# papers

title: Revisiting Audio-Visual Segmentation with Vision-Centric Transformer
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 8352-8361
cities: ["Hefei", "Beijing", "Beijing", "Beijing", "Shenzhen"]
countries: ["China", "China", "China", "China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Huang_Revisiting_Audio-Visual_Segmentation_with_Vision-Centric_Transformer_CVPR_2025_paper.html

authors:
Shaofei Huang, Rui Ling, Tianrui Hui, Hongyu Li, Xu Zhou, Shifeng Zhang, Si Liu, Richang Hong, Meng Wang

abstract:
Audio-Visual Segmentation (AVS) aims to segment sound-producing objects in video frames based on the associated audio signal. Prevailing AVS methods typically adopt an audio-centric Transformer architecture, where object queries are derived from audio features. However, audio-centric Transformers suffer from two limitations: perception ambiguity caused by the mixed nature of audio, and weakened dense prediction ability due to visual detail loss. To address these limitations, we propose a new Vision-Centric Transformer (VCT) framework that leverages vision-derived queries to iteratively fetch corresponding audio and visual information, enabling queries to better distinguish between different sounding objects from mixed audio and accurately delineate their contours. Additionally, we also introduce a Prototype Prompted Query Generation (PPQG) module within our VCT framework to generate vision-derived queries that are both semantically aware and visually rich through audio prototype prompting and pixel context grouping, facilitating audio-visual information aggregation. Extensive experiments demonstrate that our VCT framework achieves new state-of-the-art performances on three subsets of the AVSBench dataset.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Huang_Revisiting_Audio-Visual_Segmentation_with_Vision-Centric_Transformer_CVPR_2025_paper.pdf

bibtex:
@InProceedings{Huang_2025_CVPR,
    author    = {Huang, Shaofei and Ling, Rui and Hui, Tianrui and Li, Hongyu and Zhou, Xu and Zhang, Shifeng and Liu, Si and Hong, Richang and Wang, Meng},
    title     = {Revisiting Audio-Visual Segmentation with Vision-Centric Transformer},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {8352-8361}
}

raw_affiliation:
[{"affiliation": "School of Computer Science and Information Engineering, Hefei University of Technology", "city": "Hefei", "country": "China"}, {"affiliation": "Institute of Information Engineering, Chinese Academy of Sciences", "city": "Beijing", "country": "China"}, {"affiliation": "School of Computer Science and Engineering, Beihang University", "city": "Beijing", "country": "China"}, {"affiliation": "School of Arti\ufb01cial Intelligence, Beihang University", "city": "Beijing", "country": "China"}, {"affiliation": "Sangfor Technologies", "city": "Shenzhen", "country": "China"}]

affiliations:
["School of Computer Science and Information Engineering, Hefei University of Technology", "Institute of Information Engineering, Chinese Academy of Sciences", "School of Computer Science and Engineering, Beihang University", "School of Arti\ufb01cial Intelligence, Beihang University", "Sangfor Technologies"]


authors: Shuling Zhao, Fa-Ting Hong, Xiaoshui Huang, Dan Xu
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 26232-26241
affiliations: ["The Hong Kong University of Science and Technology", "Shanghai Jiao Tong University"]
cities: ["Hong Kong", "Shanghai"]
countries: ["China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Zhao_Synergizing_Motion_and_Appearance_Multi-Scale_Compensatory_Codebooks_for_Talking_Head_CVPR_2025_paper.html

title:
Synergizing Motion and Appearance: Multi-Scale Compensatory Codebooks for Talking Head Video Generation

