null | @InProceedings{He_2025_ICCV,
author = {He, Jianfang and Cao, Min and Peng, Silong and Xie, Qiong},
title = {RareCLIP: Rarity-aware Online Zero-shot Industrial Anomaly Detection},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
... | Large vision-language models such as CLIP have made significant strides in zero-shot anomaly detection through prompt engineering. However, most existing methods typically process each test image individually, ignoring the practical rarity of abnormal patches in real-world scenarios. Although some batch-based approache... |
Bi_AdaDCP_Learning_an_Adapter_with_Discrete_Cosine_Prior_for_Clear-to-Adverse_ICCV_2025_paper | AdaDCP: Learning an Adapter with Discrete Cosine Prior for Clear-to-Adverse Domain Generalization | [
"Qi Bi",
"Yixian Shen",
"Jingjun Yi",
"Gui-Song Xia"
] | https://openaccess.thecvf.com/content/ICCV2025/html/Bi_AdaDCP_Learning_an_Adapter_with_Discrete_Cosine_Prior_for_Clear-to-Adverse_ICCV_2025_paper.html | https://openaccess.thecvf.com/content/ICCV2025/papers/Bi_AdaDCP_Learning_an_Adapter_with_Discrete_Cosine_Prior_for_Clear-to-Adverse_ICCV_2025_paper.pdf | null | null | null | @InProceedings{Bi_2025_ICCV,
author = {Bi, Qi and Shen, Yixian and Yi, Jingjun and Xia, Gui-Song},
title = {AdaDCP: Learning an Adapter with Discrete Cosine Prior for Clear-to-Adverse Domain Generalization},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},... | Vision Foundation Model (VFM) provides an inherent generalization ability to unseen domains for downstream tasks. However, fine-tuning VFM to parse various adverse scenes (e.g., fog, snow, night) is particularly challenging, as these samples are difficult to collect. Using easy-to-acquire clear scenes as the source dom... |
Zhou_HERMES_A_Unified_Self-Driving_World_Model_for_Simultaneous_3D_Scene_ICCV_2025_paper | HERMES: A Unified Self-Driving World Model for Simultaneous 3D Scene Understanding and Generation | [
"Xin Zhou",
"Dingkang Liang",
"Sifan Tu",
"Xiwu Chen",
"Yikang Ding",
"Dingyuan Zhang",
"Feiyang Tan",
"Hengshuang Zhao",
"Xiang Bai"
] | https://openaccess.thecvf.com/content/ICCV2025/html/Zhou_HERMES_A_Unified_Self-Driving_World_Model_for_Simultaneous_3D_Scene_ICCV_2025_paper.html | https://openaccess.thecvf.com/content/ICCV2025/papers/Zhou_HERMES_A_Unified_Self-Driving_World_Model_for_Simultaneous_3D_Scene_ICCV_2025_paper.pdf | https://openaccess.thecvf.com/content/ICCV2025/supplemental/Zhou_HERMES_A_Unified_ICCV_2025_supplemental.pdf | 2501.14729 | cvf | @InProceedings{Zhou_2025_ICCV,
author = {Zhou, Xin and Liang, Dingkang and Tu, Sifan and Chen, Xiwu and Ding, Yikang and Zhang, Dingyuan and Tan, Feiyang and Zhao, Hengshuang and Bai, Xiang},
title = {HERMES: A Unified Self-Driving World Model for Simultaneous 3D Scene Understanding and Generation},
... | Driving World Models (DWMs) have become essential for autonomous driving by enabling future scene prediction. However, existing DWMs are limited to scene generation and fail to incorporate scene understanding, which involves interpreting and reasoning about the driving environment. In this paper, we present a unified D... |
Deng_ArgMatch_Adaptive_Refinement_Gathering_for_Efficient_Dense_Matching_ICCV_2025_paper | ArgMatch: Adaptive Refinement Gathering for Efficient Dense Matching | [
"Yuxin Deng",
"Kaining Zhang",
"Linfeng Tang",
"Jiaqi Yang",
"Jiayi Ma"
] | https://openaccess.thecvf.com/content/ICCV2025/html/Deng_ArgMatch_Adaptive_Refinement_Gathering_for_Efficient_Dense_Matching_ICCV_2025_paper.html | https://openaccess.thecvf.com/content/ICCV2025/papers/Deng_ArgMatch_Adaptive_Refinement_Gathering_for_Efficient_Dense_Matching_ICCV_2025_paper.pdf | https://openaccess.thecvf.com/content/ICCV2025/supplemental/Deng_ArgMatch_Adaptive_Refinement_ICCV_2025_supplemental.pdf | null | null | @InProceedings{Deng_2025_ICCV,
author = {Deng, Yuxin and Zhang, Kaining and Tang, Linfeng and Yang, Jiaqi and Ma, Jiayi},
title = {ArgMatch: Adaptive Refinement Gathering for Efficient Dense Matching},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
m... | Establishing dense correspondences is crucial yet computationally demanding in multi-view tasks. Although coarse-to-fine schemes mitigate computational costs, their efficiency remains limited by the substantial demands of heavy feature extractors and global matchers. In this paper, we propose Adaptive Refinement Gather... |
Hu_Enhancing_Image_Restoration_Transformer_via_Adaptive_Translation_Equivariance_ICCV_2025_paper | Enhancing Image Restoration Transformer via Adaptive Translation Equivariance | [
"JiaKui Hu",
"Zhengjian Yao",
"Lujia Jin",
"Hangzhou He",
"Yanye Lu"
] | https://openaccess.thecvf.com/content/ICCV2025/html/Hu_Enhancing_Image_Restoration_Transformer_via_Adaptive_Translation_Equivariance_ICCV_2025_paper.html | https://openaccess.thecvf.com/content/ICCV2025/papers/Hu_Enhancing_Image_Restoration_Transformer_via_Adaptive_Translation_Equivariance_ICCV_2025_paper.pdf | https://openaccess.thecvf.com/content/ICCV2025/supplemental/Hu_Enhancing_Image_Restoration_ICCV_2025_supplemental.pdf | 2506.18520 | cvf | @InProceedings{Hu_2025_ICCV,
author = {Hu, JiaKui and Yao, Zhengjian and Jin, Lujia and He, Hangzhou and Lu, Yanye},
title = {Enhancing Image Restoration Transformer via Adaptive Translation Equivariance},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
... | Translation equivariance is a fundamental inductive bias in image restoration, ensuring that translated inputs produce translated outputs. Attention mechanisms in modern restoration transformers undermine this property, adversely impacting both training convergence and generalization. To alleviate this issue, we propos... |
Liu_Free4D_Tuning-free_4D_Scene_Generation_with_Spatial-Temporal_Consistency_ICCV_2025_paper | Free4D: Tuning-free 4D Scene Generation with Spatial-Temporal Consistency | [
"Tianqi Liu",
"Zihao Huang",
"Zhaoxi Chen",
"Guangcong Wang",
"Shoukang Hu",
"Liao Shen",
"Huiqiang Sun",
"Zhiguo Cao",
"Wei Li",
"Ziwei Liu"
] | https://openaccess.thecvf.com/content/ICCV2025/html/Liu_Free4D_Tuning-free_4D_Scene_Generation_with_Spatial-Temporal_Consistency_ICCV_2025_paper.html | https://openaccess.thecvf.com/content/ICCV2025/papers/Liu_Free4D_Tuning-free_4D_Scene_Generation_with_Spatial-Temporal_Consistency_ICCV_2025_paper.pdf | https://openaccess.thecvf.com/content/ICCV2025/supplemental/Liu_Free4D_Tuning-free_4D_ICCV_2025_supplemental.pdf | 2503.20785 | cvf | @InProceedings{Liu_2025_ICCV,
author = {Liu, Tianqi and Huang, Zihao and Chen, Zhaoxi and Wang, Guangcong and Hu, Shoukang and Shen, Liao and Sun, Huiqiang and Cao, Zhiguo and Li, Wei and Liu, Ziwei},
title = {Free4D: Tuning-free 4D Scene Generation with Spatial-Temporal Consistency},
booktitle = {Pr... | We present Free4D, a novel tuning-free framework for 4D scene generation from a single image. Existing methods either focus on object-level generation, making scene-level generation infeasible, or rely on large-scale multi-view video datasets for expensive training, with limited generalization ability due to the scarci... |
Niewiadomski_Generative_Zoo_ICCV_2025_paper | Generative Zoo | [
"Tomasz Niewiadomski",
"Anastasios Yiannakidis",
"Hanz Cuevas-Velasquez",
"Soubhik Sanyal",
"Michael J. Black",
"Silvia Zuffi",
"Peter Kulits"
] | https://openaccess.thecvf.com/content/ICCV2025/html/Niewiadomski_Generative_Zoo_ICCV_2025_paper.html | https://openaccess.thecvf.com/content/ICCV2025/papers/Niewiadomski_Generative_Zoo_ICCV_2025_paper.pdf | https://openaccess.thecvf.com/content/ICCV2025/supplemental/Niewiadomski_Generative_Zoo_ICCV_2025_supplemental.pdf | 2412.08101 | cvf | @InProceedings{Niewiadomski_2025_ICCV,
author = {Niewiadomski, Tomasz and Yiannakidis, Anastasios and Cuevas-Velasquez, Hanz and Sanyal, Soubhik and Black, Michael J. and Zuffi, Silvia and Kulits, Peter},
