# papers

title: MANTA: A Large-Scale Multi-View and Visual-Text Anomaly Detection Dataset for Tiny Objects
authors: Lei Fan, Dongdong Fan, Zhiguang Hu, Yiwen Ding, Donglin Di, Kai Yi, Maurice Pagnucco, Yang Song
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 25518-25527
affiliations: ["UNSW Sydney", "Gaozhe Technology", "SCAU", "Tsinghua University", "University of Cambridge"]
cities: ["Sydney", "Unknown", "Guangzhou", "Beijing", "Cambridge"]
countries: ["Australia", "Unknown", "China", "China", "UK"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Fan_MANTA_A_Large-Scale_Multi-View_and_Visual-Text_Anomaly_Detection_Dataset_for_CVPR_2025_paper.html

abstract:
We present MANTA, a visual-text anomaly detection dataset for tiny objects. The visual component comprises over 137.3K images across 38 object categories spanning five typical domains, of which 8.6K images are labeled as anomalous with pixel-level annotations. Each image is captured from five distinct viewpoints to ensure comprehensive object coverage. The text component consists of two subsets: Declarative Knowledge, including 875 words that describe common anomalies across various domains and specific categories, with detailed explanations for \left< what, why, how \right>,  including causes and visual characteristics; and Constructivist Learning, providing 2K multiple-choice questions with varying levels of difficulty, each paired with images and corresponded answer explanations. We also propose a baseline for visual-text tasks and conduct extensive benchmarking experiments to evaluate advanced methods across different settings, highlighting the challenges and efficacy of our dataset.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Fan_MANTA_A_Large-Scale_Multi-View_and_Visual-Text_Anomaly_Detection_Dataset_for_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Fan_MANTA_A_Large-Scale_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Fan_2025_CVPR,
    author    = {Fan, Lei and Fan, Dongdong and Hu, Zhiguang and Ding, Yiwen and Di, Donglin and Yi, Kai and Pagnucco, Maurice and Song, Yang},
    title     = {MANTA: A Large-Scale Multi-View and Visual-Text Anomaly Detection Dataset for Tiny Objects},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {25518-25527}
}

raw_affiliation:
[{"affiliation": "UNSW Sydney", "city": "Sydney", "country": "Australia"}, {"affiliation": "Gaozhe Technology", "city": "Unknown", "country": "Unknown"}, {"affiliation": "SCAU", "city": "Guangzhou", "country": "China"}, {"affiliation": "Tsinghua University", "city": "Beijing", "country": "China"}, {"affiliation": "University of Cambridge", "city": "Cambridge", "country": "UK"}]


authors: Sangwoon Kwak, Joonsoo Kim, Jun Young Jeong, Won-Sik Cheong, Jihyong Oh, Munchurl Kim
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 11338-11348
cities: ["Daejeon", "Daejeon", "Seoul"]
countries: ["South Korea", "South Korea", "South Korea"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Kwak_MoDec-GS_Global-to-Local_Motion_Decomposition_and_Temporal_Interval_Adjustment_for_Compact_CVPR_2025_paper.html

title:
MoDec-GS: Global-to-Local Motion Decomposition and Temporal Interval Adjustment for Compact Dynamic 3D Gaussian Splatting

abstract:
3D Gaussian Splatting (3DGS) has made significant strides in scene representation and neural rendering, with intense efforts focused on adapting it for dynamic scenes. Despite delivering remarkable rendering quality and speed, existing methods struggle with storage demands and the representation of complex real-world motions. To address these challenges, we propose MoDec-GS, a memory-efficient Gaussian splatting framework designed to reconstruct novel views in challenging scenarios with complex motions. We introduce Global-to-Local Motion Decomposition (GLMD) to effectively capture dynamic motions in a coarse-to-fine manner. This approach leverages Global Canonical Scaffolds (Global CS) and Local Canonical Scaffolds (Local CS), which extend static Scaffold representation to dynamic video reconstruction. For Global CS, we propose Global Anchor Deformation (GAD) to efficiently represent global dynamics along complex motions by directly deforming the implicit Scaffold attributes, including anchor position, offset, and local context features. Next, we finely adjust local motions via the Local Gaussian Deformation (LGD) of Local CS explicitly. Additionally, we introduce Temporal Interval Adjustment (TIA) to automatically control the temporal coverage of each Local CS during training, enabling MoDec-GS to find optimal interval assignments based on the specified number of temporal segments. Extensive evaluations demonstrate that MoDec-GS achieves an average 70% reduction in model size over state-of-the-art methods for dynamic 3D Gaussians from real-world dynamic videos while maintaining or even improving rendering quality.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Kwak_MoDec-GS_Global-to-Local_Motion_Decomposition_and_Temporal_Interval_Adjustment_for_Compact_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Kwak_MoDec-GS_Global-to-Local_Motion_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Kwak_2025_CVPR,
    author    = {Kwak, Sangwoon and Kim, Joonsoo and Jeong, Jun Young and Cheong, Won-Sik and Oh, Jihyong and Kim, Munchurl},
    title     = {MoDec-GS: Global-to-Local Motion Decomposition and Temporal Interval Adjustment for Compact Dynamic 3D Gaussian Splatting},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {11338-11348}
}

raw_affiliation:
[{"affiliation": "Electronics and Telecommunications Research Institute", "city": "Daejeon", "country": "South Korea"}, {"affiliation": "Korea Advanced Institute of Science and Technology", "city": "Daejeon", "country": "South Korea"}, {"affiliation": "Chung-Ang University", "city": "Seoul", "country": "South Korea"}]

affiliations:
["Electronics and Telecommunications Research Institute", "Korea Advanced Institute of Science and Technology", "Chung-Ang University"]


title: DaCapo: Score Distillation as Stacked Bridge for Fast and High-quality 3D Editing
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 16304-16313
cities: ["Hangzhou", "Hangzhou", "Beijing", "Beijing", "Hong Kong", "Macau"]
countries: ["China", "China", "China", "China", "China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Huang_DaCapo_Score_Distillation_as_Stacked_Bridge_for_Fast_and_High-quality_CVPR_2025_paper.html

authors:
Yufei Huang, Bangyan Liao, Yuqi Hu, Haitao Lin, Lirong Wu, Siyuan Li, Cheng Tan, Zicheng Liu, Yunfan Liu, Zelin Zang, Chang Yu, Zhen Lei

abstract:
Score Distillation Sampling (SDS) has been successfully extended to text-driven 3D scene editing with 2D pretrained diffusion models. However, SDS-based editing methods suffer from lengthy optimization processes with slow inference and low quality. We attribute the issue of lengthy optimization to the stochastic optimization scheme used in SDS-based editing, where many steps may conflict with each other (e.g., the inherent trade-off between editing and preservation). To reduce this internal conflict and speed up the editing process, we propose to separate editing and preservation in time with a diffusion time schedule and frame the 3D editing optimization process as a diffusion bridge sampling process. Motivated by the analysis above, we introduce DaCapo, a fast diffusion sampling-like 3D editing method that incorporates a novel stacked bridge framework, which estimates a direct diffusion bridge between source and target distribution with only a pretrained 2D diffusion model. Specifically, It models the editing process as a combination of inversion and generation, where both processes happen simultaneously as a stack of Diffusion Bridges. DaCapo shows a 15x speed-up with comparable results to the state-of-the-art SDS-based method. It completes the process in just 2,500 steps on a single GPU and accommodates a variety of 3D representation methods.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Huang_DaCapo_Score_Distillation_as_Stacked_Bridge_for_Fast_and_High-quality_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Huang_DaCapo_Score_Distillation_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Huang_2025_CVPR,
    author    = {Huang, Yufei and Liao, Bangyan and Hu, Yuqi and Lin, Haitao and Wu, Lirong and Li, Siyuan and Tan, Cheng and Liu, Zicheng and Liu, Yunfan and Zang, Zelin and Yu, Chang and Lei, Zhen},
    title     = {DaCapo: Score Distillation as Stacked Bridge for Fast and High-quality 3D Editing},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {16304-16313}
}

raw_affiliation:
[{"affiliation": "Zhejiang University", "city": "Hangzhou", "country": "China"}, {"affiliation": "Westlake University", "city": "Hangzhou", "country": "China"}, {"affiliation": "MAIS, Institute of Automation, Chinese Academy of Sciences", "city": "Beijing", "country": "China"}, {"affiliation": "School of Artificial Intelligence, University of Chinese Academy of Sciences", "city": "Beijing", "country": "China"}, {"affiliation": "CAIR, HKSIS, Chinese Academy of Sciences", "city": "Hong Kong", "country": "China"}, {"affiliation": "School of Computer Science and Engineering, the Faculty of Innovation Engineering, M.U.S.T", "city": "Macau", "country": "China"}]

affiliations:
["Zhejiang University", "Westlake University", "MAIS, Institute of Automation, Chinese Academy of Sciences", "School of Artificial Intelligence, University of Chinese Academy of Sciences", "CAIR, HKSIS, Chinese Academy of Sciences", "School of Computer Science and Engineering, the Faculty of Innovation Engineering, M.U.S.T"]


title: A Selective Re-learning Mechanism for Hyperspectral Fusion Imaging
authors: Yuanye Liu, Jinyang Liu, Renwei Dian, Shutao Li
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 7437-7446
cities: ["Changsha", "Quanzhou", "Changsha"]
countries: ["China", "China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Liu_A_Selective_Re-learning_Mechanism_for_Hyperspectral_Fusion_Imaging_CVPR_2025_paper.html

abstract:
Hyperspectral fusion imaging is challenged by high computational cost due to the abundant spectral information. We find that pixels in regions with smooth spatial-spectral structure can be reconstructed well using a shallow network, while only those in regions with complex spatial-spectral structure require a deeper network. However, existing methods process all pixels uniformly, which ignores  this property. To leverage this property, we propose a Selective Re-learning Fusion Network (SRLF) that initially extracts features from all pixels uniformly and then selectively refines distorted feature points. Specifically, SRLF first employs a Preliminary Fusion Module with robust global modeling capability to generate a preliminary fusion feature. Afterward, it applies a Selective Re-learning Module to focus on improving distorted feature points in the preliminary fusion feature. To achieve targeted learning, we present a novel Spatial-Spectral Structure-Guided Selective Re-learning Mechanism (SSG-SRL) that integrates the observation model to identify the feature points with spatial or spectral distortions. Only these distorted points are sent to the corresponding re-learning blocks, reducing both computational cost and the risk of overfitting. Finally, we develop an SRLF-Net, composed of multiple cascaded SRLFs, which surpasses multiple state-of-the-art methods on several datasets with minimal computational cost.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Liu_A_Selective_Re-learning_Mechanism_for_Hyperspectral_Fusion_Imaging_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Liu_A_Selective_Re-learning_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Liu_2025_CVPR,
    author    = {Liu, Yuanye and Liu, Jinyang and Dian, Renwei and Li, Shutao},
    title     = {A Selective Re-learning Mechanism for Hyperspectral Fusion Imaging},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {7437-7446}
}

raw_affiliation:
[{"affiliation": "The School of Robotics, Hunan University, China", "city": "Changsha", "country": "China"}, {"affiliation": "Industrial Design and Machine Intelligence Innovation Research Institute, Quanzhou-Hunan University, China", "city": "Quanzhou", "country": "China"}, {"affiliation": "The College of Electrical and Information Engineering, Hunan University, China", "city": "Changsha", "country": "China"}]

affiliations:
["The School of Robotics, Hunan University, China", "Industrial Design and Machine Intelligence Innovation Research Institute, Quanzhou-Hunan University, China", "The College of Electrical and Information Engineering, Hunan University, China"]


title: SCSegamba: Lightweight Structure-Aware Vision Mamba for Crack Segmentation in Structures
authors: Hui Liu, Chen Jia, Fan Shi, Xu Cheng, Shengyong Chen
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 29406-29416
cities: ["Tianjin", "Tianjin", "Tianjin"]
countries: ["China", "China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Liu_SCSegamba_Lightweight_Structure-Aware_Vision_Mamba_for_Crack_Segmentation_in_Structures_CVPR_2025_paper.html

abstract:
Pixel-level segmentation of structural cracks across various scenarios remains a considerable challenge. Current methods encounter challenges in effectively modeling crack morphology and texture, facing challenges in balancing segmentation quality with low computational resource usage. To overcome these limitations, we propose a lightweight Structure-Aware Vision Mamba Network (SCSegamba), capable of generating high-quality pixel-level segmentation maps by leveraging both the morphological information and texture cues of crack pixels with minimal computational cost. Specifically, we developed a Structure-Aware Visual State Space module (SAVSS), which incorporates a lightweight Gated Bottleneck Convolution (GBC) and a Structure-Aware Scanning Strategy (SASS). The key insight of GBC lies in its effectiveness in modeling the morphological information of cracks, while the SASS enhances the perception of crack topology and texture by strengthening the continuity of semantic information between crack pixels. Experiments on crack benchmark datasets demonstrate that our method outperforms other state-of-the-art (SOTA) methods, achieving the highest performance with only 2.8M parameters. On the multi-scenario dataset, our method reached 0.8390 in F1 score and 0.8479 in mIoU.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Liu_SCSegamba_Lightweight_Structure-Aware_Vision_Mamba_for_Crack_Segmentation_in_Structures_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Liu_SCSegamba_Lightweight_Structure-Aware_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Liu_2025_CVPR,
    author    = {Liu, Hui and Jia, Chen and Shi, Fan and Cheng, Xu and Chen, Shengyong},
    title     = {SCSegamba: Lightweight Structure-Aware Vision Mamba for Crack Segmentation in Structures},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {29406-29416}
}

raw_affiliation:
[{"affiliation": "School of Computer Science and Engineering, Tianjin University of Technology", "city": "Tianjin", "country": "China"}, {"affiliation": "Engineering Research Center of Learning-Based Intelligent System (Ministry of Education)", "city": "Tianjin", "country": "China"}, {"affiliation": "Key Laboratory of Computer Vision and System (Ministry of Education)", "city": "Tianjin", "country": "China"}]

affiliations:
["School of Computer Science and Engineering, Tianjin University of Technology", "Engineering Research Center of Learning-Based Intelligent System (Ministry of Education)", "Key Laboratory of Computer Vision and System (Ministry of Education)"]


title: Autoregressive Sequential Pretraining for Visual Tracking
authors: Shiyi Liang, Yifan Bai, Yihong Gong, Xing Wei
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 7254-7264
affiliations: ["School of Software Engineering, Xi\u2019an Jiaotong University"]
cities: ["Xi'an"]
countries: ["China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Liang_Autoregressive_Sequential_Pretraining_for_Visual_Tracking_CVPR_2025_paper.html

abstract:
Recent advancements in visual object tracking have shifted towards a sequential generation paradigm, where object deformation and motion exhibit strong temporal dependencies. Despite the importance of these dependencies, widely adopted image-level pretrained backbones barely capture the dynamics in the consecutive video, which is the essence of tracking. Thus, we propose AutoRegressive Sequential Pretraining (ARP), an unsupervised spatio-temporal learner, via generating the evolution of object appearance and motion in video sequences.Our method leverages a diffusion model to autoregressively generate the future frame appearance, conditioned on historical embeddings extracted by a general encoder. Furthermore, to ensure trajectory coherence, the same encoder is employed to learn trajectory consistency by generating coordinate sequences in a reverse autoregressive fashion, a process we term back-tracking. Further, we integrate the pretrained ARP into ARTrackV2, creating ARPTrack, which is further fine-tuned for tracking tasks. ARPTrack achieves state-of-the-art performance across multiple benchmarks, becoming the first tracker to surpass 80% AO on GOT-10k, while maintaining high efficiency. These results demonstrate the effectiveness of our approach in capturing temporal dependencies for continuous video tracking.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Liang_Autoregressive_Sequential_Pretraining_for_Visual_Tracking_CVPR_2025_paper.pdf

bibtex:
@InProceedings{Liang_2025_CVPR,
    author    = {Liang, Shiyi and Bai, Yifan and Gong, Yihong and Wei, Xing},
    title     = {Autoregressive Sequential Pretraining for Visual Tracking},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {7254-7264}
}

raw_affiliation:
[{"affiliation": "School of Software Engineering, Xi\u2019an Jiaotong University", "city": "Xi'an", "country": "China"}]


title: Number it: Temporal Grounding Videos like Flipping Manga
authors: Yongliang Wu, Xinting Hu, Yuyang Sun, Yizhou Zhou, Wenbo Zhu, Fengyun Rao, Bernt Schiele, Xu Yang
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 13754-13765
cities: ["Nanjing", "Nanjing", "Saarbrucken", "Shenzhen", "Berkeley"]
countries: ["China", "China", "Germany", "China", "USA"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Wu_Number_it_Temporal_Grounding_Videos_like_Flipping_Manga_CVPR_2025_paper.html

abstract:
Video Large Language Models (Vid-LLMs) have made remarkable advancements in comprehending video content for QA dialogue. However, they struggle to extend this visual understanding to tasks requiring precise temporal localization, known as Video Temporal Grounding (VTG). To address this, we introduce Number-Prompt (NumPro), a novel method that empowers Vid-LLMs to bridge visual comprehension with temporal grounding by adding unique numerical identifiers to each video frame. Treating a video as a sequence of numbered frame images, NumPro transforms VTG into an intuitive process: flipping through manga panels in sequence. This allows Vid-LLMs to "read" event timelines, accurately linking visual content with corresponding temporal information. Our experiments demonstrate that NumPro significantly boosts VTG performance of top-tier Vid-LLMs without additional computational cost. Furthermore, fine-tuning on a NumPro-enhanced dataset defines a new state-of-the-art for VTG, surpassing previous top-performing methods by up to 6.9% in mIoU for moment retrieval and 8.5% in mAP for highlight detection. The code is available at https://github.com/yongliang-wu/NumPro.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Wu_Number_it_Temporal_Grounding_Videos_like_Flipping_Manga_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Wu_Number_it_Temporal_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Wu_2025_CVPR,
    author    = {Wu, Yongliang and Hu, Xinting and Sun, Yuyang and Zhou, Yizhou and Zhu, Wenbo and Rao, Fengyun and Schiele, Bernt and Yang, Xu},
    title     = {Number it: Temporal Grounding Videos like Flipping Manga},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {13754-13765}
}

raw_affiliation:
[{"affiliation": "Southeast University", "city": "Nanjing", "country": "China"}, {"affiliation": "Key Laboratory of New Generation Arti\ufb01cial Intelligence Technology and Its Interdisciplinary Applications (Southeast University), Ministry of Education, China", "city": "Nanjing", "country": "China"}, {"affiliation": "Max Planck Institute for Informatics, Saarland Informatics Campus", "city": "Saarbrucken", "country": "Germany"}, {"affiliation": "WeChat Vision, Tencent Inc.", "city": "Shenzhen", "country": "China"}, {"affiliation": "University of California, Berkeley", "city": "Berkeley", "country": "USA"}]

affiliations:
["Southeast University", "Key Laboratory of New Generation Arti\ufb01cial Intelligence Technology and Its Interdisciplinary Applications (Southeast University), Ministry of Education, China", "Max Planck Institute for Informatics, Saarland Informatics Campus", "WeChat Vision, Tencent Inc.", "University of California, Berkeley"]


title: Collaborative Tree Search for Enhancing Embodied Multi-Agent Collaboration
authors: Lizheng Zu, Lin Lin, Song Fu, Na Zhao, Pan Zhou
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 29513-29522
cities: ["Harbin", "Singapore", "Singapore", "Singapore"]
countries: ["China", "Singapore", "Singapore", "Singapore"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Zu_Collaborative_Tree_Search_for_Enhancing_Embodied_Multi-Agent_Collaboration_CVPR_2025_paper.html

abstract:
Embodied agents based on large language models (LLMs) face significant challenges in collaborative tasks, requiring effective communication and reasonable division of labor to ensure efficient and correct task completion. Previous approaches with simple communication patterns carry erroneous or incoherent agent actions, which can lead to additional risks. To address these problems, we propose Cooperative Tree Search (CoTS), a framework designed to significantly improve collaborative planning and task execution efficiency among embodied agents. CoTS guides multi-agents to discuss long-term strategic plans within a modified Monte Carlo tree, searching along LLM-driven reward functions to provide a more thoughtful and promising approach to cooperation. Another key feature of our method is the introduction of a plan evaluation module, which not only prevents agent action confusion caused by frequent plan updates but also ensures plan updates when the current plan becomes unsuitable. Experimental results show that the proposed method performs excellently in planning, communication, and collaboration on embodied environments (CWAH and TDW-MAT), efficiently completing long-term, complex tasks and significantly outperforming existing methods.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Zu_Collaborative_Tree_Search_for_Enhancing_Embodied_Multi-Agent_Collaboration_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Zu_Collaborative_Tree_Search_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Zu_2025_CVPR,
    author    = {Zu, Lizheng and Lin, Lin and Fu, Song and Zhao, Na and Zhou, Pan},
    title     = {Collaborative Tree Search for Enhancing Embodied Multi-Agent Collaboration},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {29513-29522}
}

raw_affiliation:
[{"affiliation": "Harbin Institute of Technology", "city": "Harbin", "country": "China"}, {"affiliation": "Nanyang Technological University", "city": "Singapore", "country": "Singapore"}, {"affiliation": "Singapore University of Technology and Design", "city": "Singapore", "country": "Singapore"}, {"affiliation": "Singapore Management University", "city": "Singapore", "country": "Singapore"}]

affiliations:
["Harbin Institute of Technology", "Nanyang Technological University", "Singapore University of Technology and Design", "Singapore Management University"]


title: PromptHMR: Promptable Human Mesh Recovery
authors: Yufu Wang, Yu Sun, Priyanka Patel, Kostas Daniilidis, Michael J. Black, Muhammed Kocabas
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 1148-1159
cities: ["Munich", "T\u00fcbingen", "Z\u00fcrich", "Philadelphia", "Philadelphia"]
countries: ["Germany", "Germany", "Switzerland", "USA", "USA"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Wang_PromptHMR_Promptable_Human_Mesh_Recovery_CVPR_2025_paper.html

abstract:
Human pose and shape (HPS) estimation presents challenges in diverse scenarios such as crowded scenes, person-person interactions, and single-view reconstruction. Existing approaches lack mechanisms to incorporate auxiliary "side information" that could enhance reconstruction accuracy in such challenging scenarios. Furthermore, the most accurate methods rely on cropped person detections and cannot exploit scene context while methods that process the whole image often fail to detect people and are less accurate than methods that use crops. While recent language-based methods explore HPS reasoning through large language or vision-language models, their metric accuracy is well below the state of the art. In contrast, we present PromptHMR, a transformer-based promptable method that reformulates HPS estimation through spatial and semantic prompts. Our method processes full images to maintain scene context and accepts multiple input modalities: spatial prompts like bounding boxes and masks, and semantic prompts like language descriptions or interaction labels. PromptHMR demonstrates robust performance across challenging scenarios: estimating people from bounding boxes as small as faces in crowded scenes, improving body shape estimation through language descriptions, modeling person-person interactions, and producing temporally coherent motions in videos. Experiments on benchmarks show that PromptHMR achieves state-of-the-art performance while offering flexible prompt-based control over the HPS estimation process.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Wang_PromptHMR_Promptable_Human_Mesh_Recovery_CVPR_2025_paper.pdf

bibtex:
@InProceedings{Wang_2025_CVPR,
    author    = {Wang, Yufu and Sun, Yu and Patel, Priyanka and Daniilidis, Kostas and Black, Michael J. and Kocabas, Muhammed},
    title     = {PromptHMR: Promptable Human Mesh Recovery},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {1148-1159}
}

raw_affiliation:
[{"affiliation": "Meshcapade", "city": "Munich", "country": "Germany"}, {"affiliation": "MPI for Intelligent Systems", "city": "T\u00fcbingen", "country": "Germany"}, {"affiliation": "ETH Z\u00a8urich", "city": "Z\u00fcrich", "country": "Switzerland"}, {"affiliation": "University of Pennsylvania", "city": "Philadelphia", "country": "USA"}, {"affiliation": "Archimedes", "city": "Philadelphia", "country": "USA"}]

affiliations:
["Meshcapade", "MPI for Intelligent Systems", "ETH Z\u00a8urich", "University of Pennsylvania", "Archimedes"]


title: SyncVP: Joint Diffusion for Synchronous Multi-Modal Video Prediction
authors: Enrico Pallotta, Sina Mokhtarzadeh Azar, Shuai Li, Olga Zatsarynna, Juergen Gall
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 13787-13797
affiliations: ["University of Bonn, Lamarr Institute for Machine Learning and Artificial Intelligence"]
cities: ["Bonn"]
countries: ["Germany"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Pallotta_SyncVP_Joint_Diffusion_for_Synchronous_Multi-Modal_Video_Prediction_CVPR_2025_paper.html

abstract:
Predicting future video frames is essential for decision-making systems, yet RGB frames alone often lack the information needed to fully capture the underlying complexities of the real world. To address this limitation, we propose a multi-modal framework for Synchronous Video Prediction (SyncVP) that incorporates complementary data modalities, enhancing the richness and accuracy of future predictions. SyncVP builds on pre-trained modality-specific diffusion models and introduces an efficient spatio-temporal cross-attention module to enable effective information sharing across modalities. We evaluate SyncVP on standard benchmark datasets, such as Cityscapes and BAIR, using depth as an additional modality. We furthermore demonstrate its generalization to other modalities on SYNTHIA with semantic information and ERA5-Land with climate data. Notably, SyncVP achieves state-of-the-art performance, even in scenarios where only one modality is present, demonstrating its robustness and potential for a wide range of applications.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Pallotta_SyncVP_Joint_Diffusion_for_Synchronous_Multi-Modal_Video_Prediction_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Pallotta_SyncVP_Joint_Diffusion_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Pallotta_2025_CVPR,
    author    = {Pallotta, Enrico and Azar, Sina Mokhtarzadeh and Li, Shuai and Zatsarynna, Olga and Gall, Juergen},
    title     = {SyncVP: Joint Diffusion for Synchronous Multi-Modal Video Prediction},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {13787-13797}
}

raw_affiliation:
[{"affiliation": "University of Bonn, Lamarr Institute for Machine Learning and Artificial Intelligence", "city": "Bonn", "country": "Germany"}]


title: HUSH: Holistic Panoramic 3D Scene Understanding using Spherical Harmonics
authors: Jongsung Lee, Harin Park, Byeong-Uk Lee, Kyungdon Joo
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 16599-16608
affiliations: ["Artificial Intelligence Graduate School, UNIST", "KRAFTON"]
cities: ["Ulsan", "Seongnam"]
countries: ["South Korea", "South Korea"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Lee_HUSH_Holistic_Panoramic_3D_Scene_Understanding_using_Spherical_Harmonics_CVPR_2025_paper.html

abstract:
Motivated by the efficiency of spherical harmonics (SH) in representing various physical phenomena, we propose a Holistic panoramic 3D scene Understanding framework using Spherical Harmonics, dubbed as HUSH. Our approach focuses on a unified framework adaptable to various 3D scene understanding tasks via SH bases. To achieve this, we first estimate SH coefficients, allowing for the adaptive configuration of the SH bases specific to each scene. HUSH then employs a hierarchical attention module that uses SH bases as queries to generate comprehensive scene features by integrating these scene-adaptive SH bases with image features. Additionally, we introduce an SH basis index module that adaptively emphasizes relevant SH bases to produce task-relevant features, enhancing the versatility of HUSH across different scene understanding tasks. Finally, by combining the scene features with task-relevant features in the task-specific heads, we perform various scene understanding tasks, including depth, surface normal and room layout estimation. Experiments demonstrate that HUSH achieves state-of-the-art performance on depth estimation benchmarks, highlighting the robustness and scalability of using SH in panoramic 3D scene understanding.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Lee_HUSH_Holistic_Panoramic_3D_Scene_Understanding_using_Spherical_Harmonics_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Lee_HUSH_Holistic_Panoramic_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Lee_2025_CVPR,
    author    = {Lee, Jongsung and Park, Harin and Lee, Byeong-Uk and Joo, Kyungdon},
    title     = {HUSH: Holistic Panoramic 3D Scene Understanding using Spherical Harmonics},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {16599-16608}
}

raw_affiliation:
[{"affiliation": "Artificial Intelligence Graduate School, UNIST", "city": "Ulsan", "country": "South Korea"}, {"affiliation": "KRAFTON", "city": "Seongnam", "country": "South Korea"}]


title: SkillMimic: Learning Basketball Interaction Skills from Demonstrations
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 17540-17549
cities: ["Hong Kong", "Hangzhou", "Shenzhen", "Beijing", "Shenzhen", "Shenzhen", "Pittsburgh"]
countries: ["China", "China", "China", "China", "China", "China", "USA"]
highlight: yes
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Wang_SkillMimic_Learning_Basketball_Interaction_Skills_from_Demonstrations_CVPR_2025_paper.html

authors:
Yinhuai Wang, Qihan Zhao, Runyi Yu, Hok Wai Tsui, Ailing Zeng, Jing Lin, Zhengyi Luo, Jiwen Yu, Xiu Li, Qifeng Chen, Jian Zhang, Lei Zhang, Ping Tan

abstract:
Traditional reinforcement learning methods for human-object interaction (HOI) rely on labor-intensive, manually designed skill rewards that do not generalize well across different interactions. We introduce SkillMimic, a unified data-driven framework that fundamentally changes how agents learn interaction skills by eliminating the need for skill-specific rewards. Our key insight is that a unified HOI imitation reward can effectively capture the essence of diverse interaction patterns from HOI datasets. This enables SkillMimic to learn a single policy that not only masters multiple interaction skills but also facilitates skill transitions, with both diversity and generalization improving as the HOI dataset grows. For evaluation, we collect and introduce two basketball datasets containing approximately 35 minutes of diverse basketball skills. Extensive experiments show that SkillMimic successfully masters a wide range of basketball skills including stylistic variations in dribbling, layup, and shooting. Moreover, these learned skills can be effectively composed by a high-level controller to accomplish complex and long-horizon tasks such as consecutive scoring, opening new possibilities for scalable and generalizable interaction skill learning. Project page: https://ingrid789.github.io/SkillMimic/

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Wang_SkillMimic_Learning_Basketball_Interaction_Skills_from_Demonstrations_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Wang_SkillMimic_Learning_Basketball_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Wang_2025_CVPR,
    author    = {Wang, Yinhuai and Zhao, Qihan and Yu, Runyi and Tsui, Hok Wai and Zeng, Ailing and Lin, Jing and Luo, Zhengyi and Yu, Jiwen and Li, Xiu and Chen, Qifeng and Zhang, Jian and Zhang, Lei and Tan, Ping},
    title     = {SkillMimic: Learning Basketball Interaction Skills from Demonstrations},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {17540-17549}
}

raw_affiliation:
[{"affiliation": "Hong Kong University of Science and Technology", "city": "Hong Kong", "country": "China"}, {"affiliation": "Unitree Robotics", "city": "Hangzhou", "country": "China"}, {"affiliation": "Peking University Shenzhen Graduate School", "city": "Shenzhen", "country": "China"}, {"affiliation": "Tsinghua University", "city": "Beijing", "country": "China"}, {"affiliation": "International Digital Economy Academy", "city": "Shenzhen", "country": "China"}, {"affiliation": "Tencent", "city": "Shenzhen", "country": "China"}, {"affiliation": "Carnegie Mellon University", "city": "Pittsburgh", "country": "USA"}]

affiliations:
["Hong Kong University of Science and Technology", "Unitree Robotics", "Peking University Shenzhen Graduate School", "Tsinghua University", "International Digital Economy Academy", "Tencent", "Carnegie Mellon University"]


authors: Kang You, Ziling Wei, Jing Yan, Boning Zhang, Qinghai Guo, Yaoyu Zhang, Zhezhi He
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 8796-8805
cities: ["Shanghai", "Shanghai", "Shanghai", "Shenzhen"]
countries: ["China", "China", "China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/You_VISTREAM_Improving_Computation_Efficiency_of_Visual_Streaming_Perception_via_Law-of-Charge-Conservation_CVPR_2025_paper.html

title:
VISTREAM: Improving Computation Efficiency of Visual Streaming Perception via Law-of-Charge-Conservation Inspired Spiking Neural Network

abstract:
Visual streaming perception (VSP) involves online intelligent processing of sequential frames captured by vision sensors, enabling real-time decision-making in applications such as autonomous driving, UAVs, and AR/VR. However, the computational efficiency of VSP on edge devices remains a challenge due to power constraints and the underutilization of temporal dependencies between frames. While spiking neural networks (SNNs) offer biologically inspired event-driven processing with potential energy benefits, their practical advantage over artificial neural networks (ANNs) for VSP tasks remains unproven.In this work, we introduce a novel framework, VISTREAM, which leverages the Law of Charge Conservation (LoCC) property in ST-BIF neurons and a differential encoding (DiffEncode) scheme to optimize SNN inference for VSP. By encoding temporal differences between neighboring frames and eliminating frequent membrane resets, VISTREAM achieves significant computational efficiency while maintaining accuracy equivalent to its ANN counterpart. We provide theoretical proofs of equivalence and validate VISTREAM across diverse VSP tasks, including object detection, tracking, and segmentation, demonstrating substantial energy savings without compromising performance.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/You_VISTREAM_Improving_Computation_Efficiency_of_Visual_Streaming_Perception_via_Law-of-Charge-Conservation_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/You_VISTREAM_Improving_Computation_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{You_2025_CVPR,
    author    = {You, Kang and Wei, Ziling and Yan, Jing and Zhang, Boning and Guo, Qinghai and Zhang, Yaoyu and He, Zhezhi},
    title     = {VISTREAM: Improving Computation Efficiency of Visual Streaming Perception via Law-of-Charge-Conservation Inspired Spiking Neural Network},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {8796-8805}
}

raw_affiliation:
[{"affiliation": "Intelligent Computing Research Group, SEIEE, Shanghai Jiao Tong University, CN", "city": "Shanghai", "country": "China"}, {"affiliation": "Institute of Natural Sciences and MOE-LSC, SJTU, CN", "city": "Shanghai", "country": "China"}, {"affiliation": "School of Mathematical Sciences, SJTU, CN", "city": "Shanghai", "country": "China"}, {"affiliation": "Huawei Technologies, Shenzhen, CN", "city": "Shenzhen", "country": "China"}]

affiliations:
["Intelligent Computing Research Group, SEIEE, Shanghai Jiao Tong University, CN", "Institute of Natural Sciences and MOE-LSC, SJTU, CN", "School of Mathematical Sciences, SJTU, CN", "Huawei Technologies, Shenzhen, CN"]


title: STPro: Spatial and Temporal Progressive Learning for Weakly Supervised Spatio-Temporal Grounding
authors: Aaryan Garg, Akash Kumar, Yogesh S Rawat
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 3384-3394
affiliations: ["BITS Pilani", "CRCV , University of Central Florida"]
cities: ["Pilani", "Orlando"]
countries: ["India", "USA"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Garg_STPro_Spatial_and_Temporal_Progressive_Learning_for_Weakly_Supervised_Spatio-Temporal_CVPR_2025_paper.html

abstract:
In this work, we study Weakly Supervised Spatio-Temporal Video Grounding (WSTVG), a challenging task of localizing subjects spatio-temporally in videos using only textual queries and no bounding box supervision. Inspired by recent advances in vision-language foundation models, we investigate their utility for WSTVG, leveraging their zero-shot grounding capabilities. However, we find that a simple adaptation lacks essential spatio-temporal grounding abilities. To bridge this gap, we introduce Tubelet Referral Grounding (TRG), which connects textual queries to tubelets to enable spatio-temporal predictions. Despite its promise, TRG struggles with compositional action understanding and dense scene scenarios. To address these limitations, we propose STPro, a progressive learning framework with two key modules: Sub-Action Temporal Curriculum Learning (SA-TCL), which incrementally builds compositional action understanding, and Congestion-Guided Spatial Curriculum Learning (CG-SCL), which adapts the model to complex scenes by spatially increasing task difficulty. STPro achieves state-of-the-art results on three benchmark datasets, with improvements of 1.0% on VidSTG-Declarative and 3.0% on HCSTVG-v1.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Garg_STPro_Spatial_and_Temporal_Progressive_Learning_for_Weakly_Supervised_Spatio-Temporal_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Garg_STPro_Spatial_and_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Garg_2025_CVPR,
    author    = {Garg, Aaryan and Kumar, Akash and Rawat, Yogesh S},
    title     = {STPro: Spatial and Temporal Progressive Learning for Weakly Supervised Spatio-Temporal Grounding},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {3384-3394}
}

raw_affiliation:
[{"affiliation": "BITS Pilani", "city": "Pilani", "country": "India"}, {"affiliation": "CRCV , University of Central Florida", "city": "Orlando", "country": "USA"}]


title: RGBAvatar: Reduced Gaussian Blendshapes for Online Modeling of Head Avatars
authors: Linzhou Li, Yumeng Li, Yanlin Weng, Youyi Zheng, Kun Zhou
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 10747-10757
affiliations: ["State Key Lab of CAD&CG, Zhejiang University"]
cities: ["Hangzhou"]
countries: ["China"]
highlight: yes
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Li_RGBAvatar_Reduced_Gaussian_Blendshapes_for_Online_Modeling_of_Head_Avatars_CVPR_2025_paper.html

abstract:
We present Reduced Gaussian Blendshapes Avatar (RGBAvatar), a method for reconstructing photorealistic, animatable head avatars at speeds sufficient for on-the-fly reconstruction. Unlike prior approaches that utilize linear bases from 3D morphable models (3DMM) to model Gaussian blendshapes, our method maps tracked 3DMM parameters into reduced blendshape weights with an MLP, leading to a compact set of blendshape bases. The learned compact base composition effectively captures essential facial details for specific individuals, and does not rely on the fixed base composition weights of 3DMM, leading to enhanced reconstruction quality and higher efficiency. To further expedite the reconstruction process, we develop a novel color initialization estimation method and a batch-parallel Gaussian rasterization process, achieving state-of-the-art quality with training throughput of about 630 images per second. Moreover, we propose a local-global sampling strategy that enables direct on-the-fly reconstruction, immediately reconstructing the model as video streams in real time while achieving quality comparable to offline settings. Our source code is available at https://github.com/gapszju/RGBAvatar.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Li_RGBAvatar_Reduced_Gaussian_Blendshapes_for_Online_Modeling_of_Head_Avatars_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Li_RGBAvatar_Reduced_Gaussian_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Li_2025_CVPR,
    author    = {Li, Linzhou and Li, Yumeng and Weng, Yanlin and Zheng, Youyi and Zhou, Kun},
    title     = {RGBAvatar: Reduced Gaussian Blendshapes for Online Modeling of Head Avatars},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {10747-10757}
}

raw_affiliation:
[{"affiliation": "State Key Lab of CAD&CG, Zhejiang University", "city": "Hangzhou", "country": "China"}]


title: Rashomon Sets for Prototypical-Part Networks: Editing Interpretable Models in Real-Time
authors: Jon Donnelly, Zhicheng Guo, Alina Jade Barnett, Hayden McTavish, Chaofan Chen, Cynthia Rudin
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 4528-4538
cities: ["Durham", "Durham", "Durham", "Durham", "Orono", "Durham"]
countries: ["USA", "USA", "USA", "USA", "USA", "USA"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Donnelly_Rashomon_Sets_for_Prototypical-Part_Networks_Editing_Interpretable_Models_in_Real-Time_CVPR_2025_paper.html