abstract:
Talking head video generation aims to generate a realistic talking head video that preserves the person's identity from a source image and the motion from a driving video. Despite the promising progress made in the field, it remains a challenging and critical problem to generate videos with accurate poses and fine-grained facial details simultaneously. Essentially, facial motion is often highly complex to model precisely, and the one-shot source face image cannot provide sufficient appearance guidance during generation due to dynamic pose changes. To tackle the problem, we propose to jointly learn motion and appearance codebooks and perform multi-scale codebook compensation to effectively refine both the facial motion conditions and appearance features for talking face image decoding. Specifically, the designed multi-scale motion and appearance codebooks are learned simultaneously in a unified framework to store representative global facial motion flow and appearance patterns. Then, we present a novel multi-scale motion and appearance compensation module, which utilizes a transformer-based codebook retrieval strategy to query complementary information from the two codebooks for joint motion and appearance compensation. The entire process produces motion flows of greater flexibility and appearance features with fewer distortions across different scales, resulting in a high-quality talking head video generation framework. Extensive experiments on various benchmarks validate the effectiveness of our approach and demonstrate superior generation results from both qualitative and quantitative perspectives when compared to state-of-the-art competitors. The project page is available at https://shaelynz.github.io/synergize-motion-appearance/.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Zhao_Synergizing_Motion_and_Appearance_Multi-Scale_Compensatory_Codebooks_for_Talking_Head_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Zhao_Synergizing_Motion_and_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Zhao_2025_CVPR,
    author    = {Zhao, Shuling and Hong, Fa-Ting and Huang, Xiaoshui and Xu, Dan},
    title     = {Synergizing Motion and Appearance: Multi-Scale Compensatory Codebooks for Talking Head Video Generation},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {26232-26241}
}

raw_affiliation:
[{"affiliation": "The Hong Kong University of Science and Technology", "city": "Hong Kong", "country": "China"}, {"affiliation": "Shanghai Jiao Tong University", "city": "Shanghai", "country": "China"}]


title: HOIGPT: Learning Long-Sequence Hand-Object Interaction with Language Models
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 7136-7146
affiliations: ["FAIR, Meta", "State University of New York at Buffalo"]
cities: ["Menlo Park", "Buffalo"]
countries: ["USA", "USA"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Huang_HOIGPT_Learning_Long-Sequence_Hand-Object_Interaction_with_Language_Models_CVPR_2025_paper.html

authors:
Mingzhen Huang, Fu-Jen Chu, Bugra Tekin, Kevin J. Liang, Haoyu Ma, Weiyao Wang, Xingyu Chen, Pierre Gleize, Hongfei Xue, Siwei Lyu, Kris Kitani, Matt Feiszli, Hao Tang

abstract:
We introduce HOIGPT, a token-based generative method that unifies 3D hand-object interactions (HOI) perception and generation, offering the first comprehensive solution for captioning and generating high-quality 3D HOI sequences from a diverse range of conditional signals (e.g. text, objects, partial sequences). At its core, HOIGPT utilizes a large language model to predict the bidrectional transformation between HOI sequences and natural language descriptions. Given text inputs, HOIGPT generates a sequence of hand and object meshes; given (partial) HOI sequences, HOIGPT generates text descriptions and completes the sequences. To facilitate HOI understanding with a large language model, this paper introduces two key innovations: (1) a novel physically grounded HOI tokenizer, the hand-object decomposed VQ-VAE, for discretizing HOI sequences, and (2) a motion-aware language model trained to process and generate both text and HOI tokens. Extensive experiments demonstrate that HOIGPT sets new state-of-the-art performance on both text generation (+2.01% R Precision) and HOI generation (-2.56 FID) across multiple tasks and benchmarks.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Huang_HOIGPT_Learning_Long-Sequence_Hand-Object_Interaction_with_Language_Models_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Huang_HOIGPT_Learning_Long-Sequence_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Huang_2025_CVPR,
    author    = {Huang, Mingzhen and Chu, Fu-Jen and Tekin, Bugra and Liang, Kevin J. and Ma, Haoyu and Wang, Weiyao and Chen, Xingyu and Gleize, Pierre and Xue, Hongfei and Lyu, Siwei and Kitani, Kris and Feiszli, Matt and Tang, Hao},
    title     = {HOIGPT: Learning Long-Sequence Hand-Object Interaction with Language Models},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {7136-7146}
}

raw_affiliation:
[{"affiliation": "FAIR, Meta", "city": "Menlo Park", "country": "USA"}, {"affiliation": "State University of New York at Buffalo", "city": "Buffalo", "country": "USA"}]


authors: Lin Bie, Shouan Pan, Siqi Li, Yining Zhao, Yue Gao
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 22161-22171
affiliations: ["BNRist, THUIBCS, BLBCI, School of Software, Tsinghua University"]
cities: ["Beijing"]
countries: ["China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Bie_GraphI2P_Image-to-Point_Cloud_Registration_with_Exploring_Pattern_of_Correspondence_via_CVPR_2025_paper.html

title:
GraphI2P: Image-to-Point Cloud Registration with Exploring Pattern of Correspondence via Graph Learning