title = {Generative Zoo},
booktitle = {Proceedings of the IEEE/CVF International Conference on C... | The model-based estimation of 3D animal pose and shape from images enables computational modeling of animal behavior. Training models for this purpose requires large amounts of labeled image data with precise pose and shape annotations. However, capturing such data requires the use of multi-view or marker-based motion-... |
Tong_Any-SSR_How_Recursive_Least_Squares_Works_in_Continual_Learning_of_ICCV_2025_paper | Any-SSR: How Recursive Least Squares Works in Continual Learning of Large Language Model | [
"Kai Tong",
"Kang Pan",
"Xiao Zhang",
"Erli Meng",
"Run He",
"Yawen Cui",
"Nuoyan Guo",
"Huiping Zhuang"
] | https://openaccess.thecvf.com/content/ICCV2025/html/Tong_Any-SSR_How_Recursive_Least_Squares_Works_in_Continual_Learning_of_ICCV_2025_paper.html | https://openaccess.thecvf.com/content/ICCV2025/papers/Tong_Any-SSR_How_Recursive_Least_Squares_Works_in_Continual_Learning_of_ICCV_2025_paper.pdf | https://openaccess.thecvf.com/content/ICCV2025/supplemental/Tong_Any-SSR_How_Recursive_ICCV_2025_supplemental.pdf | 2503.13575 | title_judge | @InProceedings{Tong_2025_ICCV,
author = {Tong, Kai and Pan, Kang and Zhang, Xiao and Meng, Erli and He, Run and Cui, Yawen and Guo, Nuoyan and Zhuang, Huiping},
title = {Any-SSR: How Recursive Least Squares Works in Continual Learning of Large Language Model},
booktitle = {Proceedings of the IEEE/CVF... | Large Language Models (LLMs) possess encompassing capabilities that can process diverse language-related tasks. However, finetuning on LLMs will diminish this general skills and continual finetuning will further cause severe degradation on accumulated knowledge. Recently, Continual Learning (CL) in Large Language Model... |
Wang_Instruction-Oriented_Preference_Alignment_for_Enhancing_Multi-Modal_Comprehension_Capability_of_MLLMs_ICCV_2025_paper | Instruction-Oriented Preference Alignment for Enhancing Multi-Modal Comprehension Capability of MLLMs | [
"Zitian Wang",
"Yue Liao",
"Kang Rong",
"Fengyun Rao",
"Yibo Yang",
"Si Liu"
] | https://openaccess.thecvf.com/content/ICCV2025/html/Wang_Instruction-Oriented_Preference_Alignment_for_Enhancing_Multi-Modal_Comprehension_Capability_of_MLLMs_ICCV_2025_paper.html | https://openaccess.thecvf.com/content/ICCV2025/papers/Wang_Instruction-Oriented_Preference_Alignment_for_Enhancing_Multi-Modal_Comprehension_Capability_of_MLLMs_ICCV_2025_paper.pdf | https://openaccess.thecvf.com/content/ICCV2025/supplemental/Wang_Instruction-Oriented_Preference_Alignment_ICCV_2025_supplemental.pdf | 2503.20309 | cvf | @InProceedings{Wang_2025_ICCV,
author = {Wang, Zitian and Liao, Yue and Rong, Kang and Rao, Fengyun and Yang, Yibo and Liu, Si},
title = {Instruction-Oriented Preference Alignment for Enhancing Multi-Modal Comprehension Capability of MLLMs},
booktitle = {Proceedings of the IEEE/CVF International Conf... | Preference alignment has emerged as an effective strategy to enhance the performance of Multimodal Large Language Models (MLLMs) following supervised fine-tuning. While existing preference alignment methods predominantly target hallucination factors, they overlook the factors essential for multi-modal comprehension cap... |
Chen_RapVerse_Coherent_Vocals_and_Whole-Body_Motion_Generation_from_Text_ICCV_2025_paper | RapVerse: Coherent Vocals and Whole-Body Motion Generation from Text | [
"Jiaben Chen",
"Xin Yan",
"Yihang Chen",
"Siyuan Cen",
"Zixin Wang",
"Qinwei Ma",
"Haoyu Zhen",
"Kaizhi Qian",
"Lie Lu",
"Chuang Gan"
] | https://openaccess.thecvf.com/content/ICCV2025/html/Chen_RapVerse_Coherent_Vocals_and_Whole-Body_Motion_Generation_from_Text_ICCV_2025_paper.html | https://openaccess.thecvf.com/content/ICCV2025/papers/Chen_RapVerse_Coherent_Vocals_and_Whole-Body_Motion_Generation_from_Text_ICCV_2025_paper.pdf | https://openaccess.thecvf.com/content/ICCV2025/supplemental/Chen_RapVerse_Coherent_Vocals_ICCV_2025_supplemental.pdf | 2405.20336 | cvf | @InProceedings{Chen_2025_ICCV,
author = {Chen, Jiaben and Yan, Xin and Chen, Yihang and Cen, Siyuan and Wang, Zixin and Ma, Qinwei and Zhen, Haoyu and Qian, Kaizhi and Lu, Lie and Gan, Chuang},
title = {RapVerse: Coherent Vocals and Whole-Body Motion Generation from Text},
booktitle = {Proceedings of... | In this work, we introduce a challenging task for simultaneously generating 3D holistic body motions and singing vocals directly from textual lyrics inputs, advancing beyond existing works that typically address these two modalities in isolation. To facilitate this, we first collect the RapVerse dataset, a large datase... |
Liu_MoFRR_Mixture_of_Diffusion_Models_for_Face_Retouching_Restoration_ICCV_2025_paper | MoFRR: Mixture of Diffusion Models for Face Retouching Restoration | [
"Jiaxin Liu",
"Qichao Ying",
"Zhenxing Qian",
"Sheng Li",
"Runqi Zhang",
"Jian Liu",
"Xinpeng Zhang"
] | https://openaccess.thecvf.com/content/ICCV2025/html/Liu_MoFRR_Mixture_of_Diffusion_Models_for_Face_Retouching_Restoration_ICCV_2025_paper.html | https://openaccess.thecvf.com/content/ICCV2025/papers/Liu_MoFRR_Mixture_of_Diffusion_Models_for_Face_Retouching_Restoration_ICCV_2025_paper.pdf | null | 2507.19770 | cvf | @InProceedings{Liu_2025_ICCV,
author = {Liu, Jiaxin and Ying, Qichao and Qian, Zhenxing and Li, Sheng and Zhang, Runqi and Liu, Jian and Zhang, Xinpeng},
title = {MoFRR: Mixture of Diffusion Models for Face Retouching Restoration},
booktitle = {Proceedings of the IEEE/CVF International Conference on ... | The widespread use of face retouching on social media platforms raises concerns about the authenticity of face images. While existing methods focus on detecting face retouching, how to accurately recover the original faces from the retouched ones has yet to be answered. This paper introduces Face Retouching Restoration... |
Park_SFUOD_Source-Free_Unknown_Object_Detection_ICCV_2025_paper | SFUOD: Source-Free Unknown Object Detection | [
"Keon-Hee Park",
"Seun-An Choe",
"Gyeong-Moon Park"
] | https://openaccess.thecvf.com/content/ICCV2025/html/Park_SFUOD_Source-Free_Unknown_Object_Detection_ICCV_2025_paper.html | https://openaccess.thecvf.com/content/ICCV2025/papers/Park_SFUOD_Source-Free_Unknown_Object_Detection_ICCV_2025_paper.pdf | https://openaccess.thecvf.com/content/ICCV2025/supplemental/Park_SFUOD_Source-Free_Unknown_ICCV_2025_supplemental.pdf | 2507.17373 | cvf | @InProceedings{Park_2025_ICCV,
author = {Park, Keon-Hee and Choe, Seun-An and Park, Gyeong-Moon},
title = {SFUOD: Source-Free Unknown Object Detection},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2025},
... | Source-free object detection adapts a detector pre-trained on a source domain to an unlabeled target domain without requiring access to labeled source data. While this setting is practical as it eliminates the need for the source dataset during domain adaptation, it operates under the restrictive assumption that only p... |
Patel_UniEgoMotion_A_Unified_Model_for_Egocentric_Motion_Reconstruction_Forecasting_and_ICCV_2025_paper | UniEgoMotion: A Unified Model for Egocentric Motion Reconstruction, Forecasting, and Generation | [
"Chaitanya Patel",
"Hiroki Nakamura",
"Yuta Kyuragi",
"Kazuki Kozuka",
"Juan Carlos Niebles",
"Ehsan Adeli"
] | https://openaccess.thecvf.com/content/ICCV2025/html/Patel_UniEgoMotion_A_Unified_Model_for_Egocentric_Motion_Reconstruction_Forecasting_and_ICCV_2025_paper.html | https://openaccess.thecvf.com/content/ICCV2025/papers/Patel_UniEgoMotion_A_Unified_Model_for_Egocentric_Motion_Reconstruction_Forecasting_and_ICCV_2025_paper.pdf | https://openaccess.thecvf.com/content/ICCV2025/supplemental/Patel_UniEgoMotion_A_Unified_ICCV_2025_supplemental.zip | 2508.01126 | cvf | @InProceedings{Patel_2025_ICCV,