abstract:
Interpretability is critical for machine learning models in high-stakes settings because it allows users to verify the model's reasoning. In computer vision, prototypical part models (ProtoPNets) have become the dominant model type to meet this need. Users can easily identify flaws in ProtoPNets, but fixing problems in a ProtoPNet requires slow, difficult retraining that is not guaranteed to resolve the issue. This problem is called the "interaction bottleneck." We solve the interaction bottleneck for ProtoPNets by simultaneously finding many equally good ProtoPNets (i.e., a draw from a "Rashomon set"). We show that our framework - called Proto-RSet - quickly produces many accurate, diverse ProtoPNets, allowing users to correct problems in real time while maintaining performance guarantees with respect to the training set. We demonstrate the utility of this method in two settings: 1) removing synthetic bias introduced to a bird-identification model and 2) debugging a skin cancer identification model. This tool empowers non-machine-learning experts, such as clinicians or domain experts, to quickly refine and correct machine learning models without repeated retraining by machine learning experts.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Donnelly_Rashomon_Sets_for_Prototypical-Part_Networks_Editing_Interpretable_Models_in_Real-Time_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Donnelly_Rashomon_Sets_for_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Donnelly_2025_CVPR,
    author    = {Donnelly, Jon and Guo, Zhicheng and Barnett, Alina Jade and McTavish, Hayden and Chen, Chaofan and Rudin, Cynthia},
    title     = {Rashomon Sets for Prototypical-Part Networks: Editing Interpretable Models in Real-Time},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {4528-4538}
}

raw_affiliation:
[{"affiliation": "Duke University", "city": "Durham", "country": "USA"}, {"affiliation": "Duke University", "city": "Durham", "country": "USA"}, {"affiliation": "Duke University", "city": "Durham", "country": "USA"}, {"affiliation": "Duke University", "city": "Durham", "country": "USA"}, {"affiliation": "University of Maine", "city": "Orono", "country": "USA"}, {"affiliation": "Duke University", "city": "Durham", "country": "USA"}]

affiliations:
["Duke University", "Duke University", "Duke University", "Duke University", "University of Maine", "Duke University"]


title: EEE-Bench: A Comprehensive Multimodal Electrical And Electronics Engineering Benchmark
authors: Ming Li, Jike Zhong, Tianle Chen, Yuxiang Lai, Konstantinos Psounis
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 13337-13349
cities: ["Tokyo", "Los Angeles", "Boston", "Atlanta"]
countries: ["Japan", "USA", "USA", "USA"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Li_EEE-Bench_A_Comprehensive_Multimodal_Electrical_And_Electronics_Engineering_Benchmark_CVPR_2025_paper.html

abstract:
Recent studies on large language models (LLMs) and large multimodal models (LMMs) have demonstrated promising skills in various domains including science and mathematics. However, their capability in more challenging and real-world related scenarios like engineering has not been systematically studied. To bridge this gap, we propose EEE-Bench, a multimodal benchmark aimed at assessing LMMs' capabilities in solving practical engineering tasks, using electrical and electronics engineering (EEE) as the testbed. Our benchmark consists of 2860 hand-picked and carefully curated problems spanning 10 essential subdomains such as analog circuits, control systems, etc. Compared to other domains, engineering problems are intrinsically 1) more visually complex and versatile and 2) less deterministic in solutions. Successful solutions to these problems often demand more-than-usual rigorous integration of visual and textual information as models need to understand intricate images like abstract circuits and system diagrams while taking professional instructions. Alongside EEE-Bench, we provide extensive quantitative evaluations, fine-grained analysis, and improvement methods using 17 widely-used open- and closed-sourced LLMs and LMMs and 7 popular prompting techniques. Our results reveal notable deficiencies in current foundation models for EEE, including an average performance ranging from 19.48% to 46.78% and a tendency toward "laziness" in overlooking essential visual context. In summary, we believe EEE-Bench not only reveals some noteworthy limitations of LMMs but also provides a valuable resource for advancing research on their application in practical engineering tasks, driving future improvements in their capability to handle complex, real-world scenarios.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Li_EEE-Bench_A_Comprehensive_Multimodal_Electrical_And_Electronics_Engineering_Benchmark_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Li_EEE-Bench_A_Comprehensive_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Li_2025_CVPR,
    author    = {Li, Ming and Zhong, Jike and Chen, Tianle and Lai, Yuxiang and Psounis, Konstantinos},
    title     = {EEE-Bench: A Comprehensive Multimodal Electrical And Electronics Engineering Benchmark},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {13337-13349}
}

raw_affiliation:
[{"affiliation": "University of Tokyo", "city": "Tokyo", "country": "Japan"}, {"affiliation": "University of Southern California", "city": "Los Angeles", "country": "USA"}, {"affiliation": "Boston University", "city": "Boston", "country": "USA"}, {"affiliation": "Emory University", "city": "Atlanta", "country": "USA"}]

affiliations:
["University of Tokyo", "University of Southern California", "Boston University", "Emory University"]


title: Text-Driven Fashion Image Editing with Compositional Concept Learning and Counterfactual Abduction
authors: Shanshan Huang, Haoxuan Li, Chunyuan Zheng, Mingyuan Ge, Wei Gao, Lei Wang, Li Liu
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 28726-28735
cities: ["Chongqing", "Beijing", "Beijing"]
countries: ["China", "China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Huang_Text-Driven_Fashion_Image_Editing_with_Compositional_Concept_Learning_and_Counterfactual_CVPR_2025_paper.html

abstract:
Fashion image editing is a valuable tool for designers to convey their creative ideas by visualizing design concepts. With the recent advances in text editing methods, significant progress has been made in fashion image editing. However, they face two key challenges: spurious correlations in training data often induce changes in other areas when editing an area representing the intended editing concept, and these models typically lack the ability to edit multiple concepts simultaneously. To address the above challenges, we propose a novel \underline T ext-driven \underline F ashion \underline I mage edi\underline T ing framework called T-FIT to mitigate the impact of spurious correlation by integrating counterfactual reasoning with compositional concept learning to precisely ensure compositional multi-concept fashion image editing relying solely on text descriptions. Specifically, T-FIT includes three key components. (i) Counterfactual abduction module, which learns an exogenous variable of the source image by a denoising U-Net model. (ii) Concept learning module, which identifies concepts in fashion image editing--such as clothing types and colors and projects a target concept into the space spanned from a series of textual prompts. (iii) Concept composition module, which enables simultaneous adjustments of multiple concepts by aggregating each concept's direction vector obtained from the concept learning module. Extensive experiments show that our method can achieve state-of-the-art performance on various fashion image editing tasks, including single-concept editing (e.g., sleeve length, clothing type) and multi-concept editing (e.g., color & sleeve length).

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Huang_Text-Driven_Fashion_Image_Editing_with_Compositional_Concept_Learning_and_Counterfactual_CVPR_2025_paper.pdf

bibtex:
@InProceedings{Huang_2025_CVPR,
    author    = {Huang, Shanshan and Li, Haoxuan and Zheng, Chunyuan and Ge, Mingyuan and Gao, Wei and Wang, Lei and Liu, Li},
    title     = {Text-Driven Fashion Image Editing with Compositional Concept Learning and Counterfactual Abduction},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {28726-28735}
}

raw_affiliation:
[{"affiliation": "School of Big Data & Software Engineering, Chongqing University", "city": "Chongqing", "country": "China"}, {"affiliation": "Center for Data Science, Peking University", "city": "Beijing", "country": "China"}, {"affiliation": "School of Mathematical Sciences, Peking University", "city": "Beijing", "country": "China"}]

affiliations:
["School of Big Data & Software Engineering, Chongqing University", "Center for Data Science, Peking University", "School of Mathematical Sciences, Peking University"]


proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 1839-1849
affiliations: ["Shanghai Jiao Tong University", "ByteDance", "University of Southern California"]
cities: ["Shanghai", "Beijing", "Los Angeles"]
countries: ["China", "China", "USA"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Xia_EnvPoser_Environment-aware_Realistic_Human_Motion_Estimation_from_Sparse_Observations_with_CVPR_2025_paper.html

title:
EnvPoser: Environment-aware Realistic Human Motion Estimation from Sparse Observations with Uncertainty Modeling

authors:
Songpengcheng Xia, Yu Zhang, Zhuo Su, Xiaozheng Zheng, Zheng Lv, Guidong Wang, Yongjie Zhang, Qi Wu, Lei Chu, Ling Pei

abstract:
Estimating full-body motion using the tracking signals of head and hands from VR devices holds great potential for various applications. However, the sparsity and unique distribution of observations present a significant challenge, resulting in an ill-posed problem with multiple feasible solutions (i.e., hypotheses). This amplifies uncertainty and ambiguity in full-body motion estimation, especially for the lower-body joints. Therefore, we propose a new method, EnvPoser, that employs a two-stage framework to perform full-body motion estimation using sparse tracking signals and pre-scanned environment from VR devices. EnvPoser models the multi-hypothesis nature of human motion through an uncertainty-aware estimation module in the first stage. In the second stage, we refine these multi-hypothesis estimates by integrating semantic and geometric environmental constraints, ensuring that the final motion estimation aligns realistically with both the environmental context and physical interactions.Qualitative and quantitative experiments on two public datasets demonstrate that our method achieves state-of-the-art performance, highlighting significant improvements in human motion estimation within motion-environment interaction scenarios. Project page: https://xspc.github.io/EnvPoser/.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Xia_EnvPoser_Environment-aware_Realistic_Human_Motion_Estimation_from_Sparse_Observations_with_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Xia_EnvPoser_Environment-aware_Realistic_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Xia_2025_CVPR,
    author    = {Xia, Songpengcheng and Zhang, Yu and Su, Zhuo and Zheng, Xiaozheng and Lv, Zheng and Wang, Guidong and Zhang, Yongjie and Wu, Qi and Chu, Lei and Pei, Ling},
    title     = {EnvPoser: Environment-aware Realistic Human Motion Estimation from Sparse Observations with Uncertainty Modeling},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {1839-1849}
}

raw_affiliation:
[{"affiliation": "Shanghai Jiao Tong University", "city": "Shanghai", "country": "China"}, {"affiliation": "ByteDance", "city": "Beijing", "country": "China"}, {"affiliation": "University of Southern California", "city": "Los Angeles", "country": "USA"}]


title: A Unified Framework for Heterogeneous Semi-supervised Learning
authors: Marzi Heidari, Abdullah Alchihabi, Hao Yan, Yuhong Guo
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 15371-15380
cities: ["Ottawa", "Edmonton"]
countries: ["Canada", "Canada"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Heidari_A_Unified_Framework_for_Heterogeneous_Semi-supervised_Learning_CVPR_2025_paper.html

abstract:
In this work, we introduce a novel problem setup termed as Heterogeneous Semi-Supervised Learning (HSSL), which presents unique challenges by bridging the semi-supervised learning (SSL) task and the unsupervised domain adaptation (UDA) task, and expanding standard semi-supervised learning to cope with heterogeneous training data. At its core, HSSL aims to learn a prediction model using a combination of labeled and unlabeled training data drawn separately from heterogeneous domains that share a common set of semantic categories; this model is intended to differentiate the semantic categories of test instances sampled from both the labeled and unlabeled domains. In particular, the labeled and unlabeled domains have dissimilar label distributions and class feature distributions. This heterogeneity, coupled with the assorted sources of the test data, introduces significant challenges to standard SSL and UDA methods. Therefore, we propose a novel method, Unified Framework for Heterogeneous Semi-supervised Learning (Uni-HSSL), to address HSSL by directly learning a fine-grained classifier from the heterogeneous data, which adaptively handles the inter-domain heterogeneity while leveraging both the unlabeled data and the inter-domain semantic class relationships for cross-domain knowledge transfer and adaptation. We conduct comprehensive experiments and the experimental results validate the efficacy and superior performance of the proposed Uni-HSSL over state-of-the-art semi-supervised learning and unsupervised domain adaptation methods.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Heidari_A_Unified_Framework_for_Heterogeneous_Semi-supervised_Learning_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Heidari_A_Unified_Framework_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Heidari_2025_CVPR,
    author    = {Heidari, Marzi and Alchihabi, Abdullah and Yan, Hao and Guo, Yuhong},
    title     = {A Unified Framework for Heterogeneous Semi-supervised Learning},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {15371-15380}
}

raw_affiliation:
[{"affiliation": "School of Computer Science, Carleton University, Ottawa, Canada", "city": "Ottawa", "country": "Canada"}, {"affiliation": "Canada CIFAR AI Chair, Amii, Canada", "city": "Edmonton", "country": "Canada"}]

affiliations:
["School of Computer Science, Carleton University, Ottawa, Canada", "Canada CIFAR AI Chair, Amii, Canada"]


title: Adapting Text-to-Image Generation with Feature Difference Instruction for Generic Image Restoration
authors: Chao Wang, Hehe Fan, Huichen Yang, Sarvnaz Karimi, Lina Yao, Yi Yang
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 23539-23550
cities: ["Sydney", "Sydney", "Sydney", "Hangzhou"]
countries: ["Australia", "Australia", "Australia", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Wang_Adapting_Text-to-Image_Generation_with_Feature_Difference_Instruction_for_Generic_Image_CVPR_2025_paper.html

abstract:
Diffusion-based Text-to-Image (T2I) models have demonstrated significant potential in image restoration. However, existing models continue to grapple with challenges such as complex training and prompt design. We introduce a new perspective for improving image restoration by injecting knowledge from pretrained vision-language models into current T2I models. We empirically show that the degradation and content representations in BLIP-2 can be linearly separated, providing promising degradation guidance for image restoration. Specifically, the Feature Difference Instruction (FDI) is first extracted by Q-Formers through a simple subtraction operation based on reference image pairs. Then, we propose a multi-scale FDI adapter to decouple the degradation style and corrupted artifacts, and inject the styleflow exclusively into specific blocks through adapter-tuning, thereby preventing noise interference and eschewing the need for cumbersome weight retraining.  In this way, we can train various task-specific adapters according to different degradations, achieving rich detail enhancement in the restoration results.  Furthermore, the proposed FDI adapters have attractive properties of practical value, such as composability and generalization ability for all-in-one and mixed-degradation restoration. Extensive experiments under various settings demonstrate that our method has promising repairing quality over 10 image restoration tasks and a wide range of other applications.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Wang_Adapting_Text-to-Image_Generation_with_Feature_Difference_Instruction_for_Generic_Image_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Wang_Adapting_Text-to-Image_Generation_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Wang_2025_CVPR,
    author    = {Wang, Chao and Fan, Hehe and Yang, Huichen and Karimi, Sarvnaz and Yao, Lina and Yang, Yi},
    title     = {Adapting Text-to-Image Generation with Feature Difference Instruction for Generic Image Restoration},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {23539-23550}
}

raw_affiliation:
[{"affiliation": "ReLER, AAII, University of Technology Sydney", "city": "Sydney", "country": "Australia"}, {"affiliation": "Data61, CSIRO", "city": "Sydney", "country": "Australia"}, {"affiliation": "University of New South Wales", "city": "Sydney", "country": "Australia"}, {"affiliation": "CCAI, Zhejiang University", "city": "Hangzhou", "country": "China"}]

affiliations:
["ReLER, AAII, University of Technology Sydney", "Data61, CSIRO", "University of New South Wales", "CCAI, Zhejiang University"]


authors: Quanxing Zha, Xin Liu, Shu-Juan Peng, Yiu-ming Cheung, Xing Xu, Nannan Wang
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 29680-29689
cities: ["Xiamen", "Hong Kong", "Chengdu", "Xi'an"]
countries: ["China", "China", "China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Zha_ReCon_Enhancing_True_Correspondence_Discrimination_through_Relation_Consistency_for_Robust_CVPR_2025_paper.html

title:
ReCon: Enhancing True Correspondence Discrimination through Relation Consistency for Robust Noisy Correspondence Learning

abstract:
Can we accurately identify the true correspondences from multimodal datasets containing mismatched data pairs? Existing methods primarily emphasize the similarity matching between the representations of objects across modalities, potentially neglecting the crucial relation consistency within modalities that are particularly important for distinguishing the true and false correspondences. Such an omission often runs the risk of misidentifying negatives as positives, thus leading to unanticipated performance degradation. To address this problem, we propose a general Relation Consistency learning framework, namely ReCon, to accurately discriminate the true correspondences among the multimodal data and thus effectively mitigate the adverse impact caused by mismatches. Specifically, ReCon leverages a novel relation consistency learning to ensure the dual-alignment, respectively of, the cross-modal relation consistency between different modalities and the intra-modal relation consistency within modalities. Thanks to such dual constrains on relations, ReCon significantly enhances its effectiveness for true correspondence discrimination and therefore reliably filters out the mismatched pairs to mitigate the risks of wrong supervisions. Extensive experiments on three widely-used benchmark datasets, including Flickr30K, MS-COCO, and Conceptual Captions, are conducted to demonstrate the effectiveness and superiority of ReCon compared with other SOTAs. The code is available at: https://anonymous.4open.science/r/ReCon-NCL.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Zha_ReCon_Enhancing_True_Correspondence_Discrimination_through_Relation_Consistency_for_Robust_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Zha_ReCon_Enhancing_True_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Zha_2025_CVPR,
    author    = {Zha, Quanxing and Liu, Xin and Peng, Shu-Juan and Cheung, Yiu-ming and Xu, Xing and Wang, Nannan},
    title     = {ReCon: Enhancing True Correspondence Discrimination through Relation Consistency for Robust Noisy Correspondence Learning},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {29680-29689}
}

raw_affiliation:
[{"affiliation": "Huaqiao University", "city": "Xiamen", "country": "China"}, {"affiliation": "Hong Kong Baptist University", "city": "Hong Kong", "country": "China"}, {"affiliation": "University of Electronic Science and Technology of China", "city": "Chengdu", "country": "China"}, {"affiliation": "Xidian University", "city": "Xi'an", "country": "China"}]

affiliations:
["Huaqiao University", "Hong Kong Baptist University", "University of Electronic Science and Technology of China", "Xidian University"]


authors: Chong Bao, Xiyu Zhang, Zehao Yu, Jiale Shi, Guofeng Zhang, Songyou Peng, Zhaopeng Cui
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 16377-16387
cities: ["Hangzhou", "Zurich", "T\u00fcbingen"]
countries: ["China", "Switzerland", "Germany"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Bao_Free360_Layered_Gaussian_Splatting_for_Unbounded_360-Degree_View_Synthesis_from_CVPR_2025_paper.html

title:
Free360: Layered Gaussian Splatting for Unbounded 360-Degree View Synthesis from Extremely Sparse and Unposed Views

abstract:
Neural rendering has demonstrated remarkable success in high-quality 3D neural reconstruction and novel view synthesis with dense input views and accurate poses. However, applying it to sparse, unposed views in unbounded 360* scenes remains a challenging problem. In this paper, we propose a novel neural rendering framework to accomplish the unposed and extremely sparse-view 3D reconstruction in unbounded 360* scenes. To resolve the spatial ambiguity inherent in unbounded scenes with sparse input views, we propose a layered Gaussian-based representation to effectively model the scene with distinct spatial layers. By employing a dense stereo reconstruction model to recover coarse geometry, we introduce a layer-specific bootstrap optimization to refine the noise and fill occluded regions in the reconstruction. Furthermore, we propose an iterative fusion of reconstruction and generation alongside an uncertainty-aware training approach to facilitate mutual conditioning and enhancement between these two processes. Comprehensive experiments show that our approach outperforms existing state-of-the-art methods in terms of rendering quality and surface reconstruction accuracy. Project page: https://zju3dv.github.io/free360/

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Bao_Free360_Layered_Gaussian_Splatting_for_Unbounded_360-Degree_View_Synthesis_from_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Bao_Free360_Layered_Gaussian_CVPR_2025_supplemental.zip

bibtex:
@InProceedings{Bao_2025_CVPR,
    author    = {Bao, Chong and Zhang, Xiyu and Yu, Zehao and Shi, Jiale and Zhang, Guofeng and Peng, Songyou and Cui, Zhaopeng},
    title     = {Free360: Layered Gaussian Splatting for Unbounded 360-Degree View Synthesis from Extremely Sparse and Unposed Views},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {16377-16387}
}

raw_affiliation:
[{"affiliation": "State Key Lab of CAD&CG, Zhejiang University", "city": "Hangzhou", "country": "China"}, {"affiliation": "ETH Z\u00a8urich", "city": "Zurich", "country": "Switzerland"}, {"affiliation": "University of T\u00a8ubingen, T\u00a8ubingen AI Center", "city": "T\u00fcbingen", "country": "Germany"}]

affiliations:
["State Key Lab of CAD&CG, Zhejiang University", "ETH Z\u00a8urich", "University of T\u00a8ubingen, T\u00a8ubingen AI Center"]


title: Tuning the Frequencies: Robust Training for Sinusoidal Neural Networks
authors: Tiago Novello, Diana Aldana, Andre Araujo, Luiz Velho
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 3071-3080
affiliations: ["IMPA", "Google DeepMind"]
cities: ["Rio de Janeiro", "Mountain View"]
countries: ["Brazil", "USA"]
highlight: yes
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Novello_Tuning_the_Frequencies_Robust_Training_for_Sinusoidal_Neural_Networks_CVPR_2025_paper.html

abstract:
Sinusoidal neural networks have been shown effective as implicit neural representations (INRs) of low-dimensional signals, due to their smoothness and high representation capacity. However, initializing and training them remain empirical tasks which lack on deeper understanding to guide the learning process. To fill this gap, our work introduces a theoretical framework that explains the capacity property of sinusoidal networks and offers robust control mechanisms for initialization and training. Our analysis is based on a novel amplitude-phase expansion of the sinusoidal multilayer perceptron, showing how its layer compositions produce a large number of new frequencies expressed as integer combinations of the input frequencies. This relationship can be directly used to initialize the input neurons, as a form of spectral sampling, and to bound the network's spectrum while training. Our method, referred to as TUNER (TUNing sinusoidal nEtwoRks), greatly improves the stability and convergence of sinusoidal INR training, leading to detailed reconstructions, while preventing overfitting.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Novello_Tuning_the_Frequencies_Robust_Training_for_Sinusoidal_Neural_Networks_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Novello_Tuning_the_Frequencies_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Novello_2025_CVPR,
    author    = {Novello, Tiago and Aldana, Diana and Araujo, Andre and Velho, Luiz},
    title     = {Tuning the Frequencies: Robust Training for Sinusoidal Neural Networks},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {3071-3080}
}

raw_affiliation:
[{"affiliation": "IMPA", "city": "Rio de Janeiro", "country": "Brazil"}, {"affiliation": "Google DeepMind", "city": "Mountain View", "country": "USA"}]


title: Preconditioners for the Stochastic Training of Neural Fields
authors: Shin-Fang Chng, Hemanth Saratchandran, Simon Lucey
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 27222-27232
affiliations: ["Australian Institute of Machine Learning, University of Adelaide"]
cities: ["Adelaide"]
countries: ["Australia"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Chng_Preconditioners_for_the_Stochastic_Training_of_Neural_Fields_CVPR_2025_paper.html

abstract:
Neural fields encode continuous multidimensional signals as neural networks, enabling diverse applications in computer vision, robotics, and geometry. While Adam is effective for stochastic optimization, it often requires long training times. To address this, we explore alternative optimization techniques to accelerate training without sacrificing accuracy. Traditional second-order methods like L-BFGS are unsuitable for stochastic settings. We propose a theoretical framework for training neural fields with curvature-aware diagonal preconditioners, demonstrating their effectiveness across tasks such as image reconstruction, shape modeling, and Neural Radiance Fields (NeRF).

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Chng_Preconditioners_for_the_Stochastic_Training_of_Neural_Fields_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Chng_Preconditioners_for_the_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Chng_2025_CVPR,
    author    = {Chng, Shin-Fang and Saratchandran, Hemanth and Lucey, Simon},
    title     = {Preconditioners for the Stochastic Training of Neural Fields},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {27222-27232}
}

raw_affiliation:
[{"affiliation": "Australian Institute of Machine Learning, University of Adelaide", "city": "Adelaide", "country": "Australia"}]


title: Open Ad-hoc Categorization with Contextualized Feature Learning
authors: Zilin Wang, Sangwoo Mo, Stella X. Yu, Sima Behpour, Liu Ren
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 15108-15117
affiliations: ["University of Michigan", "UC Berkeley", "Bosch Center for AI"]
cities: ["Ann Arbor", "Berkeley", "Palo Alto"]
countries: ["USA", "USA", "USA"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Wang_Open_Ad-hoc_Categorization_with_Contextualized_Feature_Learning_CVPR_2025_paper.html

abstract:
Adaptive categorization of visual scenes is essential for AI agents to handle changing tasks.  Unlike fixed common categories for plants or animals, ad-hoc categories, such as things to sell at a garage sale, are created dynamically to achieve specific tasks.  We study open ad-hoc categorization, where the goal is to infer novel concepts and categorize images based on a given context, a small set of labeled exemplars, and some unlabeled data. We have two key insights: 1) recognizing ad-hoc categories relies on the same perceptual processes as common categories; 2) novel concepts can be discovered semantically by expanding contextual cues or visually by clustering similar patterns.  We propose OAK, a simple model that introduces a single learnable context token into CLIP, trained with CLIP's objective of aligning visual and textual features and GCD's objective of clustering similar images. On Stanford and Clevr-4 datasets, OAK consistently achieves the state-of-art in accuracy and concept discovery across multiple categorizations, including 87.4% novel accuracy on Stanford Mood, surpassing CLIP and GCD by over 50%. Moreover, OAK generates interpretable saliency maps, focusing on hands for Action, faces for Mood, and backgrounds for Location, promoting transparency and trust while enabling accurate and flexible categorization.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Wang_Open_Ad-hoc_Categorization_with_Contextualized_Feature_Learning_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Wang_Open_Ad-hoc_Categorization_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Wang_2025_CVPR,
    author    = {Wang, Zilin and Mo, Sangwoo and Yu, Stella X. and Behpour, Sima and Ren, Liu},
    title     = {Open Ad-hoc Categorization with Contextualized Feature Learning},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {15108-15117}
}

raw_affiliation:
[{"affiliation": "University of Michigan", "city": "Ann Arbor", "country": "USA"}, {"affiliation": "UC Berkeley", "city": "Berkeley", "country": "USA"}, {"affiliation": "Bosch Center for AI", "city": "Palo Alto", "country": "USA"}]


title: Real-time Free-view Human Rendering from Sparse-view RGB Videos using Double Unprojected Textures
authors: Guoxing Sun, Rishabh Dabral, Heming Zhu, Pascal Fua, Christian Theobalt, Marc Habermann
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 562-573
affiliations: ["Max Planck Institute for Informatics", "VIA Research Center", "EPFL"]
cities: ["Saarbr\u00fccken", "Unknown", "Lausanne"]
countries: ["Germany", "Unknown", "Switzerland"]
highlight: yes
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Sun_Real-time_Free-view_Human_Rendering_from_Sparse-view_RGB_Videos_using_Double_CVPR_2025_paper.html

abstract:
Real-time free-view human rendering from sparse-view RGB inputs is a challenging task due to the sensor scarcity and the tight time budget. To ensure efficiency, recent methods leverage 2D CNNs operating in texture space to learn rendering primitives. However, they either jointly learn geometry and appearance, or completely ignore sparse image information for geometry estimation, significantly harming visual quality and robustness to unseen body poses. To address these issues, we present Double Unprojected Textures, which at the core disentangles coarse geometric deformation estimation from appearance synthesis, enabling robust and photorealistic 4K rendering in real-time. Specifically, we first introduce a novel image-conditioned template deformation network, which estimates the coarse deformation of the human template from a first unprojected texture. This updated geometry is then used to apply a second and more accurate texture unprojection. The resulting texture map has fewer artifacts and better alignment with input views, which benefits our learning of finer-level geometry and appearance represented by Gaussian splats. We validate the effectiveness and efficiency of the proposed method in quantitative and qualitative experiments, which significantly surpasses other state-of-the-art methods.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Sun_Real-time_Free-view_Human_Rendering_from_Sparse-view_RGB_Videos_using_Double_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Sun_Real-time_Free-view_Human_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Sun_2025_CVPR,
    author    = {Sun, Guoxing and Dabral, Rishabh and Zhu, Heming and Fua, Pascal and Theobalt, Christian and Habermann, Marc},
    title     = {Real-time Free-view Human Rendering from Sparse-view RGB Videos using Double Unprojected Textures},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {562-573}
}

raw_affiliation:
[{"affiliation": "Max Planck Institute for Informatics", "city": "Saarbr\u00fccken", "country": "Germany"}, {"affiliation": "VIA Research Center", "city": "Unknown", "country": "Unknown"}, {"affiliation": "EPFL", "city": "Lausanne", "country": "Switzerland"}]


proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 24593-24602
cities: ["Hangzhou", "Hangzhou", "Shanghai", "Shanghai"]
countries: ["China", "China", "China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Dang_ECBench_Can_Multi-modal_Foundation_Models_Understand_the_Egocentric_World_A_CVPR_2025_paper.html

title:
ECBench: Can Multi-modal Foundation Models Understand the Egocentric World? A Holistic Embodied Cognition Benchmark

authors:
Ronghao Dang, Yuqian Yuan, Wenqi Zhang, Yifei Xin, Boqiang Zhang, Long Li, Liuyi Wang, Qinyang Zeng, Xin Li, Lidong Bing

abstract:
The enhancement of generalization in robots by large vision-language models (LVLMs) is increasingly evident. Therefore, the embodied cognitive abilities of LVLMs based on egocentric videos are of great interest. However, current datasets for embodied video question answering lack comprehensive and systematic evaluation frameworks. Critical embodied cognitive issues, such as robotic self-cognition, dynamic scene perception, and hallucination, are rarely addressed. To tackle these challenges, we propose ECBench, a high-quality benchmark designed to systematically evaluate the embodied cognitive abilities of LVLMs. ECBench features a diverse range of scene video sources, open and varied question formats, and 30 dimensions of embodied cognition.  To ensure quality, balance, and high visual dependence, ECBench uses class-independent meticulous human annotation and multi-round question screening strategies. Additionally, we introduce ECEval, a comprehensive evaluation system that ensures the fairness and rationality of the indicators.  Utilizing ECBench, we conduct extensive evaluations of proprietary, open-source, and task-specific LVLMs. ECBench is pivotal in advancing the embodied cognitive capabilities of LVLMs, laying a solid foundation for developing reliable core models for embodied agents. All data and code is available at https://github.com/Rh-Dang/ECBench.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Dang_ECBench_Can_Multi-modal_Foundation_Models_Understand_the_Egocentric_World_A_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Dang_ECBench_Can_Multi-modal_CVPR_2025_supplemental.zip

bibtex:
@InProceedings{Dang_2025_CVPR,
    author    = {Dang, Ronghao and Yuan, Yuqian and Zhang, Wenqi and Xin, Yifei and Zhang, Boqiang and Li, Long and Wang, Liuyi and Zeng, Qinyang and Li, Xin and Bing, Lidong},
    title     = {ECBench: Can Multi-modal Foundation Models Understand the Egocentric World? A Holistic Embodied Cognition Benchmark},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {24593-24602}
}

raw_affiliation:
[{"affiliation": "Alibaba DAMO Academy", "city": "Hangzhou", "country": "China"}, {"affiliation": "Zhejiang University", "city": "Hangzhou", "country": "China"}, {"affiliation": "Tongji University", "city": "Shanghai", "country": "China"}, {"affiliation": "Shanda AI Research Institute", "city": "Shanghai", "country": "China"}]

affiliations:
["Alibaba DAMO Academy", "Zhejiang University", "Tongji University", "Shanda AI Research Institute"]


title: SfM-Free 3D Gaussian Splatting via Hierarchical Training
authors: Bo Ji, Angela Yao
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 21654-21663
raw_affiliation: [{"affiliation": "National University of Singapore", "city": "Singapore", "country": "Singapore"}]
affiliations: ["National University of Singapore"]
cities: ["Singapore"]
countries: ["Singapore"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Ji_SfM-Free_3D_Gaussian_Splatting_via_Hierarchical_Training_CVPR_2025_paper.html

abstract:
Standard 3D Gaussian Splatting (3DGS) relies on known or pre-computed camera poses and a sparse point cloud, obtained from structure-from-motion (SfM) preprocessing, to initialize and grow 3D Gaussians. We propose a novel SfM-Free 3DGS (SFGS) method for video input, eliminating the need for known camera poses and SfM preprocessing. Our approach introduces a hierarchical training strategy that trains and merges multiple 3D Gaussian representations -- each optimized for specific scene regions -- into a single, unified 3DGS model representing the entire scene. To compensate for large camera motions, we leverage video frame interpolation models. Additionally, we incorporate multi-source supervision to reduce overfitting and enhance representation. Experimental results reveal that our approach significantly surpasses state-of-the-art SfM-free novel view synthesis methods. On the Tanks and Temples dataset, we improve PSNR by an average of 2.25dB, with a maximum gain of 3.72dB in the best scene. On the CO3D-V2 dataset, we achieve an average PSNR boost of 1.74dB, with a top gain of 3.90dB. The code is available at \href https://github.com/jibo27/3DGS_Hierarchical_Training/  https://github.com/jibo27/3DGS_Hierarchical_Training .

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Ji_SfM-Free_3D_Gaussian_Splatting_via_Hierarchical_Training_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Ji_SfM-Free_3D_Gaussian_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Ji_2025_CVPR,
    author    = {Ji, Bo and Yao, Angela},
    title     = {SfM-Free 3D Gaussian Splatting via Hierarchical Training},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {21654-21663}
}


title: Large Self-Supervised Models Bridge the Gap in Domain Adaptive Object Detection
authors: Marc-Antoine Lavoie, Anas Mahmoud, Steven L. Waslander
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 4692-4702
affiliations: ["University of Toronto Robotics Institute"]
cities: ["Toronto"]
countries: ["Canada"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Lavoie_Large_Self-Supervised_Models_Bridge_the_Gap_in_Domain_Adaptive_Object_CVPR_2025_paper.html

abstract:
The current state-of-the-art methods in domain adaptive object detection (DAOD) use Mean Teacher self-labelling, where a teacher model, directly derived as an exponential moving average of the student model, is used to generate labels on the target domain which are then used to improve both models in a positive loop. This couples learning and generating labels on the target domain, and other recent works also leverage the generated labels to add additional domain alignment losses. We believe this coupling is brittle and excessively constrained: there is no guarantee that a student trained only on source data can generate accurate target domain labels and initiate the positive feedback loop, and much better target domain labels can likely be generated by using a large pretrained network that has been exposed to much more data. Vision foundational models are exactly such models, and they have shown impressive task generalization capabilities even when frozen. We want to leverage these models for DAOD and introduce DINO Teacher, which consists of two components. First, we train a new labeller on source data only using a large frozen DINOv2 backbone and show it generates more accurate labels than Mean Teacher. Next, we align the student's source and target image patch features with those from a DINO encoder, driving source and target representations closer to the generalizable DINO representation. We obtain state-of-the-art performance on multiple DAOD datasets.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Lavoie_Large_Self-Supervised_Models_Bridge_the_Gap_in_Domain_Adaptive_Object_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Lavoie_Large_Self-Supervised_Models_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Lavoie_2025_CVPR,
    author    = {Lavoie, Marc-Antoine and Mahmoud, Anas and Waslander, Steven L.},
    title     = {Large Self-Supervised Models Bridge the Gap in Domain Adaptive Object Detection},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {4692-4702}
}

raw_affiliation:
[{"affiliation": "University of Toronto Robotics Institute", "city": "Toronto", "country": "Canada"}]


title: Dynamic Updates for Language Adaptation in Visual-Language Tracking
authors: Xiaohai Li, Bineng Zhong, Qihua Liang, Zhiyi Mo, Jian Nong, Shuxiang Song
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 19165-19174
cities: ["Guilin", "Wuzhou"]
countries: ["China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Li_Dynamic_Updates_for_Language_Adaptation_in_Visual-Language_Tracking_CVPR_2025_paper.html

abstract:
The consistency between the semantic information provided by the multi-modal reference and the tracked object is crucial for visual-language (VL) tracking. However, existing VL tracking frameworks rely on static multi-modal references to locate dynamic objects, which can lead to semantic discrepancies and reduce the robustness of the tracker. To address this issue, we propose a novel vision-language tracking framework, named DUTrack, which captures the latest state of the target by dynamically updating multi-modal references to maintain consistency.Specifically, we introduce a Dynamic Language Update Module, which leverages a large language model to generate dynamic language descriptions for the object based on visual features and object category information. Then, we design a Dynamic Template Capture Module, which captures the regions in the image that highly match the dynamic language descriptions. Furthermore, to ensure the efficiency of description generation, we design an update strategy that assesses changes in target displacement, scale, and other factors to decide on updates. Finally, the dynamic template and language descriptions that record the latest state of the target are used to update the multi-modal references, providing more accurate reference information for subsequent inference and enhancing the robustness of the tracker.DUTrack achieves new state-of-the-art performance on four mainstream vision-language and two vision-only tracking benchmarks, including LaSOT, LaSOT_ext, TNL2K, OTB99-Lang, GOT-10K, and UAV123.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Li_Dynamic_Updates_for_Language_Adaptation_in_Visual-Language_Tracking_CVPR_2025_paper.pdf

bibtex:
@InProceedings{Li_2025_CVPR,
    author    = {Li, Xiaohai and Zhong, Bineng and Liang, Qihua and Mo, Zhiyi and Nong, Jian and Song, Shuxiang},
    title     = {Dynamic Updates for Language Adaptation in Visual-Language Tracking},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {19165-19174}
}

raw_affiliation:
[{"affiliation": "Key Laboratory of Education Blockchain and Intelligent Technology, Ministry of Education, Guangxi Normal University", "city": "Guilin", "country": "China"}, {"affiliation": "Guangxi Colleges and Universities Key Laboratory of Intelligent Software, Wuzhou University", "city": "Wuzhou", "country": "China"}]

affiliations:
["Key Laboratory of Education Blockchain and Intelligent Technology, Ministry of Education, Guangxi Normal University", "Guangxi Colleges and Universities Key Laboratory of Intelligent Software, Wuzhou University"]


title: Multi-focal Conditioned Latent Diffusion for Person Image Synthesis
authors: Jiaqi Liu, Jichao Zhang, Paolo Rota, Nicu Sebe
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 16019-16028
affiliations: ["University of Trento", "Ocean University of China"]
cities: ["Trento", "Qingdao"]
countries: ["Italy", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Liu_Multi-focal_Conditioned_Latent_Diffusion_for_Person_Image_Synthesis_CVPR_2025_paper.html

abstract:
The Latent Diffusion Model (LDM) has demonstrated strong capabilities in high-resolution image generation and has been widely employed for Pose-Guided Person Image Synthesis (PGPIS), yielding promising results. However, the compression process of LDM often results in the deterioration of details, particularly in sensitive areas such as facial features and clothing textures. In this paper, we propose a Multi-focal Conditioned Latent Diffusion (MCLD) method to address these limitations by conditioning the model on disentangled, pose-invariant features from these sensitive regions. Our approach utilizes a multi-focal condition aggregation module, which effectively integrates facial identity and texture-specific information, enhancing the model's ability to produce appearance realistic and identity-consistent images. Our method demonstrates consistent identity and appearance generation on the DeepFashion dataset and enables flexible person image editing due to its generation consistency. The code is available at https://github.com/jqliu09/mcld.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Liu_Multi-focal_Conditioned_Latent_Diffusion_for_Person_Image_Synthesis_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Liu_Multi-focal_Conditioned_Latent_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Liu_2025_CVPR,
    author    = {Liu, Jiaqi and Zhang, Jichao and Rota, Paolo and Sebe, Nicu},
    title     = {Multi-focal Conditioned Latent Diffusion for Person Image Synthesis},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {16019-16028}
}

raw_affiliation:
[{"affiliation": "University of Trento", "city": "Trento", "country": "Italy"}, {"affiliation": "Ocean University of China", "city": "Qingdao", "country": "China"}]


authors: Jiangyi Wang, Na Zhao
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 20329-20339
affiliations: ["Singapore University of Technology and Design (SUTD)"]
cities: ["Singapore"]
countries: ["Singapore"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Wang_Uncertainty_Meets_Diversity_A_Comprehensive_Active_Learning_Framework_for_Indoor_CVPR_2025_paper.html