abstract:
Although the fusion of images and LiDAR point clouds is crucial to many applications in computer vision, the relative poses of cameras and LiDAR scanners are often unknown. The general registration pipeline first establishes correspondences and then performs pose estimation based on the generated matches. However, 2D-3D correspondences are inherently challenging to establish due to the large gap between images and LiDAR point clouds. To this end, we build a bridge to alleviate the 2D-3D gap and propose a practical framework to align LiDAR point clouds to the virtual points generated by images. In this way, the modality gap is converted to the domain gap of point clouds. Moreover, we propose a virtual-spherical representation and adaptive distribution sample module to narrow the domain gap between virtual and LiDAR point clouds. Then, we explore the reliable correspondence pattern consistency through a graph-based selection process. We improve the correspondence representation through a graph neural network. Experimental results demonstrate that our method outperforms the state-of-the-art methods by more than 10.77% and 12.53% performance on the KITTI Odometry and nuScenes datasets, respectively. The results demonstrate that our method can effectively solve non-synchronized random frame registration.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Bie_GraphI2P_Image-to-Point_Cloud_Registration_with_Exploring_Pattern_of_Correspondence_via_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Bie_GraphI2P_Image-to-Point_Cloud_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Bie_2025_CVPR,
    author    = {Bie, Lin and Pan, Shouan and Li, Siqi and Zhao, Yining and Gao, Yue},
    title     = {GraphI2P: Image-to-Point Cloud Registration with Exploring Pattern of Correspondence via Graph Learning},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {22161-22171}
}

raw_affiliation:
[{"affiliation": "BNRist, THUIBCS, BLBCI, School of Software, Tsinghua University", "city": "Beijing", "country": "China"}]


title: SoftVQ-VAE: Efficient 1-Dimensional Continuous Tokenizer
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 28358-28370
affiliations: ["Carnegie Mellon University", "AMD", "William & Mary", "MBZUAI"]
cities: ["Pittsburgh", "Santa Clara", "Williamsburg", "Abu Dhabi"]
countries: ["USA", "USA", "USA", "UAE"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Chen_SoftVQ-VAE_Efficient_1-Dimensional_Continuous_Tokenizer_CVPR_2025_paper.html

authors:
Hao Chen, Ze Wang, Xiang Li, Ximeng Sun, Fangyi Chen, Jiang Liu, Jindong Wang, Bhiksha Raj, Zicheng Liu, Emad Barsoum

abstract:
Efficient image tokenization with high compression ratios remains a critical challenge for training generative models.We present SoftVQ-VAE, a continuous image tokenizer that leverages soft categorical posteriors to aggregate multiple codewords into each latent token, substantially increasing the representation capacity of the latent space. When applied to Transformer-based architectures, our approach compresses 256x256 and 512x512 images using only 32 or 64 1-dimensional tokens.Not only does SoftVQ-VAE show consistent and high-quality reconstruction, more importantly, it also achieves state-of-the-art and significantly faster image generation results across different denoising-based generative models. Remarkably, SoftVQ-VAE improves inference throughput by up to 18x for generating 256x256 images and 55x for 512x512 images while achieving competitive FID scores of 1.78 and 2.21 for SiT-XL.It also improves the training efficiency of the generative models by reducing the number of training iterations by 2.3x while maintaining comparable performance. With its fully-differentiable design and semantic-rich latent space, our experiment demonstrates that SoftVQ-VQE achieves efficient tokenization without compromising generation quality, paving the way for more efficient generative models.Code and model will be released.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Chen_SoftVQ-VAE_Efficient_1-Dimensional_Continuous_Tokenizer_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Chen_SoftVQ-VAE_Efficient_1-Dimensional_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Chen_2025_CVPR,
    author    = {Chen, Hao and Wang, Ze and Li, Xiang and Sun, Ximeng and Chen, Fangyi and Liu, Jiang and Wang, Jindong and Raj, Bhiksha and Liu, Zicheng and Barsoum, Emad},
    title     = {SoftVQ-VAE: Efficient 1-Dimensional Continuous Tokenizer},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {28358-28370}
}

raw_affiliation:
[{"affiliation": "Carnegie Mellon University", "city": "Pittsburgh", "country": "USA"}, {"affiliation": "AMD", "city": "Santa Clara", "country": "USA"}, {"affiliation": "William & Mary", "city": "Williamsburg", "country": "USA"}, {"affiliation": "MBZUAI", "city": "Abu Dhabi", "country": "UAE"}]