author = {Patel, Chaitanya and Nakamura, Hiroki and Kyuragi, Yuta and Kozuka, Kazuki and Niebles, Juan Carlos and Adeli, Ehsan},
title = {UniEgoMotion: A Unified Model for Egocentric Motion Reconstruction, Forecasting, and Generation},
booktitle = {Proceedings of the I... | Egocentric human motion generation and forecasting with scene-context is crucial for enhancing AR/VR experiences, improving human-robot interaction, advancing assistive technologies, and enabling adaptive healthcare solutions by accurately predicting and simulating movement from a first-person perspective. However, exi... |
Yin_ToolVQA_A_Dataset_for_Multi-step_Reasoning_VQA_with_External_Tools_ICCV_2025_paper | ToolVQA: A Dataset for Multi-step Reasoning VQA with External Tools | [
"Shaofeng Yin",
"Ting Lei",
"Yang Liu"
] | https://openaccess.thecvf.com/content/ICCV2025/html/Yin_ToolVQA_A_Dataset_for_Multi-step_Reasoning_VQA_with_External_Tools_ICCV_2025_paper.html | https://openaccess.thecvf.com/content/ICCV2025/papers/Yin_ToolVQA_A_Dataset_for_Multi-step_Reasoning_VQA_with_External_Tools_ICCV_2025_paper.pdf | https://openaccess.thecvf.com/content/ICCV2025/supplemental/Yin_ToolVQA_A_Dataset_ICCV_2025_supplemental.pdf | 2508.03284 | cvf | @InProceedings{Yin_2025_ICCV,
author = {Yin, Shaofeng and Lei, Ting and Liu, Yang},
title = {ToolVQA: A Dataset for Multi-step Reasoning VQA with External Tools},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {... | Integrating external tools into Large Foundation Models (LFMs) has emerged as a promising approach to enhance their problem-solving capabilities. While existing studies have demonstrated strong performance in tool-augmented Visual Question Answering (VQA), recent benchmarks re- veal significant gaps in real-world tool-... |
Brousseau_Spherical_Epipolar_Rectification_for_Deep_Two-View_Absolute_Depth_Estimation_ICCV_2025_paper | Spherical Epipolar Rectification for Deep Two-View Absolute Depth Estimation | [
"Pierre-André Brousseau",
"Sébastien Roy"
] | https://openaccess.thecvf.com/content/ICCV2025/html/Brousseau_Spherical_Epipolar_Rectification_for_Deep_Two-View_Absolute_Depth_Estimation_ICCV_2025_paper.html | https://openaccess.thecvf.com/content/ICCV2025/papers/Brousseau_Spherical_Epipolar_Rectification_for_Deep_Two-View_Absolute_Depth_Estimation_ICCV_2025_paper.pdf | https://openaccess.thecvf.com/content/ICCV2025/supplemental/Brousseau_Spherical_Epipolar_Rectification_ICCV_2025_supplemental.pdf | null | null | @InProceedings{Brousseau_2025_ICCV,
author = {Brousseau, Pierre-Andr\'e and Roy, S\'ebastien},
title = {Spherical Epipolar Rectification for Deep Two-View Absolute Depth Estimation},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October... | Absolute depth estimation from single camera sequence of images is a relevant task given that mobile machines increasingly rely on vision to navigate. Deep learning for stereo matching has been demonstrated to improve performance for stereo rectified depth estimation but these methods require straightforward left-right... |
Xia_ScenePainter_Semantically_Consistent_Perpetual_3D_Scene_Generation_with_Concept_Relation_ICCV_2025_paper | ScenePainter: Semantically Consistent Perpetual 3D Scene Generation with Concept Relation Alignment | [
"Chong Xia",
"Shengjun Zhang",
"Fangfu Liu",
"Chang Liu",
"Khodchaphun Hirunyaratsameewong",
"Yueqi Duan"
] | https://openaccess.thecvf.com/content/ICCV2025/html/Xia_ScenePainter_Semantically_Consistent_Perpetual_3D_Scene_Generation_with_Concept_Relation_ICCV_2025_paper.html | https://openaccess.thecvf.com/content/ICCV2025/papers/Xia_ScenePainter_Semantically_Consistent_Perpetual_3D_Scene_Generation_with_Concept_Relation_ICCV_2025_paper.pdf | https://openaccess.thecvf.com/content/ICCV2025/supplemental/Xia_ScenePainter_Semantically_Consistent_ICCV_2025_supplemental.zip | 2507.19058 | cvf | @InProceedings{Xia_2025_ICCV,
author = {Xia, Chong and Zhang, Shengjun and Liu, Fangfu and Liu, Chang and Hirunyaratsameewong, Khodchaphun and Duan, Yueqi},
title = {ScenePainter: Semantically Consistent Perpetual 3D Scene Generation with Concept Relation Alignment},
booktitle = {Proceedings of the I... | Perpetual 3D scene generation aims to produce long-range and coherent 3D view sequences, which is applicable for long-term video synthesis and 3D scene reconstruction. Existing methods follow a "navigate-and-imagine" fashion and rely on outpainting for successive view expansion. However, the generated view sequences su... |
Ye_ESCNetEdge-Semantic_Collaborative_Network_for_Camouflaged_Object_Detection_ICCV_2025_paper | ESCNet:Edge-Semantic Collaborative Network for Camouflaged Object Detection | [
"Sheng Ye",
"Xin Chen",
"Yan Zhang",
"Xianming Lin",
"Liujuan Cao"
] | https://openaccess.thecvf.com/content/ICCV2025/html/Ye_ESCNetEdge-Semantic_Collaborative_Network_for_Camouflaged_Object_Detection_ICCV_2025_paper.html | https://openaccess.thecvf.com/content/ICCV2025/papers/Ye_ESCNetEdge-Semantic_Collaborative_Network_for_Camouflaged_Object_Detection_ICCV_2025_paper.pdf | null | null | null | @InProceedings{Ye_2025_ICCV,
author = {Ye, Sheng and Chen, Xin and Zhang, Yan and Lin, Xianming and Cao, Liujuan},
title = {ESCNet:Edge-Semantic Collaborative Network for Camouflaged Object Detection},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
m... | Camouflaged object detection (COD) faces unique challenges where target boundaries are intrinsically ambiguous due to their textural similarity to backgrounds. Existing methods relying on single-modality features often produce fragmented predictions due to insufficient boundary constraints.To address this, we propose E... |
Jin_PixelStitch_Structure-Preserving_Pixel-Wise_Bidirectional_Warps_for_Unsupervised_Image_Stitching_ICCV_2025_paper | PixelStitch: Structure-Preserving Pixel-Wise Bidirectional Warps for Unsupervised Image Stitching | [
"Hengzhe Jin",
"Lang Nie",
"Chunyu Lin",
"Xiaomei Feng",
"Yao Zhao"
] | https://openaccess.thecvf.com/content/ICCV2025/html/Jin_PixelStitch_Structure-Preserving_Pixel-Wise_Bidirectional_Warps_for_Unsupervised_Image_Stitching_ICCV_2025_paper.html | https://openaccess.thecvf.com/content/ICCV2025/papers/Jin_PixelStitch_Structure-Preserving_Pixel-Wise_Bidirectional_Warps_for_Unsupervised_Image_Stitching_ICCV_2025_paper.pdf | https://openaccess.thecvf.com/content/ICCV2025/supplemental/Jin_PixelStitch_Structure-Preserving_Pixel-Wise_ICCV_2025_supplemental.zip | null | null | @InProceedings{Jin_2025_ICCV,
author = {Jin, Hengzhe and Nie, Lang and Lin, Chunyu and Feng, Xiaomei and Zhao, Yao},
title = {PixelStitch: Structure-Preserving Pixel-Wise Bidirectional Warps for Unsupervised Image Stitching},
booktitle = {Proceedings of the IEEE/CVF International Conference on Comput... | We propose PixelStitch, a pixel-wise bidirectional warp that learns to stitch images as well as preserve structure in an unsupervised paradigm. To produce natural stitched images, we first determine the middle plane through homography decomposition and globally project the original images toward the desired plane. Comp... |
Chen_SANA-Sprint_One-Step_Diffusion_with_Continuous-Time_Consistency_Distillation_ICCV_2025_paper | SANA-Sprint: One-Step Diffusion with Continuous-Time Consistency Distillation | [
"Junsong Chen",
"Shuchen Xue",
"Yuyang Zhao",
"Jincheng Yu",
"Sayak Paul",
"Junyu Chen",
"Han Cai",
"Song Han",
"Enze Xie"
] | https://openaccess.thecvf.com/content/ICCV2025/html/Chen_SANA-Sprint_One-Step_Diffusion_with_Continuous-Time_Consistency_Distillation_ICCV_2025_paper.html | https://openaccess.thecvf.com/content/ICCV2025/papers/Chen_SANA-Sprint_One-Step_Diffusion_with_Continuous-Time_Consistency_Distillation_ICCV_2025_paper.pdf | https://openaccess.thecvf.com/content/ICCV2025/supplemental/Chen_SANA-Sprint_One-Step_Diffusion_ICCV_2025_supplemental.pdf | 2503.09641 | title_snapshot | @InProceedings{Chen_2025_ICCV,