title:
Uncertainty Meets Diversity: A Comprehensive Active Learning Framework for Indoor 3D Object Detection

abstract:
Active learning has emerged as a promising approach to reduce the substantial annotation burden in 3D object detection tasks, spurring several initiatives in outdoor environments. However, its application in indoor environments remains unexplored. Compared to outdoor 3D datasets, indoor datasets face significant challenges, including fewer training samples per class, a greater number of classes, more severe class imbalance, and more diverse scene types and intra-class variances.This paper presents the first study on active learning for indoor 3D object detection, where we propose a novel framework tailored for this task. Our method incorporates two key criteria - uncertainty and diversity - to actively select the most ambiguous and informative unlabeled samples for annotation. The uncertainty criterion accounts for both inaccurate detections and undetected objects, ensuring that the most ambiguous samples are prioritized. Meanwhile, the diversity criterion is formulated as a joint optimization problem that maximizes the diversity of both object class distributions and scene types, using a new Class-aware Adaptive Prototype (CAP) bank. The CAP bank dynamically allocates representative prototypes to each class, helping to capture varying intra-class diversity across different categories.We evaluate our method on SUN RGB-D and ScanNetV2, where it outperforms baselines by a significant margin, achieving over 85% of fully-supervised performance with just 10% of the annotation budget.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Wang_Uncertainty_Meets_Diversity_A_Comprehensive_Active_Learning_Framework_for_Indoor_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Wang_Uncertainty_Meets_Diversity_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Wang_2025_CVPR,
    author    = {Wang, Jiangyi and Zhao, Na},
    title     = {Uncertainty Meets Diversity: A Comprehensive Active Learning Framework for Indoor 3D Object Detection},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {20329-20339}
}

raw_affiliation:
[{"affiliation": "Singapore University of Technology and Design (SUTD)", "city": "Singapore", "country": "Singapore"}]


title: CASAGPT: Cuboid Arrangement and Scene Assembly for Interior Design
authors: Weitao Feng, Hang Zhou, Jing Liao, Li Cheng, Wenbo Zhou
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 29173-29182
cities: ["Hefei", "Edmonton", "Hong Kong"]
countries: ["China", "Canada", "China"]
highlight: yes
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Feng_CASAGPT_Cuboid_Arrangement_and_Scene_Assembly_for_Interior_Design_CVPR_2025_paper.html

abstract:
We present a novel approach for indoor scene synthesis, which learns to arrange decomposed cuboid primitives to represent 3D objects within a scene. Unlike conventional methods that use bounding boxes to determine the placement and scale of 3D objects, our approach leverages cuboids as a straightforward yet highly effective alternative for modeling objects. This allows for compact scene generation while minimizing object intersections. Our approach, coined CASAGPT for Cuboid Arrangement and Scene Assembly, employs an autoregressive model to sequentially arrange cuboids, producing physically plausible scenes. By applying rejection sampling during the fine-tuning stage to filter out scenes with object collisions, our model further reduces intersections and enhances scene quality. Additionally, we introduce a refined dataset, 3DFRONT-NC, which eliminates significant noise presented in the original dataset, 3D-FRONT. Extensive experiments on the 3D-FRONT dataset as well as our dataset demonstrate that our approach consistently outperforms the state-of-the-art methods, enhancing the realism of generated scenes, and providing a promising direction for 3D scene synthesis.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Feng_CASAGPT_Cuboid_Arrangement_and_Scene_Assembly_for_Interior_Design_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Feng_CASAGPT_Cuboid_Arrangement_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Feng_2025_CVPR,
    author    = {Feng, Weitao and Zhou, Hang and Liao, Jing and Cheng, Li and Zhou, Wenbo},
    title     = {CASAGPT: Cuboid Arrangement and Scene Assembly for Interior Design},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {29173-29182}
}

raw_affiliation:
[{"affiliation": "University of Science and Technology of China", "city": "Hefei", "country": "China"}, {"affiliation": "University of Alberta", "city": "Edmonton", "country": "Canada"}, {"affiliation": "City University of Hong Kong", "city": "Hong Kong", "country": "China"}]

affiliations:
["University of Science and Technology of China", "University of Alberta", "City University of Hong Kong"]


title: Identity-Clothing Similarity Modeling for Unsupervised Clothing Change Person Re-Identification
authors: Zhiqi Pang, Junjie Wang, Lingling Zhao, Chunyu Wang
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 19251-19260
affiliations: ["Harbin Institute of Technology", "Nanjing Medical University"]
cities: ["Harbin", "Nanjing"]
countries: ["China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Pang_Identity-Clothing_Similarity_Modeling_for_Unsupervised_Clothing_Change_Person_Re-Identification_CVPR_2025_paper.html

abstract:
Clothing change person re-identification (CC-ReID) aims to match different images of the same person, even when the clothing varies across images. To reduce manual labeling costs, existing unsupervised CC-ReID methods employ clustering algorithms to generate pseudo-labels. However, they often fail to assign the same pseudo-label to two images with the same identity but different clothing--referred to as a clothing change positive pair--thus hindering clothing-invariant feature learning. To address this issue, we propose the identity-clothing similarity modeling (ICSM) framework. To effectively connect clothing change positive pairs, ICSM first performs clothing-aware learning to leverage all discriminative information, including clothing, to obtain compact clusters. It then extracts cluster-level identity and clothing features and performs inter-cluster similarity estimation to identify clothing change positive clusters, reliable negative clusters, and hard negative clusters for each compact cluster. During optimization, we design an adaptive version of existing optimization methods to enhance similarities of clothing change positive pairs, while also introducing text semantics as a supervisory signal to further promote clothing invariance. Extensive experimental results across multiple datasets validate the effectiveness of the proposed framework, demonstrating its superiority over existing unsupervised methods and its competitiveness with some supervised approaches.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Pang_Identity-Clothing_Similarity_Modeling_for_Unsupervised_Clothing_Change_Person_Re-Identification_CVPR_2025_paper.pdf

bibtex:
@InProceedings{Pang_2025_CVPR,
    author    = {Pang, Zhiqi and Wang, Junjie and Zhao, Lingling and Wang, Chunyu},
    title     = {Identity-Clothing Similarity Modeling for Unsupervised Clothing Change Person Re-Identification},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {19251-19260}
}

raw_affiliation:
[{"affiliation": "Harbin Institute of Technology", "city": "Harbin", "country": "China"}, {"affiliation": "Nanjing Medical University", "city": "Nanjing", "country": "China"}]


title: Evaluating Model Perception of Color Illusions in Photorealistic Scenes
authors: Lingjun Mao, Zineng Tang, Alane Suhr
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 7805-7814
raw_affiliation: [{"affiliation": "University of California, Berkeley", "city": "Berkeley", "country": "USA"}]
affiliations: ["University of California, Berkeley"]
cities: ["Berkeley"]
countries: ["USA"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Mao_Evaluating_Model_Perception_of_Color_Illusions_in_Photorealistic_Scenes_CVPR_2025_paper.html

abstract:
We study the perception of color illusions by vision-language models. Color illusion, where a person's visual system perceives color differently from actual color, is well-studied in human vision. However, it remains underexplored whether vision-language models (VLMs), trained on large-scale human data, exhibit similar perceptual biases when confronted with such color illusions. We propose an automated framework for generating color illusion images, resulting in RCID (Realistic Color Illusion Dataset), a dataset of 19,000 realistic illusion images. Our experiments show that all studied VLMs exhibit perceptual biases similar human vision. Finally, we train a model to distinguish both human perception and actual pixel differences.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Mao_Evaluating_Model_Perception_of_Color_Illusions_in_Photorealistic_Scenes_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Mao_Evaluating_Model_Perception_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Mao_2025_CVPR,
    author    = {Mao, Lingjun and Tang, Zineng and Suhr, Alane},
    title     = {Evaluating Model Perception of Color Illusions in Photorealistic Scenes},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {7805-7814}
}


title: MINIMA: Modality Invariant Image Matching
authors: Jiangwei Ren, Xingyu Jiang, Zizhuo Li, Dingkang Liang, Xin Zhou, Xiang Bai
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 23059-23068
affiliations: ["Huazhong University of Science and Technology", "Wuhan University"]
cities: ["Wuhan", "Wuhan"]
countries: ["China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Ren_MINIMA_Modality_Invariant_Image_Matching_CVPR_2025_paper.html

abstract:
Image matching for both cross-view and cross-modality plays a critical role in multimodal perception. In practice, the modality gap caused by different imaging systems/styles poses great challenges to the matching task. Existing works try to extract invariant features for specific modalities and train on limited datasets, showing poor generalization. In this paper, we present MINIMA, a unified image matching framework for multiple cross-modal cases. Without pursuing fancy modules, our MINIMA aims to enhance universal performance from the perspective of data scaling up. For such purpose, we propose a simple yet effective data engine that can freely produce a large dataset containing multiple modalities, rich scenarios, and accurate matching labels. Specifically, we scale up the modalities from cheap but rich RGB-only matching data, by means of generative models. Under this setting, the matching labels and rich diversity of the RGB dataset are well inherited by the generated multimodal data. Benefiting from this, we construct MD-syn, a new comprehensive dataset that fills the data gap for general multimodal image matching. With MD-syn, we can directly train any advanced matching pipeline on randomly selected modality pairs to obtain cross-modal ability. Extensive experiments on in-domain and zero-shot matching tasks, including 19 cross-modal cases, demonstrate that our MINIMA can significantly outperform the baselines and even surpass modality-specific methods. The dataset and code are available at https://github.com/LSXI7/MINIMA

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Ren_MINIMA_Modality_Invariant_Image_Matching_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Ren_MINIMA_Modality_Invariant_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Ren_2025_CVPR,
    author    = {Ren, Jiangwei and Jiang, Xingyu and Li, Zizhuo and Liang, Dingkang and Zhou, Xin and Bai, Xiang},
    title     = {MINIMA: Modality Invariant Image Matching},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {23059-23068}
}

raw_affiliation:
[{"affiliation": "Huazhong University of Science and Technology", "city": "Wuhan", "country": "China"}, {"affiliation": "Wuhan University", "city": "Wuhan", "country": "China"}]


title: OccMamba: Semantic Occupancy Prediction with State Space Models
authors: Heng Li, Yuenan Hou, Xiaohan Xing, Yuexin Ma, Xiao Sun, Yanyong Zhang
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 11949-11959
cities: ["Hefei", "Shanghai", "Stanford", "Shanghai", "Hefei"]
countries: ["China", "China", "USA", "China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Li_OccMamba_Semantic_Occupancy_Prediction_with_State_Space_Models_CVPR_2025_paper.html

abstract:
Training deep learning models for semantic occupancy prediction is challenging due to factors such as a large number of occupancy cells, severe occlusion, limited visual cues, complicated driving scenarios, etc. Recent methods often adopt transformer-based architectures given their strong capability in learning input-conditioned weights and long-range relationships. However, transformer-based networks are notorious for their quadratic computation complexity, seriously undermining their efficacy and deployment in semantic occupancy prediction. Inspired by the global modeling and linear computation complexity of the Mamba architecture, we present the first Mamba-based network for semantic occupancy prediction, termed OccMamba. Specifically, we first design the hierarchical Mamba module and local context processor to better aggregate global and local contextual information, respectively. Besides, to relieve the inherent domain gap between the linguistic and 3D domains, we present a simple yet effective 3D-to-1D reordering scheme, i.e., height-prioritized 2D Hilbert expansion. It can maximally retain the spatial structure of 3D voxels as well as facilitate the processing of Mamba blocks. Endowed with the aforementioned designs, our OccMamba is capable of directly and efficiently processing large volumes of dense scene grids, achieving state-of-the-art performance across three prevalent occupancy prediction benchmarks, including OpenOccupancy, SemanticKITTI, and SemanticPOSS. Notably, on OpenOccupancy, our OccMamba outperforms the previous state-of-the-art Co-Occ by 5.1% IoU and 4.3% mIoU, respectively. Our implementation is open-sourced and available at: https://github.com/USTCLH/OccMamba.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Li_OccMamba_Semantic_Occupancy_Prediction_with_State_Space_Models_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Li_OccMamba_Semantic_Occupancy_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Li_2025_CVPR,
    author    = {Li, Heng and Hou, Yuenan and Xing, Xiaohan and Ma, Yuexin and Sun, Xiao and Zhang, Yanyong},
    title     = {OccMamba: Semantic Occupancy Prediction with State Space Models},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {11949-11959}
}

raw_affiliation:
[{"affiliation": "University of Science and Technology of China", "city": "Hefei", "country": "China"}, {"affiliation": "Shanghai AI Laboratory", "city": "Shanghai", "country": "China"}, {"affiliation": "Stanford University", "city": "Stanford", "country": "USA"}, {"affiliation": "ShanghaiTech University", "city": "Shanghai", "country": "China"}, {"affiliation": "Institute of Arti\ufb01cial Intelligence, Hefei Comprehensive National Science Center", "city": "Hefei", "country": "China"}]

affiliations:
["University of Science and Technology of China", "Shanghai AI Laboratory", "Stanford University", "ShanghaiTech University", "Institute of Arti\ufb01cial Intelligence, Hefei Comprehensive National Science Center"]


title: 3D Convex Splatting: Radiance Field Rendering with 3D Smooth Convexes
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 21360-21369
affiliations: ["University of Li `ege", "KAUST", "University of Oxford"]
cities: ["Li `ege", "Thuwal", "Oxford"]
countries: ["Belgium", "Saudi Arabia", "United Kingdom"]
highlight: yes
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Held_3D_Convex_Splatting_Radiance_Field_Rendering_with_3D_Smooth_Convexes_CVPR_2025_paper.html

authors:
Jan Held, Renaud Vandeghen, Abdullah Hamdi, Adrien Deliege, Anthony Cioppa, Silvio Giancola, Andrea Vedaldi, Bernard Ghanem, Marc Van Droogenbroeck

abstract:
Recent advances in radiance field reconstruction, such as 3D Gaussian Splatting (3DGS), have achieved high-quality novel view synthesis and fast rendering by representing scenes with compositions of Gaussian primitives. However, 3D Gaussians present several limitations for scene reconstruction. Accurately capturing hard edges is challenging without significantly increasing the number of Gaussians, creating a large memory footprint. Moreover, they struggle to represent flat surfaces, as they are diffused in space. Without hand-crafted regularizers, they tend to disperse irregularly around the actual surface. To circumvent these issues, we introduce a novel method, named 3D Convex Splatting (3DCS), which leverages 3D smooth convexes as primitives for modeling geometrically-meaningful radiance fields from multi-view images. Smooth convex shapes offer greater flexibility than Gaussians, allowing for a better representation of 3D scenes with hard edges and dense volumes using fewer primitives. Powered by our efficient CUDA-based rasterizer, 3DCS achieves superior performance over 3DGS on benchmarks such as Mip-NeRF360, Tanks and Temples, and Deep Blending. Specifically, our method attains an improvement of up to 0.81 in PSNR and 0.026 in LPIPS compared to 3DGS while maintaining high rendering speeds and reducing the number of required primitives. Our results highlight the potential of 3D Convex Splatting to become the new standard for high-quality scene reconstruction and novel view synthesis. The project page is https://convexsplatting.github.io/ .

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Held_3D_Convex_Splatting_Radiance_Field_Rendering_with_3D_Smooth_Convexes_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Held_3D_Convex_Splatting_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Held_2025_CVPR,
    author    = {Held, Jan and Vandeghen, Renaud and Hamdi, Abdullah and Deliege, Adrien and Cioppa, Anthony and Giancola, Silvio and Vedaldi, Andrea and Ghanem, Bernard and Van Droogenbroeck, Marc},
    title     = {3D Convex Splatting: Radiance Field Rendering with 3D Smooth Convexes},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {21360-21369}
}

raw_affiliation:
[{"affiliation": "University of Li `ege", "city": "Li `ege", "country": "Belgium"}, {"affiliation": "KAUST", "city": "Thuwal", "country": "Saudi Arabia"}, {"affiliation": "University of Oxford", "city": "Oxford", "country": "United Kingdom"}]


title: 3D Prior Is All You Need: Cross-Task Few-shot 2D Gaze Estimation
authors: Yihua Cheng, Hengfei Wang, Zhongqun Zhang, Yang Yue, Boeun Kim, Feng Lu, Hyung Jin Chang
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 23891-23900
affiliations: ["University of Birmingham", "Beihang University", "Dankook University", "KETI"]
cities: ["Birmingham", "Beijing", "Yongin", "Gyeonggi-do"]
countries: ["UK", "China", "South Korea", "South Korea"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Cheng_3D_Prior_Is_All_You_Need_Cross-Task_Few-shot_2D_Gaze_CVPR_2025_paper.html

abstract:
3D and 2D gaze estimation share the fundamental objective of capturing eye movements but are traditionally treated as two distinct research domains. In this paper, we introduce a novel cross-task few-shot 2D gaze estimation approach, aiming to adapt a pre-trained 3D gaze estimation network for 2D gaze prediction on unseen devices using only a few training images. This task is highly challenging due to the domain gap between 3D and 2D gaze, unknown screen poses, and limited training data. To address these challenges, we propose a novel framework that bridges the gap between 3D and 2D gaze. Our framework contains a physics-based differentiable projection module with learnable parameters to model screen poses and project 3D gaze into 2D gaze. The framework is fully differentiable and can integrate into existing 3D gaze networks without modifying their original architecture. Additionally, we introduce a dynamic pseudo-labelling strategy for flipped images, which is particularly challenging for 2D labels due to unknown screen poses. To overcome this, we reverse the projection process by converting 2D labels to 3D space, where flipping is performed. Notably, this 3D space is not aligned with the camera coordinate system, so we learn a dynamic transformation matrix to compensate for this misalignment. We evaluate our method on MPIIGaze, EVE, and GazeCapture datasets, collected respectively on laptops, desktop computers, and mobile devices. The superior performance highlights the effectiveness of our approach, and demonstrates its strong potential for real-world applications.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Cheng_3D_Prior_Is_All_You_Need_Cross-Task_Few-shot_2D_Gaze_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Cheng_3D_Prior_Is_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Cheng_2025_CVPR,
    author    = {Cheng, Yihua and Wang, Hengfei and Zhang, Zhongqun and Yue, Yang and Kim, Boeun and Lu, Feng and Chang, Hyung Jin},
    title     = {3D Prior Is All You Need: Cross-Task Few-shot 2D Gaze Estimation},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {23891-23900}
}

raw_affiliation:
[{"affiliation": "University of Birmingham", "city": "Birmingham", "country": "UK"}, {"affiliation": "Beihang University", "city": "Beijing", "country": "China"}, {"affiliation": "Dankook University", "city": "Yongin", "country": "South Korea"}, {"affiliation": "KETI", "city": "Gyeonggi-do", "country": "South Korea"}]


title: Cheb-GR: Rethinking K-nearest Neighbor Search in Re-ranking for Person Re-identification
authors: Jinxi Yang, He Li, Bo Du, Mang Ye
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 19261-19270
cities: ["Wuhan"]
countries: ["China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Yang_Cheb-GR_Rethinking_K-nearest_Neighbor_Search_in_Re-ranking_for_Person_Re-identification_CVPR_2025_paper.html

abstract:
Person re-identification (ReID) is the task of matching individuals across different camera views. Existing approaches typically employ neural networks to extract discriminative features, ranking gallery images based on their similarities to probe images. While effective, these methods are often enhanced through re-ranking, a post-processing step that refines initial retrieval results without requiring additional model training. However, current re-ranking methods mostly rely on k-nearest neighbor search to extract similar images that might have the same identity as the query, which is time-consuming with a high computation burden, limiting their applications in reality. We rethink the effect of the k-nearest neighbor search and introduce the Chebyshev's Theorem-guided Graph Re-ranking (Cheb-GR) method, which adopts the adaptive neighbor search guided by Chebyshev's Theorem over the k-nearest neighbor search for efficient neighbor selection. Our method leverages graph convolution operations to refine image features and achieve robust re-ranking, leading to enhanced retrieval performance. Furthermore, we provide a theoretical analysis based on Chebyshev's Inequality to elucidate the factors contributing to the strong performance of the proposed method. Our method significantly reduces the computation costs while maintaining relatively strong performance. Through extensive experiments in both general and cross-domain settings, we demonstrate the effectiveness of Cheb-GR and its potential for real-world applications.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Yang_Cheb-GR_Rethinking_K-nearest_Neighbor_Search_in_Re-ranking_for_Person_Re-identification_CVPR_2025_paper.pdf

bibtex:
@InProceedings{Yang_2025_CVPR,
    author    = {Yang, Jinxi and Li, He and Du, Bo and Ye, Mang},
    title     = {Cheb-GR: Rethinking K-nearest Neighbor Search in Re-ranking for Person Re-identification},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {19261-19270}
}

raw_affiliation:
[{"affiliation": "National Engineering Research Center for Multimedia Software, School of Computer Science, Wuhan University", "city": "Wuhan", "country": "China"}]

affiliations:
["National Engineering Research Center for Multimedia Software, School of Computer Science, Wuhan University"]


title: Spotting the Unexpected (STU): A 3D LiDAR Dataset for Anomaly Segmentation in Autonomous Driving
authors: Alexey Nekrasov, Malcolm Burdorf, Stewart Worrall, Bastian Leibe, Julie Stephany Berrio Perez
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 11875-11885
affiliations: ["RWTH Aachen University", "The University of Sydney"]
cities: ["Aachen", "Sydney"]
countries: ["Germany", "Australia"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Nekrasov_Spotting_the_Unexpected_STU_A_3D_LiDAR_Dataset_for_Anomaly_CVPR_2025_paper.html

abstract:
To operate safely, autonomous vehicles (AVs) need to detect and handle unexpected objects or anomalies on the road. While significant research exists for anomaly detection and segmentation in 2D, research progress in 3D is underexplored. Existing datasets lack high-quality multimodal data that are typically found in AVs. This paper presents a novel dataset for anomaly segmentation in driving scenarios. To the best of our knowledge, it is the first publicly available dataset focused on road anomaly segmentation with dense 3D semantic labeling, incorporating both LiDAR and camera data, as well as sequential information to enable anomaly detection across various ranges. This capability is critical for the safe navigation of autonomous vehicles. We adapted and evaluated several baseline models for 3D segmentation, highlighting the challenges of 3D anomaly detection in driving environments. Our dataset and evaluation code will be openly available, facilitating the testing and performance comparison of different approaches.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Nekrasov_Spotting_the_Unexpected_STU_A_3D_LiDAR_Dataset_for_Anomaly_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Nekrasov_Spotting_the_Unexpected_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Nekrasov_2025_CVPR,
    author    = {Nekrasov, Alexey and Burdorf, Malcolm and Worrall, Stewart and Leibe, Bastian and Perez, Julie Stephany Berrio},
    title     = {Spotting the Unexpected (STU): A 3D LiDAR Dataset for Anomaly Segmentation in Autonomous Driving},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {11875-11885}
}

raw_affiliation:
[{"affiliation": "RWTH Aachen University", "city": "Aachen", "country": "Germany"}, {"affiliation": "The University of Sydney", "city": "Sydney", "country": "Australia"}]


authors: Ji Hyeok Jung, Eun Tae Kim, Seoyeon Kim, Joo Ho Lee, Bumsoo Kim, Buru Chang
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 14257-14267
affiliations: ["Sogang University", "Chungang University", "Korea University"]
cities: ["Seoul", "Seoul", "Seoul"]
countries: ["South Korea", "South Korea", "South Korea"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Jung_Is_Right_Right_Enhancing_Object_Orientation_Understanding_in_Multimodal_Large_CVPR_2025_paper.html

title:
Is `Right' Right? Enhancing Object Orientation Understanding in Multimodal Large Language Models through Egocentric Instruction Tuning

abstract:
Multimodal large language models (MLLMs) act as essential interfaces, connecting humans with AI technologies in multimodal applications. However, current MLLMs face challenges in accurately interpreting object orientation in images due to inconsistent orientation annotations in training data, hindering the development of a coherent orientation understanding. To overcome this, we propose egocentric instruction tuning, which aligns MLLMs' orientation understanding with the user's perspective, based on a consistent annotation standard derived from the user's egocentric viewpoint. We first generate egocentric instruction data that leverages MLLMs' ability to recognize object details and applies prior knowledge for orientation understanding. Using this data, we perform instruction tuning to enhance the model's capability for accurate orientation interpretation. In addition, we introduce EgoOrientBench, a benchmark that evaluates MLLMs' orientation understanding across three tasks using images collected from diverse domains. Experimental results on this benchmark show that egocentric instruction tuning significantly improves orientation understanding without compromising overall MLLM performance. The instruction data and benchmark dataset are available on our project page at https://github.com/jhCOR/EgoOrientBench.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Jung_Is_Right_Right_Enhancing_Object_Orientation_Understanding_in_Multimodal_Large_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Jung_Is_Right_Right_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Jung_2025_CVPR,
    author    = {Jung, Ji Hyeok and Kim, Eun Tae and Kim, Seoyeon and Lee, Joo Ho and Kim, Bumsoo and Chang, Buru},
    title     = {Is `Right' Right? Enhancing Object Orientation Understanding in Multimodal Large Language Models through Egocentric Instruction Tuning},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {14257-14267}
}

raw_affiliation:
[{"affiliation": "Sogang University", "city": "Seoul", "country": "South Korea"}, {"affiliation": "Chungang University", "city": "Seoul", "country": "South Korea"}, {"affiliation": "Korea University", "city": "Seoul", "country": "South Korea"}]


title: GCC: Generative Color Constancy via Diffusing a Color Checker
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 10868-10878
affiliations: ["National Yang Ming Chiao Tung University", "MediaTek Inc."]
cities: ["Hsinchu", "Hsinchu"]
countries: ["Taiwan", "Taiwan"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Chang_GCC_Generative_Color_Constancy_via_Diffusing_a_Color_Checker_CVPR_2025_paper.html

authors:
Chen-Wei Chang, Cheng-De Fan, Chia-Che Chang, Yi-Chen Lo, Yu-Chee Tseng, Jiun-Long Huang, Yu-Lun Liu

abstract:
Color constancy methods often struggle to generalize across different camera sensors due to varying spectral sensitivities. We present GCC, which leverages diffusion models to inpaint color checkers into images for illumination estimation. Our key innovations include (1) a single-step deterministic inference approach that inpaints color checkers reflecting scene illumination, (2) a Laplacian decomposition technique that preserves checker structure while allowing illumination-dependent color adaptation, and (3) a mask-based data augmentation strategy for handling imprecise color checker annotations. By harnessing rich priors from pre-trained diffusion models, GCC demonstrates strong robustness in challenging cross-camera scenarios. These results highlight our method's effective generalization capability across different camera characteristics without requiring sensor-specific training, making it a versatile and practical solution for real-world applications.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Chang_GCC_Generative_Color_Constancy_via_Diffusing_a_Color_Checker_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Chang_GCC_Generative_Color_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Chang_2025_CVPR,
    author    = {Chang, Chen-Wei and Fan, Cheng-De and Chang, Chia-Che and Lo, Yi-Chen and Tseng, Yu-Chee and Huang, Jiun-Long and Liu, Yu-Lun},
    title     = {GCC: Generative Color Constancy via Diffusing a Color Checker},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {10868-10878}
}

raw_affiliation:
[{"affiliation": "National Yang Ming Chiao Tung University", "city": "Hsinchu", "country": "Taiwan"}, {"affiliation": "MediaTek Inc.", "city": "Hsinchu", "country": "Taiwan"}]


title: Do Visual Imaginations Improve Vision-and-Language Navigation Agents?
authors: Akhil Perincherry, Jacob Krantz, Stefan Lee
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 3846-3855
raw_affiliation: [{"affiliation": "Oregon State University", "city": "Corvallis", "country": "USA"}]
affiliations: ["Oregon State University"]
cities: ["Corvallis"]
countries: ["USA"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Perincherry_Do_Visual_Imaginations_Improve_Vision-and-Language_Navigation_Agents_CVPR_2025_paper.html

abstract:
Vision-and-Language Navigation (VLN) agents are tasked with navigating an unseen environment using natural language instructions. In this work, we study if visual representations of sub-goals implied by the instructions can serve as navigational cues and lead to increased navigation performance. To synthesize these visual representations or "imaginations", we leverage a text-to-image diffusion model on landmark references contained in segmented instructions. These imaginations are provided to VLN agents as an added modality to act as landmark cues and an auxiliary loss is added to explicitly encourage relating these with their corresponding referring expressions. Our findings reveal an increase in success rate (SR) of ~1 point and up to ~0.5 points in success scaled by inverse path length (SPL) across agents. These results suggest that the proposed approach reinforces visual understanding compared to relying on language instructions alone.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Perincherry_Do_Visual_Imaginations_Improve_Vision-and-Language_Navigation_Agents_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Perincherry_Do_Visual_Imaginations_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Perincherry_2025_CVPR,
    author    = {Perincherry, Akhil and Krantz, Jacob and Lee, Stefan},
    title     = {Do Visual Imaginations Improve Vision-and-Language Navigation Agents?},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {3846-3855}
}


title: On Denoising Walking Videos for Gait Recognition
authors: Dongyang Jin, Chao Fan, Jingzhe Ma, Jingkai Zhou, Weihua Chen, Shiqi Yu
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 12347-12357
cities: ["Shenzhen", "Shenzhen", "Shenzhen", "Hangzhou", "Hangzhou"]
countries: ["China", "China", "China", "China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Jin_On_Denoising_Walking_Videos_for_Gait_Recognition_CVPR_2025_paper.html

abstract:
To capture individual gait patterns, excluding identity-irrelevant cues in walking videos, such as clothing texture and color, remains a persistent challenge for vision-based gait recognition. Traditional silhouette and pose-based methods, though theoretically effective at removing such distractions, often fall short of high accuracy due to their sparse and less informative inputs. To address this, emerging end-to-end methods focus on directly denoising RGB videos using global optimization and human-defined priors. Building on this trend, we propose a novel gait denoising method, DenosingGait. Inspired by the philosophy that "what I cannot create, I do not understand",  we turn to generative diffusion models, uncovering how these models can partially filter out irrelevant factors for improved gait understanding. Based on this generation-driven denoising, we introduce feature matching, a kind of popular geometrical constraint in optical flow and depth estimation, to compact multi-channel float-encoded RGB information into two-channel direction vectors that represent local structural features, where within-frame matching captures spatial details and cross-frame matching conveys temporal dynamics. Experiments on the CCPG, CAISA-B*, and SUSTech1K datasets demonstrate that DenoisingGait achieves a new SoTA performance in most cases for both within-domain and cross-domain evaluations. Code is available at https://github.com/ShiqiYu/OpenGait.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Jin_On_Denoising_Walking_Videos_for_Gait_Recognition_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Jin_On_Denoising_Walking_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Jin_2025_CVPR,
    author    = {Jin, Dongyang and Fan, Chao and Ma, Jingzhe and Zhou, Jingkai and Chen, Weihua and Yu, Shiqi},
    title     = {On Denoising Walking Videos for Gait Recognition},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {12347-12357}
}

raw_affiliation:
[{"affiliation": "Department of Computer Science and Engineering, Southern University of Science and Technology, China", "city": "Shenzhen", "country": "China"}, {"affiliation": "National Engineering Laboratory for Big Data System Computing Technology, Shenzhen University, China", "city": "Shenzhen", "country": "China"}, {"affiliation": "Shenzhen Polytechnic University, China", "city": "Shenzhen", "country": "China"}, {"affiliation": "Zhejiang University, China", "city": "Hangzhou", "country": "China"}, {"affiliation": "Alibaba Group, China", "city": "Hangzhou", "country": "China"}]

affiliations:
["Department of Computer Science and Engineering, Southern University of Science and Technology, China", "National Engineering Laboratory for Big Data System Computing Technology, Shenzhen University, China", "Shenzhen Polytechnic University, China", "Zhejiang University, China", "Alibaba Group, China"]


title: Conformal Prediction for Zero-Shot Models
authors: Julio Silva-Rodríguez, Ismail Ben Ayed, Jose Dolz
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 19931-19941
raw_affiliation: [{"affiliation": "ETS Montr\u00b4eal", "city": "Montreal", "country": "Canada"}]
affiliations: ["ETS Montr\u00b4eal"]
cities: ["Montreal"]
countries: ["Canada"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Silva-Rodriguez_Conformal_Prediction_for_Zero-Shot_Models_CVPR_2025_paper.html

abstract:
Vision-language models pre-trained at large scale have shown unprecedented adaptability and generalization to downstream tasks. Although its discriminative potential has been widely explored, its reliability and uncertainty are still overlooked. In this work, we investigate the capabilities of CLIP models under the split conformal prediction paradigm, which provides theoretical guarantees to black-box models based on a small, labeled calibration set. In contrast to the main body of literature on conformal predictors in vision classifiers, foundation models exhibit a particular characteristic: they are pre-trained on a one-time basis on an inaccessible source domain, different from the transferred task. This domain drift negatively affects the efficiency of the conformal sets and poses additional challenges. To alleviate this issue, we propose Conf-OT, a transfer learning setting that operates transductive over the combined calibration and query sets. Solving an optimal transport problem, the proposed method bridges the domain gap between pre-training and adaptation without requiring additional data splits but still maintaining coverage guarantees. We comprehensively explore this conformal prediction strategy on a broad span of 15 datasets and three non-conformity scores. Conf-OT provides consistent relative improvements of up to 20% on set efficiency while being 15 times faster than popular transductive approaches.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Silva-Rodriguez_Conformal_Prediction_for_Zero-Shot_Models_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Silva-Rodriguez_Conformal_Prediction_for_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Silva-Rodriguez_2025_CVPR,
    author    = {Silva-Rodr{\'\i}guez, Julio and Ben Ayed, Ismail and Dolz, Jose},
    title     = {Conformal Prediction for Zero-Shot Models},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {19931-19941}
}


title: PhysAnimator: Physics-Guided Generative Cartoon Animation
authors: Tianyi Xie, Yiwei Zhao, Ying Jiang, Chenfanfu Jiang
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 10793-10804
affiliations: ["Net\ufb02ix", "UCLA"]
cities: ["Los Gatos", "Los Angeles"]
countries: ["USA", "USA"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Xie_PhysAnimator_Physics-Guided_Generative_Cartoon_Animation_CVPR_2025_paper.html

abstract:
Creating hand-drawn animation sequences is labor-intensive and demands professional expertise. We introduce PhysAnimator, a novel approach for generating physically plausible meanwhile anime-stylized animation from static anime illustrations. Our method seamlessly integrates physics-based simulations with data-driven generative models to produce dynamic and visually compelling animations. To capture the fluidity and exaggeration characteristic of anime, we perform image-space deformable body simulations on extracted mesh geometries. We enhance artistic control by introducing customizable energy strokes and incorporating rigging point support, enabling the creation of tailored animation effects such as wind interactions. Finally, we extract and warp sketches from the simulation sequence, generating a texture-agnostic representation, and employ a sketch-guided video diffusion model to synthesize high-quality animation frames. The resulting animations exhibit temporal consistency and visual plausibility, demonstrating the effectiveness of our method in creating dynamic anime-style animations.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Xie_PhysAnimator_Physics-Guided_Generative_Cartoon_Animation_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Xie_PhysAnimator_Physics-Guided_Generative_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Xie_2025_CVPR,
    author    = {Xie, Tianyi and Zhao, Yiwei and Jiang, Ying and Jiang, Chenfanfu},
    title     = {PhysAnimator: Physics-Guided Generative Cartoon Animation},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {10793-10804}
}

raw_affiliation:
[{"affiliation": "Net\ufb02ix", "city": "Los Gatos", "country": "USA"}, {"affiliation": "UCLA", "city": "Los Angeles", "country": "USA"}]


title: SeriesBench: A Benchmark for Narrative-Driven Drama Series Understanding
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 28995-29004
cities: ["Beijing", "Beijing", "Hangzhou", "Unknown"]
countries: ["China", "China", "China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Zhang_SeriesBench_A_Benchmark_for_Narrative-Driven_Drama_Series_Understanding_CVPR_2025_paper.html

authors:
Chenkai Zhang, Yiming Lei, Zeming Liu, Haitao Leng, ShaoGuo Liu, Tingting Gao, Qingjie Liu, Yunhong Wang

abstract:
With the rapid development of Multi-modal Large Language Models (MLLMs), an increasing number of benchmarks have been established to evaluate the video understanding capabilities of these models. However, these benchmarks focus on standalone videos and only assess "visual elements" like human actions and object states. In reality, contemporary videos often encompass complex and continuous narratives, typically presented as a series. To address this challenge, we propose SeriesBench, a benchmark consisting of 105 carefully curated narrative-driven series, covering 28 specialized tasks that require deep narrative understanding to solve. Specifically, we first select a diverse set of drama series spanning various genres. Then, we introduce a novel long-span narrative annotation method, combined with a full-information transformation approach to convert manual annotations into diverse task formats. To further enhance the model's capacity for detailed analysis of plot structures and character relationships within series, we propose a novel narrative reasoning framework, PC-DCoT. Extensive results on SeriesBench indicate that existing MLLMs still face significant challenges in understanding narrative-driven series, while PC-DCoT enables these MLLMs to achieve performance improvements. Overall, our SeriesBench and PC-DCoT highlight the critical necessity of advancing model capabilities for understanding narrative-driven series, guiding future MLLMs development. SeriesBench is publicly available at https://github.com/zackhxn/SeriesBench-CVPR2025.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Zhang_SeriesBench_A_Benchmark_for_Narrative-Driven_Drama_Series_Understanding_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Zhang_SeriesBench_A_Benchmark_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Zhang_2025_CVPR,
    author    = {Zhang, Chenkai and Lei, Yiming and Liu, Zeming and Leng, Haitao and Liu, ShaoGuo and Gao, Tingting and Liu, Qingjie and Wang, Yunhong},
    title     = {SeriesBench: A Benchmark for Narrative-Driven Drama Series Understanding},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {28995-29004}
}

raw_affiliation:
[{"affiliation": "State Key Laboratory of Virtual Reality Technology and Systems, Beihang University", "city": "Beijing", "country": "China"}, {"affiliation": "School of Computer Science and Engineering, Beihang University, Beijing, China", "city": "Beijing", "country": "China"}, {"affiliation": "Hangzhou Innovation Institute, Beihang University, Hangzhou China", "city": "Hangzhou", "country": "China"}, {"affiliation": "Kuaishou Technology", "city": "Unknown", "country": "China"}]

affiliations:
["State Key Laboratory of Virtual Reality Technology and Systems, Beihang University", "School of Computer Science and Engineering, Beihang University, Beijing, China", "Hangzhou Innovation Institute, Beihang University, Hangzhou China", "Kuaishou Technology"]


authors: Quan Zhang, Jinwei Fang, Rui Yuan, Xi Tang, Yuxin Qi, Ke Zhang, Chun Yuan
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 24139-24148
cities: ["Beijing", "Hefei", "Shanghai"]
countries: ["China", "China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Zhang_Weakly_Supervised_Temporal_Action_Localization_via_Dual-Prior_Collaborative_Learning_Guided_CVPR_2025_paper.html

title:
Weakly Supervised Temporal Action Localization via Dual-Prior Collaborative Learning Guided by Multimodal Large Language Models