title: FedCS: Coreset Selection for Federated Learning
authors: Chenhe Hao, Weiying Xie, Daixun Li, Haonan Qin, Hangyu Ye, Leyuan Fang, Yunsong Li
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 15434-15443
affiliations: ["Xidian University", "Hunan University"]
cities: ["Xi'an", "Changsha"]
countries: ["China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Hao_FedCS_Coreset_Selection_for_Federated_Learning_CVPR_2025_paper.html

abstract:
Federated Learning (FL) is an emerging direction in distributed machine learning that enables jointly training a model without sharing the data. However, as the size of datasets grows exponentially, computational costs of FL increase. In this paper, we propose the first Coreset Selection criterion for Federated Learning (FedCS) by exploring the Distance Contrast (DC) in feature space. Our FedCS is inspired by the discovery that DC can indicate the intrinsic properties inherent to samples regardless of the networks. Based on the observation, we develop a method that is mathematically formulated to prune samples with high DC. The principle behind our pruning is that high DC samples either contain less information or represent rare extreme cases, thus removal of them can enhance the aggregation performance. Besides, we experimentally show that samples with low DC usually contain substantial information and reflect the common features of samples within their classes, such that they are suitable for constructing coreset. With only two time of linear-logarithmic complexity operation, FedCS leads to significant improvements over the methods using whole dataset in terms of computational costs, with similar accuracies. For example, on the CIFAR-10 dataset with Dirichlet coefficient \alpha=0.1, FedCS achieves 58.88% accuracy using only 44% of the entire dataset, whereas other methods require twice the data volume as FedCS for same performance.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Hao_FedCS_Coreset_Selection_for_Federated_Learning_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Hao_FedCS_Coreset_Selection_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Hao_2025_CVPR,
    author    = {Hao, Chenhe and Xie, Weiying and Li, Daixun and Qin, Haonan and Ye, Hangyu and Fang, Leyuan and Li, Yunsong},
    title     = {FedCS: Coreset Selection for Federated Learning},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {15434-15443}
}

raw_affiliation:
[{"affiliation": "Xidian University", "city": "Xi'an", "country": "China"}, {"affiliation": "Hunan University", "city": "Changsha", "country": "China"}]


title: DPC: Dual-Prompt Collaboration for Tuning Vision-Language Models
authors: Haoyang Li, Liang Wang, Chao Wang, Jing Jiang, Yan Peng, Guodong Long
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 25623-25632
affiliations: ["Shanghai University", "University of Technology Sydney"]
cities: ["Shanghai", "Sydney"]
countries: ["China", "Australia"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Li_DPC_Dual-Prompt_Collaboration_for_Tuning_Vision-Language_Models_CVPR_2025_paper.html

abstract:
The Base-New Trade-off (BNT) problem universally exists during the optimization of CLIP-based prompt tuning, where continuous fine-tuning on base (target) classes leads to a simultaneous decrease of generalization ability on new (unseen) classes. Existing approaches attempt to regulate the prompt tuning process to balance BNT by appending constraints. However, imposed on the same target prompt, these constraints fail to fully avert the mutual exclusivity between the optimization directions for base and new. As a novel solution to this challenge, we propose the plug-and-play Dual-Prompt Collaboration (DPC) framework, the first that decoupling the optimization processes of base and new tasks at the prompt level. Specifically, we clone a learnable parallel prompt based on the backbone prompt, and introduce a variable Weighting-Decoupling framework to independently control the optimization directions of dual prompts specific to base or new tasks, thus avoiding the conflict in generalization. Meanwhile, we propose a Dynamic Hard Negative Optimizer, utilizing dual prompts to construct a more challenging optimization task on base classes for enhancement. For interpretability, we prove the feature channel invariance of the prompt vector during the optimization process, providing theoretical support for the Weighting-Decoupling of DPC. Extensive experiments on multiple backbones demonstrate that DPC can significantly improve base performance without introducing any external knowledge beyond the base classes, while maintaining generalization to new classes. Code is available at: https://github.com/JREion/DPC.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Li_DPC_Dual-Prompt_Collaboration_for_Tuning_Vision-Language_Models_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Li_DPC_Dual-Prompt_Collaboration_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Li_2025_CVPR,
    author    = {Li, Haoyang and Wang, Liang and Wang, Chao and Jiang, Jing and Peng, Yan and Long, Guodong},
    title     = {DPC: Dual-Prompt Collaboration for Tuning Vision-Language Models},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {25623-25632}
}