author = {Chen, Junsong and Xue, Shuchen and Zhao, Yuyang and Yu, Jincheng and Paul, Sayak and Chen, Junyu and Cai, Han and Han, Song and Xie, Enze},
title = {SANA-Sprint: One-Step Diffusion with Continuous-Time Consistency Distillation},
booktitle = {Proceedings of the... | This paper presents SANA-Sprint, an efficient diffusion model for ultra-fast text-to-image (T2I) generation. SANA-Sprint is built on a pre-trained foundation model and augmented with hybrid distillation, dramatically reducing inference steps from 20 to 1-4.We introduce three key innovations: (1) We propose a training-f... |
Yin_Information-Bottleneck_Driven_Binary_Neural_Network_for_Change_Detection_ICCV_2025_paper | Information-Bottleneck Driven Binary Neural Network for Change Detection | [
"Kaijie Yin",
"Zhiyuan Zhang",
"Shu Kong",
"Tian Gao",
"Cheng-Zhong Xu",
"Hui Kong"
] | https://openaccess.thecvf.com/content/ICCV2025/html/Yin_Information-Bottleneck_Driven_Binary_Neural_Network_for_Change_Detection_ICCV_2025_paper.html | https://openaccess.thecvf.com/content/ICCV2025/papers/Yin_Information-Bottleneck_Driven_Binary_Neural_Network_for_Change_Detection_ICCV_2025_paper.pdf | null | 2507.03504 | cvf | @InProceedings{Yin_2025_ICCV,
author = {Yin, Kaijie and Zhang, Zhiyuan and Kong, Shu and Gao, Tian and Xu, Cheng-Zhong and Kong, Hui},
title = {Information-Bottleneck Driven Binary Neural Network for Change Detection},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Visi... | In this paper, we propose Binarized Change Detection (BiCD), the first binary neural network (BNN) designed specifically for change detection. Conventional network binarization approaches, which directly quantize both weights and activations in change detection models, severely limit the network's ability to represent ... |
Amara_Erasing_More_Than_Intended_How_Concept_Erasure_Degrades_the_Generation_ICCV_2025_paper | Erasing More Than Intended? How Concept Erasure Degrades the Generation of Non-Target Concepts | [
"Ibtihel Amara",
"Ahmed Imtiaz Humayun",
"Ivana Kajic",
"Zarana Parekh",
"Natalie Harris",
"Sarah Young",
"Chirag Nagpal",
"Najoung Kim",
"Junfeng He",
"Cristina Nader Vasconcelos",
"Deepak Ramachandran",
"Golnoosh Farnadi",
"Katherine Heller",
"Mohammad Havaei",
"Negar Rostamzadeh"
] | https://openaccess.thecvf.com/content/ICCV2025/html/Amara_Erasing_More_Than_Intended_How_Concept_Erasure_Degrades_the_Generation_ICCV_2025_paper.html | https://openaccess.thecvf.com/content/ICCV2025/papers/Amara_Erasing_More_Than_Intended_How_Concept_Erasure_Degrades_the_Generation_ICCV_2025_paper.pdf | https://openaccess.thecvf.com/content/ICCV2025/supplemental/Amara_Erasing_More_Than_ICCV_2025_supplemental.pdf | 2501.09833 | cvf | @InProceedings{Amara_2025_ICCV,
author = {Amara, Ibtihel and Humayun, Ahmed Imtiaz and Kajic, Ivana and Parekh, Zarana and Harris, Natalie and Young, Sarah and Nagpal, Chirag and Kim, Najoung and He, Junfeng and Vasconcelos, Cristina Nader and Ramachandran, Deepak and Farnadi, Golnoosh and Heller, Katherine and ... | Concept erasure techniques have recently gained significant attention for their potential to remove unwanted concepts from text-to-image models. While these methods often demonstrate promising results in controlled settings, their robustness in real-world applications and suitability for deployment remain uncertain. In... |
Sastry_Global_and_Local_Entailment_Learning_for_Natural_World_Imagery_ICCV_2025_paper | Global and Local Entailment Learning for Natural World Imagery | [
"Srikumar Sastry",
"Aayush Dhakal",
"Eric Xing",
"Subash Khanal",
"Nathan Jacobs"
] | https://openaccess.thecvf.com/content/ICCV2025/html/Sastry_Global_and_Local_Entailment_Learning_for_Natural_World_Imagery_ICCV_2025_paper.html | https://openaccess.thecvf.com/content/ICCV2025/papers/Sastry_Global_and_Local_Entailment_Learning_for_Natural_World_Imagery_ICCV_2025_paper.pdf | https://openaccess.thecvf.com/content/ICCV2025/supplemental/Sastry_Global_and_Local_ICCV_2025_supplemental.pdf | 2506.21476 | cvf | @InProceedings{Sastry_2025_ICCV,
author = {Sastry, Srikumar and Dhakal, Aayush and Xing, Eric and Khanal, Subash and Jacobs, Nathan},
title = {Global and Local Entailment Learning for Natural World Imagery},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},... | Learning the hierarchical structure of data in vision-language models is a significant challenge. Previous works have attempted to address this challenge by employing entailment learning. However, these approaches fail to model the transitive nature of entailment explicitly, which establishes the relationship between o... |
Wang_Ross3D_Reconstructive_Visual_Instruction_Tuning_with_3D-Awareness_ICCV_2025_paper | Ross3D: Reconstructive Visual Instruction Tuning with 3D-Awareness | [
"Haochen Wang",
"Yucheng Zhao",
"Tiancai Wang",
"Haoqiang Fan",
"Xiangyu Zhang",
"Zhaoxiang Zhang"
] | https://openaccess.thecvf.com/content/ICCV2025/html/Wang_Ross3D_Reconstructive_Visual_Instruction_Tuning_with_3D-Awareness_ICCV_2025_paper.html | https://openaccess.thecvf.com/content/ICCV2025/papers/Wang_Ross3D_Reconstructive_Visual_Instruction_Tuning_with_3D-Awareness_ICCV_2025_paper.pdf | https://openaccess.thecvf.com/content/ICCV2025/supplemental/Wang_Ross3D_Reconstructive_Visual_ICCV_2025_supplemental.pdf | 2504.01901 | cvf | @InProceedings{Wang_2025_ICCV,
author = {Wang, Haochen and Zhao, Yucheng and Wang, Tiancai and Fan, Haoqiang and Zhang, Xiangyu and Zhang, Zhaoxiang},
title = {Ross3D: Reconstructive Visual Instruction Tuning with 3D-Awareness},
booktitle = {Proceedings of the IEEE/CVF International Conference on Com... | The rapid development of Large Multimodal Models (LMMs) for 2D images and videos has spurred efforts to adapt these models for interpreting 3D scenes. However, the absence of large-scale 3D vision-language datasets has posed a significant obstacle. To address this issue, typical approaches focus on injecting 3D awarene... |
You_LVFace_Progressive_Cluster_Optimization_for_Large_Vision_Models_in_Face_ICCV_2025_paper | LVFace: Progressive Cluster Optimization for Large Vision Models in Face Recognition | [
"Jinghan You",
"Shanglin Li",
"Yuanrui Sun",
"Jiangchuan Wei",
"Mingyu Guo",
"Chao Feng",
"Jiao Ran"
] | https://openaccess.thecvf.com/content/ICCV2025/html/You_LVFace_Progressive_Cluster_Optimization_for_Large_Vision_Models_in_Face_ICCV_2025_paper.html | https://openaccess.thecvf.com/content/ICCV2025/papers/You_LVFace_Progressive_Cluster_Optimization_for_Large_Vision_Models_in_Face_ICCV_2025_paper.pdf | null | 2501.13420 | cvf | @InProceedings{You_2025_ICCV,
author = {You, Jinghan and Li, Shanglin and Sun, Yuanrui and Wei, Jiangchuan and Guo, Mingyu and Feng, Chao and Ran, Jiao},
title = {LVFace: Progressive Cluster Optimization for Large Vision Models in Face Recognition},
booktitle = {Proceedings of the IEEE/CVF Internatio... | Vision Transformers (ViTs) have revolutionized large-scale visual modeling, yet remain underexplored in face recognition (FR) where CNNs still dominate. We identify a critical bottleneck: CNN-inspired training paradigms fail to unlock ViT's potential, leading to suboptimal performance and convergence instability.To add... |
Xie_Dataset_Ownership_Verification_for_Pre-trained_Masked_Models_ICCV_2025_paper | Dataset Ownership Verification for Pre-trained Masked Models | [
"Yuechen Xie",
"Jie Song",
"Yicheng Shan",
"Xiaoyan Zhang",
"Yuanyu Wan",
"Shengxuming Zhang",
"Jiarui Duan",
"Mingli Song"
] | https://openaccess.thecvf.com/content/ICCV2025/html/Xie_Dataset_Ownership_Verification_for_Pre-trained_Masked_Models_ICCV_2025_paper.html | https://openaccess.thecvf.com/content/ICCV2025/papers/Xie_Dataset_Ownership_Verification_for_Pre-trained_Masked_Models_ICCV_2025_paper.pdf | https://openaccess.thecvf.com/content/ICCV2025/supplemental/Xie_Dataset_Ownership_Verification_ICCV_2025_supplemental.zip | 2507.12022 | cvf | @InProceedings{Xie_2025_ICCV,