abstract:
Recent breakthroughs in Multimodal Large Language Models (MLLMs) have gained significant recognition within the deep learning community, where the fusion of the Video Foundation Models (VFMs) and Large Language Models(LLMs) has proven instrumental in constructing robust video understanding systems, effectively surmounting constraints associated with predefined visual tasks. These sophisticated MLLMs exhibit remarkable proficiency in comprehending videos, swiftly attaining unprecedented performance levels across diverse benchmarks. However, their operation demands substantial memory and computational resources, underscoring the continued importance of traditional models in video comprehension tasks. In this paper, we introduce a novel learning paradigm termed MLLM4WTAL. This paradigm harnesses the potential of MLLM to offer temporal action key semantics and complete semantic textual cues for conventional Weakly-supervised Temporal Action Localization (WTAL) methods. MLLM4WTAL facilitates the enhancement of WTAL by leveraging MLLM guidance. It achieves this by integrating two distinct modules: Key Semantic Matching (KSM) and Complete Semantic Reconstruction (CSR). These modules work in tandem to effectively address prevalent issues like incomplete and over-complete outcomes common in WTAL methods. Rigorous experiments are conducted to validate the efficacy of our proposed approach in augmenting the performance of various heterogeneous WTAL models.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Zhang_Weakly_Supervised_Temporal_Action_Localization_via_Dual-Prior_Collaborative_Learning_Guided_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Zhang_Weakly_Supervised_Temporal_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Zhang_2025_CVPR,
    author    = {Zhang, Quan and Fang, Jinwei and Yuan, Rui and Tang, Xi and Qi, Yuxin and Zhang, Ke and Yuan, Chun},
    title     = {Weakly Supervised Temporal Action Localization via Dual-Prior Collaborative Learning Guided by Multimodal Large Language Models},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {24139-24148}
}

raw_affiliation:
[{"affiliation": "Tsinghua University", "city": "Beijing", "country": "China"}, {"affiliation": "University of Science and Technology of China", "city": "Hefei", "country": "China"}, {"affiliation": "Shanghai Jiao Tong University", "city": "Shanghai", "country": "China"}]

affiliations:
["Tsinghua University", "University of Science and Technology of China", "Shanghai Jiao Tong University"]


authors: Zhuguanyu Wu, Shihe Wang, Jiayi Zhang, Jiaxin Chen, Yunhong Wang
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 14891-14900
cities: ["Beijing", "Beijing"]
countries: ["China", "China"]
highlight: yes
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Wu_FIMA-Q_Post-Training_Quantization_for_Vision_Transformers_by_Fisher_Information_Matrix_CVPR_2025_paper.html

title:
FIMA-Q: Post-Training Quantization for Vision Transformers by Fisher Information Matrix Approximation

abstract:
Post-training quantization (PTQ) has stood out as a cost-effective and promising model compression approach over recent years, as it eliminates the need for retraining on the entire dataset. Unfortunately, most existing PTQ methods for Vision Transformers (ViTs) exhibit a notable drop in accuracy, especially in low-bit cases. To tackle these challenges, we analyze the extensively utilized Hessian-guided quantization loss, and uncover certain limitations within the approximated pre-activation Hessian. Following the block-by-block reconstruction paradigm of PTQ, we first derive a quantization loss based on the Fisher Information Matrix (FIM). Due to the large scale of the complete FIM, we establish the relationship between KL divergence and FIM in the PTQ scenario to enable fast computation of the quantization loss during reconstruction. Subsequently, we develop a Diagonal Plus Low-Rank (DPLR) estimation on FIM to achieve a more nuanced quantization loss. Our extensive experiments, conducted across various vision tasks with distinct representative ViT-based architectures on public benchmark datasets, demonstrate that our method outperforms the state-of-the-art approaches, especially in the case of low-bit quantization. The source code is available at https://github.com/ShiheWang/FIMA-Q.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Wu_FIMA-Q_Post-Training_Quantization_for_Vision_Transformers_by_Fisher_Information_Matrix_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Wu_FIMA-Q_Post-Training_Quantization_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Wu_2025_CVPR,
    author    = {Wu, Zhuguanyu and Wang, Shihe and Zhang, Jiayi and Chen, Jiaxin and Wang, Yunhong},
    title     = {FIMA-Q: Post-Training Quantization for Vision Transformers by Fisher Information Matrix Approximation},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {14891-14900}
}

raw_affiliation:
[{"affiliation": "State Key Laboratory of Virtual Reality Technology and Systems, Beihang University, China", "city": "Beijing", "country": "China"}, {"affiliation": "School of Computer Science and Engineering, Beihang University, Beijing, China", "city": "Beijing", "country": "China"}]

affiliations:
["State Key Laboratory of Virtual Reality Technology and Systems, Beihang University, China", "School of Computer Science and Engineering, Beihang University, Beijing, China"]


title: HotSpot: Signed Distance Function Optimization with an Asymptotically Sufficient Condition
authors: Zimo Wang, Cheng Wang, Taiki Yoshino, Sirui Tao, Ziyang Fu, Tzu-Mao Li
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 1276-1286
raw_affiliation: [{"affiliation": "UC San Diego", "city": "San Diego", "country": "USA"}]
affiliations: ["UC San Diego"]
cities: ["San Diego"]
countries: ["USA"]
highlight: yes
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Wang_HotSpot_Signed_Distance_Function_Optimization_with_an_Asymptotically_Sufficient_Condition_CVPR_2025_paper.html

abstract:
We propose a method, HotSpot, for optimizing neural signed distance functions. Existing losses, such as the eikonal loss, act as necessary but insufficient constraints and cannot guarantee that the recovered implicit function represents a true distance function, even if the output minimizes these losses almost everywhere. Furthermore, the eikonal loss suffers from stability issues in optimization. Finally, in conventional methods, regularization losses that penalize surface area distort the reconstructed signed distance function. We address these challenges by designing a loss function using the solution of a screened Poisson equation. Our loss, when minimized, provides an asymptotically sufficient condition to ensure the output converges to a true distance function. Our loss also leads to stable optimization and naturally penalizes large surface areas. We present theoretical analysis and experiments on both challenging 2D and 3D datasets and show that our method provides better surface reconstruction and a more accurate distance approximation.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Wang_HotSpot_Signed_Distance_Function_Optimization_with_an_Asymptotically_Sufficient_Condition_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Wang_HotSpot_Signed_Distance_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Wang_2025_CVPR,
    author    = {Wang, Zimo and Wang, Cheng and Yoshino, Taiki and Tao, Sirui and Fu, Ziyang and Li, Tzu-Mao},
    title     = {HotSpot: Signed Distance Function Optimization with an Asymptotically Sufficient Condition},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {1276-1286}
}


title: GliaNet: Adaptive Neural Network Structure Learning with Glia-Driven
authors: Mengqiao Han, Liyuan Pan, Xiabi Liu
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 25240-25249
cities: ["Yangling", "Beijing"]
countries: ["China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Han_GliaNet_Adaptive_Neural_Network_Structure_Learning_with_Glia-Driven_CVPR_2025_paper.html

abstract:
Neural networks derived from the M-P model have excelled in various visual tasks. However, as a simplified simulation version of the brain neural pathway, their structures are locked during training, causing over-fitting and over-parameterization. Although recent models have begun using the biomimetic concept and empirical pruning, they still result in irrational pruning, potentially affecting the accuracy of the model. In this paper, we introduce the Glia unit, composed of oligodendrocytes (Oli) and astrocytes (Ast), to emulate the exact workflow of the mammalian brain, thereby enhancing the biological plausibility of neural functions. Oli selects neurons involved in signal transmission during neural communication and, together with Ast, adaptively optimizes the neural structure. Specifically, we first construct the artificial Glia-Neuron (G-N) model, which is formulated at the instance, group, and interaction levels with adaptive and collaborative mechanisms. Then, we construct GliaNet based on our G-N model, whose structure and connections can be continuously optimized during training. Experiments show that our GliaNet advances state-of-the-art on multiple tasks while significantly reducing its parameters.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Han_GliaNet_Adaptive_Neural_Network_Structure_Learning_with_Glia-Driven_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Han_GliaNet_Adaptive_Neural_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Han_2025_CVPR,
    author    = {Han, Mengqiao and Pan, Liyuan and Liu, Xiabi},
    title     = {GliaNet: Adaptive Neural Network Structure Learning with Glia-Driven},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {25240-25249}
}

raw_affiliation:
[{"affiliation": "College of Information Engineering, Northwest Agricultural and Forestry (A&F) University", "city": "Yangling", "country": "China"}, {"affiliation": "School of Computer Science, Beijing Institute of Technology", "city": "Beijing", "country": "China"}]

affiliations:
["College of Information Engineering, Northwest Agricultural and Forestry (A&F) University", "School of Computer Science, Beijing Institute of Technology"]


title: BACON: Improving Clarity of Image Captions via Bag-of-Concept Graphs
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 14380-14389
cities: ["Shanghai", "Hangzhou", "Philadelphia", "Waterloo"]
countries: ["China", "China", "USA", "Canada"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Yang_BACON_Improving_Clarity_of_Image_Captions_via_Bag-of-Concept_Graphs_CVPR_2025_paper.html

authors:
Zhantao Yang, Ruili Feng, Keyu Yan, Huangji Wang, Zhicai Wang, Shangwen Zhu, Han Zhang, Jie Xiao, Pingyu Wu, Kai Zhu, Jixuan Chen, Chen-Wei Xie, Yue Yang, Hongyang Zhang, Yu Liu, Fan Cheng

abstract:
Advancements in large Vision-Language Models have brought precise, accurate image captioning, vital for advancing multi-modal image understanding and processing. Yet these captions often carry lengthy, intertwined contexts that are difficult to parse and frequently overlook essential cues, posing a great barrier for models like GroundingDINO and SDXL, which lack the strong text encoding and syntax analysis needed to fully leverage dense captions.To address this, we propose BACON, a prompting method that breaks down VLM-generated captions into disentangled, structured elements such as objects, relationships, styles, and themes. This approach not only minimizes confusion from handling complex contexts but also allows for efficient transfer into a JSON dictionary, enabling models without linguistic processing capabilities to easily access key information.We annotated 100,000 image-caption pairs using BACON with GPT-4V and trained an LLaVA captioner on this dataset, enabling it to produce BACON-style captions without relying on costly GPT-4V resources. Evaluations of overall quality, precision, and recall--as well as user studies--demonstrate that the resulting caption model consistently outperforms other state-of-the-art VLM models in generating high-quality captions.Additionally, we show that BACON-style captions exhibit better clarity when applied to various models, enabling them to accomplish previously unattainable tasks or surpass existing SOTA solutions without training. For example, BACON-style captions help groundingDINO achieve 1.51 times higher recall scores on open-vocabulary object detection tasks compared to leading methods.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Yang_BACON_Improving_Clarity_of_Image_Captions_via_Bag-of-Concept_Graphs_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Yang_BACON_Improving_Clarity_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Yang_2025_CVPR,
    author    = {Yang, Zhantao and Feng, Ruili and Yan, Keyu and Wang, Huangji and Wang, Zhicai and Zhu, Shangwen and Zhang, Han and Xiao, Jie and Wu, Pingyu and Zhu, Kai and Chen, Jixuan and Xie, Chen-Wei and Yang, Yue and Zhang, Hongyang and Liu, Yu and Cheng, Fan},
    title     = {BACON: Improving Clarity of Image Captions via Bag-of-Concept Graphs},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {14380-14389}
}

raw_affiliation:
[{"affiliation": "Shanghai Jiao Tong University", "city": "Shanghai", "country": "China"}, {"affiliation": "Alibaba group", "city": "Hangzhou", "country": "China"}, {"affiliation": "University of Pennsylvania", "city": "Philadelphia", "country": "USA"}, {"affiliation": "University of Waterloo", "city": "Waterloo", "country": "Canada"}]

affiliations:
["Shanghai Jiao Tong University", "Alibaba group", "University of Pennsylvania", "University of Waterloo"]


title: EntitySAM: Segment Everything in Video
authors: Mingqiao Ye, Seoung Wug Oh, Lei Ke, Joon-Young Lee
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 24234-24243
affiliations: ["EPFL", "Adobe Research", "Carnegie Mellon University"]
cities: ["Lausanne", "San Jose", "Pittsburgh"]
countries: ["Switzerland", "USA", "USA"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Ye_EntitySAM_Segment_Everything_in_Video_CVPR_2025_paper.html

abstract:
Automatically tracking and segmenting every video entity remains a significant challenge. Despite rapid advancements in video segmentation, even state-of-the-art models like SAM 2 struggle to consistently track all entities across a video--a task we refer to as Video Entity Segmentation.We propose EntitySAM, a framework for zero-shot video entity segmentation. EntitySAM extends SAM 2 by removing the need for explicit prompts, allowing automatic discovery and tracking of all entities, including those appearing in later frames. We incorporate query-based entity discovery and association into SAM 2, inspired by transformer-based object detectors. Specifically, we introduce an entity decoder to facilitate inter-object communication and an automatic prompt generator using learnable object queries. Additionally, we add a semantic encoder to enhance SAM 2's semantic awareness, improving segmentation quality. Trained on image-level mask annotations without category information from the COCO dataset, EntitySAM demonstrates strong generalization on four zero-shot video segmentation tasks: Video Entity, Panoptic, Instance, and Semantic Segmentation. Results on six popular benchmarks show that EntitySAM outperforms previous unified video segmentation methods and strong baselines, setting new standards for zero-shot video segmentation.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Ye_EntitySAM_Segment_Everything_in_Video_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Ye_EntitySAM_Segment_Everything_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Ye_2025_CVPR,
    author    = {Ye, Mingqiao and Oh, Seoung Wug and Ke, Lei and Lee, Joon-Young},
    title     = {EntitySAM: Segment Everything in Video},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {24234-24243}
}

raw_affiliation:
[{"affiliation": "EPFL", "city": "Lausanne", "country": "Switzerland"}, {"affiliation": "Adobe Research", "city": "San Jose", "country": "USA"}, {"affiliation": "Carnegie Mellon University", "city": "Pittsburgh", "country": "USA"}]


title: GS-2DGS: Geometrically Supervised 2DGS for Reflective Object Reconstruction
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 21547-21557
affiliations: ["Australian National University", "CSIRO", "Indian Institute of Technology Madras"]
cities: ["Canberra", "Canberra", "Chennai"]
countries: ["Australia", "Australia", "India"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Tong_GS-2DGS_Geometrically_Supervised_2DGS_for_Reflective_Object_Reconstruction_CVPR_2025_paper.html

authors:
Jinguang Tong, Xuesong Li, Fahira Afzal Maken, Sundaram Muthu, Lars Petersson, Chuong Nguyen, Hongdong Li

abstract:
3D modeling of highly reflective objects remains challenging due to strong view-dependent appearances. While previous SDF-based methods can recover high-quality meshes, they are often time-consuming and tend to produce over-smoothed surfaces. In contrast, 3D Gaussian Splatting (3DGS) offers the advantage of high speed and detailed real-time rendering, but extracting surfaces from the Gaussians can be noisy due to the lack of geometric constraints. To bridge the gap between these approaches, we propose a novel reconstruction method called GS-2DGS for reflective objects based on 2D Gaussian Splatting (2DGS). Our approach combines the rapid rendering capabilities of Gaussian Splatting with additional geometric information from a foundation model. Experimental results on synthetic and real datasets demonstrate that our method significantly outperforms Gaussian-based techniques in terms of reconstruction and relighting and achieves performance comparable to SDF-based methods while being an order of magnitude faster.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Tong_GS-2DGS_Geometrically_Supervised_2DGS_for_Reflective_Object_Reconstruction_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Tong_GS-2DGS_Geometrically_Supervised_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Tong_2025_CVPR,
    author    = {Tong, Jinguang and Li, Xuesong and Maken, Fahira Afzal and Muthu, Sundaram and Petersson, Lars and Nguyen, Chuong and Li, Hongdong},
    title     = {GS-2DGS: Geometrically Supervised 2DGS for Reflective Object Reconstruction},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {21547-21557}
}

raw_affiliation:
[{"affiliation": "Australian National University", "city": "Canberra", "country": "Australia"}, {"affiliation": "CSIRO", "city": "Canberra", "country": "Australia"}, {"affiliation": "Indian Institute of Technology Madras", "city": "Chennai", "country": "India"}]


authors: Longrong Yang, Dong Shen, Chaoxiang Cai, Kaibing Chen, Fan Yang, Tingting Gao, Di Zhang, Xi Li
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 9402-9412
cities: ["Hangzhou", "Beijing", "Hangzhou"]
countries: ["China", "China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Yang_Libra-Merging_Importance-redundancy_and_Pruning-merging_Trade-off_for_Acceleration_Plug-in_in_Large_CVPR_2025_paper.html

title:
Libra-Merging: Importance-redundancy and Pruning-merging Trade-off for Acceleration Plug-in in Large Vision-Language Model

abstract:
Large Vision-Language Models (LVLMs) have achieved significant progress in recent years. However, the expensive inference cost limits the realistic deployment of LVLMs. Some works find that visual tokens are redundant and compress tokens to reduce the inference cost. These works identify important non-redundant tokens as target tokens, then prune the remaining tokens (non-target tokens) or merge them into target tokens. However, target token identification faces the token importance-redundancy dilemma. Besides, token merging and pruning face a dilemma between disrupting target token information and losing non-target token information. To solve these problems, we propose a novel visual token compression scheme, named Libra-Merging. In target token identification, Libra-Merging selects the most important tokens from spatially discrete intervals, achieving a more robust token importance-redundancy trade-off than relying on a hyper-parameter. In token compression, when non-target tokens are dissimilar to target tokens, Libra-Merging does not merge them into the target tokens, thus avoiding disrupting target token information. Meanwhile, Libra-Merging condenses these non-target tokens into an information compensation token to prevent losing important non-target token information. Our method can serve as a plug-in for diverse LVLMs, and extensive experimental results demonstrate its effectiveness. The code will be publicly available at https://github.com/longrongyang/Libra-Merging.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Yang_Libra-Merging_Importance-redundancy_and_Pruning-merging_Trade-off_for_Acceleration_Plug-in_in_Large_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Yang_Libra-Merging_Importance-redundancy_and_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Yang_2025_CVPR,
    author    = {Yang, Longrong and Shen, Dong and Cai, Chaoxiang and Chen, Kaibing and Yang, Fan and Gao, Tingting and Zhang, Di and Li, Xi},
    title     = {Libra-Merging: Importance-redundancy and Pruning-merging Trade-off for Acceleration Plug-in in Large Vision-Language Model},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {9402-9412}
}

raw_affiliation:
[{"affiliation": "College of Computer Science and Technology, Zhejiang University", "city": "Hangzhou", "country": "China"}, {"affiliation": "Kuaishou Technology", "city": "Beijing", "country": "China"}, {"affiliation": "School of Software Technology, Zhejiang University", "city": "Hangzhou", "country": "China"}]

affiliations:
["College of Computer Science and Technology, Zhejiang University", "Kuaishou Technology", "School of Software Technology, Zhejiang University"]


title: VasTSD: Learning 3D Vascular Tree-state Space Diffusion Model for Angiography Synthesis
authors: Zhifeng Wang, Renjiao Yi, Xin Wen, Chenyang Zhu, Kai Xu
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 15693-15702
affiliations: ["National University of Defense Technology", "Xiangjiang Laboratory"]
cities: ["Changsha", "Changsha"]
countries: ["China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Wang_VasTSD_Learning_3D_Vascular_Tree-state_Space_Diffusion_Model_for_Angiography_CVPR_2025_paper.html

abstract:
Angiography imaging is a medical imaging technique that enhances the visibility of blood vessels within the body by using contrast agents. Angiographic images can effectively assist in the diagnosis of vascular diseases. However, contrast agents may bring extra radiation exposure which is harmful to patients with health risks. To mitigate these concerns, in this paper, we aim to automatically generate angiography from non-angiographic inputs, by leveraging and enhancing the inherent physical properties of vascular structures. Previous methods relying on 2D slice-based angiography synthesis struggle with maintaining continuity in 3D vascular structures and exhibit limited effectiveness across different imaging modalities. We propose VasTSD, a 3D vascular tree-state space diffusion model to synthesize angiography from 3D non-angiographic volumes, with a novel state space serialization approach that dynamically constructs vascular tree topologies, integrating these with a diffusion-based generative model to ensure the generation of anatomically continuous vasculature in 3D volumes. A pre-trained vision embedder is employed to construct vascular state space representations, enabling consistent modeling of vascular structures across multiple modalities. Extensive experiments on various angiographic datasets demonstrate the superiority of VasTSD over prior works, achieving enhanced continuity of blood vessels in synthesized angiographic synthesis in multiple modalities and anatomical regions.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Wang_VasTSD_Learning_3D_Vascular_Tree-state_Space_Diffusion_Model_for_Angiography_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Wang_VasTSD_Learning_3D_CVPR_2025_supplemental.zip

bibtex:
@InProceedings{Wang_2025_CVPR,
    author    = {Wang, Zhifeng and Yi, Renjiao and Wen, Xin and Zhu, Chenyang and Xu, Kai},
    title     = {VasTSD: Learning 3D Vascular Tree-state Space Diffusion Model for Angiography Synthesis},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {15693-15702}
}

raw_affiliation:
[{"affiliation": "National University of Defense Technology", "city": "Changsha", "country": "China"}, {"affiliation": "Xiangjiang Laboratory", "city": "Changsha", "country": "China"}]


title: PanSplat: 4K Panorama Synthesis with Feed-Forward Gaussian Splatting
authors: Cheng Zhang, Haofei Xu, Qianyi Wu, Camilo Cruz Gambardella, Dinh Phung, Jianfei Cai
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 11437-11447
affiliations: ["Monash University", "Building 4.0 CRC", "ETH Zurich"]
cities: ["Melbourne", "Caulfield East", "Zurich"]
countries: ["Australia", "Australia", "Switzerland"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Zhang_PanSplat_4K_Panorama_Synthesis_with_Feed-Forward_Gaussian_Splatting_CVPR_2025_paper.html

abstract:
With the advent of portable 360deg cameras, panorama has gained significant attention in applications like virtual reality (VR), virtual tours, robotics, and autonomous driving. As a result, wide-baseline panorama view synthesis has emerged as a vital task, where high resolution, fast inference, and memory efficiency are essential. Nevertheless, existing methods typically focus on lower resolutions (512 x1024) due to demanding memory and computational requirements. In this paper, we present PanSplat, a generalizable, feed-forward approach that efficiently supports resolution up to 4K (2048 x4096). Our approach features a tailored spherical 3D Gaussian pyramid with a Fibonacci lattice arrangement, enhancing image quality while reducing information redundancy. To accommodate the demands of high resolution, we propose a pipeline that integrates a hierarchical spherical cost volume and localized Gaussian heads, enabling two-step deferred backpropagation for memory-efficient training on a single A100 GPU. Experiments demonstrate that PanSplat achieves state-of-the-art results with superior efficiency and image quality across both synthetic and real-world datasets.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Zhang_PanSplat_4K_Panorama_Synthesis_with_Feed-Forward_Gaussian_Splatting_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Zhang_PanSplat_4K_Panorama_CVPR_2025_supplemental.zip

bibtex:
@InProceedings{Zhang_2025_CVPR,
    author    = {Zhang, Cheng and Xu, Haofei and Wu, Qianyi and Gambardella, Camilo Cruz and Phung, Dinh and Cai, Jianfei},
    title     = {PanSplat: 4K Panorama Synthesis with Feed-Forward Gaussian Splatting},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {11437-11447}
}

raw_affiliation:
[{"affiliation": "Monash University", "city": "Melbourne", "country": "Australia"}, {"affiliation": "Building 4.0 CRC", "city": "Caulfield East", "country": "Australia"}, {"affiliation": "ETH Zurich", "city": "Zurich", "country": "Switzerland"}]


title: WISNet: Pseudo Label Generation on Unbalanced and Patch Annotated Waste Images
authors: Shifan Zhang, Hongzi Zhu, Yinan He, Minyi Guo, Ziyang Lou, Shan Chang
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 15076-15085
affiliations: ["Shanghai Jiao Tong University", "Tencent AI Lab", "Donghua University"]
cities: ["Shanghai", "Shenzhen", "Shanghai"]
countries: ["China", "China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Zhang_WISNet_Pseudo_Label_Generation_on_Unbalanced_and_Patch_Annotated_Waste_CVPR_2025_paper.html

abstract:
Computer-vision-based assessment on waste sorting is desired to replace manpower supervision in Shanghai city. Due to the hardness of labeling a multitude of waste images, it is infeasible to train a semantic segmentation model for this purpose directly. In this work, we construct a new dataset consisting of 12,208 waste images, upon which seed regions (i.e., patches) are annotated and classified into 21 categories in a crowdsourcing fashion. To obtain pixel-level labels to train an effective segmentation model, we propose a weakly-supervised waste image pseudo label generation scheme, called WISNet. Specifically, we train a cohesive feature extractor with contrastive prototype learning, incorporating an unsupervised classification pretext task to help the extractor focus on more discriminative regions even with the same category. Furthermore, we propose an effective iterative patch expansion method to generate accurate pixel-level pseudo labels. Given these generated pseudo labels, a few-shot segmentation model can be trained to segment waste images. We implement and deploy WISNet in two real-world scenarios and conduct intensive experiments. Results show that WISNet can achieve a state-of-the-art 40.2% final segmentation mIoU on our waste benchmark, outperforming all other baselines and demonstrating the efficacy of WISNet.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Zhang_WISNet_Pseudo_Label_Generation_on_Unbalanced_and_Patch_Annotated_Waste_CVPR_2025_paper.pdf

bibtex:
@InProceedings{Zhang_2025_CVPR,
    author    = {Zhang, Shifan and Zhu, Hongzi and He, Yinan and Guo, Minyi and Lou, Ziyang and Chang, Shan},
    title     = {WISNet: Pseudo Label Generation on Unbalanced and Patch Annotated Waste Images},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {15076-15085}
}

raw_affiliation:
[{"affiliation": "Shanghai Jiao Tong University", "city": "Shanghai", "country": "China"}, {"affiliation": "Tencent AI Lab", "city": "Shenzhen", "country": "China"}, {"affiliation": "Donghua University", "city": "Shanghai", "country": "China"}]


title: MixerMDM: Learnable Composition of Human Motion Diffusion Models
authors: Pablo Ruiz-Ponce, German Barquero, Cristina Palmero, Sergio Escalera, José García-Rodríguez
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 12380-12390
cities: ["Alicante", "Barcelona", "London"]
countries: ["Spain", "Spain", "UK"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Ruiz-Ponce_MixerMDM_Learnable_Composition_of_Human_Motion_Diffusion_Models_CVPR_2025_paper.html

abstract:
Generating human motion guided by conditions such as textual descriptions is challenging due to the need for datasets with pairs of high-quality motion and their corresponding conditions. The difficulty increases when aiming for finer control in the generation. To that end, prior works have proposed to combine several motion diffusion models pre-trained on datasets with different types of conditions, thus allowing control with multiple conditions. However, the proposed merging strategies overlook that the optimal way to combine the generation processes might depend on the particularities of each pre-trained generative model and also the specific textual descriptions. In this context, we introduce MixerMDM, the first learnable model composition technique for combining pre-trained text-conditioned human motion diffusion models. Unlike previous approaches, MixerMDM provides a dynamic mixing strategy that is trained in an adversarial fashion to learn to combine the denoising process of each model depending on the set of conditions driving the generation. By using MixerMDM to combine single- and multi-person motion diffusion models, we achieve fine-grained control on the dynamics of every person individually, and also on the overall interaction. Furthermore, we propose a new evaluation technique that, for the first time in this task, measures the interaction and individual quality by computing the alignment between the mixed generated motions and their conditions as well as the capabilities of MixerMDM to adapt the mixing throughout the denoising process depending on the motions to mix.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Ruiz-Ponce_MixerMDM_Learnable_Composition_of_Human_Motion_Diffusion_Models_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Ruiz-Ponce_MixerMDM_Learnable_Composition_CVPR_2025_supplemental.zip

bibtex:
@InProceedings{Ruiz-Ponce_2025_CVPR,
    author    = {Ruiz-Ponce, Pablo and Barquero, German and Palmero, Cristina and Escalera, Sergio and Garc{\'\i}a-Rodr{\'\i}guez, Jos\'e},
    title     = {MixerMDM: Learnable Composition of Human Motion Diffusion Models},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {12380-12390}
}

raw_affiliation:
[{"affiliation": "Universidad de Alicante", "city": "Alicante", "country": "Spain"}, {"affiliation": "Universitat de Barcelona and Computer Vision Center", "city": "Barcelona", "country": "Spain"}, {"affiliation": "King\u2019s College London", "city": "London", "country": "UK"}]

affiliations:
["Universidad de Alicante", "Universitat de Barcelona and Computer Vision Center", "King\u2019s College London"]


title: Hand-held Object Reconstruction from RGB Video with Dynamic Interaction
authors: Shijian Jiang, Qi Ye, Rengan Xie, Yuchi Huo, Jiming Chen
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 12220-12230
cities: ["Hangzhou", "Hangzhou", "Hangzhou", "Hangzhou"]
countries: ["China", "China", "China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Jiang_Hand-held_Object_Reconstruction_from_RGB_Video_with_Dynamic_Interaction_CVPR_2025_paper.html

abstract:
This work aims to reconstruct the 3D geometry of a rigid object manipulated by one or both hands using monocular RGB video. Previous methods rely on Structure-from-Motion or hand priors to estimate relative motion between the object and camera, which typically assume textured objects or single-hand interactions. To accurately recover object geometry in dynamic interactions, we incorporate priors from 3D generation model into object pose estimation and propose semantic consistency constraints to solve the challenge of shape and texture discrepancy between the generated priors and observations. The poses are initialized, followed by joint optimization of the object poses and implicit neural representation. During optimization, a novel pose outlier voting strategy with inter-view consistency is proposed to correct large pose errors. Experiments on three datasets demonstrate that our method significantly outperforms the state-of-the-art in reconstruction quality for both single- and two-hand scenarios. Our project page: https://east-j.github.io/dynhor/

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Jiang_Hand-held_Object_Reconstruction_from_RGB_Video_with_Dynamic_Interaction_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Jiang_Hand-held_Object_Reconstruction_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Jiang_2025_CVPR,
    author    = {Jiang, Shijian and Ye, Qi and Xie, Rengan and Huo, Yuchi and Chen, Jiming},
    title     = {Hand-held Object Reconstruction from RGB Video with Dynamic Interaction},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {12220-12230}
}

raw_affiliation:
[{"affiliation": "College of Control Science and Engineering, Zhejiang University", "city": "Hangzhou", "country": "China"}, {"affiliation": "Key Lab of CS&AUS of Zhejiang Province", "city": "Hangzhou", "country": "China"}, {"affiliation": "State Key Lab of CAD&CG, Zhejiang University", "city": "Hangzhou", "country": "China"}, {"affiliation": "Zhejiang Lab", "city": "Hangzhou", "country": "China"}]

affiliations:
["College of Control Science and Engineering, Zhejiang University", "Key Lab of CS&AUS of Zhejiang Province", "State Key Lab of CAD&CG, Zhejiang University", "Zhejiang Lab"]


title: LEDiff: Latent Exposure Diffusion for HDR Generation
authors: Chao Wang, Zhihao Xia, Thomas Leimkuhler, Karol Myszkowski, Xuaner Zhang
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 453-464
affiliations: ["MPI Informatik", "Adobe"]
cities: ["Saarbr\u00fccken", "San Jose"]
countries: ["Germany", "USA"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Wang_LEDiff_Latent_Exposure_Diffusion_for_HDR_Generation_CVPR_2025_paper.html

abstract:
While consumer displays increasingly support more than 10 stops of dynamic range, most image assets -- such as internet photographs and generative AI content -- remain limited to 8-bit low dynamic range (LDR), constraining their utility across high dynamic range (HDR) applications. Currently, no generative model can produce high-bit, high-dynamic range content in a generalizable way. Existing LDR-to-HDR conversion methods often struggle to produce photorealistic details and physically-plausible dynamic range in the clipped areas. We introduce LEDiff, a method that enables a generative model with HDR content generation through latent space fusion inspired by image-space exposure fusion techniques. It also functions as an LDR-to-HDR converter, expanding the dynamic range of existing low-dynamic range images. Our approach uses a small HDR dataset to enable a pretrained diffusion model to recover detail and dynamic range in clipped highlights and shadows. LEDiff brings HDR capabilities to existing generative models and converts any LDR image to HDR, creating photorealistic HDR outputs for image generation, image-based lighting (HDR environment map generation), and photographic effects such as depth of field simulation, where linear HDR data is essential for realistic quality.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Wang_LEDiff_Latent_Exposure_Diffusion_for_HDR_Generation_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Wang_LEDiff_Latent_Exposure_CVPR_2025_supplemental.zip

bibtex:
@InProceedings{Wang_2025_CVPR,
    author    = {Wang, Chao and Xia, Zhihao and Leimkuhler, Thomas and Myszkowski, Karol and Zhang, Xuaner},
    title     = {LEDiff: Latent Exposure Diffusion for HDR Generation},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {453-464}
}

raw_affiliation:
[{"affiliation": "MPI Informatik", "city": "Saarbr\u00fccken", "country": "Germany"}, {"affiliation": "Adobe", "city": "San Jose", "country": "USA"}]


title: Video Depth Anything: Consistent Depth Estimation for Super-Long Videos
authors: Sili Chen, Hengkai Guo, Shengnan Zhu, Feihu Zhang, Zilong Huang, Jiashi Feng, Bingyi Kang
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 22831-22840
raw_affiliation: [{"affiliation": "ByteDance", "city": "Beijing", "country": "China"}]
affiliations: ["ByteDance"]
cities: ["Beijing"]
countries: ["China"]
highlight: yes
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Chen_Video_Depth_Anything_Consistent_Depth_Estimation_for_Super-Long_Videos_CVPR_2025_paper.html

abstract:
Depth Anything has achieved remarkable success in monocular depth estimation with strong generalization ability. However, it suffers from temporal inconsistency in videos, hindering its practical applications. Various methods have been proposed to alleviate this issue by leveraging video generation models or introducing priors from optical flow and camera poses. Nonetheless, these methods are only applicable to short videos (10 seconds) and require a trade-off between quality and computational efficiency. We propose Video Depth Anything for high-quality, consistent depth estimation in super-long videos (over several minutes) without sacrificing efficiency. We base our model on Depth Anything V2 and replace its head with an efficient spatial-temporal head. We design a straightforward yet effective temporal consistency loss by constraining the temporal depth gradient, eliminating the need for additional geometric priors. The model is trained on a joint dataset of video depth and unlabeled images, similar to Depth Anything V2. Moreover, a novel key-frame-based strategy is developed for long video inference.  Experiments show that our model can be applied to arbitrarily long videos without compromising quality, consistency, or generalization ability. Comprehensive evaluations on multiple video benchmarks demonstrate that our approach sets a new state-of-the-art in zero-shot video depth estimation. We offer models of different scales to support a range of scenarios, with our smallest model capable of real-time performance at 30 FPS.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Chen_Video_Depth_Anything_Consistent_Depth_Estimation_for_Super-Long_Videos_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Chen_Video_Depth_Anything_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Chen_2025_CVPR,
    author    = {Chen, Sili and Guo, Hengkai and Zhu, Shengnan and Zhang, Feihu and Huang, Zilong and Feng, Jiashi and Kang, Bingyi},
    title     = {Video Depth Anything: Consistent Depth Estimation for Super-Long Videos},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {22831-22840}
}


authors: Ziyang Luo, Haoning Wu, Dongxu Li, Jing Ma, Mohan Kankanhalli, Junnan Li
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 8461-8474
cities: ["San Francisco", "Hong Kong", "Singapore", "Singapore", "Canberra"]
countries: ["USA", "China", "Singapore", "Singapore", "Australia"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Luo_VideoAutoArena_An_Automated_Arena_for_Evaluating_Large_Multimodal_Models_in_CVPR_2025_paper.html

title:
VideoAutoArena: An Automated Arena for Evaluating Large Multimodal Models in Video Analysis through User Simulation

abstract:
Large multimodal models (LMMs) with advanced video analysis capabilities have recently garnered significant attention. However, most evaluations rely on traditional methods like multiple-choice question answering in benchmarks such as VideoMME and LongVideoBench, which are prone to lack the depth needed to capture the complex demands of real-world users. To address this limitation--and due to the prohibitive cost and slow pace of human annotation for video tasks--we introduce VideoAutoArena, an arena-style benchmark inspired by LMSYS Chatbot Arena's framework, designed to automatically assess LMMs' video analysis abilities. VideoAutoArena utilizes user simulation to generate open-ended, adaptive questions that rigorously assess model performance in video understanding. The benchmark features an automated, scalable evaluation framework, incorporating a modified ELO Rating System for fair and continuous comparisons across multiple LMMs. To validate our automated judging system, we construct a "gold standard" using a carefully curated subset of human annotations, demonstrating that our arena strongly aligns with human judgment while maintaining scalability. Additionally, we introduce a fault-driven evolution strategy, progressively increasing question complexity to push models toward handling more challenging video analysis scenarios. Experimental results demonstrate that VideoAutoArena effectively differentiates among state-of-the-art LMMs, providing insights into model strengths and areas for improvement. To further streamline our evaluation, we introduce VideoAutoBench as an auxiliary benchmark, where human annotators label winners in a subset of VideoAutoArena battles. We use GPT-4o as a judge to compare responses against these human-validated answers. Together, VideoAutoArena and VideoAutoBench offer a cost-effective, and scalable framework for evaluating LMMs in user-centric video analysis.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Luo_VideoAutoArena_An_Automated_Arena_for_Evaluating_Large_Multimodal_Models_in_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Luo_VideoAutoArena_An_Automated_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Luo_2025_CVPR,
    author    = {Luo, Ziyang and Wu, Haoning and Li, Dongxu and Ma, Jing and Kankanhalli, Mohan and Li, Junnan},
    title     = {VideoAutoArena: An Automated Arena for Evaluating Large Multimodal Models in Video Analysis through User Simulation},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {8461-8474}
}

raw_affiliation:
[{"affiliation": "Salesforce AI Research", "city": "San Francisco", "country": "USA"}, {"affiliation": "Hong Kong Baptist University", "city": "Hong Kong", "country": "China"}, {"affiliation": "National University of Singapore", "city": "Singapore", "country": "Singapore"}, {"affiliation": "Nanyang Technological University", "city": "Singapore", "country": "Singapore"}, {"affiliation": "The Australian National University", "city": "Canberra", "country": "Australia"}]

affiliations:
["Salesforce AI Research", "Hong Kong Baptist University", "National University of Singapore", "Nanyang Technological University", "The Australian National University"]


title: InstanceCap: Improving Text-to-Video Generation via Instance-aware Structured Caption
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 28974-28983
affiliations: ["Nanjing University", "ByteDance", "Nankai University"]
cities: ["Nanjing", "Beijing", "Tianjin"]
countries: ["China", "China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Fan_InstanceCap_Improving_Text-to-Video_Generation_via_Instance-aware_Structured_Caption_CVPR_2025_paper.html

authors:
Tiehan Fan, Kepan Nan, Rui Xie, Penghao Zhou, Zhenheng Yang, Chaoyou Fu, Xiang Li, Jian Yang, Ying Tai

abstract:
Text-to-video generation has evolved rapidly in recent years, delivering remarkable results. Training typically relies on video-caption paired data, which plays a crucial role in enhancing generation performance. However, current video captions often suffer from insufficient details, hallucinations and imprecise motion depiction, affecting the fidelity and consistency of generated videos. In this work, we propose a novel instance-aware structured caption framework, termed \mathtt InstanceCap , to achieve instance-level and fine-grained video caption for the first time. Based on this scheme, we design an auxiliary models cluster to convert original video into instances to enhance instance fidelity. Video instances are further used to refine dense prompts into structured phrases, achieving concise yet precise descriptions. Furthermore, a 22K \mathtt InstanceVid  dataset is curated for training, and an enhancement pipeline that tailored to \mathtt InstanceCap  structure is proposed for inference. Experimental results demonstrate that our proposed \mathtt InstanceCap  significantly outperform previous models, ensuring high fidelity between captions and videos while reducing hallucinations.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Fan_InstanceCap_Improving_Text-to-Video_Generation_via_Instance-aware_Structured_Caption_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Fan_InstanceCap_Improving_Text-to-Video_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Fan_2025_CVPR,
    author    = {Fan, Tiehan and Nan, Kepan and Xie, Rui and Zhou, Penghao and Yang, Zhenheng and Fu, Chaoyou and Li, Xiang and Yang, Jian and Tai, Ying},
    title     = {InstanceCap: Improving Text-to-Video Generation via Instance-aware Structured Caption},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {28974-28983}
}