raw_affiliation:
[{"affiliation": "Shanghai University", "city": "Shanghai", "country": "China"}, {"affiliation": "University of Technology Sydney", "city": "Sydney", "country": "Australia"}]


title: Dual-Granularity Semantic Guided Sparse Routing Diffusion Model for General Pansharpening
authors: Yinghui Xing, Litao Qu, Shizhou Zhang, Di Xu, Yingkun Yang, Yanning Zhang
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 12658-12668
affiliations: ["Northwestern Polytechnical University", "Huawei Cloud Computing Technologies Co., Ltd"]
cities: ["Xi'an", "Shenzhen"]
countries: ["China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Xing_Dual-Granularity_Semantic_Guided_Sparse_Routing_Diffusion_Model_for_General_Pansharpening_CVPR_2025_paper.html

abstract:
Pansharpening aims at integrating complementary information from panchromatic and multispectral images. Available deep-learning based pansharpening methods typically perform exceptionally with particular satellite datasets. At the same time, it has been observed that these models also exhibit scene dependence, for example, if the majority of the training samples come from the urban scenes, the model's performance may decline in the river scene. To address the domain gap produced by varying satellite sensors and distinct scenes, we propose a dual-granularity semantic guided sparse routing diffusion model for general pansharpening. By utilizing the large Vision-Language Models (VLMs) in the field of geoscience, e.g, GeoChat, we introduce the dual granularity semantics to generate dynamic sparse routing scores for adaptation of different satellite sensors and scenes. This scene-level and region-level dual-granularity semantic information serves as guidance for dynamically activating specialized experts within the diffusion model. Extensive experiments on WorldView-3, QuickBird, and GaoFen-2 datasets show the effectiveness of our proposed method. Notably, the proposed method outperforms the comparison approaches in adapting to new satellite sensors and scenes. The codes are available at https://github.com/codgodtao/SGDiff.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Xing_Dual-Granularity_Semantic_Guided_Sparse_Routing_Diffusion_Model_for_General_Pansharpening_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Xing_Dual-Granularity_Semantic_Guided_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Xing_2025_CVPR,
    author    = {Xing, Yinghui and Qu, Litao and Zhang, Shizhou and Xu, Di and Yang, Yingkun and Zhang, Yanning},
    title     = {Dual-Granularity Semantic Guided Sparse Routing Diffusion Model for General Pansharpening},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {12658-12668}
}

raw_affiliation:
[{"affiliation": "Northwestern Polytechnical University", "city": "Xi'an", "country": "China"}, {"affiliation": "Huawei Cloud Computing Technologies Co., Ltd", "city": "Shenzhen", "country": "China"}]


authors: Zengqun Zhao, Ziquan Liu, Yu Cao, Shaogang Gong, Ioannis Patras
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 28748-28758
affiliations: ["Centre for Multimodal AI, Queen Mary University of London"]
cities: ["London"]
countries: ["UK"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Zhao_AIM-Fair_Advancing_Algorithmic_Fairness_via_Selectively_Fine-Tuning_Biased_Models_with_CVPR_2025_paper.html

title:
AIM-Fair: Advancing Algorithmic Fairness via Selectively Fine-Tuning Biased Models with Contextual Synthetic Data

abstract:
Recent advances in generative models have sparked research on improving model fairness with AI-generated data. However, existing methods often face limitations in the diversity and quality of synthetic data, leading to compromised fairness and overall model accuracy. Moreover, many approaches rely on the availability of demographic group labels, which are often costly to annotate. This paper proposes AIM-Fair, aiming to overcome these limitations and harness the potential of cutting-edge generative models in promoting algorithmic fairness. We investigate a fine-tuning paradigm starting from a biased model initially trained on real-world data without demographic annotations. This model is then fine-tuned using unbiased synthetic data generated by a state-of-the-art diffusion model to improve its fairness. Two key challenges are identified in this fine-tuning paradigm, 1) the low quality of synthetic data, which can still happen even with advanced generative models, and 2) the domain and bias gap between real and synthetic data. To address the limitation of synthetic data quality, we propose Contextual Synthetic Data Generation (CSDG) to generate data using a text-to-image diffusion model (T2I) with prompts generated by a context-aware LLM, ensuring both data diversity and control of bias in synthetic data. To resolve domain and bias shifts, we introduce a novel selective fine-tuning scheme in which only model parameters more sensitive to bias and less sensitive to domain shift are updated. Experiments on CelebA and UTKFace datasets show that our AIM-Fair improves model fairness while maintaining utility, outperforming both fully and partially fine-tuned approaches to model fairness.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Zhao_AIM-Fair_Advancing_Algorithmic_Fairness_via_Selectively_Fine-Tuning_Biased_Models_with_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Zhao_AIM-Fair_Advancing_Algorithmic_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Zhao_2025_CVPR,
    author    = {Zhao, Zengqun and Liu, Ziquan and Cao, Yu and Gong, Shaogang and Patras, Ioannis},
    title     = {AIM-Fair: Advancing Algorithmic Fairness via Selectively Fine-Tuning Biased Models with Contextual Synthetic Data},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {28748-28758}
}