author = {Xie, Yuechen and Song, Jie and Shan, Yicheng and Zhang, Xiaoyan and Wan, Yuanyu and Zhang, Shengxuming and Duan, Jiarui and Song, Mingli},
title = {Dataset Ownership Verification for Pre-trained Masked Models},
booktitle = {Proceedings of the IEEE/CVF Internati... | High-quality open-source datasets have emerged as a pivotal catalyst driving the swift advancement of deep learning, while facing the looming threat of potential exploitation. Protecting these datasets is of paramount importance for the interests of their owners. The verification of dataset ownership has evolved into a... |
Kong_VLR-Driver_Large_Vision-Language-Reasoning_Models_for_Embodied_Autonomous_Driving_ICCV_2025_paper | VLR-Driver: Large Vision-Language-Reasoning Models for Embodied Autonomous Driving | [
"Fanjie Kong",
"Yitong Li",
"Weihuang Chen",
"Chen Min",
"Yizhe Li",
"Zhiqiang Gao",
"Haoyang Li",
"Zhongyu Guo",
"Hongbin Sun"
] | https://openaccess.thecvf.com/content/ICCV2025/html/Kong_VLR-Driver_Large_Vision-Language-Reasoning_Models_for_Embodied_Autonomous_Driving_ICCV_2025_paper.html | https://openaccess.thecvf.com/content/ICCV2025/papers/Kong_VLR-Driver_Large_Vision-Language-Reasoning_Models_for_Embodied_Autonomous_Driving_ICCV_2025_paper.pdf | null | null | null | @InProceedings{Kong_2025_ICCV,
author = {Kong, Fanjie and Li, Yitong and Chen, Weihuang and Min, Chen and Li, Yizhe and Gao, Zhiqiang and Li, Haoyang and Guo, Zhongyu and Sun, Hongbin},
title = {VLR-Driver: Large Vision-Language-Reasoning Models for Embodied Autonomous Driving},
booktitle = {Proceedi... | The rise of embodied intelligence and multi-modal large language models has led to exciting advancements in the field of autonomous driving, establishing it as a prominent research focus in both academia and industry. However, when confronted with intricate and ambiguous traffic scenarios, the lack of logical reasoning... |
Lyu_ResGS_Residual_Densification_of_3D_Gaussian_for_Efficient_Detail_Recovery_ICCV_2025_paper | ResGS: Residual Densification of 3D Gaussian for Efficient Detail Recovery | [
"Yanzhe Lyu",
"Kai Cheng",
"Xin Kang",
"Xuejin Chen"
] | https://openaccess.thecvf.com/content/ICCV2025/html/Lyu_ResGS_Residual_Densification_of_3D_Gaussian_for_Efficient_Detail_Recovery_ICCV_2025_paper.html | https://openaccess.thecvf.com/content/ICCV2025/papers/Lyu_ResGS_Residual_Densification_of_3D_Gaussian_for_Efficient_Detail_Recovery_ICCV_2025_paper.pdf | https://openaccess.thecvf.com/content/ICCV2025/supplemental/Lyu_ResGS_Residual_Densification_ICCV_2025_supplemental.zip | 2412.07494 | cvf | @InProceedings{Lyu_2025_ICCV,
author = {Lyu, Yanzhe and Cheng, Kai and Kang, Xin and Chen, Xuejin},
title = {ResGS: Residual Densification of 3D Gaussian for Efficient Detail Recovery},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {Octo... | Recently, 3D Gaussian Splatting (3D-GS) has prevailed in novel view synthesis, achieving high fidelity and efficiency. However, it often struggles to capture rich details and complete geometry. Our analysis reveals that the 3D-GS densification operation lacks adaptiveness and faces a dilemma between geometry coverage a... |
Yu_Language_Driven_Occupancy_Prediction_ICCV_2025_paper | Language Driven Occupancy Prediction | [
"Zhu Yu",
"Bowen Pang",
"Lizhe Liu",
"Runmin Zhang",
"Qiang Li",
"Si-Yuan Cao",
"Maochun Luo",
"Mingxia Chen",
"Sheng Yang",
"Hui-Liang Shen"
] | https://openaccess.thecvf.com/content/ICCV2025/html/Yu_Language_Driven_Occupancy_Prediction_ICCV_2025_paper.html | https://openaccess.thecvf.com/content/ICCV2025/papers/Yu_Language_Driven_Occupancy_Prediction_ICCV_2025_paper.pdf | https://openaccess.thecvf.com/content/ICCV2025/supplemental/Yu_Language_Driven_Occupancy_ICCV_2025_supplemental.pdf | 2411.16072 | cvf | @InProceedings{Yu_2025_ICCV,
author = {Yu, Zhu and Pang, Bowen and Liu, Lizhe and Zhang, Runmin and Li, Qiang and Cao, Si-Yuan and Luo, Maochun and Chen, Mingxia and Yang, Sheng and Shen, Hui-Liang},
title = {Language Driven Occupancy Prediction},
booktitle = {Proceedings of the IEEE/CVF Internationa... | We introduce LOcc, an effective and generalizable framework for open-vocabulary occupancy (OVO) prediction. Previous approaches typically supervise the networks through coarse voxel-to-text correspondences via image features as intermediates or noisy and sparse correspondences from voxel-based model-view projections. T... |
Schmidt_Prior2Former_-_Evidential_Modeling_of_Mask_Transformers_for_Assumption-Free_Open-World_ICCV_2025_paper | Prior2Former - Evidential Modeling of Mask Transformers for Assumption-Free Open-World Panoptic Segmentation | [
"Sebastian Schmidt",
"Julius Koerner",
"Dominik Fuchsgruber",
"Stefano Gasperini",
"Federico Tombari",
"Stephan Günnemann"
] | https://openaccess.thecvf.com/content/ICCV2025/html/Schmidt_Prior2Former_-_Evidential_Modeling_of_Mask_Transformers_for_Assumption-Free_Open-World_ICCV_2025_paper.html | https://openaccess.thecvf.com/content/ICCV2025/papers/Schmidt_Prior2Former_-_Evidential_Modeling_of_Mask_Transformers_for_Assumption-Free_Open-World_ICCV_2025_paper.pdf | https://openaccess.thecvf.com/content/ICCV2025/supplemental/Schmidt_Prior2Former_-_Evidential_ICCV_2025_supplemental.pdf | 2504.04841 | title_snapshot | @InProceedings{Schmidt_2025_ICCV,
author = {Schmidt, Sebastian and Koerner, Julius and Fuchsgruber, Dominik and Gasperini, Stefano and Tombari, Federico and G\"unnemann, Stephan},
title = {Prior2Former - Evidential Modeling of Mask Transformers for Assumption-Free Open-World Panoptic Segmentation},
b... | In panoptic segmentation, individual instances must be separated within semantic classes. As state-of-the-art methods rely on a pre-defined set of classes, they struggle with novel categories and out-of-distribution (OOD) data. This is particularly problematic in safety-critical applications, such as autonomous driving... |
Liu_E-NeMF_Event-based_Neural_Motion_Field_for_Novel_Space-time_View_Synthesis_ICCV_2025_paper | E-NeMF: Event-based Neural Motion Field for Novel Space-time View Synthesis of Dynamic Scenes | [
"Yan Liu",
"Zehao Chen",
"Haojie Yan",
"De Ma",
"Huajin Tang",
"Qian Zheng",
"Gang Pan"
] | https://openaccess.thecvf.com/content/ICCV2025/html/Liu_E-NeMF_Event-based_Neural_Motion_Field_for_Novel_Space-time_View_Synthesis_ICCV_2025_paper.html | https://openaccess.thecvf.com/content/ICCV2025/papers/Liu_E-NeMF_Event-based_Neural_Motion_Field_for_Novel_Space-time_View_Synthesis_ICCV_2025_paper.pdf | null | null | null | @InProceedings{Liu_2025_ICCV,
author = {Liu, Yan and Chen, Zehao and Yan, Haojie and Ma, De and Tang, Huajin and Zheng, Qian and Pan, Gang},
title = {E-NeMF: Event-based Neural Motion Field for Novel Space-time View Synthesis of Dynamic Scenes},
booktitle = {Proceedings of the IEEE/CVF International ... | Synthesizing novel space-time views from a monocular video is a highly ill-posed problem, and its effectiveness relies on accurately reconstructing motion and appearance of the dynamic scene.Frame-based methods for novel space-time view synthesis in dynamic scenes rely on simplistic motion assumptions due to the absenc... |
Chen_Event-based_Tiny_Object_Detection_A_Benchmark_Dataset_and_Baseline_ICCV_2025_paper | Event-based Tiny Object Detection: A Benchmark Dataset and Baseline | [
"Nuo Chen",
"Chao Xiao",
"Yimian Dai",
"Shiman He",
"Miao Li",
"Wei An"
] | https://openaccess.thecvf.com/content/ICCV2025/html/Chen_Event-based_Tiny_Object_Detection_A_Benchmark_Dataset_and_Baseline_ICCV_2025_paper.html | https://openaccess.thecvf.com/content/ICCV2025/papers/Chen_Event-based_Tiny_Object_Detection_A_Benchmark_Dataset_and_Baseline_ICCV_2025_paper.pdf | https://openaccess.thecvf.com/content/ICCV2025/supplemental/Chen_Event-based_Tiny_Object_ICCV_2025_supplemental.pdf | 2506.23575 | cvf | @InProceedings{Chen_2025_ICCV,