raw_affiliation:
[{"affiliation": "Nanjing University", "city": "Nanjing", "country": "China"}, {"affiliation": "ByteDance", "city": "Beijing", "country": "China"}, {"affiliation": "Nankai University", "city": "Tianjin", "country": "China"}]


title: AudCast: Audio-Driven Human Video Generation by Cascaded Diffusion Transformers
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 10678-10689
cities: ["Beijing", "Beijing", "Singapore", "Beijing", "Hefei", "Thuwal"]
countries: ["China", "China", "Singapore", "China", "China", "Saudi Arabia"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Guan_AudCast_Audio-Driven_Human_Video_Generation_by_Cascaded_Diffusion_Transformers_CVPR_2025_paper.html

authors:
Jiazhi Guan, Kaisiyuan Wang, Zhiliang Xu, Quanwei Yang, Yasheng Sun, Shengyi He, Borong Liang, Yukang Cao, Yingying Li, Haocheng Feng, Errui Ding, Jingdong Wang, Youjian Zhao, Hang Zhou, Ziwei Liu

abstract:
Despite the recent progress of audio-driven video generation, existing methods mostly focus on driving facial movements, leading to non-coherent head and body dynamics. Moving forward, it is desirable yet challenging to generate holistic human videos with both accurate lip-sync and delicate co-speech gestures w.r.t. given audio. In this work, we propose AudCast, a generalized audio-driven human video generation framework adopting a cascade Diffusion-Transformers (DiTs) paradigm, which synthesizes holistic human videos based on a reference image and a given audio. 1) Firstly, an audio-conditioned Holistic Human DiT architecture is proposed to directly drive the movements of any human body with vivid gesture dynamics. 2) Then to enhance hand and face details that are well-knownly difficult to handle, a Regional Refinement DiT leverages regional 3D fitting as the bridge to reform the signals, producing the final results.  Extensive experiments demonstrate that our framework generates high-fidelity audio-driven holistic human videos with temporal coherence and fine facial and hand details.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Guan_AudCast_Audio-Driven_Human_Video_Generation_by_Cascaded_Diffusion_Transformers_CVPR_2025_paper.pdf

bibtex:
@InProceedings{Guan_2025_CVPR,
    author    = {Guan, Jiazhi and Wang, Kaisiyuan and Xu, Zhiliang and Yang, Quanwei and Sun, Yasheng and He, Shengyi and Liang, Borong and Cao, Yukang and Li, Yingying and Feng, Haocheng and Ding, Errui and Wang, Jingdong and Zhao, Youjian and Zhou, Hang and Liu, Ziwei},
    title     = {AudCast: Audio-Driven Human Video Generation by Cascaded Diffusion Transformers},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {10678-10689}
}

raw_affiliation:
[{"affiliation": "DCST, Tsinghua University", "city": "Beijing", "country": "China"}, {"affiliation": "Baidu Inc.", "city": "Beijing", "country": "China"}, {"affiliation": "S-Lab, Nanyang Technological University", "city": "Singapore", "country": "Singapore"}, {"affiliation": "Zhongguancun Laboratory", "city": "Beijing", "country": "China"}, {"affiliation": "University of Science and Technology of China", "city": "Hefei", "country": "China"}, {"affiliation": "KAUST", "city": "Thuwal", "country": "Saudi Arabia"}]

affiliations:
["DCST, Tsinghua University", "Baidu Inc.", "S-Lab, Nanyang Technological University", "Zhongguancun Laboratory", "University of Science and Technology of China", "KAUST"]


authors: Ziteng Cui, Xuangeng Chu, Tatsuya Harada
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 26472-26482
affiliations: ["The University of Tokyo", "RIKEN AIP"]
cities: ["Tokyo", "Wako"]
countries: ["Japan", "Japan"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Cui_Luminance-GS_Adapting_3D_Gaussian_Splatting_to_Challenging_Lighting_Conditions_with_CVPR_2025_paper.html

title:
Luminance-GS: Adapting 3D Gaussian Splatting to Challenging Lighting Conditions with View-Adaptive Curve Adjustment

abstract:
Capturing high-quality photographs under diverse real-world lighting conditions is challenging, as both natural lighting (e.g., low-light) and camera exposure settings (e.g., exposure time) significantly impact image quality. This challenge becomes more pronounced in multi-view scenarios, where variations in lighting and image signal processor (ISP) settings across viewpoints introduce photometric inconsistencies. Such lighting degradations and view-dependent variations pose substantial challenges to novel view synthesis (NVS) frameworks based on Neural Radiance Fields (NeRF) and 3D Gaussian Splatting (3DGS). To address this, we introduce Luminance-GS, a novel approach to achieving high-quality novel view synthesis results under diverse challenging lighting conditions using 3DGS. By adopting per-view color matrix mapping and view adaptive curve adjustments, Luminance-GS achieves state-of-the-art (SOTA) results across various lighting conditions--including low-light, overexposure, and varying exposure--while not altering the original 3DGS explicit representation. Compared to previous NeRF- and 3DGS-based baselines, Luminance-GS provides real-time rendering speed with improved reconstruction quality. The source code is available at https://github.com/cuiziteng/Luminance-GS.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Cui_Luminance-GS_Adapting_3D_Gaussian_Splatting_to_Challenging_Lighting_Conditions_with_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Cui_Luminance-GS_Adapting_3D_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Cui_2025_CVPR,
    author    = {Cui, Ziteng and Chu, Xuangeng and Harada, Tatsuya},
    title     = {Luminance-GS: Adapting 3D Gaussian Splatting to Challenging Lighting Conditions with View-Adaptive Curve Adjustment},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {26472-26482}
}

raw_affiliation:
[{"affiliation": "The University of Tokyo", "city": "Tokyo", "country": "Japan"}, {"affiliation": "RIKEN AIP", "city": "Wako", "country": "Japan"}]


title: EventSplat: 3D Gaussian Splatting from Moving Event Cameras for Real-time Rendering
authors: Toshiya Yura, Ashkan Mirzaei, Igor Gilitschenski
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 26876-26886
affiliations: ["Sony Semiconductor Solutions Corporation", "University of Toronto"]
cities: ["Tokyo", "Toronto"]
countries: ["Japan", "Canada"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Yura_EventSplat_3D_Gaussian_Splatting_from_Moving_Event_Cameras_for_Real-time_CVPR_2025_paper.html

abstract:
We introduce a method for using event camera data in novel view synthesis via Gaussian Splatting. Event cameras offer exceptional temporal resolution and a high dynamic range. Leveraging these capabilities allows us to effectively address the novel view synthesis challenge in the presence of fast camera motion. For initialization of the optimization process, our approach uses prior knowledge encoded in an event-to-video model. We also use spline interpolation for obtaining high quality poses along the event camera trajectory. This enhances the reconstruction quality from fast-moving cameras while overcoming the computational limitations traditionally associated with event-based Neural Radiance Field (NeRF) methods. Our experimental evaluation demonstrates that our results achieve higher visual fidelity and better performance than existing event-based NeRF approaches while being an order of magnitude faster to render.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Yura_EventSplat_3D_Gaussian_Splatting_from_Moving_Event_Cameras_for_Real-time_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Yura_EventSplat_3D_Gaussian_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Yura_2025_CVPR,
    author    = {Yura, Toshiya and Mirzaei, Ashkan and Gilitschenski, Igor},
    title     = {EventSplat: 3D Gaussian Splatting from Moving Event Cameras for Real-time Rendering},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {26876-26886}
}

raw_affiliation:
[{"affiliation": "Sony Semiconductor Solutions Corporation", "city": "Tokyo", "country": "Japan"}, {"affiliation": "University of Toronto", "city": "Toronto", "country": "Canada"}]


title: Thinking in Space: How Multimodal Large Language Models See, Remember, and Recall Spaces
authors: Jihan Yang, Shusheng Yang, Anjali W. Gupta, Rilyn Han, Li Fei-Fei, Saining Xie
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 10632-10643
affiliations: ["New York University", "Yale University", "Stanford University"]
cities: ["New York", "New Haven", "Stanford"]
countries: ["USA", "USA", "USA"]
oral: yes
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Yang_Thinking_in_Space_How_Multimodal_Large_Language_Models_See_Remember_CVPR_2025_paper.html

abstract:
Humans possess the visual-spatial intelligence to remember spaces from sequential visual observations. However, can Multimodal Large Language Models (MLLMs) trained on million-scale video datasets also "think in space" from videos? We present a novel video-based visual-spatial intelligence benchmark (VSI-Bench) of over 5,000 question-answer pairs, and find that MLLMs exhibit competitive--though subhuman--visual-spatial intelligence. We probe models to express how they think in space both linguistically and visually and find that while spatial reasoning capabilities remain the primary bottleneck for MLLMs to reach higher benchmark performance, local world models and spatial awareness do emerge within these models. Notably, prevailing linguistic reasoning techniques (e.g., chain-of-thought, self-consistency, tree-of-thoughts) fail to improve performance, whereas  explicitly generating cognitive maps during question-answering enhances MLLMs' spatial distance awareness.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Yang_Thinking_in_Space_How_Multimodal_Large_Language_Models_See_Remember_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Yang_Thinking_in_Space_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Yang_2025_CVPR,
    author    = {Yang, Jihan and Yang, Shusheng and Gupta, Anjali W. and Han, Rilyn and Fei-Fei, Li and Xie, Saining},
    title     = {Thinking in Space: How Multimodal Large Language Models See, Remember, and Recall Spaces},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {10632-10643}
}

raw_affiliation:
[{"affiliation": "New York University", "city": "New York", "country": "USA"}, {"affiliation": "Yale University", "city": "New Haven", "country": "USA"}, {"affiliation": "Stanford University", "city": "Stanford", "country": "USA"}]


title: 3D Student Splatting and Scooping
authors: Jialin Zhu, Jiangbei Yue, Feixiang He, He Wang
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 21045-21054
affiliations: ["University College London", "University of Leeds", "AI Centre, University College London"]
cities: ["London", "Leeds", "London"]
countries: ["UK", "UK", "UK"]
oral: yes
award_candidate: yes
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Zhu_3D_Student_Splatting_and_Scooping_CVPR_2025_paper.html

abstract:
Recently, 3D Gaussian Splatting (3DGS) provides a new framework for novel view synthesis, and has spiked a new wave of research in neural rendering and related applications. As 3DGS is becoming a foundational component of many models, any improvement on 3DGS itself can bring huge benefits. To this end, we aim to improve the fundamental paradigm and formulation of 3DGS. We argue that as an unnormalized mixture model, it needs to be neither Gaussians nor splatting. We subsequently propose a new mixture model consisting of flexible Student's t distributions, with both positive (splatting) and negative (scooping) densities. We name our model Student Splatting and Scooping, or SSS. When providing better expressivity, SSS also poses new challenges in learning. Therefore, we also propose a new principled sampling approach for optimization. Through exhaustive evaluation and comparison, across multiple datasets, settings, and metrics, we demonstrate that SSS outperforms existing methods in terms of quality and parameter efficiency, e.g. achieving matching or better quality with similar numbers of components, and obtaining comparable results while reducing the component number by as much as 82%.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Zhu_3D_Student_Splatting_and_Scooping_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Zhu_3D_Student_Splatting_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Zhu_2025_CVPR,
    author    = {Zhu, Jialin and Yue, Jiangbei and He, Feixiang and Wang, He},
    title     = {3D Student Splatting and Scooping},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {21045-21054}
}

raw_affiliation:
[{"affiliation": "University College London", "city": "London", "country": "UK"}, {"affiliation": "University of Leeds", "city": "Leeds", "country": "UK"}, {"affiliation": "AI Centre, University College London", "city": "London", "country": "UK"}]


title: World-consistent Video Diffusion with Explicit 3D Modeling
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 21685-21695
affiliations: ["Apple", "The Chinese University of Hong Kong"]
cities: ["Cupertino", "Hong Kong"]
countries: ["USA", "China"]
highlight: yes
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Zhang_World-consistent_Video_Diffusion_with_Explicit_3D_Modeling_CVPR_2025_paper.html

authors:
Qihang Zhang, Shuangfei Zhai, Miguel Ángel Bautista Martin, Kevin Miao, Alexander Toshev, Joshua Susskind, Jiatao Gu

abstract:
Recent advancements in diffusion models have set new benchmarks in image and video generation, enabling realistic visual synthesis across single- and multi-frame contexts. However, these models still struggle with efficiently and explicitly generating 3D-consistent content. To address this, we propose World-consistent Video Diffusion (WVD), a novel framework that incorporates explicit 3D supervision using XYZ images, which encode global 3D coordinates for each image pixel. More specifically, we train a diffusion transformer to learn the joint distribution of RGB and XYZ frames. This approach supports multi-task adaptability via a flexible inpainting strategy. For example, WVD can estimate XYZ frames from ground-truth RGB or generate novel RGB frames using XYZ projections along a specified camera trajectory. In doing so, WVD unifies tasks like single-image-to-3D generation, multi-view stereo, and camera-controlled video generation. Our approach demonstrates competitive performance across multiple benchmarks, providing a scalable solution for 3D-consistent video and image generation with a single pretrained model.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Zhang_World-consistent_Video_Diffusion_with_Explicit_3D_Modeling_CVPR_2025_paper.pdf

bibtex:
@InProceedings{Zhang_2025_CVPR,
    author    = {Zhang, Qihang and Zhai, Shuangfei and Martin, Miguel \'Angel Bautista and Miao, Kevin and Toshev, Alexander and Susskind, Joshua and Gu, Jiatao},
    title     = {World-consistent Video Diffusion with Explicit 3D Modeling},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {21685-21695}
}

raw_affiliation:
[{"affiliation": "Apple", "city": "Cupertino", "country": "USA"}, {"affiliation": "The Chinese University of Hong Kong", "city": "Hong Kong", "country": "China"}]


title: A Stitch in Time Saves Nine: Small VLM is a Precise Guidance for Accelerating Large VLMs
authors: Wangbo Zhao, Yizeng Han, Jiasheng Tang, Zhikai Li, Yibing Song, Kai Wang, Zhangyang Wang, Yang You
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 19814-19824
cities: ["Singapore", "Hangzhou", "Hangzhou", "Austin"]
countries: ["Singapore", "China", "China", "USA"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Zhao_A_Stitch_in_Time_Saves_Nine_Small_VLM_is_a_CVPR_2025_paper.html

abstract:
Vision-language models (VLMs) have shown remarkable success across various multi-modal tasks, yet large VLMs encounter significant efficiency challenges due to processing numerous visual tokens. A promising approach to accelerating large VLM inference is using partial information, such as attention maps from specific layers, to assess token importance and prune less essential tokens. However, our study reveals three key insights: (i) Partial attention information is insufficient for accurately identifying critical visual tokens, resulting in suboptimal performance, especially at low token retention ratios; (ii) Global attention information, such as the attention map aggregated across all layers, more effectively preserves essential tokens and maintains performance under aggressive pruning. However, it requires a full inference pass, which increases computational load and is therefore impractical in existing methods; and (iii) The global attention map aggregated from a small VLM closely resembles that of a large VLM, suggesting an efficient alternative. Based on these findings, we introduce \underline S mall VLM \underline G uidance for \underline L arge VLMs (SGL). Specifically, we employ the aggregated attention map from a small VLM guide the pruning of visual tokens in a large VLM. Additionally, we develop a small VLM early exiting mechanism to make full use of the small VLM's predictions, dynamically invoking the larger VLM only when necessary, yielding a superior trade-off between accuracy and computational cost. Extensive evaluations across 11 benchmarks demonstrate the effectiveness and generalizability of our method, achieving up to 91% pruning ratio for visual tokens while retaining competitive performance.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Zhao_A_Stitch_in_Time_Saves_Nine_Small_VLM_is_a_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Zhao_A_Stitch_in_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Zhao_2025_CVPR,
    author    = {Zhao, Wangbo and Han, Yizeng and Tang, Jiasheng and Li, Zhikai and Song, Yibing and Wang, Kai and Wang, Zhangyang and You, Yang},
    title     = {A Stitch in Time Saves Nine: Small VLM is a Precise Guidance for Accelerating Large VLMs},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {19814-19824}
}

raw_affiliation:
[{"affiliation": "National University of Singapore", "city": "Singapore", "country": "Singapore"}, {"affiliation": "DAMO Academy, Alibaba Group", "city": "Hangzhou", "country": "China"}, {"affiliation": "Hupan Lab", "city": "Hangzhou", "country": "China"}, {"affiliation": "The University of Texas at Austin", "city": "Austin", "country": "USA"}]

affiliations:
["National University of Singapore", "DAMO Academy, Alibaba Group", "Hupan Lab", "The University of Texas at Austin"]


title: Zero-Shot Novel View and Depth Synthesis with Multi-View Geometric Diffusion
authors: Vitor Guizilini, Muhammad Zubair Irshad, Dian Chen, Greg Shakhnarovich, Rares Ambrus
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 764-776
affiliations: ["Toyota Research Institute (TRI)", "Toyota Technological Institute at Chicago (TTIC)"]
cities: ["Los Altos", "Chicago"]
countries: ["USA", "USA"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Guizilini_Zero-Shot_Novel_View_and_Depth_Synthesis_with_Multi-View_Geometric_Diffusion_CVPR_2025_paper.html

abstract:
Current methods for 3D scene reconstruction from sparse posed images employ intermediate 3D representations such as neural fields, voxel grids, or 3D Gaussians, to achieve multi-view consistent scene appearance and geometry. In this paper we introduce MVGD, a diffusion-based architecture capable of direct pixel-level generation of images and depth maps from novel viewpoints, given an arbitrary number of input views. Our method uses raymap conditioning to both augment visual features with spatial information from different viewpoints, as well as to guide the generation of images and depth maps from novel views. A key aspect of our approach is the multi-task generation of images and depth maps, using learnable task embeddings to guide the diffusion process towards specific modalities. We train this model on a collection of more than 60 million multi-view samples from publicly available datasets, and propose techniques to enable efficient and consistent learning in such diverse conditions.  We also propose a novel strategy that enables the efficient training of larger models by incrementally fine-tuning smaller ones, with promising scaling behavior. Through extensive experiments, we report state-of-the-art results in multiple novel view synthesis benchmarks, as well as multi-view stereo and video depth estimation.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Guizilini_Zero-Shot_Novel_View_and_Depth_Synthesis_with_Multi-View_Geometric_Diffusion_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Guizilini_Zero-Shot_Novel_View_CVPR_2025_supplemental.zip

bibtex:
@InProceedings{Guizilini_2025_CVPR,
    author    = {Guizilini, Vitor and Irshad, Muhammad Zubair and Chen, Dian and Shakhnarovich, Greg and Ambrus, Rares},
    title     = {Zero-Shot Novel View and Depth Synthesis with Multi-View Geometric Diffusion},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {764-776}
}

raw_affiliation:
[{"affiliation": "Toyota Research Institute (TRI)", "city": "Los Altos", "country": "USA"}, {"affiliation": "Toyota Technological Institute at Chicago (TTIC)", "city": "Chicago", "country": "USA"}]


title: Unveiling Visual Perception in Language Models: An Attention Head Analysis Approach
authors: Jing Bi, Junjia Guo, Yunlong Tang, Lianggong Bruce Wen, Zhang Liu, Bingjie Wang, Chenliang Xu
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 4135-4144
affiliations: ["University of Rochester", "Corning Inc."]
cities: ["Rochester", "Corning"]
countries: ["USA", "USA"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Bi_Unveiling_Visual_Perception_in_Language_Models_An_Attention_Head_Analysis_CVPR_2025_paper.html

abstract:
Recent advancements in Multimodal Large Language Models (MLLMs) have demonstrated remarkable progress in visual understanding. This impressive leap raises a compelling question: how can language models, initially trained solely on linguistic data, effectively interpret and process visual content? This paper aims to address this question with systematic investigation across 4 model families and 4 model scales, uncovering a unique class of attention heads that focus specifically on visual content. Our analysis reveals a strong correlation between the behavior of these attention heads, the distribution of attention weights, and their concentration on visual tokens within the input. These findings enhance our understanding of how LLMs adapt to multimodal tasks, demonstrating their potential to bridge the gap between textual and visual understanding. This work paves the way for the development of AI systems capable of engaging with diverse modalities.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Bi_Unveiling_Visual_Perception_in_Language_Models_An_Attention_Head_Analysis_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Bi_Unveiling_Visual_Perception_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Bi_2025_CVPR,
    author    = {Bi, Jing and Guo, Junjia and Tang, Yunlong and Wen, Lianggong Bruce and Liu, Zhang and Wang, Bingjie and Xu, Chenliang},
    title     = {Unveiling Visual Perception in Language Models: An Attention Head Analysis Approach},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {4135-4144}
}

raw_affiliation:
[{"affiliation": "University of Rochester", "city": "Rochester", "country": "USA"}, {"affiliation": "Corning Inc.", "city": "Corning", "country": "USA"}]


title: SemanticDraw: Towards Real-Time Interactive Content Creation from Image Diffusion Models
authors: Jaerin Lee, Daniel Sungho Jung, Kanggeon Lee, Kyoung Mu Lee
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 13021-13030
cities: ["Seoul", "Seoul", "Seoul"]
countries: ["Korea", "Korea", "Korea"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Lee_SemanticDraw_Towards_Real-Time_Interactive_Content_Creation_from_Image_Diffusion_Models_CVPR_2025_paper.html

abstract:
We introduce SemanticDraw, a new paradigm of interactive content creation where high-quality images are generated in near real-time from given multiple hand-drawn regions, each encoding prescribed semantic meaning. In order to maximize the productivity of content creators and to fully realize their artistic imagination, it requires both quick interactive interfaces and fine-grained regional controls in their tools. Despite astonishing generation quality from recent diffusion models, we find that existing approaches for regional controllability are very slow (52 seconds for 512 x 512 image) while not compatible with acceleration methods such as LCM, blocking their huge potential in interactive content creation. From this observation, we build our solution for interactive content creation in two steps: (1) we establish compatibility between region-based controls and acceleration techniques for diffusion models, maintaining high fidelity of multi-prompt image generation with x 10 reduced number of inference steps, (2) we increase the generation throughput with our new multi-prompt stream batch pipeline, enabling low-latency generation from multiple, region-based text prompts on a single RTX 2080 Ti GPU. Our proposed framework is generalizable to any existing diffusion models and acceleration schedulers, allowing sub-second (0.64 seconds) image content creation application upon well-established image diffusion models.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Lee_SemanticDraw_Towards_Real-Time_Interactive_Content_Creation_from_Image_Diffusion_Models_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Lee_SemanticDraw_Towards_Real-Time_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Lee_2025_CVPR,
    author    = {Lee, Jaerin and Jung, Daniel Sungho and Lee, Kanggeon and Lee, Kyoung Mu},
    title     = {SemanticDraw: Towards Real-Time Interactive Content Creation from Image Diffusion Models},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {13021-13030}
}

raw_affiliation:
[{"affiliation": "ASRI, Department of ECE, Seoul National University", "city": "Seoul", "country": "Korea"}, {"affiliation": "Interdisciplinary Program in Artificial Intelligence, Seoul National University", "city": "Seoul", "country": "Korea"}, {"affiliation": "SNU-LG AI Research Center, Seoul National University", "city": "Seoul", "country": "Korea"}]

affiliations:
["ASRI, Department of ECE, Seoul National University", "Interdisciplinary Program in Artificial Intelligence, Seoul National University", "SNU-LG AI Research Center, Seoul National University"]


authors: Hritam Basak, Zhaozheng Yin
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 9816-9828
affiliations: ["Dept. of Computer Science, Stony Brook University"]
cities: ["Stony Brook"]
countries: ["USA"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Basak_SemiDAViL_Semi-supervised_Domain_Adaptation_with_Vision-Language_Guidance_for_Semantic_Segmentation_CVPR_2025_paper.html

title:
SemiDAViL: Semi-supervised Domain Adaptation with Vision-Language Guidance for Semantic Segmentation

abstract:
Domain Adaptation (DA) and Semi-supervised Learning (SSL) converge in Semi-supervised Domain Adaptation (SSDA), where the objective is to transfer knowledge from a source domain to a target domain using a combination of limited labeled target samples and abundant unlabeled target data. Although intuitive, a simple amalgamation of DA and SSL is suboptimal in semantic segmentation due to two major reasons: (1) previous methods, while able to learn good segmentation boundaries, are prone to confuse classes with similar visual appearance due to limited supervision; and (2) skewed and imbalanced training data distribution preferring source representation learning whereas impeding from exploring limited information about tailed classes. Language guidance can serve as a pivotal semantic bridge, facilitating robust class discrimination and mitigating visual ambiguities by leveraging the rich semantic relationships encoded in pre-trained language models to enhance feature representations across domains. Therefore, we propose the first language-guided SSDA setting for semantic segmentation in this work. Specifically, we harness the semantic generalization capabilities inherent in vision-language models (VLMs) to establish a synergistic framework within the SSDA paradigm. To address the inherent class-imbalance challenges in long-tailed distributions, we introduce class-balanced segmentation loss formulations that effectively regularize the learning process. Through extensive experimentation across diverse domain adaptation scenarios, our approach demonstrates substantial performance improvements over contemporary state-of-the-art (SoTA) methodologies.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Basak_SemiDAViL_Semi-supervised_Domain_Adaptation_with_Vision-Language_Guidance_for_Semantic_Segmentation_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Basak_SemiDAViL_Semi-supervised_Domain_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Basak_2025_CVPR,
    author    = {Basak, Hritam and Yin, Zhaozheng},
    title     = {SemiDAViL: Semi-supervised Domain Adaptation with Vision-Language Guidance for Semantic Segmentation},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {9816-9828}
}

raw_affiliation:
[{"affiliation": "Dept. of Computer Science, Stony Brook University", "city": "Stony Brook", "country": "USA"}]


title: Learning Partonomic 3D Reconstruction from Image Collections
authors: Xiaoqian Ruan, Pei Yu, Dian Jia, Hyeonjeong Park, Peixi Xiong, Wei Tang
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 26734-26744
affiliations: ["University of Illinois Chicago", "Microsoft", "Intel"]
cities: ["Chicago", "Redmond", "Santa Clara"]
countries: ["USA", "USA", "USA"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Ruan_Learning_Partonomic_3D_Reconstruction_from_Image_Collections_CVPR_2025_paper.html

abstract:
Reconstructing the 3D shape of an object from a single-view image is a fundamental task in computer vision. Recent advances in differentiable rendering have enabled 3D reconstruction from image collections using only 2D annotations. However, these methods mainly focus on whole-object reconstruction and overlook object partonomy, which is essential for intelligent agents interacting with physical environments. This paper aims at learning partonomic 3D reconstruction from collections of images with only 2D annotations. Our goal is not only to reconstruct the shape of an object from a single-view image but also to decompose the shape into meaningful semantic parts. To handle the expanded solution space and frequent part occlusions in single-view images, we introduce a novel approach that represents, parses, and learns the structural compositionality of 3D objects. This approach comprises: (1) a compact and expressive compositional representation of object geometry, achieved through disentangled modeling of large shape variations, constituent parts, and detailed part deformations as multi-granularity neural fields; (2) a part transformer that recovers precise partonomic geometry and handles occlusions, through effective part-to-pixel grounding and part-to-part relational modeling; and (3) a 2D-supervised learning method that jointly learns the compositional representation and part transformer, by bridging object shape and parts, image synthesis, and differentiable rendering. Extensive experiments on ShapeNetPart, PartNet, and CUB-200-2011 demonstrate the effectiveness of our approach on both overall and partonomic reconstruction. Code, models, and data are avaliable at https://github.com/XiaoqianRuan1/Partonomic_Reconstruction.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Ruan_Learning_Partonomic_3D_Reconstruction_from_Image_Collections_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Ruan_Learning_Partonomic_3D_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Ruan_2025_CVPR,
    author    = {Ruan, Xiaoqian and Yu, Pei and Jia, Dian and Park, Hyeonjeong and Xiong, Peixi and Tang, Wei},
    title     = {Learning Partonomic 3D Reconstruction from Image Collections},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {26734-26744}
}

raw_affiliation:
[{"affiliation": "University of Illinois Chicago", "city": "Chicago", "country": "USA"}, {"affiliation": "Microsoft", "city": "Redmond", "country": "USA"}, {"affiliation": "Intel", "city": "Santa Clara", "country": "USA"}]


title: Arc2Avatar: Generating Expressive 3D Avatars from a Single Image via ID Guidance
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 10770-10782
raw_affiliation: [{"affiliation": "Imperial College London", "city": "London", "country": "UK"}]
affiliations: ["Imperial College London"]
cities: ["London"]
countries: ["UK"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Gerogiannis_Arc2Avatar_Generating_Expressive_3D_Avatars_from_a_Single_Image_via_CVPR_2025_paper.html

authors:
Dimitrios Gerogiannis, Foivos Paraperas Papantoniou, Rolandos Alexandros Potamias, Alexandros Lattas, Stefanos Zafeiriou

abstract:
Inspired by the effectiveness of 3D Gaussian Splatting (3DGS) in reconstructing detailed 3D scenes within multi-view setups and the emergence of large 2D human foundation models, we introduce Arc2Avatar, the first SDS-based method utilizing a human face foundation model as guidance with just a single image as input. To achieve that, we extend such a model for diverse-view human head generation by fine-tuning on synthetic data and modifying its conditioning. Our avatars maintain a dense correspondence with a human face mesh template, allowing blendshape-based expression generation. This is achieved through a modified 3DGS approach, connectivity regularizers, and a strategic initialization tailored for our task. Additionally, we propose an optional efficient SDS-based correction step to refine the blendshape expressions, enhancing realism and diversity. Experiments demonstrate that Arc2Avatar achieves state-of-the-art realism and identity preservation, effectively addressing color issues by allowing the use of very low guidance, enabled by our strong identity prior and initialization strategy, without compromising detail.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Gerogiannis_Arc2Avatar_Generating_Expressive_3D_Avatars_from_a_Single_Image_via_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Gerogiannis_Arc2Avatar_Generating_Expressive_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Gerogiannis_2025_CVPR,
    author    = {Gerogiannis, Dimitrios and Papantoniou, Foivos Paraperas and Potamias, Rolandos Alexandros and Lattas, Alexandros and Zafeiriou, Stefanos},
    title     = {Arc2Avatar: Generating Expressive 3D Avatars from a Single Image via ID Guidance},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {10770-10782}
}


title: Seeing Speech and Sound: Distinguishing and Locating Audio Sources in Visual Scenes
authors: Hyeonggon Ryu, Seongyu Kim, Joon Son Chung, Arda Senocak
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 13540-13549
affiliations: ["Korea Advanced Institute of Science and Technology"]
cities: ["Daejeon"]
countries: ["South Korea"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Ryu_Seeing_Speech_and_Sound_Distinguishing_and_Locating_Audio_Sources_in_CVPR_2025_paper.html

abstract:
We present a unified model capable of simultaneously grounding both spoken language and non-speech sounds within a visual scene, addressing key limitations in current audio-visual grounding models. Existing approaches are typically limited to handling either speech or non-speech sounds independently, or at best, together but sequentially without mixing. This limitation prevents them from capturing the complexity of real-world audio sources that are often mixed. Our approach introduces a "mix-and-separate" framework with audio-visual alignment objectives that jointly learn correspondence and disentanglement using mixed audio. Through these objectives, our model learns to produce distinct embeddings for each audio type, enabling effective disentanglement and grounding across mixed audio sources.Additionally, we created a new dataset to evaluate simultaneous grounding of mixed audio sources, demonstrating that our model outperforms prior methods. Our approach also achieves state-of-the-art performance in standard segmentation and cross-modal retrieval tasks, highlighting the benefits of our mix-and-separate approach.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Ryu_Seeing_Speech_and_Sound_Distinguishing_and_Locating_Audio_Sources_in_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Ryu_Seeing_Speech_and_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Ryu_2025_CVPR,
    author    = {Ryu, Hyeonggon and Kim, Seongyu and Chung, Joon Son and Senocak, Arda},
    title     = {Seeing Speech and Sound: Distinguishing and Locating Audio Sources in Visual Scenes},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {13540-13549}
}

raw_affiliation:
[{"affiliation": "Korea Advanced Institute of Science and Technology", "city": "Daejeon", "country": "South Korea"}]


title: Structure from Collision
authors: Takuhiro Kaneko
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 16314-16324
raw_affiliation: [{"affiliation": "NTT Corporation", "city": "Tokyo", "country": "Japan"}]
affiliations: ["NTT Corporation"]
cities: ["Tokyo"]
countries: ["Japan"]
highlight: yes
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Kaneko_Structure_from_Collision_CVPR_2025_paper.html

abstract:
Recent advancements in neural 3D representations, such as neural radiance fields (NeRF) and 3D Gaussian splatting (3DGS), have made accurate estimation of the 3D structure from multiview images possible. However, this capability is limited to estimating the visible external structure, and it is still difficult to identify the invisible internal structure hidden behind the surface. To overcome this limitation, we address a new task called structure from collision (SfC), which aims to estimate the structure (including the invisible internal one) of an object from the appearance changes at collision. To solve this task, we propose a novel model called SfC-NeRF, which optimizes the invisible internal structure of the object through a video sequence under physical, appearance (i.e., visible external structure)-preserving, and keyframe constraints. In particular, to avoid falling into undesirable local optima owing to its ill-posed nature, we propose volume annealing, i.e., searching for the global optima by repeatedly reducing and expanding the volume. Extensive experiments on 115 objects involving diverse structures (i.e., various cavity shapes, locations, and sizes) and various material properties reveal the properties of SfC and demonstrate the effectiveness of the proposed SfC-NeRF.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Kaneko_Structure_from_Collision_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Kaneko_Structure_from_Collision_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Kaneko_2025_CVPR,
    author    = {Kaneko, Takuhiro},
    title     = {Structure from Collision},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {16314-16324}
}


authors: Tong Wang, Mingkang Wang, Zhongze Wang, Hongkai Wang, Qi Xu, Fengyu Cong, Hongming Xu
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 25920-25929
affiliations: ["Dalian University of Technology", "East China University of Science and Technology"]
cities: ["Dalian", "Shanghai"]
countries: ["China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Wang_ODA-GAN_Orthogonal_Decoupling_Alignment_GAN_Assisted_by_Weakly-supervised_Learning_for_CVPR_2025_paper.html

title:
ODA-GAN: Orthogonal Decoupling Alignment GAN Assisted by Weakly-supervised Learning for Virtual Immunohistochemistry Staining

abstract:
Recently, virtual staining has emerged as a promising alternative to revolutionize histological staining by digitally generating stains. However, most existing methods suffer from the curse of staining unreality and unreliability. In this paper, we propose the Orthogonal Decoupling Alignment Generative Adversarial Network (ODA-GAN) for unpaired virtual immunohistochemistry (IHC) staining. Our approach is based on the assumption that an image consists of IHC staining-related features, which influence staining distribution and intensity, and staining-unrelated features, such as tissue morphology. Leveraging a pathology foundation model, we first develop a weakly-supervised segmentation pipeline as an alternative to expert annotations. We introduce an Orthogonal MLP (O-MLP) module to project image features into an orthogonal space, decoupling them into staining-related and unrelated components. Additionally, we propose a Dual-stream PatchNCE (DPNCE) loss to resolve contrastive learning contradictions in the staining-related space, thereby enhancing staining accuracy. To further improve realism, we introduce a Multi-layer Domain Alignment (MDA) module to bridge the domain gap between generated and real IHC images. Evaluations on three benchmark datasets show that our ODA-GAN reaches state-of-the-art (SOTA) performance. Our source code is available at https://github.com/ittong/ODA-GAN.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Wang_ODA-GAN_Orthogonal_Decoupling_Alignment_GAN_Assisted_by_Weakly-supervised_Learning_for_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Wang_ODA-GAN_Orthogonal_Decoupling_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Wang_2025_CVPR,
    author    = {Wang, Tong and Wang, Mingkang and Wang, Zhongze and Wang, Hongkai and Xu, Qi and Cong, Fengyu and Xu, Hongming},
    title     = {ODA-GAN: Orthogonal Decoupling Alignment GAN Assisted by Weakly-supervised Learning for Virtual Immunohistochemistry Staining},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {25920-25929}
}

raw_affiliation:
[{"affiliation": "Dalian University of Technology", "city": "Dalian", "country": "China"}, {"affiliation": "East China University of Science and Technology", "city": "Shanghai", "country": "China"}]


title: EVOS: Efficient Implicit Neural Training via EVOlutionary Selector
authors: Weixiang Zhang, Shuzhao Xie, Chengwei Ren, Siyi Xie, Chen Tang, Shijia Ge, Mingzi Wang, Zhi Wang
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 30472-30482
cities: ["Shenzhen", "Xi'an", "Hong Kong"]
countries: ["China", "China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Zhang_EVOS_Efficient_Implicit_Neural_Training_via_EVOlutionary_Selector_CVPR_2025_paper.html

abstract:
We propose EVOlutionary Selector (EVOS), an efficient training paradigm for accelerating Implicit Neural Representation (INR). Unlike conventional INR training that feeds all samples through the neural network in each iteration, our approach restricts training to strategically selected points, reducing computational overhead by eliminating redundant forward passes.Specifically, we treat each sample as an individual in an evolutionary process, where only those fittest ones survive and merit inclusion in training, adaptively evolving with the neural network dynamics. While this is conceptually similar to Evolutionary Algorithms, their distinct objectives (selection for acceleration vs. iterative solution optimization) require a fundamental redefinition of evolutionary mechanisms for our context.In response, we design sparse fitness evaluation, frequency-guided crossover, and augmented unbiased mutation to comprise EVOS. These components respectively guide sample selection with reduced computational cost, enhance performance through frequency-domain balance, and mitigate selection bias from cached evaluation. Extensive experiments demonstrate that our method achieves approximately 48%-66% reduction in training time while ensuring superior convergence without additional cost, establishing state-of-the-art acceleration among recent sampling-based strategies.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Zhang_EVOS_Efficient_Implicit_Neural_Training_via_EVOlutionary_Selector_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Zhang_EVOS_Efficient_Implicit_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Zhang_2025_CVPR,
    author    = {Zhang, Weixiang and Xie, Shuzhao and Ren, Chengwei and Xie, Siyi and Tang, Chen and Ge, Shijia and Wang, Mingzi and Wang, Zhi},
    title     = {EVOS: Efficient Implicit Neural Training via EVOlutionary Selector},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {30472-30482}
}

raw_affiliation:
[{"affiliation": "Tsinghua Shenzhen International Graduate School, Tsinghua University", "city": "Shenzhen", "country": "China"}, {"affiliation": "Xi\u2019an Jiaotong University", "city": "Xi'an", "country": "China"}, {"affiliation": "The Chinese University of Hong Kong", "city": "Hong Kong", "country": "China"}]

affiliations:
["Tsinghua Shenzhen International Graduate School, Tsinghua University", "Xi\u2019an Jiaotong University", "The Chinese University of Hong Kong"]


title: Crab: A Unified Audio-Visual Scene Understanding Model with Explicit Cooperation
authors: Henghui Du, Guangyao Li, Chang Zhou, Chunjie Zhang, Alan Zhao, Di Hu
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 18804-18814
cities: ["Beijing", "Beijing", "Beijing"]
countries: ["China", "China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Du_Crab_A_Unified_Audio-Visual_Scene_Understanding_Model_with_Explicit_Cooperation_CVPR_2025_paper.html

abstract:
In recent years, numerous tasks have been proposed to encourage model to develop specified capability in understanding audio-visual scene, primarily categorized into temporal localization, spatial localization, spatio-temporal reasoning, and pixel-level understanding. Instead, human possesses a unified understanding ability for diversified tasks. Therefore, designing an audio-visual model with general capability to unify these tasks is of great value. However, simply joint training for all tasks can lead to interference due to the heterogeneity of audiovisual data and complex relationship among tasks. We argue that this problem can be solved through explicit cooperation among tasks. To achieve this goal, we propose a unified learning method which achieves explicit inter-task cooperation from both the perspectives of data and model thoroughly. Specifically, considering the labels of existing datasets are simple words, we carefully refine these datasets and construct an Audio-Visual Unified Instruction-tuning dataset with Explicit reasoning process (AV-UIE), which clarifies the cooperative relationship among tasks. Subsequently, to facilitate concrete cooperation in learning stage, an interaction-aware LoRA structure with multiple LoRA heads is designed to learn different aspects of audiovisual data interaction. By unifying the explicit cooperation across the data and model aspect, our method not only surpasses existing unified audio-visual model on multiple tasks, but also outperforms most specialized models for certain tasks. Furthermore, we also visualize the process of explicit cooperation and surprisingly find that each LoRA head has certain audio-visual understanding ability. Code and dataset: https://github.com/GeWu-Lab/Crab