author = {Chen, Nuo and Xiao, Chao and Dai, Yimian and He, Shiman and Li, Miao and An, Wei},
title = {Event-based Tiny Object Detection: A Benchmark Dataset and Baseline},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
... | Small object detection (SOD) in anti-UAV task is a challenging problem due to the small size of UAVs and complex backgrounds. Traditional frame-based cameras struggle to detect small objects in complex environments due to their low frame rates, limited dynamic range, and data redundancy. Event cameras, with microsecond... |
Xiao_Optimal_Transport_for_Brain-Image_Alignment_Unveiling_Redundancy_and_Synergy_in_ICCV_2025_paper | Optimal Transport for Brain-Image Alignment: Unveiling Redundancy and Synergy in Neural Information Processing | [
"Yang Xiao",
"Wang Lu",
"Jie Ji",
"Ruimeng Ye",
"Gen Li",
"Xiaolong Ma",
"Bo Hui"
] | https://openaccess.thecvf.com/content/ICCV2025/html/Xiao_Optimal_Transport_for_Brain-Image_Alignment_Unveiling_Redundancy_and_Synergy_in_ICCV_2025_paper.html | https://openaccess.thecvf.com/content/ICCV2025/papers/Xiao_Optimal_Transport_for_Brain-Image_Alignment_Unveiling_Redundancy_and_Synergy_in_ICCV_2025_paper.pdf | null | 2503.10663 | cvf | @InProceedings{Xiao_2025_ICCV,
author = {Xiao, Yang and Lu, Wang and Ji, Jie and Ye, Ruimeng and Li, Gen and Ma, Xiaolong and Hui, Bo},
title = {Optimal Transport for Brain-Image Alignment: Unveiling Redundancy and Synergy in Neural Information Processing},
booktitle = {Proceedings of the IEEE/CVF In... | The design of artificial neural networks (ANNs) is inspired by the structure of the human brain, and in turn, ANNs offer a potential means to interpret and understand brain signals. Existing methods primarily align brain signals with stimulus signals using Mean Squared Error (MSE), which focuses only on local point-wis... |
Lu_Adversarial_Distribution_Matching_for_Diffusion_Distillation_Towards_Efficient_Image_and_ICCV_2025_paper | Adversarial Distribution Matching for Diffusion Distillation Towards Efficient Image and Video Synthesis | [
"Yanzuo Lu",
"Yuxi Ren",
"Xin Xia",
"Shanchuan Lin",
"Xing Wang",
"Xuefeng Xiao",
"Andy J. Ma",
"Xiaohua Xie",
"Jian-Huang Lai"
] | https://openaccess.thecvf.com/content/ICCV2025/html/Lu_Adversarial_Distribution_Matching_for_Diffusion_Distillation_Towards_Efficient_Image_and_ICCV_2025_paper.html | https://openaccess.thecvf.com/content/ICCV2025/papers/Lu_Adversarial_Distribution_Matching_for_Diffusion_Distillation_Towards_Efficient_Image_and_ICCV_2025_paper.pdf | https://openaccess.thecvf.com/content/ICCV2025/supplemental/Lu_Adversarial_Distribution_Matching_ICCV_2025_supplemental.pdf | 2507.18569 | cvf | @InProceedings{Lu_2025_ICCV,
author = {Lu, Yanzuo and Ren, Yuxi and Xia, Xin and Lin, Shanchuan and Wang, Xing and Xiao, Xuefeng and Ma, Andy J. and Xie, Xiaohua and Lai, Jian-Huang},
title = {Adversarial Distribution Matching for Diffusion Distillation Towards Efficient Image and Video Synthesis},
b... | Distribution Matching Distillation (DMD) is a promising score distillation technique that compresses pre-trained teacher diffusion models into efficient one-step or multi-step student generators.Nevertheless, its reliance on the reverse Kullback-Leibler (KL) divergence minimization potentially induces mode collapse (or... |
Majumder_Switch-a-View_View_Selection_Learned_from_Unlabeled_In-the-wild_Videos_ICCV_2025_paper | Switch-a-View: View Selection Learned from Unlabeled In-the-wild Videos | [
"Sagnik Majumder",
"Tushar Nagarajan",
"Ziad Al-Halah",
"Kristen Grauman"
] | https://openaccess.thecvf.com/content/ICCV2025/html/Majumder_Switch-a-View_View_Selection_Learned_from_Unlabeled_In-the-wild_Videos_ICCV_2025_paper.html | https://openaccess.thecvf.com/content/ICCV2025/papers/Majumder_Switch-a-View_View_Selection_Learned_from_Unlabeled_In-the-wild_Videos_ICCV_2025_paper.pdf | https://openaccess.thecvf.com/content/ICCV2025/supplemental/Majumder_Switch-a-View_View_Selection_ICCV_2025_supplemental.pdf | 2412.18386 | title_snapshot | @InProceedings{Majumder_2025_ICCV,
author = {Majumder, Sagnik and Nagarajan, Tushar and Al-Halah, Ziad and Grauman, Kristen},
title = {Switch-a-View: View Selection Learned from Unlabeled In-the-wild Videos},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)}... | We introduce Switch-a-View, a model that learns to automatically select the viewpoint to display at each timepoint when creating a how-to video. The key insight of our approach is how to train such a model from unlabeled--but human-edited--video samples. We pose a pretext task that pseudo-labels segments in the trainin... |
Zhang_E-SAM_Training-Free_Segment_Every_Entity_Model_ICCV_2025_paper | E-SAM: Training-Free Segment Every Entity Model | [
"Weiming Zhang",
"Dingwen Xiao",
"Lei Chen",
"Lin Wang"
] | https://openaccess.thecvf.com/content/ICCV2025/html/Zhang_E-SAM_Training-Free_Segment_Every_Entity_Model_ICCV_2025_paper.html | https://openaccess.thecvf.com/content/ICCV2025/papers/Zhang_E-SAM_Training-Free_Segment_Every_Entity_Model_ICCV_2025_paper.pdf | https://openaccess.thecvf.com/content/ICCV2025/supplemental/Zhang_E-SAM_Training-Free_Segment_ICCV_2025_supplemental.pdf | 2503.12094 | title_snapshot | @InProceedings{Zhang_2025_ICCV,
author = {Zhang, Weiming and Xiao, Dingwen and Chen, Lei and Wang, Lin},
title = {E-SAM: Training-Free Segment Every Entity Model},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = ... | Entity Segmentation (ES) aims at identifying and segmenting distinct entities within an image without the need for predefined class labels. This characteristic makes ES well-suited to open-world applications with adaptation to diverse and dynamically changing environments, where new and previously unseen entities may a... |
Huang_ViewSRD_3D_Visual_Grounding_via_Structured_Multi-View_Decomposition_ICCV_2025_paper | ViewSRD: 3D Visual Grounding via Structured Multi-View Decomposition | [
"Ronggang Huang",
"Haoxin Yang",
"Yan Cai",
"Xuemiao Xu",
"Huaidong Zhang",
"Shengfeng He"
] | https://openaccess.thecvf.com/content/ICCV2025/html/Huang_ViewSRD_3D_Visual_Grounding_via_Structured_Multi-View_Decomposition_ICCV_2025_paper.html | https://openaccess.thecvf.com/content/ICCV2025/papers/Huang_ViewSRD_3D_Visual_Grounding_via_Structured_Multi-View_Decomposition_ICCV_2025_paper.pdf | https://openaccess.thecvf.com/content/ICCV2025/supplemental/Huang_ViewSRD_3D_Visual_ICCV_2025_supplemental.pdf | 2507.11261 | cvf | @InProceedings{Huang_2025_ICCV,
author = {Huang, Ronggang and Yang, Haoxin and Cai, Yan and Xu, Xuemiao and Zhang, Huaidong and He, Shengfeng},
title = {ViewSRD: 3D Visual Grounding via Structured Multi-View Decomposition},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer... | 3D visual grounding aims to identify and localize objects in a 3D space based on textual descriptions. However, existing methods struggle with disentangling targets from anchors in complex multi-anchor queries and resolving inconsistencies in spatial descriptions caused by perspective variations.To tackle these challen... |
He_UniPortrait_A_Unified_Framework_for_Identity-Preserving_Single-_and_Multi-Human_Image_ICCV_2025_paper | UniPortrait: A Unified Framework for Identity-Preserving Single- and Multi-Human Image Personalization | [
"Junjie He",
"Yifeng Geng",
"Liefeng Bo"
] | https://openaccess.thecvf.com/content/ICCV2025/html/He_UniPortrait_A_Unified_Framework_for_Identity-Preserving_Single-_and_Multi-Human_Image_ICCV_2025_paper.html | https://openaccess.thecvf.com/content/ICCV2025/papers/He_UniPortrait_A_Unified_Framework_for_Identity-Preserving_Single-_and_Multi-Human_Image_ICCV_2025_paper.pdf | https://openaccess.thecvf.com/content/ICCV2025/supplemental/He_UniPortrait_A_Unified_ICCV_2025_supplemental.pdf | 2408.05939 | title_snapshot | @InProceedings{He_2025_ICCV,
author = {He, Junjie and Geng, Yifeng and Bo, Liefeng},
title = {UniPortrait: A Unified Framework for Identity-Preserving Single- and Multi-Human Image Personalization},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
mont... | This paper presents UniPortrait, an innovative human image personalization framework that unifies single- and multi-ID customization with high face fidelity, extensive facial editability, free-form input description, and diverse layout generation. UniPortrait consists of only two plug-and-play modules: an ID embedding ... |
Wu_FDPT_Federated_Discrete_Prompt_Tuning_for_Black-Box_Visual-Language_Models_ICCV_2025_paper | FDPT: Federated Discrete Prompt Tuning for Black-Box Visual-Language Models | [
"Jiaqi Wu",
"Simin Chen",
"Jing Tang",
"Yuzhe Yang",
"Yiming Chen",
"Lixu Wang",
"Song Lin",
"Zehua Wang",
"Wei Chen",
"Zijian Tian"
] | https://openaccess.thecvf.com/content/ICCV2025/html/Wu_FDPT_Federated_Discrete_Prompt_Tuning_for_Black-Box_Visual-Language_Models_ICCV_2025_paper.html | https://openaccess.thecvf.com/content/ICCV2025/papers/Wu_FDPT_Federated_Discrete_Prompt_Tuning_for_Black-Box_Visual-Language_Models_ICCV_2025_paper.pdf | https://openaccess.thecvf.com/content/ICCV2025/supplemental/Wu_FDPT_Federated_Discrete_ICCV_2025_supplemental.pdf | null | null | @InProceedings{Wu_2025_ICCV,
author = {Wu, Jiaqi and Chen, Simin and Tang, Jing and Yang, Yuzhe and Chen, Yiming and Wang, Lixu and Lin, Song and Wang, Zehua and Chen, Wei and Tian, Zijian},
title = {FDPT: Federated Discrete Prompt Tuning for Black-Box Visual-Language Models},
booktitle = {Proceeding... | General-purpose Vision-Language Models (VLMs) have driven major advancements in multimodal AI. Fine-tuning these models with task-specific data enhances adaptability to various downstream tasks but suffers from privacy risks. While potential solutions like federated learning can address user data privacy concerns, mode... |
Kim_Exploiting_Diffusion_Prior_for_Task-driven_Image_Restoration_ICCV_2025_paper | Exploiting Diffusion Prior for Task-driven Image Restoration | [
"Jaeha Kim",
"Junghun Oh",
"Kyoung Mu Lee"
] | https://openaccess.thecvf.com/content/ICCV2025/html/Kim_Exploiting_Diffusion_Prior_for_Task-driven_Image_Restoration_ICCV_2025_paper.html | https://openaccess.thecvf.com/content/ICCV2025/papers/Kim_Exploiting_Diffusion_Prior_for_Task-driven_Image_Restoration_ICCV_2025_paper.pdf | https://openaccess.thecvf.com/content/ICCV2025/supplemental/Kim_Exploiting_Diffusion_Prior_ICCV_2025_supplemental.pdf | 2507.22459 | cvf | @InProceedings{Kim_2025_ICCV,
author = {Kim, Jaeha and Oh, Junghun and Lee, Kyoung Mu},
title = {Exploiting Diffusion Prior for Task-driven Image Restoration},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {202... | Task-driven image restoration (TDIR) has recently emerged to address performance drops in high-level vision tasks caused by low-quality (LQ) inputs. Previous TDIR methods struggle to handle practical scenarios in which images are degraded by multiple complex factors, leaving minimal clues for restoration. This motivate... |
Kuroki_CE-FAM_Concept-Based_Explanation_via_Fusion_of_Activation_Maps_ICCV_2025_paper | CE-FAM: Concept-Based Explanation via Fusion of Activation Maps | [
"Michihiro Kuroki",
"Toshihiko Yamasaki"
] | https://openaccess.thecvf.com/content/ICCV2025/html/Kuroki_CE-FAM_Concept-Based_Explanation_via_Fusion_of_Activation_Maps_ICCV_2025_paper.html | https://openaccess.thecvf.com/content/ICCV2025/papers/Kuroki_CE-FAM_Concept-Based_Explanation_via_Fusion_of_Activation_Maps_ICCV_2025_paper.pdf | null | 2509.23849 | title_snapshot | @InProceedings{Kuroki_2025_ICCV,
author = {Kuroki, Michihiro and Yamasaki, Toshihiko},
title = {CE-FAM: Concept-Based Explanation via Fusion of Activation Maps},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2... | Although saliency maps can highlight important regions to explain the reasoning behind image classification in artificial intelligence (AI), the meaning of these regions is left to the user's interpretation. In contrast, concept-based explanations decompose AI predictions into human-understandable concepts, clarifying ... |
Wang_Dual-level_Prototype_Learning_for_Composite_Degraded_Image_Restoration_ICCV_2025_paper | Dual-level Prototype Learning for Composite Degraded Image Restoration | [
"Zhongze Wang",
"Haitao Zhao",
"Lujian Yao",
"Jingchao Peng",
"Kaijie Zhao"
] | https://openaccess.thecvf.com/content/ICCV2025/html/Wang_Dual-level_Prototype_Learning_for_Composite_Degraded_Image_Restoration_ICCV_2025_paper.html | https://openaccess.thecvf.com/content/ICCV2025/papers/Wang_Dual-level_Prototype_Learning_for_Composite_Degraded_Image_Restoration_ICCV_2025_paper.pdf | https://openaccess.thecvf.com/content/ICCV2025/supplemental/Wang_Dual-level_Prototype_Learning_ICCV_2025_supplemental.pdf | null | null | @InProceedings{Wang_2025_ICCV,
author = {Wang, Zhongze and Zhao, Haitao and Yao, Lujian and Peng, Jingchao and Zhao, Kaijie},
title = {Dual-level Prototype Learning for Composite Degraded Image Restoration},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},... | Images captured under severe weather conditions often suffer from complex, composite degradations, varying in intensity. In this paper, we introduce a novel method, Dual-Level Prototype Learning (DPL), to tackle the challenging task of composite degraded image restoration. Unlike previous methods that rely on fixed emb... |
Fang_Forensic-MoE_Exploring_Comprehensive_Synthetic_Image_Detection_Traces_with_Mixture_of_ICCV_2025_paper | Forensic-MoE: Exploring Comprehensive Synthetic Image Detection Traces with Mixture of Experts | [
"Mingqi Fang",
"Ziguang Li",
"Lingyun Yu",
"Quanwei Yang",
"Hongtao Xie",
"Yongdong Zhang"
] | https://openaccess.thecvf.com/content/ICCV2025/html/Fang_Forensic-MoE_Exploring_Comprehensive_Synthetic_Image_Detection_Traces_with_Mixture_of_ICCV_2025_paper.html | https://openaccess.thecvf.com/content/ICCV2025/papers/Fang_Forensic-MoE_Exploring_Comprehensive_Synthetic_Image_Detection_Traces_with_Mixture_of_ICCV_2025_paper.pdf | https://openaccess.thecvf.com/content/ICCV2025/supplemental/Fang_Forensic-MoE_Exploring_Comprehensive_ICCV_2025_supplemental.pdf | null | null | @InProceedings{Fang_2025_ICCV,
author = {Fang, Mingqi and Li, Ziguang and Yu, Lingyun and Yang, Quanwei and Xie, Hongtao and Zhang, Yongdong},
title = {Forensic-MoE: Exploring Comprehensive Synthetic Image Detection Traces with Mixture of Experts},
booktitle = {Proceedings of the IEEE/CVF Internation... | Recently, synthetic images have evolved incredibly realistic with the development of generative techniques. To avoid the spread of misinformation and identify synthetic content, research on synthetic image detection becomes urgent. Unfortunately, limited to the singular forensic perspective, existing methods struggle t... |
Jeong_Robust_Adverse_Weather_Removal_via_Spectral-based_Spatial_Grouping_ICCV_2025_paper | Robust Adverse Weather Removal via Spectral-based Spatial Grouping | [
"Yuhwan Jeong",
"Yunseo Yang",
"Youngho Yoon",
"Kuk-Jin Yoon"
] | https://openaccess.thecvf.com/content/ICCV2025/html/Jeong_Robust_Adverse_Weather_Removal_via_Spectral-based_Spatial_Grouping_ICCV_2025_paper.html | https://openaccess.thecvf.com/content/ICCV2025/papers/Jeong_Robust_Adverse_Weather_Removal_via_Spectral-based_Spatial_Grouping_ICCV_2025_paper.pdf | https://openaccess.thecvf.com/content/ICCV2025/supplemental/Jeong_Robust_Adverse_Weather_ICCV_2025_supplemental.pdf | 2507.22498 | cvf | @InProceedings{Jeong_2025_ICCV,
author = {Jeong, Yuhwan and Yang, Yunseo and Yoon, Youngho and Yoon, Kuk-Jin},