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Du_Crab_A_Unified_Audio-Visual_Scene_Understanding_Model_with_Explicit_Cooperation_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Du_Crab_A_Unified_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Du_2025_CVPR,
    author    = {Du, Henghui and Li, Guangyao and Zhou, Chang and Zhang, Chunjie and Zhao, Alan and Hu, Di},
    title     = {Crab: A Unified Audio-Visual Scene Understanding Model with Explicit Cooperation},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {18804-18814}
}

raw_affiliation:
[{"affiliation": "Gaoling School of Artificial Intelligence, Renmin University of China", "city": "Beijing", "country": "China"}, {"affiliation": "Department of Computer Science and Technology, Tsinghua University", "city": "Beijing", "country": "China"}, {"affiliation": "AI Technology Center, Online Video Business Unit, Tencent PCG", "city": "Beijing", "country": "China"}]

affiliations:
["Gaoling School of Artificial Intelligence, Renmin University of China", "Department of Computer Science and Technology, Tsinghua University", "AI Technology Center, Online Video Business Unit, Tencent PCG"]


title: Nullu: Mitigating Object Hallucinations in Large Vision-Language Models via HalluSpace Projection
authors: Le Yang, Ziwei Zheng, Boxu Chen, Zhengyu Zhao, Chenhao Lin, Chao Shen
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 14635-14645
raw_affiliation: [{"affiliation": "Xi\u2019an Jiaotong University", "city": "Xi'an", "country": "China"}]
affiliations: ["Xi\u2019an Jiaotong University"]
cities: ["Xi'an"]
countries: ["China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Yang_Nullu_Mitigating_Object_Hallucinations_in_Large_Vision-Language_Models_via_HalluSpace_CVPR_2025_paper.html

abstract:
Recent studies have shown that large vision-language models (LVLMs) often suffer from the issue of object hallucinations (OH). To mitigate this issue, we introduce an efficient method that edits the model weights based on an unsafe subspace, which we call HalluSpace in this paper. With truthful and hallucinated text prompts accompanying the visual content as inputs, the HalluSpace can be identified by extracting the hallucinated embedding features and removing the truthful representations in LVLMs. By orthogonalizing the model weights, input features will be projected into the Null space of the HalluSpace to reduce OH, based on which we name our method Nullu. We reveal that HalluSpaces generally contain prior information in the large language models (LLMs) applied to build LVLMs, which have been shown as essential causes of OH in previous studies. Therefore, null space projection suppresses the LLMs' priors to filter out the hallucinated features, resulting in contextually accurate outputs. Experiments show that our method can effectively mitigate OH across different LVLM families without extra inference costs and also show strong performance in general LVLM benchmarks. Code is released at https://github.com/Ziwei-Zheng/Nullu.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Yang_Nullu_Mitigating_Object_Hallucinations_in_Large_Vision-Language_Models_via_HalluSpace_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Yang_Nullu_Mitigating_Object_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Yang_2025_CVPR,
    author    = {Yang, Le and Zheng, Ziwei and Chen, Boxu and Zhao, Zhengyu and Lin, Chenhao and Shen, Chao},
    title     = {Nullu: Mitigating Object Hallucinations in Large Vision-Language Models via HalluSpace Projection},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {14635-14645}
}


authors: Bingzhi Chen, Sisi Fu, Xiaocheng Fang, Jieyi Cai, Boya Zhang, Minhua Lu, Yishu Liu
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 15570-15579
cities: ["Zhuhai", "Beijing", "Tianjin", "Shenzhen", "Shenzhen"]
countries: ["China", "China", "China", "China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Chen_OralXrays-9_Towards_Hospital-Scale_Panoramic_X-ray_Anomaly_Detection_via_Personalized_Multi-Object_CVPR_2025_paper.html

title:
OralXrays-9: Towards Hospital-Scale Panoramic X-ray Anomaly Detection via Personalized Multi-Object Query-Aware Mining

abstract:
In clinical practice, panoramic dental radiography is a widely employed imaging technique that can provide a detailed and comprehensive view of dental structures and surrounding tissues for identifying various oral anomalies. However, due to the complexity of oral anomalies and the scarcity of available data, existing research still suffers from substantial challenges in automated oral anomaly detection. To this end, this paper presents a new hospital-scale panoramic X-ray benchmark, namely "OralXrays-9", which consists of 12,688 panoramic X-ray images with 84,113 meticulously annotated instances across nine common oral anomalies. Correspondingly, we propose a personalized Multi-Object Query-Aware Mining (MOQAM) paradigm, which jointly incorporates the Distribution-IoU Region Proposal Network (DI-RPN) and Class-Balanced Spherical Contrastive Regularization (CB-SCR) mechanisms to address the challenges posed by multi-scale variations and class-imbalanced distributions. To the best of our knowledge, this is the first attempt to develop AI-driven diagnostic systems specifically designed for multi-object oral anomaly detection, utilizing publicly available data resources. Extensive experiments on the newly-published OralXrays-9 dataset and real-world nature scenarios consistently demonstrate the superiority of our MOQAM in revolutionizing oral healthcare practices.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Chen_OralXrays-9_Towards_Hospital-Scale_Panoramic_X-ray_Anomaly_Detection_via_Personalized_Multi-Object_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Chen_OralXrays-9_Towards_Hospital-Scale_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Chen_2025_CVPR,
    author    = {Chen, Bingzhi and Fu, Sisi and Fang, Xiaocheng and Cai, Jieyi and Zhang, Boya and Lu, Minhua and Liu, Yishu},
    title     = {OralXrays-9: Towards Hospital-Scale Panoramic X-ray Anomaly Detection via Personalized Multi-Object Query-Aware Mining},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {15570-15579}
}

raw_affiliation:
[{"affiliation": "Beijing Institute of Technology, Zhuhai", "city": "Zhuhai", "country": "China"}, {"affiliation": "Chinese People\u2019s Liberation Army General Hospital", "city": "Beijing", "country": "China"}, {"affiliation": "Nankai University", "city": "Tianjin", "country": "China"}, {"affiliation": "Shenzhen University", "city": "Shenzhen", "country": "China"}, {"affiliation": "Harbin Institute of Technology, Shenzhen", "city": "Shenzhen", "country": "China"}]

affiliations:
["Beijing Institute of Technology, Zhuhai", "Chinese People\u2019s Liberation Army General Hospital", "Nankai University", "Shenzhen University", "Harbin Institute of Technology, Shenzhen"]


title: MEET: Towards Memory-Efficient Temporal Sparse Deep Neural Networks
authors: Zeqi Zhu, Ibrahim Batuhan Akkaya, Luc Waeijen, Egor Bondarev, Arash Pourtaherian, Orlando Moreira
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 29309-29320
affiliations: ["Snap Inc.", "Eindhoven University of Technology"]
cities: ["Los Angeles", "Eindhoven"]
countries: ["USA", "Netherlands"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Zhu_MEET_Towards_Memory-Efficient_Temporal_Sparse_Deep_Neural_Networks_CVPR_2025_paper.html

abstract:
Deep Neural Networks (DNNs) are accurate but compute-intensive, leading to substantial energy consumption during inference. Exploiting temporal redundancy through \Delta-\Sigma convolution in video processing has proven to greatly enhance computation efficiency. However, temporal \Delta-\Sigma DNNs typically require substantial memory for storing neuron states to compute inter-frame differences, hindering their on-chip deployment. To mitigate this memory cost, directly compressing the states can disrupt the linearity of temporal \Delta-\Sigma convolution, causing accumulated errors in long-term \Delta-\Sigma processing. Thus, we propose MEET, an optimization framework for MEmory-Efficient Temporal \Delta-\Sigma DNNs. MEET transfers the state compression challenge to a well-established weight compression problem by trading fewer activations for more weights and introduces a co-design of network architecture and suppression method to optimize for mixed spatial-temporal execution. Evaluations on three vision applications demonstrate a reduction of 5.1~13.3 xin total memory compared to the most computation-efficient temporal DNNs, while preserving the computation efficiency and model accuracy in long-term \Delta-\Sigma processing. MEET facilitates the deployment of temporal \Delta-\Sigma DNNs within on-chip memory of embedded event-driven platforms, empowering low-power edge processing.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Zhu_MEET_Towards_Memory-Efficient_Temporal_Sparse_Deep_Neural_Networks_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Zhu_MEET_Towards_Memory-Efficient_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Zhu_2025_CVPR,
    author    = {Zhu, Zeqi and Akkaya, Ibrahim Batuhan and Waeijen, Luc and Bondarev, Egor and Pourtaherian, Arash and Moreira, Orlando},
    title     = {MEET: Towards Memory-Efficient Temporal Sparse Deep Neural Networks},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {29309-29320}
}

raw_affiliation:
[{"affiliation": "Snap Inc.", "city": "Los Angeles", "country": "USA"}, {"affiliation": "Eindhoven University of Technology", "city": "Eindhoven", "country": "Netherlands"}]


title: SplatAD: Real-Time Lidar and Camera Rendering with 3D Gaussian Splatting for Autonomous Driving
authors: Georg Hess, Carl Lindström, Maryam Fatemi, Christoffer Petersson, Lennart Svensson
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 11982-11992
affiliations: ["Zenseact", "Chalmers University of Technology"]
cities: ["Gothenburg", "Gothenburg"]
countries: ["Sweden", "Sweden"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Hess_SplatAD_Real-Time_Lidar_and_Camera_Rendering_with_3D_Gaussian_Splatting_CVPR_2025_paper.html

abstract:
Ensuring the safety of autonomous robots, such as self-driving vehicles, requires extensive testing across diverse driving scenarios. Simulation is a key ingredient for conducting such testing in a cost-effective and scalable way. Neural rendering methods have gained popularity, as they can build simulation environments from collected logs in a data-driven manner. However, existing neural radiance field (NeRF) methods for sensor-realistic rendering of camera and lidar data suffer from low rendering speeds, limiting their applicability for large-scale testing. While 3D Gaussian Splatting (3DGS) enables real-time rendering, current methods are limited to camera data and are unable to render lidar data essential for autonomous driving. To address these limitations, we propose SplatAD, the first 3DGS-based method for realistic, real-time rendering of dynamic scenes for both camera and lidar data. SplatAD accurately models key sensor-specific phenomena such as rolling shutter effects, lidar intensity, and lidar ray dropouts, using purpose-built algorithms to optimize rendering efficiency. Evaluation across three autonomous driving datasets demonstrates that SplatAD achieves state-of-the-art rendering quality with up to +2 PSNR for NVS and +3 PSNR for reconstruction while increasing rendering speed over NeRF-based methods by an order of magnitude. Code to be released upon publication.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Hess_SplatAD_Real-Time_Lidar_and_Camera_Rendering_with_3D_Gaussian_Splatting_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Hess_SplatAD_Real-Time_Lidar_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Hess_2025_CVPR,
    author    = {Hess, Georg and Lindstr\"om, Carl and Fatemi, Maryam and Petersson, Christoffer and Svensson, Lennart},
    title     = {SplatAD: Real-Time Lidar and Camera Rendering with 3D Gaussian Splatting for Autonomous Driving},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {11982-11992}
}

raw_affiliation:
[{"affiliation": "Zenseact", "city": "Gothenburg", "country": "Sweden"}, {"affiliation": "Chalmers University of Technology", "city": "Gothenburg", "country": "Sweden"}]


title: Audio-Visual Instance Segmentation
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 13550-13560
cities: ["Beijing", "Guildford", "Berkeley", "Beijing", "Pittsburgh", "Beijing", "Abu Dhabi", "Wuhan"]
countries: ["China", "UK", "USA", "China", "USA", "China", "UAE", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Guo_Audio-Visual_Instance_Segmentation_CVPR_2025_paper.html

authors:
Ruohao Guo, Xianghua Ying, Yaru Chen, Dantong Niu, Guangyao Li, Liao Qu, Yanyu Qi, Jinxing Zhou, Bowei Xing, Wenzhen Yue, Ji Shi, Qixun Wang, Peiliang Zhang, Buwen Liang

abstract:
In this paper, we propose a new multi-modal task, termed audio-visual instance segmentation (AVIS), which aims to simultaneously identify, segment and track individual sounding object instances in audible videos. To facilitate this research, we introduce a high-quality benchmark named AVISeg, containing over 90K instance masks from 26 semantic categories in 926 long videos. Additionally, we propose a strong baseline model for this task. Our model first localizes sound source within each frame, and condenses object-specific contexts into concise tokens. Then it builds long-range audio-visual dependencies between these tokens using window-based attention, and tracks sounding objects among the entire video sequences. Extensive experiments reveal that our method performs best on AVISeg, surpassing the existing methods from related tasks. We further conduct the evaluation on several multi-modal large models. Unfortunately, they exhibits subpar performance on instance-level sound source localization and temporal perception. We expect that AVIS will inspire the community towards a more comprehensive multi-modal understanding. Dataset and code is available at https://github.com/ruohaoguo/avis.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Guo_Audio-Visual_Instance_Segmentation_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Guo_Audio-Visual_Instance_Segmentation_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Guo_2025_CVPR,
    author    = {Guo, Ruohao and Ying, Xianghua and Chen, Yaru and Niu, Dantong and Li, Guangyao and Qu, Liao and Qi, Yanyu and Zhou, Jinxing and Xing, Bowei and Yue, Wenzhen and Shi, Ji and Wang, Qixun and Zhang, Peiliang and Liang, Buwen},
    title     = {Audio-Visual Instance Segmentation},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {13550-13560}
}

raw_affiliation:
[{"affiliation": "State Key Laboratory of General Artificial Intelligence, School of Intelligence Science and Technology, Peking University", "city": "Beijing", "country": "China"}, {"affiliation": "University of Surrey", "city": "Guildford", "country": "UK"}, {"affiliation": "UC Berkeley", "city": "Berkeley", "country": "USA"}, {"affiliation": "Tsinghua University", "city": "Beijing", "country": "China"}, {"affiliation": "CMU", "city": "Pittsburgh", "country": "USA"}, {"affiliation": "China Agricultural University", "city": "Beijing", "country": "China"}, {"affiliation": "MBZUAI", "city": "Abu Dhabi", "country": "UAE"}, {"affiliation": "Wuhan University of Technology", "city": "Wuhan", "country": "China"}]

affiliations:
["State Key Laboratory of General Artificial Intelligence, School of Intelligence Science and Technology, Peking University", "University of Surrey", "UC Berkeley", "Tsinghua University", "CMU", "China Agricultural University", "MBZUAI", "Wuhan University of Technology"]


title: Probabilistic Prompt Distribution Learning for Animal Pose Estimation
authors: Jiyong Rao, Brian Nlong Zhao, Yu Wang
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 29438-29447
affiliations: ["School of Computer Science and T echnology , T ongji University", "Stanford University"]
cities: ["Shanghai", "Stanford"]
countries: ["China", "USA"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Rao_Probabilistic_Prompt_Distribution_Learning_for_Animal_Pose_Estimation_CVPR_2025_paper.html

abstract:
Multi-species animal pose estimation has emerged as a challenging yet critical task, hindered by substantial visual diversity and uncertainty. This paper challenges the problem by efficient prompt learning for Vision-Language Pretrained (VLP) models, e.g. CLIP, aiming to resolve the cross-species generalization problem. At the core of the solution lies in the prompt designing, probabilistic prompt modeling and cross-modal adaptation, thereby enabling prompts to compensate for cross-modal information and effectively overcome large data variances under unbalanced data distribution. To this end, we propose a novel probabilistic prompting approach to fully explore textual descriptions, which could alleviate the diversity issues caused by long-tail property and increase the adaptability of prompts on unseen category instance. Specifically, we first introduce a set of learnable prompts and propose a diversity loss to maintain distinctiveness among prompts, thus representing diverse image attributes. Diverse textual probabilistic representations are sampled and used as the guidance for the pose estimation. Subsequently, we explore three different cross-modal fusion strategies at spatial level to alleviate the adverse impacts of visual uncertainty. Extensive experiments on multi-species animal pose benchmarks show that our method achieves the state-of-the-art performance under both supervised and zero-shot settings.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Rao_Probabilistic_Prompt_Distribution_Learning_for_Animal_Pose_Estimation_CVPR_2025_paper.pdf

bibtex:
@InProceedings{Rao_2025_CVPR,
    author    = {Rao, Jiyong and Zhao, Brian Nlong and Wang, Yu},
    title     = {Probabilistic Prompt Distribution Learning for Animal Pose Estimation},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {29438-29447}
}

raw_affiliation:
[{"affiliation": "School of Computer Science and T echnology , T ongji University", "city": "Shanghai", "country": "China"}, {"affiliation": "Stanford University", "city": "Stanford", "country": "USA"}]


title: dFLMoE: Decentralized Federated Learning via Mixture of Experts for Medical Data Analysis
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 10203-10213
cities: ["Beijing", "Beijing", "Beijing", "Buffalo"]
countries: ["China", "China", "China", "USA"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Xie_dFLMoE_Decentralized_Federated_Learning_via_Mixture_of_Experts_for_Medical_CVPR_2025_paper.html

authors:
Luyuan Xie, Tianyu Luan, Wenyuan Cai, Guochen Yan, Zhaoyu Chen, Nan Xi, Yuejian Fang, Qingni Shen, Zhonghai Wu, Junsong Yuan

abstract:
Federated learning has wide applications in the medical field. It enables knowledge sharing among different healthcare institutes while protecting patients' privacy. However, existing federated learning systems are typically centralized, requiring clients to upload client-specific knowledge to a central server for aggregation. This centralized approach would integrate the knowledge from each client into a centralized server, and the knowledge would be already undermined during the centralized integration before it reaches back to each client. Besides, the centralized approach also creates a dependency on the central server, which may affect training stability if the server malfunctions or connections are unstable. To address these issues, we propose a decentralized federated learning framework named dFLMoE. In our framework, clients directly exchange lightweight head models with each other. After exchanging, each client treats both local and received head models as individual experts, and utilizes a client-specific Mixture of Experts (MoE) approach to make collective decisions. This design not only reduces the knowledge damage with client-specific aggregations but also removes the dependency on the central server to enhance the robustness of the framework. We validate our framework on multiple medical tasks, demonstrating that our method evidently outperforms state-of-the-art approaches under both model homogeneity and heterogeneity settings.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Xie_dFLMoE_Decentralized_Federated_Learning_via_Mixture_of_Experts_for_Medical_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Xie_dFLMoE_Decentralized_Federated_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Xie_2025_CVPR,
    author    = {Xie, Luyuan and Luan, Tianyu and Cai, Wenyuan and Yan, Guochen and Chen, Zhaoyu and Xi, Nan and Fang, Yuejian and Shen, Qingni and Wu, Zhonghai and Yuan, Junsong},
    title     = {dFLMoE: Decentralized Federated Learning via Mixture of Experts for Medical Data Analysis},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {10203-10213}
}

raw_affiliation:
[{"affiliation": "School of Software and Microelectronics, Peking University", "city": "Beijing", "country": "China"}, {"affiliation": "PKU-OCTA Laboratory for Blockchain and Privacy Computing", "city": "Beijing", "country": "China"}, {"affiliation": "National Engineering Research Center for Software Engineering, Peking University", "city": "Beijing", "country": "China"}, {"affiliation": "State University of New York at Buffalo", "city": "Buffalo", "country": "USA"}]

affiliations:
["School of Software and Microelectronics, Peking University", "PKU-OCTA Laboratory for Blockchain and Privacy Computing", "National Engineering Research Center for Software Engineering, Peking University", "State University of New York at Buffalo"]


title: Reconstructing Humans with a Biomechanically Accurate Skeleton
authors: Yan Xia, Xiaowei Zhou, Etienne Vouga, Qixing Huang, Georgios Pavlakos
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 5355-5365
affiliations: ["The University of Texas at Austin", "Zhejiang University"]
cities: ["Austin", "Hangzhou"]
countries: ["USA", "China"]
oral: yes
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Xia_Reconstructing_Humans_with_a_Biomechanically_Accurate_Skeleton_CVPR_2025_paper.html

abstract:
In this paper, we introduce a method for reconstructing 3D humans from a single image using a biomechanically accurate skeleton model. To achieve this, we train a transformer that takes an image as input and estimates the parameters of the model. Due to the lack of training data for this task, we build a pipeline to produce pseudo ground truth model parameters for single images and implement a training procedure that iteratively refines these pseudo labels. Compared to state-of-the-art methods for 3D human mesh recovery, our model achieves competitive performance on standard benchmarks, while it significantly outperforms them in settings with extreme 3D poses and viewpoints. Additionally, we show that previous reconstruction methods frequently violate joint angle limits, leading to unnatural rotations. In contrast, our approach leverages the biomechanically plausible degrees of freedom making more realistic joint rotation estimates. We validate our approach across multiple human pose estimation benchmarks. We make the code, models and data available at: https://isshikihugh.github.io/HSMR/

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Xia_Reconstructing_Humans_with_a_Biomechanically_Accurate_Skeleton_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Xia_Reconstructing_Humans_with_CVPR_2025_supplemental.zip

bibtex:
@InProceedings{Xia_2025_CVPR,
    author    = {Xia, Yan and Zhou, Xiaowei and Vouga, Etienne and Huang, Qixing and Pavlakos, Georgios},
    title     = {Reconstructing Humans with a Biomechanically Accurate Skeleton},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {5355-5365}
}

raw_affiliation:
[{"affiliation": "The University of Texas at Austin", "city": "Austin", "country": "USA"}, {"affiliation": "Zhejiang University", "city": "Hangzhou", "country": "China"}]


title: AdaCM^2: On Understanding Extremely Long-Term Video with Adaptive Cross-Modality Memory Reduction
authors: Yuanbin Man, Ying Huang, Chengming Zhang, Bingzhe Li, Wei Niu, Miao Yin
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 8534-8544
cities: ["Arlington", "Houston", "Dallas", "Athens"]
countries: ["USA", "USA", "USA", "USA"]
highlight: yes
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Man_AdaCM2_On_Understanding_Extremely_Long-Term_Video_with_Adaptive_Cross-Modality_Memory_CVPR_2025_paper.html

abstract:
The advancements in large language models (LLMs) have propelled the improvement of video understanding tasks by incorporating LLMs with visual models. However, most existing LLM-based models (e.g., VideoLLaMA, VideoChat) are constrained to processing short-duration videos. Recent attempts to understand long-term videos by extracting and compressing visual features into a fixed memory size. Nevertheless, those methods leverage only visual modality to merge video tokens and overlook the correlation between visual and textual queries, leading to difficulties in effectively handling complex question-answering tasks. To address the challenges of long videos and complex prompts, we propose AdaCM^2, which, for the first time, introduces an adaptive cross-modality memory reduction approach to video-text alignment in an auto-regressive manner on video streams. Our extensive experiments on various video understanding tasks, such as video captioning, video question answering, and video classification, demonstrate that AdaCM^2 achieves state-of-the-art performance across multiple datasets while significantly reducing memory usage. Notably, it achieves a 4.5% improvement across multiple tasks in the LVU dataset with a GPU memory consumption reduction of up to 65%.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Man_AdaCM2_On_Understanding_Extremely_Long-Term_Video_with_Adaptive_Cross-Modality_Memory_CVPR_2025_paper.pdf

bibtex:
@InProceedings{Man_2025_CVPR,
    author    = {Man, Yuanbin and Huang, Ying and Zhang, Chengming and Li, Bingzhe and Niu, Wei and Yin, Miao},
    title     = {AdaCM{\textasciicircum}2: On Understanding Extremely Long-Term Video with Adaptive Cross-Modality Memory Reduction},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {8534-8544}
}

raw_affiliation:
[{"affiliation": "Department of CSE, University of Texas at Arlington", "city": "Arlington", "country": "USA"}, {"affiliation": "Department of CS, University of Houston", "city": "Houston", "country": "USA"}, {"affiliation": "Department of CS, University of Texas at Dallas", "city": "Dallas", "country": "USA"}, {"affiliation": "School of Computing, University of Georgia", "city": "Athens", "country": "USA"}]

affiliations:
["Department of CSE, University of Texas at Arlington", "Department of CS, University of Houston", "Department of CS, University of Texas at Dallas", "School of Computing, University of Georgia"]


proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 29915-29926
cities: ["Xi'an", "Xi'an", "Singapore", "Unknown", "Boston", "Hangzhou"]
countries: ["China", "China", "Singapore", "Unknown", "USA", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/An_Mitigating_Object_Hallucinations_in_Large_Vision-Language_Models_with_Assembly_of_CVPR_2025_paper.html

title:
Mitigating Object Hallucinations in Large Vision-Language Models with Assembly of Global and Local Attention

authors:
Wenbin An, Feng Tian, Sicong Leng, Jiahao Nie, Haonan Lin, Qianying Wang, Ping Chen, Xiaoqin Zhang, Shijian Lu

abstract:
Despite great success across various multimodal tasks, Large Vision-Language Models (LVLMs) often encounter object hallucinations with generated textual responses being inconsistent with the actual objects in images. We examine different LVLMs and pinpoint that one root cause of object hallucinations lies with deficient attention on discriminative image features. Specifically, LVLMs often predominantly attend to prompt-irrelevant global features instead of prompt-relevant local features, undermining their visual grounding capacity and leading to object hallucinations. We propose Assembly of Global and Local Attention (AGLA) , a training-free and plug-and-play approach that mitigates hallucinations by assembling global features for response generation and local features for visual discrimination simultaneously.  Specifically, we introduce an image-prompt matching scheme that captures prompt-relevant local features from images, leading to an augmented view of the input image where prompt-relevant content is highlighted while irrelevant distractions are suppressed.  Hallucinations can thus be mitigated with a calibrated logit distribution that is from generative global features of the original image and discriminative local features of the augmented image. Extensive experiments show the superiority of AGLA in LVLM hallucination mitigation, demonstrating its wide applicability across both discriminative and generative tasks. Our code is available at https://github.com/Lackel/AGLA.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/An_Mitigating_Object_Hallucinations_in_Large_Vision-Language_Models_with_Assembly_of_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/An_Mitigating_Object_Hallucinations_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{An_2025_CVPR,
    author    = {An, Wenbin and Tian, Feng and Leng, Sicong and Nie, Jiahao and Lin, Haonan and Wang, Qianying and Chen, Ping and Zhang, Xiaoqin and Lu, Shijian},
    title     = {Mitigating Object Hallucinations in Large Vision-Language Models with Assembly of Global and Local Attention},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {29915-29926}
}

raw_affiliation:
[{"affiliation": "Xi\u2019an Jiaotong University", "city": "Xi'an", "country": "China"}, {"affiliation": "National Engineering Laboratory for Big Data Analytics", "city": "Xi'an", "country": "China"}, {"affiliation": "Nanyang Technological University", "city": "Singapore", "country": "Singapore"}, {"affiliation": "Lenovo Research", "city": "Unknown", "country": "Unknown"}, {"affiliation": "University of Massachusetts Boston", "city": "Boston", "country": "USA"}, {"affiliation": "Zhejiang University of Technology", "city": "Hangzhou", "country": "China"}]

affiliations:
["Xi\u2019an Jiaotong University", "National Engineering Laboratory for Big Data Analytics", "Nanyang Technological University", "Lenovo Research", "University of Massachusetts Boston", "Zhejiang University of Technology"]


title: VGGT: Visual Geometry Grounded Transformer
authors: Jianyuan Wang, Minghao Chen, Nikita Karaev, Andrea Vedaldi, Christian Rupprecht, David Novotny
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 5294-5306
affiliations: ["Visual Geometry Group, University of Oxford", "Meta AI"]
cities: ["Oxford", "Menlo Park"]
countries: ["UK", "USA"]
oral: yes
award_candidate: yes
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Wang_VGGT_Visual_Geometry_Grounded_Transformer_CVPR_2025_paper.html

abstract:
We present VGGT, a feed-forward neural network that directly infers all key 3D attributes of a scene, including camera parameters, point maps, depth maps, and 3D point tracks, from one, a few, or hundreds of its views. This approach is a step forward in 3D computer vision, where models have typically been constrained to and specialized for single tasks. It is also simple and efficient, reconstructing images in under one second, and still outperforming alternatives that require post-processing with visual geometry optimization techniques. The network achieves state-of-the-art results in multiple 3D tasks, including camera parameter estimation, multi-view depth estimation, dense point cloud reconstruction, and 3D point tracking. We also show that using pretrained VGGT as a feature backbone significantly enhances downstream tasks, such as non-rigid point tracking and feed-forward novel view synthesis. Code and models are publicly available at https://github.com/facebookresearch/vggt.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Wang_VGGT_Visual_Geometry_Grounded_Transformer_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Wang_VGGT_Visual_Geometry_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Wang_2025_CVPR,
    author    = {Wang, Jianyuan and Chen, Minghao and Karaev, Nikita and Vedaldi, Andrea and Rupprecht, Christian and Novotny, David},
    title     = {VGGT: Visual Geometry Grounded Transformer},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {5294-5306}
}

raw_affiliation:
[{"affiliation": "Visual Geometry Group, University of Oxford", "city": "Oxford", "country": "UK"}, {"affiliation": "Meta AI", "city": "Menlo Park", "country": "USA"}]


title: Silent Branding Attack: Trigger-free Data Poisoning Attack on Text-to-Image Diffusion Models
authors: Sangwon Jang, June Suk Choi, Jaehyeong Jo, Kimin Lee, Sung Ju Hwang
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 8203-8212
affiliations: ["KAIST", "DeepAuto.ai"]
cities: ["Daejeon", "Seoul"]
countries: ["South Korea", "South Korea"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Jang_Silent_Branding_Attack_Trigger-free_Data_Poisoning_Attack_on_Text-to-Image_Diffusion_CVPR_2025_paper.html

abstract:
Text-to-image diffusion models have achieved remarkable success in generating high-quality contents from text prompts. However, their reliance on publicly available data and the growing trend of data sharing for fine-tuning make these models particularly vulnerable to data poisoning attacks. In this work, we introduce the Silent Branding Attack, a novel data poisoning method that manipulates text-to-image diffusion models to generate images containing specific brand logos or symbols without any text triggers. We find that when certain visual patterns are repeatedly in the training data, the model learns to reproduce them naturally in its outputs, even without prompt mentions. Leveraging this, we develop an automated data poisoning algorithm that unobtrusively injects logos into original images, ensuring they blend naturally and remain undetected. Models trained on this poisoned dataset generate images containing logos without degrading image quality or text alignment. We experimentally validate our silent branding attack across two realistic settings on large-scale high-quality image datasets and style personalization datasets, achieving high success rates even without a specific text trigger. Human evaluation and quantitative metrics including logo detection show that our method can stealthily embed logos.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Jang_Silent_Branding_Attack_Trigger-free_Data_Poisoning_Attack_on_Text-to-Image_Diffusion_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Jang_Silent_Branding_Attack_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Jang_2025_CVPR,
    author    = {Jang, Sangwon and Choi, June Suk and Jo, Jaehyeong and Lee, Kimin and Hwang, Sung Ju},
    title     = {Silent Branding Attack: Trigger-free Data Poisoning Attack on Text-to-Image Diffusion Models},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {8203-8212}
}

raw_affiliation:
[{"affiliation": "KAIST", "city": "Daejeon", "country": "South Korea"}, {"affiliation": "DeepAuto.ai", "city": "Seoul", "country": "South Korea"}]


title: UniSTD: Towards Unified Spatio-Temporal Learning across Diverse Disciplines
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 29213-29224
cities: ["Hong Kong", "Shanghai", "Shanghai", "Hong Kong"]
countries: ["China", "China", "China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Tang_UniSTD_Towards_Unified_Spatio-Temporal_Learning_across_Diverse_Disciplines_CVPR_2025_paper.html

authors:
Chen Tang, Xinzhu Ma, Encheng Su, Xiufeng Song, Xiaohong Liu, Wei-Hong Li, Lei Bai, Wanli Ouyang, Xiangyu Yue

abstract:
Traditional spatiotemporal models generally rely on task-specific architectures, which limit their generalizability and scalability across diverse tasks due to domain-specific design requirements. In this paper, we introduce UniSTD, a unified Transformer-based framework for spatiotemporal modeling, which is inspired by advances in recent foundation models with the two-stage pretraining-then-adaption paradigm. Specifically, our work demonstrates that task-agnostic pretraining on 2D vision and vision-text datasets can build a generalizable model foundation for spatiotemporal learning, followed by specialized joint training on spatiotemporal datasets to enhance task-specific adaptability. To improve the learning capabilities across domains, our framework employs a rank-adaptive mixture-of-expert adaptation by using fractional interpolation to relax the discrete variables so that can be optimized in the continuous space.  Additionally, we introduce a temporal module to incorporate temporal dynamics explicitly. We evaluate our approach on a large-scale dataset covering 10 tasks across 4 disciplines, demonstrating that a unified spatiotemporal model can achieve scalable, cross-task learning and support up to 10 tasks simultaneously within one model while reducing training costs in multi-domain applications. Code will be available at https://github.com/1hunters/UniSTD.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Tang_UniSTD_Towards_Unified_Spatio-Temporal_Learning_across_Diverse_Disciplines_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Tang_UniSTD_Towards_Unified_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Tang_2025_CVPR,
    author    = {Tang, Chen and Ma, Xinzhu and Su, Encheng and Song, Xiufeng and Liu, Xiaohong and Li, Wei-Hong and Bai, Lei and Ouyang, Wanli and Yue, Xiangyu},
    title     = {UniSTD: Towards Unified Spatio-Temporal Learning across Diverse Disciplines},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {29213-29224}
}

raw_affiliation:
[{"affiliation": "MMLab, The Chinese University of Hong Kong", "city": "Hong Kong", "country": "China"}, {"affiliation": "Shanghai AI Lab", "city": "Shanghai", "country": "China"}, {"affiliation": "Shanghai Jiaotong University", "city": "Shanghai", "country": "China"}, {"affiliation": "Shun Hing Institute of Advanced Engineering, The Chinese University of Hong Kong", "city": "Hong Kong", "country": "China"}]

affiliations:
["MMLab, The Chinese University of Hong Kong", "Shanghai AI Lab", "Shanghai Jiaotong University", "Shun Hing Institute of Advanced Engineering, The Chinese University of Hong Kong"]


title: Visual Consensus Prompting for Co-Salient Object Detection
authors: Jie Wang, Nana Yu, Zihao Zhang, Yahong Han
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 9591-9600
affiliations: ["College of Intelligence and Computing, Tianjin University"]
cities: ["Tianjin"]
countries: ["China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Wang_Visual_Consensus_Prompting_for_Co-Salient_Object_Detection_CVPR_2025_paper.html

abstract:
Existing co-salient object detection (CoSOD) methods generally employ a three-stage architecture (i.e., encoding, consensus extraction & dispersion, and prediction) along with a typical full fine-tuning paradigm. Although they yield certain benefits, they exhibit two notable limitations: 1) This architecture relies on encoded features to facilitate consensus extraction, but the meticulously extracted consensus does not provide timely guidance to the encoding stage. 2) This paradigm involves globally updating all parameters of the model, which is parameter-inefficient and hinders the effective representation of knowledge within the foundation model for this task. Therefore, in this paper, we propose an interaction-effective and parameter-efficient concise architecture for the CoSOD task, addressing two key limitations. It introduces, for the first time, a parameter-efficient prompt tuning paradigm and seamlessly embeds consensus into the prompts to formulate task-specific Visual Consensus Prompts (VCP). Our VCP aims to induce the frozen foundation model to perform better on CoSOD tasks by formulating task-specific visual consensus prompts with minimized tunable parameters. Concretely, the primary insight of the purposeful Consensus Prompt Generator (CPG) is to enforce limited tunable parameters to focus on co-salient representations and generate consensus prompts. The formulated Consensus Prompt Disperser (CPD) leverages consensus prompts to form task-specific visual consensus prompts, thereby arousing the powerful potential of pre-trained models in addressing CoSOD tasks. Extensive experiments demonstrate that our concise VCP outperforms 13 cutting-edge full fine-tuning models, achieving the new state of the art (with 6.8% improvement in F_m metrics on the most challenging CoCA dataset). Source code has been available at https://github.com/WJ-CV/VCP.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Wang_Visual_Consensus_Prompting_for_Co-Salient_Object_Detection_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Wang_Visual_Consensus_Prompting_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Wang_2025_CVPR,
    author    = {Wang, Jie and Yu, Nana and Zhang, Zihao and Han, Yahong},
    title     = {Visual Consensus Prompting for Co-Salient Object Detection},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {9591-9600}
}

raw_affiliation:
[{"affiliation": "College of Intelligence and Computing, Tianjin University", "city": "Tianjin", "country": "China"}]


title: Mani-GS: Gaussian Splatting Manipulation with Triangular Mesh
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 21392-21402
affiliations: ["The Hong Kong University of Science and Technology", "Tencent", "Nanjing University"]
cities: ["Hong Kong", "Shenzhen", "Nanjing"]
countries: ["China", "China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Gao_Mani-GS_Gaussian_Splatting_Manipulation_with_Triangular_Mesh_CVPR_2025_paper.html

authors:
Xiangjun Gao, Xiaoyu Li, Yiyu Zhuang, Qi Zhang, Wenbo Hu, Chaopeng Zhang, Yao Yao, Ying Shan, Long Quan

abstract:
Neural 3D representations, such as Neural Radiation Fields (NeRF), excel at producing photorealistic rendering results but lack the flexibility for manipulation and editing which is crucial for content creation. However, manipulating NeRF is not highly controllable and requires a long training and inference time. With the emergence of 3D Gaussian Splatting (3DGS), extremely high-fidelity novel view synthesis can be achieved using an explicit point-based 3D representation with much faster training and rendering speed. However, there is still a lack of effective means to manipulate 3DGS freely while maintaining rendering quality. In this work, we aim to tackle the challenge of achieving manipulable photo-realistic rendering. We propose to utilize a triangular mesh to manipulate 3DGS directly with self-adaptation. This approach reduces the need to design various algorithms for different types of 3DGS manipulation. By utilizing a triangle shape-aware Gaussian binding and adapting method, we can achieve 3DGS manipulation and preserve high-fidelity rendering. In addition, our method is also effective with inaccurate meshes extracted from 3DGS. Experiments demonstrate our method's effectiveness and superiority over baseline approaches.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Gao_Mani-GS_Gaussian_Splatting_Manipulation_with_Triangular_Mesh_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Gao_Mani-GS_Gaussian_Splatting_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Gao_2025_CVPR,
    author    = {Gao, Xiangjun and Li, Xiaoyu and Zhuang, Yiyu and Zhang, Qi and Hu, Wenbo and Zhang, Chaopeng and Yao, Yao and Shan, Ying and Quan, Long},
    title     = {Mani-GS: Gaussian Splatting Manipulation with Triangular Mesh},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {21392-21402}
}