title = {Robust Adverse Weather Removal via Spectral-based Spatial Grouping},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {O... | Adverse weather conditions cause diverse and complex degradation patterns, driving the development of All-in-One (AiO) models. However, recent AiO solutions still struggle to capture diverse degradations, since global filtering methods like direct operations on the frequency domain fail to handle highly variable and lo... |
Wang_PLAN_Proactive_Low-Rank_Allocation_for_Continual_Learning_ICCV_2025_paper | PLAN: Proactive Low-Rank Allocation for Continual Learning | [
"Xiequn Wang",
"Zhan Zhuang",
"Yu Zhang"
] | https://openaccess.thecvf.com/content/ICCV2025/html/Wang_PLAN_Proactive_Low-Rank_Allocation_for_Continual_Learning_ICCV_2025_paper.html | https://openaccess.thecvf.com/content/ICCV2025/papers/Wang_PLAN_Proactive_Low-Rank_Allocation_for_Continual_Learning_ICCV_2025_paper.pdf | https://openaccess.thecvf.com/content/ICCV2025/supplemental/Wang_PLAN_Proactive_Low-Rank_ICCV_2025_supplemental.pdf | 2510.21188 | title_snapshot | @InProceedings{Wang_2025_ICCV,
author = {Wang, Xiequn and Zhuang, Zhan and Zhang, Yu},
title = {PLAN: Proactive Low-Rank Allocation for Continual Learning},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2025},... | Continual learning (CL) requires models to continuously adapt to new tasks without forgetting past knowledge. In this work, we propose \underline P roactive \underline L ow-rank \underline A llocatio\underline N (PLAN), a framework that extends Low-Rank Adaptation (LoRA) to enable efficient and interference-aware fine... |
Wan_EMoTive_Event-guided_Trajectory_Modeling_for_3D_Motion_Estimation_ICCV_2025_paper | EMoTive: Event-guided Trajectory Modeling for 3D Motion Estimation | [
"Zengyu Wan",
"Wei Zhai",
"Yang Cao",
"Zhengjun Zha"
] | https://openaccess.thecvf.com/content/ICCV2025/html/Wan_EMoTive_Event-guided_Trajectory_Modeling_for_3D_Motion_Estimation_ICCV_2025_paper.html | https://openaccess.thecvf.com/content/ICCV2025/papers/Wan_EMoTive_Event-guided_Trajectory_Modeling_for_3D_Motion_Estimation_ICCV_2025_paper.pdf | https://openaccess.thecvf.com/content/ICCV2025/supplemental/Wan_EMoTive_Event-guided_Trajectory_ICCV_2025_supplemental.pdf | 2503.11371 | cvf | @InProceedings{Wan_2025_ICCV,
author = {Wan, Zengyu and Zhai, Wei and Cao, Yang and Zha, Zhengjun},
title = {EMoTive: Event-guided Trajectory Modeling for 3D Motion Estimation},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
... | Visual 3D motion estimation aims to infer the motion of 2D pixels in 3D space based on visual cues. The key challenge arises from depth variation induced spatio-temporal motion inconsistencies, disrupting the assumptions of local spatial or temporal motion smoothness in previous motion estimation frameworks. In contras... |
Wang_RobuSTereo_Robust_Zero-Shot_Stereo_Matching_under_Adverse_Weather_ICCV_2025_paper | RobuSTereo: Robust Zero-Shot Stereo Matching under Adverse Weather | [
"Yuran Wang",
"Yingping Liang",
"Yutao Hu",
"Ying Fu"
] | https://openaccess.thecvf.com/content/ICCV2025/html/Wang_RobuSTereo_Robust_Zero-Shot_Stereo_Matching_under_Adverse_Weather_ICCV_2025_paper.html | https://openaccess.thecvf.com/content/ICCV2025/papers/Wang_RobuSTereo_Robust_Zero-Shot_Stereo_Matching_under_Adverse_Weather_ICCV_2025_paper.pdf | https://openaccess.thecvf.com/content/ICCV2025/supplemental/Wang_RobuSTereo_Robust_Zero-Shot_ICCV_2025_supplemental.zip | 2507.01653 | cvf | @InProceedings{Wang_2025_ICCV,
author = {Wang, Yuran and Liang, Yingping and Hu, Yutao and Fu, Ying},
title = {RobuSTereo: Robust Zero-Shot Stereo Matching under Adverse Weather},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
... | Learning-based stereo matching models struggle in adverse weather conditions due to the scarcity of corresponding training data and the challenges in extracting discriminative features from degraded images. These limitations significantly hinder zero-shot generalization to out-of-distribution weather conditions. In thi... |
Liu_SemGes_Semantics-aware_Co-Speech_Gesture_Generation_using_Semantic_Coherence_and_Relevance_ICCV_2025_paper | SemGes: Semantics-aware Co-Speech Gesture Generation using Semantic Coherence and Relevance Learning | [
"Lanmiao Liu",
"Esam Ghaleb",
"Asli Ozyurek",
"Zerrin Yumak"
] | https://openaccess.thecvf.com/content/ICCV2025/html/Liu_SemGes_Semantics-aware_Co-Speech_Gesture_Generation_using_Semantic_Coherence_and_Relevance_ICCV_2025_paper.html | https://openaccess.thecvf.com/content/ICCV2025/papers/Liu_SemGes_Semantics-aware_Co-Speech_Gesture_Generation_using_Semantic_Coherence_and_Relevance_ICCV_2025_paper.pdf | https://openaccess.thecvf.com/content/ICCV2025/supplemental/Liu_SemGes_Semantics-aware_Co-Speech_ICCV_2025_supplemental.pdf | 2507.19359 | cvf | @InProceedings{Liu_2025_ICCV,
author = {Liu, Lanmiao and Ghaleb, Esam and Ozyurek, Asli and Yumak, Zerrin},
title = {SemGes: Semantics-aware Co-Speech Gesture Generation using Semantic Coherence and Relevance Learning},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vis... | Creating a virtual avatar with semantically coherent gestures that are aligned with speech is a challenging task. Existing gesture generation research mainly focused on generating rhythmic beat gestures, neglecting the semantic context of the gestures. In this paper, we propose a novel approach for semantic grounding i... |
Kim_From_Sharp_to_Blur_Unsupervised_Domain_Adaptation_for_2D_Human_ICCV_2025_paper | From Sharp to Blur: Unsupervised Domain Adaptation for 2D Human Pose Estimation Under Extreme Motion Blur Using Event Cameras | [
"Youngho Kim",
"Hoonhee Cho",
"Kuk-Jin Yoon"
] | https://openaccess.thecvf.com/content/ICCV2025/html/Kim_From_Sharp_to_Blur_Unsupervised_Domain_Adaptation_for_2D_Human_ICCV_2025_paper.html | https://openaccess.thecvf.com/content/ICCV2025/papers/Kim_From_Sharp_to_Blur_Unsupervised_Domain_Adaptation_for_2D_Human_ICCV_2025_paper.pdf | null | 2507.22438 | cvf | @InProceedings{Kim_2025_ICCV,
author = {Kim, Youngho and Cho, Hoonhee and Yoon, Kuk-Jin},
title = {From Sharp to Blur: Unsupervised Domain Adaptation for 2D Human Pose Estimation Under Extreme Motion Blur Using Event Cameras},
booktitle = {Proceedings of the IEEE/CVF International Conference on Compu... | Human pose estimation is critical for applications such as rehabilitation, sports analytics, and AR/VR systems. However, rapid motion and low-light conditions often introduce motion blur, significantly degrading pose estimation due to the domain gap between sharp and blurred images. Most datasets assume stable conditio... |
Zhou_HyPiDecoder_Hybrid_Pixel_Decoder_for_Efficient_Segmentation_and_Detection_ICCV_2025_paper | HyPiDecoder: Hybrid Pixel Decoder for Efficient Segmentation and Detection | [
"Fengzhe Zhou",
"Humphrey Shi"
] | https://openaccess.thecvf.com/content/ICCV2025/html/Zhou_HyPiDecoder_Hybrid_Pixel_Decoder_for_Efficient_Segmentation_and_Detection_ICCV_2025_paper.html | https://openaccess.thecvf.com/content/ICCV2025/papers/Zhou_HyPiDecoder_Hybrid_Pixel_Decoder_for_Efficient_Segmentation_and_Detection_ICCV_2025_paper.pdf | null | null | null | @InProceedings{Zhou_2025_ICCV,
author = {Zhou, Fengzhe and Shi, Humphrey},
title = {HyPiDecoder: Hybrid Pixel Decoder for Efficient Segmentation and Detection},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {20... | Recently, Mask2Former has achieved significant success as a universal image segmentation framework, with its Multi-Scale Deformable Attention (MSDeformAttn) Pixel Decoder becoming a widely adopted component in current segmentation models. However, the inefficiency of MSDeformAttn has become a performance bottleneck for... |
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