raw_affiliation:
[{"affiliation": "The Hong Kong University of Science and Technology", "city": "Hong Kong", "country": "China"}, {"affiliation": "Tencent", "city": "Shenzhen", "country": "China"}, {"affiliation": "Nanjing University", "city": "Nanjing", "country": "China"}]


title: UniHOPE: A Unified Approach for Hand-Only and Hand-Object Pose Estimation
authors: Yinqiao Wang, Hao Xu, Pheng-Ann Heng, Chi-Wing Fu
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 12231-12241
cities: ["Hong Kong", "Hong Kong", "Hong Kong"]
countries: ["China", "China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Wang_UniHOPE_A_Unified_Approach_for_Hand-Only_and_Hand-Object_Pose_Estimation_CVPR_2025_paper.html

abstract:
Estimating the 3D pose of hand and potential hand-held object from monocular images is a longstanding challenge. Yet, existing methods are specialized, focusing on either bare-hand or hand interacting with object. No method can flexibly handle both scenarios and their performance degrades when applied to the other scenario. In this paper, we propose UniHOPE, a unified approach for general 3D hand-object pose estimation, flexibly adapting both scenarios. Technically, we design a grasp-aware feature fusion module to integrate hand-object features with an object switcher to dynamically control the hand-object pose estimation according to grasping status. Further, to uplift the robustness of hand pose estimation regardless of object presence, we generate realistic de-occluded image pairs to train the model to learn object-induced hand occlusions, and formulate multi-level feature enhancement techniques for learning occlusion-invariant features. Extensive experiments on three commonly-used benchmarks demonstrate UniHOPE's SOTA performance in addressing hand-only and hand-object scenarios. Code will be released on https://github.com/JoyboyWang/UniHOPE_Pytorch.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Wang_UniHOPE_A_Unified_Approach_for_Hand-Only_and_Hand-Object_Pose_Estimation_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Wang_UniHOPE_A_Unified_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Wang_2025_CVPR,
    author    = {Wang, Yinqiao and Xu, Hao and Heng, Pheng-Ann and Fu, Chi-Wing},
    title     = {UniHOPE: A Unified Approach for Hand-Only and Hand-Object Pose Estimation},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {12231-12241}
}

raw_affiliation:
[{"affiliation": "Department of Computer Science and Engineering", "city": "Hong Kong", "country": "China"}, {"affiliation": "Institute of Medical Intelligence and XR", "city": "Hong Kong", "country": "China"}, {"affiliation": "The Chinese University of Hong Kong", "city": "Hong Kong", "country": "China"}]

affiliations:
["Department of Computer Science and Engineering", "Institute of Medical Intelligence and XR", "The Chinese University of Hong Kong"]


title: RL-RC-DoT: A Block-level RL agent for Task-Aware Video Compression
authors: Uri Gadot, Assaf Shocher, Shie Mannor, Gal Chechik, Assaf Hallak
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 12533-12542
affiliations: ["Technion", "NVIDIA Research"]
cities: ["Haifa", "Santa Clara"]
countries: ["Israel", "USA"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Gadot_RL-RC-DoT_A_Block-level_RL_agent_for_Task-Aware_Video_Compression_CVPR_2025_paper.html

abstract:
Video encoders optimize compression for human perception by minimizing reconstruction error under bit-rate constraints. In many modern applications such as autonomous driving, an overwhelming majority of videos serve as input for AI systems performing tasks like object recognition or segmentation, rather than being watched by humans. It is therefore useful to optimize the encoder for a downstream task instead of for perceptual image quality. However, a major challenge is how to combine such downstream optimization with existing standard video encoders, which are highly efficient and popular. Here, we address this challenge by controlling the Quantization Parameters (QPs) at the macro-block level to optimize the downstream task. This granular control allows us to prioritize encoding for task-relevant regions within each frame. We formulate this optimization problem as a Reinforcement Learning (RL) task, where the agent learns to balance long-term implications of choosing QPs on both task performance and bit-rate constraints. Notably, our policy does not require the downstream task as an input during inference, making it suitable for streaming applications and edge devices such as vehicles. We demonstrate significant improvements in two tasks, car detection, and ROI (saliency) encoding. Our approach improves task performance for a given bit rate compared to traditional task agnostic encoding methods, paving the way for more efficient task-aware video compression.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Gadot_RL-RC-DoT_A_Block-level_RL_agent_for_Task-Aware_Video_Compression_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Gadot_RL-RC-DoT_A_Block-level_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Gadot_2025_CVPR,
    author    = {Gadot, Uri and Shocher, Assaf and Mannor, Shie and Chechik, Gal and Hallak, Assaf},
    title     = {RL-RC-DoT: A Block-level RL agent for Task-Aware Video Compression},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {12533-12542}
}

raw_affiliation:
[{"affiliation": "Technion", "city": "Haifa", "country": "Israel"}, {"affiliation": "NVIDIA Research", "city": "Santa Clara", "country": "USA"}]


title: Quantization without Tears
authors: Minghao Fu, Hao Yu, Jie Shao, Junjie Zhou, Ke Zhu, Jianxin Wu
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 4462-4472
cities: ["Nanjing", "Nanjing"]
countries: ["China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Fu_Quantization_without_Tears_CVPR_2025_paper.html

abstract:
Deep neural networks, while achieving remarkable success across diverse tasks, demand significant resources, including computation, GPU memory, bandwidth, storage, and energy. Network quantization, as a standard compression and acceleration technique, reduces storage costs and enables potential inference acceleration by discretizing network weights and activations into a finite set of integer values. However, current quantization methods are often complex and sensitive, requiring extensive task-specific hyperparameters, where even a single misconfiguration can impair model performance, limiting generality across different models and tasks. In this paper, we propose Quantization without Tears (QwT), a method that simultaneously achieves quantization speed, accuracy, simplicity, and generality. The key insight of QwT is to incorporate a lightweight additional structure into the quantized network to mitigate information loss during quantization. This structure consists solely of a small set of linear layers, keeping the method simple and efficient. More importantly, it provides a closed-form solution, allowing us to improve accuracy effortlessly under 2 minutes. Extensive experiments across various vision, language, and multimodal tasks demonstrate that QwT is both highly effective and versatile. In fact, our approach offers a robust solution for network quantization that combines simplicity, accuracy, and adaptability, which provides new insights for the design of novel quantization paradigms.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Fu_Quantization_without_Tears_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Fu_Quantization_without_Tears_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Fu_2025_CVPR,
    author    = {Fu, Minghao and Yu, Hao and Shao, Jie and Zhou, Junjie and Zhu, Ke and Wu, Jianxin},
    title     = {Quantization without Tears},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {4462-4472}
}

raw_affiliation:
[{"affiliation": "National Key Laboratory for Novel Software Technology, Nanjing University, China", "city": "Nanjing", "country": "China"}, {"affiliation": "School of Artificial Intelligence, Nanjing University, China", "city": "Nanjing", "country": "China"}]

affiliations:
["National Key Laboratory for Novel Software Technology, Nanjing University, China", "School of Artificial Intelligence, Nanjing University, China"]


authors: Xun Jiang, Zhiyi Huang, Xing Xu, Jingkuan Song, Fumin Shen, Heng Tao Shen
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 8615-8624
cities: ["Chengdu", "Shanghai"]
countries: ["China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Jiang_PHGC_Procedural_Heterogeneous_Graph_Completion_for_Natural_Language_Task_Verification_CVPR_2025_paper.html

title:
PHGC: Procedural Heterogeneous Graph Completion for Natural Language Task Verification in Egocentric Videos

abstract:
Natural Language-based Egocentric Task Verification (NLETV) aims to equip agents to determine if operation flows of procedural tasks in egocentric videos align with natural language instructions. Describing rules with natural language provides generalizable applications, but also raises cross-modal heterogeneity and hierarchical misalignment challenges. In this paper, we proposed a novel approach termed Procedural Heterogeneous Graph Completion (PHGC), which addresses these challenges with heterogeneous graphs representing the logic in rules and operation flows. Specifically, our PHGC method mainly consists of three key components: (1) Heterogeneous Graph Construction module that defines objective states and operation flows as vertices, with temporal and sequential relations as edges. (2) Cross-Modal Path Finding module that aligns semantic relations between hierarchical video and text elements. (3) Discriminative Entity Representation module excavates hidden entities that integrate general logical relations and discriminative cues to reveal final verification results. Additionally, we further constructed a new dataset called CSV-NL comprised of realistic videos. Extensive experiments on the two benchmark datasets covering both digital and physical scenarios, i.e., EgoTV and CSV-NL, demonstrate that our proposed PHGC establishes state-of-the-art performance across different settings. Our code and dataset are available at https://github.com/XunCHN/PHGC.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Jiang_PHGC_Procedural_Heterogeneous_Graph_Completion_for_Natural_Language_Task_Verification_CVPR_2025_paper.pdf

bibtex:
@InProceedings{Jiang_2025_CVPR,
    author    = {Jiang, Xun and Huang, Zhiyi and Xu, Xing and Song, Jingkuan and Shen, Fumin and Shen, Heng Tao},
    title     = {PHGC: Procedural Heterogeneous Graph Completion for Natural Language Task Verification in Egocentric Videos},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {8615-8624}
}

raw_affiliation:
[{"affiliation": "Center for Future Media & School of Computer Science and Engineering, University of Electronic Science and Technology of China, China", "city": "Chengdu", "country": "China"}, {"affiliation": "School of Computer Science and Technology, Tongji University, China", "city": "Shanghai", "country": "China"}]

affiliations:
["Center for Future Media & School of Computer Science and Engineering, University of Electronic Science and Technology of China, China", "School of Computer Science and Technology, Tongji University, China"]


title: Recognition-Synergistic Scene Text Editing
authors: Zhengyao Fang, Pengyuan Lyu, Jingjing Wu, Chengquan Zhang, Jun Yu, Guangming Lu, Wenjie Pei
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 13104-13113
cities: ["Shenzhen", "Shenzhen", "Shenzhen", "Beijing"]
countries: ["China", "China", "China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Fang_Recognition-Synergistic_Scene_Text_Editing_CVPR_2025_paper.html

abstract:
Scene text editing aims to modify text content within scene images while maintaining style consistency. Traditional methods achieve this by explicitly disentangling style and content from the source image and then fusing the style with the target content, while ensuring content consistency using a pre-trained recognition model. Despite notable progress, these methods suffer from complex pipelines, leading to suboptimal performance in complex scenarios. In this work, we introduce Recognition-Synergistic Scene Text Editing (RS-STE), a novel approach that fully exploits the intrinsic synergy of text recognition for editing. Our model seamlessly integrates text recognition with text editing within a unified framework, and leverages the recognition model's ability to implicitly disentangle style and content while ensuring content consistency. Specifically, our approach employs a multi-modal parallel decoder based on transformer architecture, which predicts both text content and stylized images in parallel. Additionally, our cyclic self-supervised fine-tuning strategy enables effective training on unpaired real-world data without ground truth, enhancing style and content consistency through a twice-cyclic generation process. Built on a relatively simple architecture, RS-STE achieves state-of-the-art performance on both synthetic and real-world benchmarks, and further demonstrates the effectiveness of leveraging the generated hard cases to boost the performance of downstream recognition tasks. Code will be made publicly available.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Fang_Recognition-Synergistic_Scene_Text_Editing_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Fang_Recognition-Synergistic_Scene_Text_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Fang_2025_CVPR,
    author    = {Fang, Zhengyao and Lyu, Pengyuan and Wu, Jingjing and Zhang, Chengquan and Yu, Jun and Lu, Guangming and Pei, Wenjie},
    title     = {Recognition-Synergistic Scene Text Editing},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {13104-13113}
}

raw_affiliation:
[{"affiliation": "Harbin Institute of Technology, Shenzhen", "city": "Shenzhen", "country": "China"}, {"affiliation": "Peng Cheng Laboratory", "city": "Shenzhen", "country": "China"}, {"affiliation": "Tencent", "city": "Shenzhen", "country": "China"}, {"affiliation": "Department of Computer Vision Technology, Baidu Inc.", "city": "Beijing", "country": "China"}]

affiliations:
["Harbin Institute of Technology, Shenzhen", "Peng Cheng Laboratory", "Tencent", "Department of Computer Vision Technology, Baidu Inc."]


title: Towards Consistent Multi-Task Learning: Unlocking the Potential of Task-Specific Parameters
authors: Xiaohan Qin, Xiaoxing Wang, Junchi Yan
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 10067-10076
affiliations: ["School of Computer Science & School of Artificial Intelligience, Shanghai Jiao Tong University"]
cities: ["Shanghai"]
countries: ["China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Qin_Towards_Consistent_Multi-Task_Learning_Unlocking_the_Potential_of_Task-Specific_Parameters_CVPR_2025_paper.html

abstract:
Multi-task learning (MTL) has gained widespread application for its ability to transfer knowledge across tasks, improving resource efficiency and generalization. However, gradient conflicts from different tasks remain a major challenge in MTL. Previous gradient-based and loss-based methods primarily focus on gradient optimization in shared parameters, often overlooking the potential of task-specific parameters. This work points out that task-specific parameters not only capture task-specific information but also influence the gradients propagated to shared parameters, which in turn affects gradient conflicts. Motivated by this insight, we propose ConsMTL, which models MTL as a bi-level optimization problem: in the upper-level optimization, we perform gradient aggregation on shared parameters to find a joint update vector that minimizes gradient conflicts; in the lower-level optimization, we introduce an additional loss for task-specific parameters guiding the k gradients of shared parameters to gradually converge towards the joint update vector. Our design enables the optimization of both shared and task-specific parameters to consistently alleviate gradient conflicts. Extensive experiments show that ConsMTL achieves state-of-the-art performance across various benchmarks with task numbers ranging from 2 to 40, demonstrating its superior performance.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Qin_Towards_Consistent_Multi-Task_Learning_Unlocking_the_Potential_of_Task-Specific_Parameters_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Qin_Towards_Consistent_Multi-Task_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Qin_2025_CVPR,
    author    = {Qin, Xiaohan and Wang, Xiaoxing and Yan, Junchi},
    title     = {Towards Consistent Multi-Task Learning: Unlocking the Potential of Task-Specific Parameters},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {10067-10076}
}

raw_affiliation:
[{"affiliation": "School of Computer Science & School of Artificial Intelligience, Shanghai Jiao Tong University", "city": "Shanghai", "country": "China"}]


title: WildAvatar: Learning In-the-wild 3D Avatars from the Web
authors: Zihao Huang, Shoukang Hu, Guangcong Wang, Tianqi Liu, Yuhang Zang, Zhiguo Cao, Wei Li, Ziwei Liu
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 15963-15975
cities: ["Wuhan", "Singapore", "Dongguan", "Shanghai"]
countries: ["China", "Singapore", "China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Huang_WildAvatar_Learning_In-the-wild_3D_Avatars_from_the_Web_CVPR_2025_paper.html

abstract:
Existing research on avatar creation is typically limited to laboratory datasets, which require high costs against scalability and exhibit insufficient representation of the real world. On the other hand, the web abounds with off-the-shelf real-world human videos, but these videos vary in quality and require accurate annotations for avatar creation. To this end, we propose an automatic annotating pipeline with filtering protocols to curate these humans from the web. Our pipeline surpasses state-of-the-art methods on the EMDB benchmark, and the filtering protocols boost verification metrics on web videos. We then curate WildAvatar, a web-scale in-the-wild human avatar creation dataset extracted from YouTube, with 10,000+ different human subjects and scenes. WildAvatar is at least 10xricher than previous datasets for 3D human avatar creation and closer to the real world. To explore its potential, we demonstrate the quality and generalizability of avatar creation methods on WildAvatar. We will publicly release our code, data source links and annotations to push forward 3D human avatar creation and other related fields for real-world applications.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Huang_WildAvatar_Learning_In-the-wild_3D_Avatars_from_the_Web_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Huang_WildAvatar_Learning_In-the-wild_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Huang_2025_CVPR,
    author    = {Huang, Zihao and Hu, Shoukang and Wang, Guangcong and Liu, Tianqi and Zang, Yuhang and Cao, Zhiguo and Li, Wei and Liu, Ziwei},
    title     = {WildAvatar: Learning In-the-wild 3D Avatars from the Web},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {15963-15975}
}

raw_affiliation:
[{"affiliation": "School of AIA, Huazhong University of Science and Technology", "city": "Wuhan", "country": "China"}, {"affiliation": "S-Lab, Nanyang Technological University", "city": "Singapore", "country": "Singapore"}, {"affiliation": "Great Bay University", "city": "Dongguan", "country": "China"}, {"affiliation": "Shanghai AI Laboratory", "city": "Shanghai", "country": "China"}]

affiliations:
["School of AIA, Huazhong University of Science and Technology", "S-Lab, Nanyang Technological University", "Great Bay University", "Shanghai AI Laboratory"]


title: BooW-VTON: Boosting In-the-Wild Virtual Try-On via Mask-Free Pseudo Data Training
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 26399-26408
cities: ["Tianjin", "Hangzhou", "Hefei", "Hangzhou"]
countries: ["China", "China", "China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Zhang_BooW-VTON_Boosting_In-the-Wild_Virtual_Try-On_via_Mask-Free_Pseudo_Data_Training_CVPR_2025_paper.html

authors:
Xuanpu Zhang, Dan Song, Pengxin Zhan, Tianyu Chang, Jianhao Zeng, Qingguo Chen, Weihua Luo, An-An Liu

abstract:
Image-based virtual try-on is an increasingly popular and important task to generate realistic try-on images of the specific person.Recent methods model virtual try-on as image mask-inpaint task, which requires masking the person image and results in significant loss of spatial information. Especially, for in-the-wild try-on scenarios with complex poses and occlusions, mask-based methods often introduce noticeable artifacts. Our research found that a mask-free approach can fully leverage spatial and lighting information from the original person image, enabling high-quality virtual try-on. Consequently, we propose a novel training paradigm for a mask-free try-on diffusion model. We ensure the model's mask-free try-on capability by creating high-quality pseudo-data and further enhance its handling of complex spatial information through effective in-the-wild data augmentation. Besides, a try-on localization loss is designed to concentrate on try-on area while suppressing garment features in non-try-on areas, ensuring precise rendering of garments and preservation of fore/back-ground. In the end, we introduce BooW-VTON, the mask-free virtual try-on diffusion model, which delivers SOTA try-on quality without parsing cost. Extensive qualitative and quantitative experiments have demonstrated superior performance in wild scenarios with such a low-demand input.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Zhang_BooW-VTON_Boosting_In-the-Wild_Virtual_Try-On_via_Mask-Free_Pseudo_Data_Training_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Zhang_BooW-VTON_Boosting_In-the-Wild_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Zhang_2025_CVPR,
    author    = {Zhang, Xuanpu and Song, Dan and Zhan, Pengxin and Chang, Tianyu and Zeng, Jianhao and Chen, Qingguo and Luo, Weihua and Liu, An-An},
    title     = {BooW-VTON: Boosting In-the-Wild Virtual Try-On via Mask-Free Pseudo Data Training},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {26399-26408}
}

raw_affiliation:
[{"affiliation": "Tianjin University", "city": "Tianjin", "country": "China"}, {"affiliation": "Alibaba Group", "city": "Hangzhou", "country": "China"}, {"affiliation": "University of Science and Technology of China", "city": "Hefei", "country": "China"}, {"affiliation": "Westlake University", "city": "Hangzhou", "country": "China"}]

affiliations:
["Tianjin University", "Alibaba Group", "University of Science and Technology of China", "Westlake University"]


title: Rectified Diffusion Guidance for Conditional Generation
authors: Mengfei Xia, Nan Xue, Yujun Shen, Ran Yi, Tieliang Gong, Yong-Jin Liu
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 13371-13380
cities: ["Beijing", "Hangzhou", "Shanghai", "Xi'an"]
countries: ["China", "China", "China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Xia_Rectified_Diffusion_Guidance_for_Conditional_Generation_CVPR_2025_paper.html

abstract:
Classifier-Free Guidance (CFG), which combines the conditional and unconditional score functions with two coefficients summing to one, serves as a practical technique for diffusion model sampling. Theoretically, however, denoising with CFG cannot be expressed as a reciprocal diffusion process, which may consequently leave some hidden risks during use. In this work, we revisit the theory behind CFG and rigorously confirm that the improper configuration of the combination coefficients (*i.e.*, the widely used summing-to-one version) brings about expectation shift of the generative distribution. To rectify this issue, we propose ReCFG with a relaxation on the guidance coefficients such that denoising with ReCFG strictly aligns with the diffusion theory. We further show that our approach enjoys a **closed-form** solution given the guidance strength. That way, the rectified coefficients can be readily pre-computed via traversing the observed data, leaving the sampling speed barely affected. Empirical evidence on real-world data demonstrate the compatibility of our post-hoc design with existing state-of-the-art diffusion models, including both class-conditioned ones (*e.g.*, EDM2 on ImageNet) and text-conditioned ones (*e.g.*, SD3 on CC12M), without any retraining. Code is available at https://github.com/thuxmf/recfg.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Xia_Rectified_Diffusion_Guidance_for_Conditional_Generation_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Xia_Rectified_Diffusion_Guidance_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Xia_2025_CVPR,
    author    = {Xia, Mengfei and Xue, Nan and Shen, Yujun and Yi, Ran and Gong, Tieliang and Liu, Yong-Jin},
    title     = {Rectified Diffusion Guidance for Conditional Generation},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {13371-13380}
}

raw_affiliation:
[{"affiliation": "Tsinghua University", "city": "Beijing", "country": "China"}, {"affiliation": "Ant Group", "city": "Hangzhou", "country": "China"}, {"affiliation": "Shanghai Jiao Tong University", "city": "Shanghai", "country": "China"}, {"affiliation": "Xi\u2019an Jiao Tong University", "city": "Xi'an", "country": "China"}]

affiliations:
["Tsinghua University", "Ant Group", "Shanghai Jiao Tong University", "Xi\u2019an Jiao Tong University"]


title: SceneFactor: Factored Latent 3D Diffusion for Controllable 3D Scene Generation
authors: Aleksey Bokhovkin, Quan Meng, Shubham Tulsiani, Angela Dai
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 628-639
cities: ["Munich", "Munich", "Pittsburgh", "Munich"]
countries: ["Germany", "Germany", "USA", "Germany"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Bokhovkin_SceneFactor_Factored_Latent_3D_Diffusion_for_Controllable_3D_Scene_Generation_CVPR_2025_paper.html

abstract:
We present SceneFactor, a diffusion-based approach for large-scale 3D scene generation that enables controllable generation and effortless editing. SceneFactor enables text-guided 3D scene synthesis through our factored diffusion formulation, leveraging latent semantic and geometric manifolds for generation of arbitrary-sized 3D scenes. While text input enables easy, controllable generation, text guidance remains imprecise for intuitive, localized editing and manipulation of the generated 3D scenes. Our factored semantic diffusion generates a proxy semantic space composed of semantic 3D boxes that enables controllable editing of generated scenes by adding, removing, changing the size of the semantic 3D proxy boxes that guides high-fidelity, consistent 3D geometric editing. Extensive experiments demonstrate that our approach enables high-fidelity 3D scene synthesis with effective controllable editing through our factored diffusion approach.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Bokhovkin_SceneFactor_Factored_Latent_3D_Diffusion_for_Controllable_3D_Scene_Generation_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Bokhovkin_SceneFactor_Factored_Latent_CVPR_2025_supplemental.zip

bibtex:
@InProceedings{Bokhovkin_2025_CVPR,
    author    = {Bokhovkin, Aleksey and Meng, Quan and Tulsiani, Shubham and Dai, Angela},
    title     = {SceneFactor: Factored Latent 3D Diffusion for Controllable 3D Scene Generation},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {628-639}
}

raw_affiliation:
[{"affiliation": "Technical University of Munich", "city": "Munich", "country": "Germany"}, {"affiliation": "Technical University of Munich", "city": "Munich", "country": "Germany"}, {"affiliation": "Carnegie Mellon University", "city": "Pittsburgh", "country": "USA"}, {"affiliation": "Technical University of Munich", "city": "Munich", "country": "Germany"}]

affiliations:
["Technical University of Munich", "Technical University of Munich", "Carnegie Mellon University", "Technical University of Munich"]


authors: Yifang Xu, Benxiang Zhai, Yunzhuo Sun, Ming Li, Yang Li, Sidan Du
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 5625-5635
cities: ["Nanjing", "Dalian", "Nanjing"]
countries: ["China", "China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Xu_HiFi-Portrait_Zero-shot_Identity-preserved_Portrait_Generation_with_High-fidelity_Multi-face_Fusion_CVPR_2025_paper.html

title:
HiFi-Portrait: Zero-shot Identity-preserved Portrait Generation with High-fidelity Multi-face Fusion

abstract:
Recent advancements in diffusion-based technologies have made significant strides, particularly in identity-preserved portrait generation (IPG). However, when using multiple reference images from the same ID, existing methods typically produce lower-fidelity portraits and struggle to customize face attributes precisely. To address these issues, this paper presents HiFi-Portrait, a high-fidelity method for zero-shot portrait generation. Specifically, we first introduce the face refiner and landmark generator to obtain fine-grained multi-face features and 3D-aware face landmarks. The landmarks include the reference ID and the target attributes. Then, we design HiFi-Net to fuse multi-face features and align them with landmarks, which improves ID fidelity and face control. In addition, we devise an automated pipeline to construct an ID-based dataset for training HiFi-Portrait. Extensive experimental results demonstrate that our method surpasses the SOTA approaches in face similarity and controllability. Furthermore, our method is also compatible with previous SDXL-based works.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Xu_HiFi-Portrait_Zero-shot_Identity-preserved_Portrait_Generation_with_High-fidelity_Multi-face_Fusion_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Xu_HiFi-Portrait_Zero-shot_Identity-preserved_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Xu_2025_CVPR,
    author    = {Xu, Yifang and Zhai, Benxiang and Sun, Yunzhuo and Li, Ming and Li, Yang and Du, Sidan},
    title     = {HiFi-Portrait: Zero-shot Identity-preserved Portrait Generation with High-fidelity Multi-face Fusion},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {5625-5635}
}

raw_affiliation:
[{"affiliation": "Nanjing University", "city": "Nanjing", "country": "China"}, {"affiliation": "Dalian University of Technology", "city": "Dalian", "country": "China"}, {"affiliation": "Nanjing University of Information Science and Technology", "city": "Nanjing", "country": "China"}]

affiliations:
["Nanjing University", "Dalian University of Technology", "Nanjing University of Information Science and Technology"]


title: IAAO: Interactive Affordance Learning for Articulated Objects in 3D Environments
authors: Can Zhang, Gim Hee Lee
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 12132-12142
affiliations: ["Department of Computer Science, National University of Singapore"]
cities: ["Singapore"]
countries: ["Singapore"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Zhang_IAAO_Interactive_Affordance_Learning_for_Articulated_Objects_in_3D_Environments_CVPR_2025_paper.html

abstract:
This work presents IAAO, a novel framework that builds an explicit 3D model for intelligent agents to gain understanding of articulated objects in their environment through interaction. Unlike prior methods that rely on task-specific networks and assumptions about movable parts, our IAAO leverages large foundation models to estimate interactive affordances and part articulations in three stages. We first build hierarchical features and label fields for each object state using 3D Gaussian Splatting (3DGS) by distilling mask features and view-consistent labels from multi-view images. We then perform object- and part-level queries on the 3D Gaussian primitives to identify static and articulated elements, estimating global transformations and local articulation parameters along with affordances. Finally, scenes from different states are merged and refined based on the estimated transformations, enabling robust affordance-based interaction and manipulation of objects. Experimental results demonstrate the effectiveness of our method.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Zhang_IAAO_Interactive_Affordance_Learning_for_Articulated_Objects_in_3D_Environments_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Zhang_IAAO_Interactive_Affordance_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Zhang_2025_CVPR,
    author    = {Zhang, Can and Lee, Gim Hee},
    title     = {IAAO: Interactive Affordance Learning for Articulated Objects in 3D Environments},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {12132-12142}
}

raw_affiliation:
[{"affiliation": "Department of Computer Science, National University of Singapore", "city": "Singapore", "country": "Singapore"}]


title: FloVD: Optical Flow Meets Video Diffusion Model for Enhanced Camera-Controlled Video Synthesis
authors: Wonjoon Jin, Qi Dai, Chong Luo, Seung-Hwan Baek, Sunghyun Cho
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 2040-2049
affiliations: ["POSTECH", "Microsoft Research Asia"]
cities: ["Pohang", "Beijing"]
countries: ["South Korea", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Jin_FloVD_Optical_Flow_Meets_Video_Diffusion_Model_for_Enhanced_Camera-Controlled_CVPR_2025_paper.html

abstract:
We present FloVD, a novel video diffusion model for camera-controllable video generation. FloVD leverages optical flow to represent the motions of the camera and moving objects. This approach offers two key benefits. Since optical flow can be directly estimated from videos, our approach allows for the use of arbitrary training videos without ground-truth camera parameters. Moreover, as background optical flow encodes 3D correlation across different viewpoints, our method enables detailed camera control by leveraging the background motion. To synthesize natural object motion while supporting detailed camera control, our framework adopts a two-stage video synthesis pipeline consisting of optical flow generation and flow-conditioned video synthesis. Extensive experiments demonstrate the superiority of our method over previous approaches in terms of accurate camera control and natural object motion synthesis.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Jin_FloVD_Optical_Flow_Meets_Video_Diffusion_Model_for_Enhanced_Camera-Controlled_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Jin_FloVD_Optical_Flow_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Jin_2025_CVPR,
    author    = {Jin, Wonjoon and Dai, Qi and Luo, Chong and Baek, Seung-Hwan and Cho, Sunghyun},
    title     = {FloVD: Optical Flow Meets Video Diffusion Model for Enhanced Camera-Controlled Video Synthesis},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {2040-2049}
}

raw_affiliation:
[{"affiliation": "POSTECH", "city": "Pohang", "country": "South Korea"}, {"affiliation": "Microsoft Research Asia", "city": "Beijing", "country": "China"}]


title: RAD: Region-Aware Diffusion Models for Image Inpainting
authors: Sora Kim, Sungho Suh, Minsik Lee
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 2439-2448
affiliations: ["Hanyang University", "Korea University", "DFKI", "Hanyang University ERICA"]
cities: ["Seoul", "Seoul", "Saarbr\u00fccken", "Ansan"]
countries: ["South Korea", "South Korea", "Germany", "South Korea"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Kim_RAD_Region-Aware_Diffusion_Models_for_Image_Inpainting_CVPR_2025_paper.html

abstract:
Diffusion models have achieved remarkable success in image generation, with applications broadening across various domains.Inpainting is one such application that can benefit significantly from diffusion models. Existing methods either hijack the reverse process of a pretrained diffusion model or cast the problem into a larger framework, i.e., conditioned generation. However, these approaches often require nested loops in the generation process or additional components for conditioning. In this paper, we present region-aware diffusion models (RAD) for inpainting with a simple yet effective reformulation of the vanilla diffusion models. RAD utilizes a different noise schedule for each pixel, which allows local regions to be generated asynchronously while considering the global image context. A plain reverse process requires no additional components, enabling RAD to achieve inference time up to 100 times faster than the state-of-the-art approaches. Moreover, we employ low-rank adaptation (LoRA) to fine-tune RAD based on other pretrained diffusion models, reducing computational burdens in training as well. Experiments demonstrated that RAD provides state-of-the-art results both qualitatively and quantitatively, on the FFHQ, LSUN Bedroom, and ImageNet datasets.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Kim_RAD_Region-Aware_Diffusion_Models_for_Image_Inpainting_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Kim_RAD_Region-Aware_Diffusion_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Kim_2025_CVPR,
    author    = {Kim, Sora and Suh, Sungho and Lee, Minsik},
    title     = {RAD: Region-Aware Diffusion Models for Image Inpainting},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {2439-2448}
}

raw_affiliation:
[{"affiliation": "Hanyang University", "city": "Seoul", "country": "South Korea"}, {"affiliation": "Korea University", "city": "Seoul", "country": "South Korea"}, {"affiliation": "DFKI", "city": "Saarbr\u00fccken", "country": "Germany"}, {"affiliation": "Hanyang University ERICA", "city": "Ansan", "country": "South Korea"}]


title: RaSS: Improving Denoising Diffusion Samplers with Reinforced Active Sampling Scheduler
authors: Xin Ding, Lei Yu, Xin Li, Zhijun Tu, Hanting Chen, Jie Hu, Zhibo Chen
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 12923-12933
affiliations: ["University of Science and Technology of China", "Huawei Noah\u2019s Ark Lab"]
cities: ["Hefei", "Shenzhen"]
countries: ["China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Ding_RaSS_Improving_Denoising_Diffusion_Samplers_with_Reinforced_Active_Sampling_Scheduler_CVPR_2025_paper.html

abstract:
Recent years have witnessed the great success of denoising diffusion samplers in improving the generative capability and sampling efficiency given a pre-trained diffusion model. However, most sampling schedulers in diffusion models lack the sampling dynamics and planning capability for future generation results, leading to suboptimal solutions. To overcome this, we propose the Reinforced Active Sampling Scheduler, termed RaSS, intending to find the optimal sampling trajectory by actively planning and adjusting the sampling steps for each sampling process in time. Concretely, RaSS divides the whole sampling process into five stages and introduces a reinforcement learning (RL) agent to continuously monitor the generated instance and perceive the potential generation results, thereby achieving optimal instance- and state-adaptive sampling steps decision. Meanwhile, a sampling reward is designed to assist the planning capability of the RL agent by balancing the sampling efficiency and generation quality. The RaSS is a plug-and-play module, which is applicable to multiple denoising diffusion samplers of diffusion models. Extensive experiments on different benchmarks have shown that our RaSS can consistently improve the generation quality and efficiency across various tasks, without introducing significant computational overhead.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Ding_RaSS_Improving_Denoising_Diffusion_Samplers_with_Reinforced_Active_Sampling_Scheduler_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Ding_RaSS_Improving_Denoising_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Ding_2025_CVPR,
    author    = {Ding, Xin and Yu, Lei and Li, Xin and Tu, Zhijun and Chen, Hanting and Hu, Jie and Chen, Zhibo},
    title     = {RaSS: Improving Denoising Diffusion Samplers with Reinforced Active Sampling Scheduler},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {12923-12933}
}

raw_affiliation:
[{"affiliation": "University of Science and Technology of China", "city": "Hefei", "country": "China"}, {"affiliation": "Huawei Noah\u2019s Ark Lab", "city": "Shenzhen", "country": "China"}]


title: Supervising Sound Localization by In-the-wild Egomotion
authors: Anna Min, Ziyang Chen, Hang Zhao, Andrew Owens
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 23936-23946
affiliations: ["University of Michigan", "Tsinghua University", "Shanghai Qi Zhi Institute"]
cities: ["Ann Arbor", "Beijing", "Shanghai"]
countries: ["USA", "China", "China"]
highlight: yes
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Min_Supervising_Sound_Localization_by_In-the-wild_Egomotion_CVPR_2025_paper.html

abstract:
We present a method for learning binaural sound localization from ego-motion in videos. When the camera moves in a video, the direction of sound sources will change along with it. We train an audio model to predict sound directions that are consistent with visual estimates of camera motion, which we obtain using methods from multi-view geometry. This provides a weak but plentiful form of supervision that we combine with traditional binaural cues. To evaluate this idea, we propose a dataset of real-world audio-visual videos with ego-motion. We show that our model can successfully learn from this real-world data, and that it obtains strong performance on sound localization tasks.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Min_Supervising_Sound_Localization_by_In-the-wild_Egomotion_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Min_Supervising_Sound_Localization_CVPR_2025_supplemental.zip

bibtex:
@InProceedings{Min_2025_CVPR,
    author    = {Min, Anna and Chen, Ziyang and Zhao, Hang and Owens, Andrew},
    title     = {Supervising Sound Localization by In-the-wild Egomotion},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {23936-23946}
}

raw_affiliation:
[{"affiliation": "University of Michigan", "city": "Ann Arbor", "country": "USA"}, {"affiliation": "Tsinghua University", "city": "Beijing", "country": "China"}, {"affiliation": "Shanghai Qi Zhi Institute", "city": "Shanghai", "country": "China"}]


title: AutoLUT: LUT-Based Image Super-Resolution with Automatic Sampling and Adaptive Residual Learning
authors: Yuheng Xu, Shijie Yang, Xin Liu, Jie Liu, Jie Tang, Gangshan Wu
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 23131-23140
affiliations: ["State Key Laboratory for Novel Software Technology, Nanjing University"]
cities: ["Nanjing"]
countries: ["China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Xu_AutoLUT_LUT-Based_Image_Super-Resolution_with_Automatic_Sampling_and_Adaptive_Residual_CVPR_2025_paper.html

abstract:
In recent years, the increasing popularity of Hi-DPI screens has driven a rising demand for high-resolution images. However, the limited computational power of edge devices poses a challenge in deploying complex super-resolution neural networks, highlighting the need for efficient methods. While prior works have made significant progress, they have not fully exploited pixel-level information. Moreover, their reliance on fixed sampling patterns limits both accuracy and the ability to capture fine details in low-resolution images. To address these challenges, we introduce two plug-and-play modules designed to capture and leverage pixel information effectively in Look-Up Table (LUT) based super-resolution networks. Our method introduces Automatic Sampling (AutoSample), a flexible LUT sampling approach where sampling weights are dynamically learned during training to adapt to pixel variations and expand the receptive field without added inference cost. We also incorporate Adaptive Residual Learning (AdaRL) to enhance inter-layer connections, enabling detailed information flow and improving the network's ability to reconstruct fine details. Our method achieves significant performance improvements on both MuLUT and SPF-LUT while maintaining similar storage sizes. Specifically, for MuLUT, we achieve a PSNR improvement of approximately +0.20 dB improvement on average across five datasets . For SPF-LUT, with more than a 50% reduction in storage space and about a 2/3 reduction in inference time, our method still maintains performance comparable to the original.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Xu_AutoLUT_LUT-Based_Image_Super-Resolution_with_Automatic_Sampling_and_Adaptive_Residual_CVPR_2025_paper.pdf

bibtex:
@InProceedings{Xu_2025_CVPR,
    author    = {Xu, Yuheng and Yang, Shijie and Liu, Xin and Liu, Jie and Tang, Jie and Wu, Gangshan},
    title     = {AutoLUT: LUT-Based Image Super-Resolution with Automatic Sampling and Adaptive Residual Learning},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {23131-23140}
}

raw_affiliation:
[{"affiliation": "State Key Laboratory for Novel Software Technology, Nanjing University", "city": "Nanjing", "country": "China"}]


title: Understanding Fine-tuning CLIP for Open-vocabulary Semantic Segmentation in Hyperbolic Space
authors: Zelin Peng, Zhengqin Xu, Zhilin Zeng, Changsong Wen, Yu Huang, Menglin Yang, Feilong Tang, Wei Shen
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 4562-4572
cities: ["Shanghai", "Guangzhou", "Abu Dhabi", "Melbourne"]
countries: ["China", "China", "UAE", "Australia"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Peng_Understanding_Fine-tuning_CLIP_for_Open-vocabulary_Semantic_Segmentation_in_Hyperbolic_Space_CVPR_2025_paper.html

abstract:
CLIP, a foundational vision-language model, has emerged as a powerful tool for open-vocabulary semantic segmentation. While freezing the text encoder preserves its powerful embeddings, recent studies show that fine-tuning both the text and image encoders jointly significantly enhances segmentation performance, especially for classes from open sets. In this work, we explain this phenomenon from the perspective of hierarchical alignment, since during fine-tuning, the hierarchy level of image embeddings shifts from image-level to pixel-level. We achieve this by leveraging hyperbolic space, which naturally encoders hierarchical structures. Our key observation is that, during fine-tuning, the hyperbolic radius of CLIP's text embeddings decreases, facilitating better alignment with the pixel-level hierarchical structure of visual data. Building on this insight, we propose HyperCLIP, a novel fine-tuning strategy that adjusts the hyperbolic radius of the text embeddings through scaling transformations. By doing so, HyperCLIP equips CLIP with segmentation capability while introducing only a small number of learnable parameters. Our experiments demonstrate that HyperCLIP achieves state-of-the-art performance on open-vocabulary semantic segmentation tasks across three benchmarks, while fine-tuning only approximately 4% of the total parameters of CLIP. More importantly, we observe that after adjustment, CLIP's text embeddings exhibit a relatively fixed hyperbolic radius across datasets, suggesting that the segmentation task has a characteristic level in hyperbolic space.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Peng_Understanding_Fine-tuning_CLIP_for_Open-vocabulary_Semantic_Segmentation_in_Hyperbolic_Space_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Peng_Understanding_Fine-tuning_CLIP_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Peng_2025_CVPR,
    author    = {Peng, Zelin and Xu, Zhengqin and Zeng, Zhilin and Wen, Changsong and Huang, Yu and Yang, Menglin and Tang, Feilong and Shen, Wei},
    title     = {Understanding Fine-tuning CLIP for Open-vocabulary Semantic Segmentation in Hyperbolic Space},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {4562-4572}
}

raw_affiliation:
[{"affiliation": "MoE Key Lab of Artificial Intelligence, AI Institute, Shanghai Jiao Tong University", "city": "Shanghai", "country": "China"}, {"affiliation": "Hong Kong University of Science and Technology (Guangzhou)", "city": "Guangzhou", "country": "China"}, {"affiliation": "MBZUAI", "city": "Abu Dhabi", "country": "UAE"}, {"affiliation": "Monash University", "city": "Melbourne", "country": "Australia"}]

affiliations:
["MoE Key Lab of Artificial Intelligence, AI Institute, Shanghai Jiao Tong University", "Hong Kong University of Science and Technology (Guangzhou)", "MBZUAI", "Monash University"]


title: TexGaussian: Generating High-quality PBR Material via Octree-based 3D Gaussian Splatting
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 551-561
cities: ["Beijing", "Beijing", "Beijing"]
countries: ["China", "China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Xiong_TexGaussian_Generating_High-quality_PBR_Material_via_Octree-based_3D_Gaussian_Splatting_CVPR_2025_paper.html

authors:
Bojun Xiong, Jialun Liu, Jiakui Hu, Chenming Wu, Jinbo Wu, Xing Liu, Chen Zhao, Errui Ding, Zhouhui Lian

abstract:
Physically Based Rendering (PBR) materials play a crucial role in modern graphics, enabling photorealistic rendering across diverse environment maps. Developing an effective and efficient algorithm that is capable of automatically generating high-quality PBR materials rather than RGB texture for 3D meshes can significantly streamline the 3D content creation. Most existing methods leverage pre-trained 2D diffusion models for multi-view image synthesis, which often leads to severe inconsistency between the generated textures and input 3D meshes. This paper presents TexGaussian, a novel method that uses octant-aligned 3D Gaussian Splatting for rapid PBR material generation. Specifically, we place each 3D Gaussian on the finest leaf node of the octree built from the input 3D mesh to render the multi-view images not only for the albedo map but also for roughness and metallic. Moreover, our model is trained in a regression manner instead of diffusion denoising, capable of generating the PBR material for a 3D mesh in a single feed-forward process. Extensive experiments on publicly available benchmarks demonstrate that our method synthesizes more visually pleasing PBR materials and runs faster than previous methods in both unconditional and text-conditional scenarios, exhibiting better consistency with the given geometry. Our code and trained models are available at https://3d-aigc.github.io/TexGaussian.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Xiong_TexGaussian_Generating_High-quality_PBR_Material_via_Octree-based_3D_Gaussian_Splatting_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Xiong_TexGaussian_Generating_High-quality_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Xiong_2025_CVPR,
    author    = {Xiong, Bojun and Liu, Jialun and Hu, Jiakui and Wu, Chenming and Wu, Jinbo and Liu, Xing and Zhao, Chen and Ding, Errui and Lian, Zhouhui},
    title     = {TexGaussian: Generating High-quality PBR Material via Octree-based 3D Gaussian Splatting},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {551-561}
}

raw_affiliation:
[{"affiliation": "Wangxuan Institute of Computer Technology, Peking University, China", "city": "Beijing", "country": "China"}, {"affiliation": "Baidu VIS", "city": "Beijing", "country": "China"}, {"affiliation": "Institute of Medical Technology, Peking University, China", "city": "Beijing", "country": "China"}]

affiliations:
["Wangxuan Institute of Computer Technology, Peking University, China", "Baidu VIS", "Institute of Medical Technology, Peking University, China"]


title: OSV: One Step is Enough for High-Quality Image to Video Generation
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 12585-12594
cities: ["Shanghai", "Hong Kong", "Hong Kong", "Hangzhou", "Shenzhen"]
countries: ["China", "China", "China", "China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Mao_OSV_One_Step_is_Enough_for_High-Quality_Image_to_Video_CVPR_2025_paper.html

authors:
Xiaofeng Mao, Zhengkai Jiang, Fu-yun Wang, Jiangning Zhang, Hao Chen, Mingmin Chi, Yabiao Wang, Wenhan Luo

abstract:
Video diffusion models have shown great potential in generating high-quality videos, making them an increasingly popular focus. However, their inherent iterative nature leads to substantial computational and time costs. Although techniques such as consistency distillation and adversarial training have been employed to accelerate video diffusion by reducing inference steps, these methods often simply transfer the generation approaches from Image diffusion models to video diffusion models. As a result, these methods frequently fall short in terms of both performance and training stability. In this work, we introduce a two-stage training framework that effectively combines consistency distillation with adversarial training to address these challenges. Additionally, we propose a novel video discriminator design, which eliminates the need for decoding the video latents and improves the final performance. Our model is capable of producing high-quality videos in merely one-step, with the flexibility to perform multi-step refinement for further performance enhancement. Our quantitative evaluation on the OpenVid-1M benchmark shows that our model significantly outperforms existing methods. Notably, our 1-step performance (FVD 171.15) exceeds the 8-step performance of the  consistency distillation based method, AnimateLCM (FVD 184.79), and approaches the 25-step performance of advanced Stable Video Diffusion (FVD 156.94).

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Mao_OSV_One_Step_is_Enough_for_High-Quality_Image_to_Video_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Mao_OSV_One_Step_CVPR_2025_supplemental.zip

bibtex:
@InProceedings{Mao_2025_CVPR,
    author    = {Mao, Xiaofeng and Jiang, Zhengkai and Wang, Fu-yun and Zhang, Jiangning and Chen, Hao and Chi, Mingmin and Wang, Yabiao and Luo, Wenhan},
    title     = {OSV: One Step is Enough for High-Quality Image to Video Generation},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {12585-12594}
}

raw_affiliation:
[{"affiliation": "Fudan University", "city": "Shanghai", "country": "China"}, {"affiliation": "Hong Kong University of Science and Technology", "city": "Hong Kong", "country": "China"}, {"affiliation": "The Chinese University of Hong Kong", "city": "Hong Kong", "country": "China"}, {"affiliation": "Zhejiang University", "city": "Hangzhou", "country": "China"}, {"affiliation": "Tencent Y ouTu Lab", "city": "Shenzhen", "country": "China"}]

affiliations:
["Fudan University", "Hong Kong University of Science and Technology", "The Chinese University of Hong Kong", "Zhejiang University", "Tencent Y ouTu Lab"]


title: Fuzzy Multimodal Learning for Trusted Cross-modal Retrieval
authors: Siyuan Duan, Yuan Sun, Dezhong Peng, Zheng Liu, Xiaomin Song, Peng Hu
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 20747-20756
cities: ["Chengdu", "Chengdu", "Chengdu"]
countries: ["China", "China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Duan_Fuzzy_Multimodal_Learning_for_Trusted_Cross-modal_Retrieval_CVPR_2025_paper.html

abstract:
Cross-modal retrieval aims to match related samples across distinct modalities, facilitating the retrieval and discovery of heterogeneous information. Although existing methods show promising performance, most are deterministic models and are unable to capture the uncertainty inherent in the retrieval outputs, leading to potentially unreliable results. To address this issue, we propose a novel framework called FUzzy Multimodal lEarning (FUME), which is able to self-estimate epistemic uncertainty, thereby embracing trusted cross-modal retrieval. Specifically, our FUME leverages the Fuzzy Set Theory to view the outputs of the classification network as a set of membership degrees and quantify category credibility by incorporating both possibility and necessity measures. However, directly optimizing the category credibility could mislead the model by over-optimizing the necessity for unmatched categories. To overcome this challenge, we present a novel fuzzy multimodal learning strategy, which utilizes label information to guide necessity optimization in the right direction, thereby indirectly optimizing category credibility and achieving accurate decision uncertainty quantification. Furthermore, we design an uncertainty merging scheme that accounts for decision uncertainties, thus further refining uncertainty estimates and boosting the trustworthiness of retrieval results. Extensive experiments on five benchmark datasets demonstrate that FUME remarkably improves both retrieval performance and reliability, offering a prospective solution for cross-modal retrieval in high-stakes applications. Code is available at https://github.com/siyuancncd/FUME .

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Duan_Fuzzy_Multimodal_Learning_for_Trusted_Cross-modal_Retrieval_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Duan_Fuzzy_Multimodal_Learning_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Duan_2025_CVPR,
    author    = {Duan, Siyuan and Sun, Yuan and Peng, Dezhong and Liu, Zheng and Song, Xiaomin and Hu, Peng},
    title     = {Fuzzy Multimodal Learning for Trusted Cross-modal Retrieval},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {20747-20756}
}

raw_affiliation:
[{"affiliation": "College of Computer Science, Sichuan University, Chengdu, 610095, China.", "city": "Chengdu", "country": "China"}, {"affiliation": "Sichuan National Innovation New Vision UHD Video Technology Co., Ltd, Chengdu, 610041, China", "city": "Chengdu", "country": "China"}, {"affiliation": "Tianfu Jincheng Laboratory, Chengdu, 610093, China.", "city": "Chengdu", "country": "China"}]

affiliations:
["College of Computer Science, Sichuan University, Chengdu, 610095, China.", "Sichuan National Innovation New Vision UHD Video Technology Co., Ltd, Chengdu, 610041, China", "Tianfu Jincheng Laboratory, Chengdu, 610093, China."]


title: AniGS: Animatable Gaussian Avatar from a Single Image with Inconsistent Gaussian Reconstruction
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 21148-21158
cities: ["Hangzhou", "Shenzhen", "Nanjing", "Wuhan"]
countries: ["China", "China", "China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Qiu_AniGS_Animatable_Gaussian_Avatar_from_a_Single_Image_with_Inconsistent_CVPR_2025_paper.html

authors:
Lingteng Qiu, Shenhao Zhu, Qi Zuo, Xiaodong Gu, Yuan Dong, Junfei Zhang, Chao Xu, Zhe Li, Weihao Yuan, Liefeng Bo, Guanying Chen, Zilong Dong

abstract:
Generating animatable human avatars from a single image is essential for various digital human modeling applications. Existing 3D reconstruction methods often struggle to capture fine details in animatable models, while generative approaches for controllable animation, though avoiding explicit 3D modeling, suffer from viewpoint inconsistencies in extreme poses and computational inefficiencies. In this paper, we address these challenges by leveraging the power of generative models to produce detailed multi-view canonical pose images, which help resolve ambiguities in animatable human reconstruction. We then propose a robust method for 3D reconstruction of inconsistent images, enabling real-time rendering during inference. Specifically, we adapt a transformer-based Text-to-Video model to generate multi-view canonical pose images and normal maps, pretraining on a large-scale monocular video dataset to improve generalization. To handle view inconsistencies, we recast the reconstruction problem as a 4D task and introduce an efficient 3D modeling approach using 4D Gaussian Splatting. Experiments demonstrate that our method achieves photorealistic, real-time animation of 3D human avatars from in-the-wild images, showcasing its effectiveness and generalization capability.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Qiu_AniGS_Animatable_Gaussian_Avatar_from_a_Single_Image_with_Inconsistent_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Qiu_AniGS_Animatable_Gaussian_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Qiu_2025_CVPR,
    author    = {Qiu, Lingteng and Zhu, Shenhao and Zuo, Qi and Gu, Xiaodong and Dong, Yuan and Zhang, Junfei and Xu, Chao and Li, Zhe and Yuan, Weihao and Bo, Liefeng and Chen, Guanying and Dong, Zilong},
    title     = {AniGS: Animatable Gaussian Avatar from a Single Image with Inconsistent Gaussian Reconstruction},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {21148-21158}
}

raw_affiliation:
[{"affiliation": "Tongyi Lab, Alibaba Group", "city": "Hangzhou", "country": "China"}, {"affiliation": "Shenzhen Campus of Sun Yat-sen University", "city": "Shenzhen", "country": "China"}, {"affiliation": "Nanjing University", "city": "Nanjing", "country": "China"}, {"affiliation": "Huazhong University of Science and Technology", "city": "Wuhan", "country": "China"}]

affiliations:
["Tongyi Lab, Alibaba Group", "Shenzhen Campus of Sun Yat-sen University", "Nanjing University", "Huazhong University of Science and Technology"]


title: GUI-Xplore: Empowering Generalizable GUI Agents with One Exploration
authors: Yuchen Sun, Shanhui Zhao, Tao Yu, Hao Wen, Samith Va, Mengwei Xu, Yuanchun Li, Chongyang Zhang
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 19477-19486
cities: ["Shanghai", "Beijing", "Shanghai", "Beijing", "Beijing"]
countries: ["China", "China", "China", "China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Sun_GUI-Xplore_Empowering_Generalizable_GUI_Agents_with_One_Exploration_CVPR_2025_paper.html

abstract:
GUI agents hold significant potential to enhance the experience and efficiency of human-device interaction. However, current methods face challenges in generalizing across applications (apps) and tasks, primarily due to two fundamental limitations in existing datasets. First, these datasets overlook developer-induced structural variations among apps, limiting the transferability of knowledge across diverse software environments. Second, many of them focus solely on navigation tasks, which restricts their capacity to represent comprehensive software architectures and complex user interactions.  To address these challenges, we introduce GUI-Xplore, a dataset meticulously designed to enhance cross-application and cross-task generalization via an exploration-and-reasoning framework. GUI-Xplore integrates pre-recorded exploration videos providing contextual insights, alongside five hierarchically structured downstream tasks designed to comprehensively evaluate GUI agent capabilities. To fully exploit GUI-Xplore's unique features, we propose Xplore-Agent, a GUI agent framework that combines Action-aware GUI Modeling with Graph-Guided Environment Reasoning. Further experiments indicate that Xplore-Agent achieves a 10% improvement over existing methods in unfamiliar environments, yet there remains significant potential for further enhancement towards truly generalizable GUI agents.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Sun_GUI-Xplore_Empowering_Generalizable_GUI_Agents_with_One_Exploration_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Sun_GUI-Xplore_Empowering_Generalizable_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Sun_2025_CVPR,
    author    = {Sun, Yuchen and Zhao, Shanhui and Yu, Tao and Wen, Hao and Va, Samith and Xu, Mengwei and Li, Yuanchun and Zhang, Chongyang},
    title     = {GUI-Xplore: Empowering Generalizable GUI Agents with One Exploration},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {19477-19486}
}

raw_affiliation:
[{"affiliation": "School of Information Science and Electronic Engineering, Shanghai Jiao Tong University", "city": "Shanghai", "country": "China"}, {"affiliation": "Institute for AI Industry Research (AIR), Tsinghua University", "city": "Beijing", "country": "China"}, {"affiliation": "MoE Key Lab of Artificial Intelligence, AI Institute, Shanghai Jiao Tong University", "city": "Shanghai", "country": "China"}, {"affiliation": "Beijing Academy of Artificial Intelligence (BAAI)", "city": "Beijing", "country": "China"}, {"affiliation": "Beijing University of Posts and Telecommunications", "city": "Beijing", "country": "China"}]

affiliations:
["School of Information Science and Electronic Engineering, Shanghai Jiao Tong University", "Institute for AI Industry Research (AIR), Tsinghua University", "MoE Key Lab of Artificial Intelligence, AI Institute, Shanghai Jiao Tong University", "Beijing Academy of Artificial Intelligence (BAAI)", "Beijing University of Posts and Telecommunications"]


title: Few-Shot Recognition via Stage-Wise Retrieval-Augmented Finetuning
authors: Tian Liu, Huixin Zhang, Shubham Parashar, Shu Kong
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 15086-15097
affiliations: ["Texas A&M University", "University of Macau", "Institute of Collaborative Innovation"]
cities: ["College Station", "Macau", "Macau"]
countries: ["USA", "China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Liu_Few-Shot_Recognition_via_Stage-Wise_Retrieval-Augmented_Finetuning_CVPR_2025_paper.html

abstract:
Few-shot recognition (FSR) aims to train a classification model with only a few labeled examples of each concept concerned by a downstream task, where data annotation cost can be prohibitively high. We develop methods to solve FSR by leveraging a pretrained Vision-Language Model (VLM). We particularly explore retrieval-augmented learning (RAL), which retrieves open data, e.g., the VLM's pretraining dataset, to learn models for better serving downstream tasks. RAL has been studied in zero-shot recognition but remains under-explored in FSR. Although applying RAL to FSR may seem straightforward, we observe interesting and novel challenges and opportunities. First, somewhat surprisingly, finetuning a VLM on a large amount of retrieved data underperforms state-of-the-art zero-shot methods. This is due to the imbalanced distribution of retrieved data and its domain gaps with the few-shot examples in the downstream task. Second, more surprisingly, we find that simply finetuning a VLM solely on few-shot examples significantly outperforms previous FSR methods, and finetuning on the mix of retrieved and few-shot data yields even better results. Third, to mitigate the imbalanced distribution and domain gap issues, we propose Stage-Wise retrieval-Augmented fineTuning (SWAT), which involves end-to-end finetuning on mixed data in the first stage and retraining the classifier on the few-shot data in the second stage. Extensive experiments on nine popular benchmarks demonstrate that SWAT significantly outperforms previous methods by >6% accuracy.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Liu_Few-Shot_Recognition_via_Stage-Wise_Retrieval-Augmented_Finetuning_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Liu_Few-Shot_Recognition_via_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Liu_2025_CVPR,
    author    = {Liu, Tian and Zhang, Huixin and Parashar, Shubham and Kong, Shu},
    title     = {Few-Shot Recognition via Stage-Wise Retrieval-Augmented Finetuning},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {15086-15097}
}

raw_affiliation:
[{"affiliation": "Texas A&M University", "city": "College Station", "country": "USA"}, {"affiliation": "University of Macau", "city": "Macau", "country": "China"}, {"affiliation": "Institute of Collaborative Innovation", "city": "Macau", "country": "China"}]


title: Concept Replacer: Replacing Sensitive Concepts in Diffusion Models via Precision Localization
authors: Lingyun Zhang, Yu Xie, Yanwei Fu, Ping Chen
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 8172-8181
affiliations: ["Fudan University", "Purple Mountain Laboratories", "Fudan University", "Fudan University"]
cities: ["Shanghai", "Nanjing", "Shanghai", "Shanghai"]
countries: ["China", "China", "China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Zhang_Concept_Replacer_Replacing_Sensitive_Concepts_in_Diffusion_Models_via_Precision_CVPR_2025_paper.html

abstract:
As large-scale diffusion models continue to advance, they excel at producing high-quality images but often generate unwanted content, such as sexually explicit or violent content. Existing methods for concept removal generally guide the image generation process but can unintentionally modify unrelated regions, leading to inconsistencies with the original model. We propose a novel approach for targeted concept replacing in diffusion models, enabling specific concepts to be removed without affecting non-target areas. Our method introduces a dedicated concept localizer for precisely identifying the target concept during the denoising process, trained with few-shot learning to require minimal labeled data. Within the identified region, we introduce a training-free Dual Prompts Cross-Attention (DPCA) module to substitute the target concept, ensuring minimal disruption to surrounding content. We evaluate our method on concept localization precision and replacement efficiency. Experimental results demonstrate that our method achieves superior precision in localizing target concepts and performs coherent concept replacement with minimal impact on non-target areas, outperforming existing approaches.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Zhang_Concept_Replacer_Replacing_Sensitive_Concepts_in_Diffusion_Models_via_Precision_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Zhang_Concept_Replacer_Replacing_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Zhang_2025_CVPR,
    author    = {Zhang, Lingyun and Xie, Yu and Fu, Yanwei and Chen, Ping},
    title     = {Concept Replacer: Replacing Sensitive Concepts in Diffusion Models via Precision Localization},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {8172-8181}
}

raw_affiliation:
[{"affiliation": "Fudan University", "city": "Shanghai", "country": "China"}, {"affiliation": "Purple Mountain Laboratories", "city": "Nanjing", "country": "China"}, {"affiliation": "Fudan University", "city": "Shanghai", "country": "China"}, {"affiliation": "Fudan University", "city": "Shanghai", "country": "China"}]


title: A Regularization-Guided Equivariant Approach for Image Restoration
authors: Yulu Bai, Jiahong Fu, Qi Xie, Deyu Meng
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 2300-2310
cities: ["Xi'an", "Taipa", "Shenzhen"]
countries: ["China", "Macao", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Bai_A_Regularization-Guided_Equivariant_Approach_for_Image_Restoration_CVPR_2025_paper.html

abstract:
Equivariant and invariant deep learning models have been developed to exploit intrinsic symmetries in data, demonstrating significant effectiveness in certain scenarios. However, these methods often suffer from limited representation accuracy and rely on strict symmetry assumptions that may not hold in practice. These limitations pose a significant drawback for image restoration tasks, which demands high accuracy and precise symmetry representation. To address these challenges, we propose a rotation-equivariant regularization strategy that adaptively enforces the appropriate symmetry constraints on the data while preserving the network's representational accuracy. Specifically, we introduce EQ-Reg, a regularizer designed to enhance rotation equivariance, which  innovatively extends the insights of data-augmentation-based and equivariant-based methodologies. This is achieved through self-supervised learning and the spatial rotation and cyclic channel shift of feature maps deduce in the equivariant framework. Our approach firstly enables a non-strictly equivariant network suitable for image restoration, providing a simple and adaptive mechanism for adjusting equivariance based on task. Extensive experiments across three low-level tasks demonstrate the superior accuracy and generalization capability of our method, outperforming state-of-the-art approaches.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Bai_A_Regularization-Guided_Equivariant_Approach_for_Image_Restoration_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Bai_A_Regularization-Guided_Equivariant_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Bai_2025_CVPR,
    author    = {Bai, Yulu and Fu, Jiahong and Xie, Qi and Meng, Deyu},
    title     = {A Regularization-Guided Equivariant Approach for Image Restoration},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {2300-2310}
}

raw_affiliation:
[{"affiliation": "School of Mathematics and Statistics, Xi\u2019an Jiaotong University", "city": "Xi'an", "country": "China"}, {"affiliation": "Macau University of Science and Technology", "city": "Taipa", "country": "Macao"}, {"affiliation": "Pengcheng Laboratory", "city": "Shenzhen", "country": "China"}]

affiliations:
["School of Mathematics and Statistics, Xi\u2019an Jiaotong University", "Macau University of Science and Technology", "Pengcheng Laboratory"]


title: RestorGS: Depth-aware Gaussian Splatting for Efficient 3D Scene Restoration
authors: Yuanjian Qiao, Mingwen Shao, Lingzhuang Meng, Kai Xu
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 11177-11186
cities: ["Qingdao"]
countries: ["China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Qiao_RestorGS_Depth-aware_Gaussian_Splatting_for_Efficient_3D_Scene_Restoration_CVPR_2025_paper.html

abstract:
3D Gaussian Splatting (3DGS) has recently achieved remarkable progress in novel view synthesis. However, existing methods rely heavily on high-quality data for rendering and struggle to handle degraded scenes with multi-view inconsistency, leading to inferior rendering quality. To address this challenge, we propose a novel Depth-aware Gaussian Splatting for efficient 3D scene Restoration, called RestorGS, which flexibly restores multiple degraded scenes using a unified framework. Specifically, RestorGS consists of two core designs: Appearance Decoupling and Depth-Guided Modeling. The former exploits appearance learning over spherical harmonics to decouple clear and degraded Gaussian, thus separating the clear views from the degraded ones.  Collaboratively, the latter leverages the depth information to guide the degradation modeling, thereby facilitating the decoupling process. Benefiting from the above optimization strategy, our method achieves high-quality restoration while enabling real-time rendering speed.  Extensive experiments show that our RestorGS outperforms existing methods significantly in underwater, nighttime, and hazy scenes.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Qiao_RestorGS_Depth-aware_Gaussian_Splatting_for_Efficient_3D_Scene_Restoration_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Qiao_RestorGS_Depth-aware_Gaussian_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Qiao_2025_CVPR,
    author    = {Qiao, Yuanjian and Shao, Mingwen and Meng, Lingzhuang and Xu, Kai},
    title     = {RestorGS: Depth-aware Gaussian Splatting for Efficient 3D Scene Restoration},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {11177-11186}
}

raw_affiliation:
[{"affiliation": "Shandong Key Laboratory of Intelligent Oil & Gas Industrial Software, Qingdao Institute of Software, College of Computer Science and Technology, China University of Petroleum (East China)", "city": "Qingdao", "country": "China"}]

affiliations:
["Shandong Key Laboratory of Intelligent Oil & Gas Industrial Software, Qingdao Institute of Software, College of Computer Science and Technology, China University of Petroleum (East China)"]


authors: Yuan Li, Ziqian Bai, Feitong Tan, Zhaopeng Cui, Sean Fanello, Yinda Zhang
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 21107-21116
affiliations: ["State Key Lab of CAD&CG, Zhejiang University", "Google"]
cities: ["Hangzhou", "Mountain View"]
countries: ["China", "USA"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Li_IM-Portrait_Learning_3D-aware_Video_Diffusion_for_Photorealistic_Talking_Heads_from_CVPR_2025_paper.html

title:
IM-Portrait: Learning 3D-aware Video Diffusion for Photorealistic Talking Heads from Monocular VideosC

abstract:
We propose a novel 3D-aware diffusion-based method for generating photorealistic talking head videos directly from a single identity image and explicit control signals (e.g., expressions). Our method generates Multiplane Images (MPIs) that ensure geometric consistency, making them ideal for immersive viewing experiences like binocular videos for VR headsets.Unlike existing methods that often require a separate stage or joint optimization to reconstruct a 3D representation (such as NeRF or 3D Gaussians), our approach directly generates the final output through a single denoising process, eliminating the need for post-processing steps to render novel views efficiently.To effectively learn from monocular videos, we introduce a training mechanism that reconstructs the output MPI randomly in either the target or the reference camera space. This approach enables the model to simultaneously learn sharp image details and underlying 3D information.Extensive experiments demonstrate the effectiveness of our method, which achieves competitive avatar quality and novel-view rendering capabilities, even without explicit 3D reconstruction or high-quality multi-view training data.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Li_IM-Portrait_Learning_3D-aware_Video_Diffusion_for_Photorealistic_Talking_Heads_from_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Li_IM-Portrait_Learning_3D-aware_CVPR_2025_supplemental.zip

bibtex:
@InProceedings{Li_2025_CVPR,
    author    = {Li, Yuan and Bai, Ziqian and Tan, Feitong and Cui, Zhaopeng and Fanello, Sean and Zhang, Yinda},
    title     = {IM-Portrait: Learning 3D-aware Video Diffusion for Photorealistic Talking Heads from Monocular VideosC},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {21107-21116}
}

raw_affiliation:
[{"affiliation": "State Key Lab of CAD&CG, Zhejiang University", "city": "Hangzhou", "country": "China"}, {"affiliation": "Google", "city": "Mountain View", "country": "USA"}]


title: Deep Fair Multi-View Clustering with Attention KAN
authors: HaiMing Xu, Qianqian Wang, Boyue Wang, Quanxue Gao
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 5061-5070
affiliations: ["Xidian University", "Beijing University of Technology"]
cities: ["Xi'an", "Beijing"]
countries: ["China", "China"]
highlight: yes
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Xu_Deep_Fair_Multi-View_Clustering_with_Attention_KAN_CVPR_2025_paper.html

abstract:
Multi-view clustering is effective in unsupervised multi-view data analysis and has received considerable attention. However, most existing methods excessively emphasize certain attributes, resulting in unfair clustering outcomes, i.e., certain sensitive attributes dominate the clustering results. Moreover, existing methods struggle to effectively capture complex nonlinear relationships and interactions across views, limiting their ability to achieve optimal clustering performance. Therefore, in this work, we propose a novel method, Deep Fair Multi-View Clustering with Attention Kolmogorov-Arnold Network (DFMVC-AKAN), to generate fair clustering results while maintaining robust performance. DFMVC-AKAN integrates attention mechanisms into Kolmogorov-Arnold Networks (KAN) to exploit the complex nonlinear inter-view relationships. Specifically, KAN provides a nonlinear feature representation capable of efficiently approximating arbitrary multivariate continuous functions, augmented by a hybrid attention mechanism which enables the model to dynamically focus on the most relevant features. Finally, we refine the clustering assignments with a distribution alignment module to ensure fair outcomes across diverse groups while maintaining discriminative ability. Experimental results on four datasets containing sensitive attributes demonstrate that DFMVC-AKAN significantly improves fairness and clustering performance compared to state-of-the-art methods.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Xu_Deep_Fair_Multi-View_Clustering_with_Attention_KAN_CVPR_2025_paper.pdf

bibtex:
@InProceedings{Xu_2025_CVPR,
    author    = {Xu, HaiMing and Wang, Qianqian and Wang, Boyue and Gao, Quanxue},
    title     = {Deep Fair Multi-View Clustering with Attention KAN},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {5061-5070}
}

raw_affiliation:
[{"affiliation": "Xidian University", "city": "Xi'an", "country": "China"}, {"affiliation": "Beijing University of Technology", "city": "Beijing", "country": "China"}]


authors: Xi Wang, Hongzhen Li, Heng Fang, Yichen Peng, Haoran Xie, Xi Yang, Chuntao Li
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 2912-2923
cities: ["Changchun", "Stockholm", "Tokyo", "Nomi"]
countries: ["China", "Sweden", "Japan", "Japan"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Wang_LineArt_A_Knowledge-guided_Training-free_High-quality_Appearance_Transfer_for_Design_Drawing_CVPR_2025_paper.html

title:
LineArt: A Knowledge-guided Training-free High-quality Appearance Transfer for Design Drawing with Diffusion Model

abstract:
Image rendering from line drawings is vital in design and image generation technologies reduce costs, yet professional line drawings demand preserving complex details. Text prompts struggle with accuracy, and image translation struggles with consistency and fine-grained control. We present LineArt, a framework that transfers complex appearance onto detailed design drawings, facilitating design and artistic creation. It generates high-fidelity appearance while preserving structural accuracy by simulating hierarchical visual cognition and integrating human artistic experience to guide the diffusion process. LineArt overcomes the limitations of current methods in terms of difficulty in fine-grained control and style degradation in design drawings. It requires no precise 3D modeling, physical property specifications, or network training, making it more convenient for design tasks. LineArt consists of two stages: a multi-frequency lines fusion module to supplement the input design drawing with detailed structural information and a two-part painting process for Base Layer Shaping and Surface Layer Coloring. We also present a new design drawing dataset, ProLines, for evaluation. The experiments show that LineArt performs better in accuracy, realism, and material precision compared to SOTAs.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Wang_LineArt_A_Knowledge-guided_Training-free_High-quality_Appearance_Transfer_for_Design_Drawing_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Wang_LineArt_A_Knowledge-guided_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Wang_2025_CVPR,
    author    = {Wang, Xi and Li, Hongzhen and Fang, Heng and Peng, Yichen and Xie, Haoran and Yang, Xi and Li, Chuntao},
    title     = {LineArt: A Knowledge-guided Training-free High-quality Appearance Transfer for Design Drawing with Diffusion Model},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {2912-2923}
}

raw_affiliation:
[{"affiliation": "Jilin University", "city": "Changchun", "country": "China"}, {"affiliation": "KTH Royal Institute of Technology", "city": "Stockholm", "country": "Sweden"}, {"affiliation": "Institute of Science Tokyo", "city": "Tokyo", "country": "Japan"}, {"affiliation": "Japan Advanced Institute of Science and Technology (JAIST)", "city": "Nomi", "country": "Japan"}]

affiliations:
["Jilin University", "KTH Royal Institute of Technology", "Institute of Science Tokyo", "Japan Advanced Institute of Science and Technology (JAIST)"]


title: 4Real-Video: Learning Generalizable Photo-Realistic 4D Video Diffusion
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 17723-17732
affiliations: ["Snap Inc", "Umass Amherst", "KAUST"]
cities: ["Los Angeles", "Amherst", "Thuwal"]
countries: ["USA", "USA", "Saudi Arabia"]
highlight: yes
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Wang_4Real-Video_Learning_Generalizable_Photo-Realistic_4D_Video_Diffusion_CVPR_2025_paper.html

authors:
Chaoyang Wang, Peiye Zhuang, Tuan Duc Ngo, Willi Menapace, Aliaksandr Siarohin, Michael Vasilkovsky, Ivan Skorokhodov, Sergey Tulyakov, Peter Wonka, Hsin-Ying Lee

abstract:
We propose 4Real-Video, a novel framework for generating 4D videos, organized as a grid of video frames with both time and viewpoint axes.  In this grid, each row contains frames sharing the same timestep, while each column contains frames from the same viewpoint. One stream performs viewpoint updates on columns, and the other stream performs temporal updates on rows. After each diffusion transformer layer, a newly designed synchronization layer exchanges information between the two token streams. We propose two implementations of the synchronization layer, using either hard or soft synchronization.This feedforward architecture improves upon previous work in three ways: higher inference speed, enhanced visual quality (measured by FVD, CLIP, and VideoScore), and improved temporal and viewpoint consistency (measured by VideoScore, GIM-Confidence, and Dust3R-Confidence).

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Wang_4Real-Video_Learning_Generalizable_Photo-Realistic_4D_Video_Diffusion_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Wang_4Real-Video_Learning_Generalizable_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Wang_2025_CVPR,
    author    = {Wang, Chaoyang and Zhuang, Peiye and Ngo, Tuan Duc and Menapace, Willi and Siarohin, Aliaksandr and Vasilkovsky, Michael and Skorokhodov, Ivan and Tulyakov, Sergey and Wonka, Peter and Lee, Hsin-Ying},
    title     = {4Real-Video: Learning Generalizable Photo-Realistic 4D Video Diffusion},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {17723-17732}
}

raw_affiliation:
[{"affiliation": "Snap Inc", "city": "Los Angeles", "country": "USA"}, {"affiliation": "Umass Amherst", "city": "Amherst", "country": "USA"}, {"affiliation": "KAUST", "city": "Thuwal", "country": "Saudi Arabia"}]


title: DynaMoDe-NeRF: Motion-aware Deblurring Neural Radiance Field for Dynamic Scenes
authors: Ashish Kumar, Rajagopalan A. N.
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 21728-21738
raw_affiliation: [{"affiliation": "Indian Institute of Technology Madras", "city": "Chennai", "country": "India"}]
affiliations: ["Indian Institute of Technology Madras"]
cities: ["Chennai"]
countries: ["India"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Kumar_DynaMoDe-NeRF_Motion-aware_Deblurring_Neural_Radiance_Field_for_Dynamic_Scenes_CVPR_2025_paper.html

abstract:
Neural Radiance Fields (NeRFs) have made significant advances in rendering novel photorealistic views for both static and dynamic scenes. However, most prior works assume ideal conditions of artifact-free visual inputs i.e., images and videos. In real scenarios, artifacts such as object motion blur, camera motion blur, or lens defocus blur are ubiquitous. Some recent studies have explored novel view synthesis using blurred input frames by examining either camera motion blur, defocus blur, or both. However, these studies are limited to static scenes. In this work, we enable NeRFs to deal with object motion blur whose local nature stems from the interplay between object velocity and camera exposure time. Often, the object motion is unknown and time varying, and this adds to the complexity of scene reconstruction. Sports videos are a prime example of how rapid object motion can significantly degrade video quality for static cameras by introducing motion blur. We present an approach for realizing motion blur-free novel views of dynamic scenes from input videos with object motion blur captured from static cameras spanning multiple poses. We propose a NeRF-based analytical framework that elegantly correlates object three-dimensional (3D) motion across views as well as time to the observed blurry videos. Our proposed method DynaMoDe-NeRF (Dynamic Motion-aware Deblurring NeRF) is self-supervised and reconstructs the dynamic 3D scene, renders sharp novel views by blind deblurring, and recovers the underlying 3D motion and blur parameters. We provide comprehensive experimental analysis on synthetic and real data to validate our approach. To the best of our knowledge, this is the first work to address localized object motion blur in the NeRF domain.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Kumar_DynaMoDe-NeRF_Motion-aware_Deblurring_Neural_Radiance_Field_for_Dynamic_Scenes_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Kumar_DynaMoDe-NeRF_Motion-aware_Deblurring_CVPR_2025_supplemental.zip

bibtex:
@InProceedings{Kumar_2025_CVPR,
    author    = {Kumar, Ashish and N., Rajagopalan A.},
    title     = {DynaMoDe-NeRF: Motion-aware Deblurring Neural Radiance Field for Dynamic Scenes},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {21728-21738}
}


authors: Kangsan Kim, Geon Park, Youngwan Lee, Woongyeong Yeo, Sung Ju Hwang
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 3295-3305
affiliations: ["KAIST", "DeepAuto.ai", "ETRI"]
cities: ["Daejeon", "Seoul", "Daejeon"]
countries: ["South Korea", "South Korea", "South Korea"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Kim_VideoICL_Confidence-based_Iterative_In-context_Learning_for_Out-of-Distribution_Video_Understanding_CVPR_2025_paper.html

title:
VideoICL: Confidence-based Iterative In-context Learning for Out-of-Distribution Video Understanding

abstract:
Recent advancements in video large multimodal models (LMMs) have significantly improved their video understanding and reasoning capabilities. However, their performance drops on out-of-distribution (OOD) tasks that are underrepresented in training data. Traditional methods like fine-tuning on OOD datasets are impractical due to high computational costs. While In-context learning (ICL) with demonstration examples has shown promising generalization performance in language tasks and image-language tasks without fine-tuning, applying ICL to video-language tasks faces challenges due to the limited context length in Video LMMs, as videos require longer token lengths. To address these issues, we propose VideoICL, a novel video in-context learning framework for OOD tasks that introduces a similarity-based relevant example selection strategy and a confidence-based iterative inference approach. This allows to select the most relevant examples and rank them based on similarity, to be used for inference. If the generated response has low confidence, our framework selects new examples and performs inference again, iteratively refining the results until a high-confidence response is obtained. This approach improves OOD video understanding performance by extending effective context length without incurring high costs. The experimental results on multiple benchmarks demonstrate significant performance gains, especially in domain-specific scenarios, laying the groundwork for broader video comprehension applications.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Kim_VideoICL_Confidence-based_Iterative_In-context_Learning_for_Out-of-Distribution_Video_Understanding_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Kim_VideoICL_Confidence-based_Iterative_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Kim_2025_CVPR,
    author    = {Kim, Kangsan and Park, Geon and Lee, Youngwan and Yeo, Woongyeong and Hwang, Sung Ju},
    title     = {VideoICL: Confidence-based Iterative In-context Learning for Out-of-Distribution Video Understanding},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {3295-3305}
}

raw_affiliation:
[{"affiliation": "KAIST", "city": "Daejeon", "country": "South Korea"}, {"affiliation": "DeepAuto.ai", "city": "Seoul", "country": "South Korea"}, {"affiliation": "ETRI", "city": "Daejeon", "country": "South Korea"}]