raw_affiliation:
[{"affiliation": "Centre for Multimodal AI, Queen Mary University of London", "city": "London", "country": "UK"}]


title: Robust Multi-Object 4D Generation for In-the-wild Videos
authors: Wen-Hsuan Chu, Lei Ke, Jianmeng Liu, Mingxiao Huo, Pavel Tokmakov, Katerina Fragkiadaki
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 22067-22077
affiliations: ["Carnegie Mellon University", "Toyota Research Institute"]
cities: ["Pittsburgh", "Los Altos"]
countries: ["USA", "USA"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Chu_Robust_Multi-Object_4D_Generation_for_In-the-wild_Videos_CVPR_2025_paper.html

abstract:
We address the challenge of generating dynamic 4D scenes from monocular multi-object videos with heavy occlusions and introduce Robust4DGen, a novel approach that integrates rendering-based deformable 3D Gaussian optimization with generative priors for view synthesis. While existing view-synthesis models excel at novel view generation for isolated objects, they struggle with full scenes due to their complexity and data demands. To overcome this, Robust4DGen decomposes scenes into individual objects, optimizing a differentiable set of deformable Gaussians per object while capturing 2D occlusions from a 3D perspective through joint Gaussian splatting. Joint splatting ensures occlusion-aware rendering losses in observed frames while explicit object decomposition allows the usage of object-centric diffusion models for object completion in unobserved viewpoints. To reconcile the differences between object-centric priors and the global frame-centric coordinate system of the video, Robust4DGen employs differentiable transformations to unify the rendering and generative constraints within a single framework. The result is a model capable of generating 4D objects across space and time while producing 2D and 3D point tracks from monocular videos. To rigorously evaluate the quality of scene generation and the accuracy of the motion under multi-object occlusions, we introduce MOSE-PTS, a subset of the challenging MOSE benchmark, which we annotated with high-quality 2D point tracks. Quantitative evaluations and perceptual human studies confirm that Robust4DGen generates more realistic novel views of scenes and produces more accurate point tracks compared to existing approaches.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Chu_Robust_Multi-Object_4D_Generation_for_In-the-wild_Videos_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Chu_Robust_Multi-Object_4D_CVPR_2025_supplemental.zip

bibtex:
@InProceedings{Chu_2025_CVPR,
    author    = {Chu, Wen-Hsuan and Ke, Lei and Liu, Jianmeng and Huo, Mingxiao and Tokmakov, Pavel and Fragkiadaki, Katerina},
    title     = {Robust Multi-Object 4D Generation for In-the-wild Videos},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {22067-22077}
}

raw_affiliation:
[{"affiliation": "Carnegie Mellon University", "city": "Pittsburgh", "country": "USA"}, {"affiliation": "Toyota Research Institute", "city": "Los Altos", "country": "USA"}]


title: OmniMMI: A Comprehensive Multi-modal Interaction Benchmark in Streaming Video Contexts
authors: Yuxuan Wang, Yueqian Wang, Bo Chen, Tong Wu, Dongyan Zhao, Zilong Zheng
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 18925-18935
cities: ["Beijing", "Beijing", "Beijing", "Shanghai"]
countries: ["China", "China", "China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Wang_OmniMMI_A_Comprehensive_Multi-modal_Interaction_Benchmark_in_Streaming_Video_Contexts_CVPR_2025_paper.html

abstract:
The rapid advancement of multi-modal language models (MLLMs) like GPT-4o has propelled the development of Omni language models, designed to process and proactively respond to continuous streams of multi-modal data. Despite their potential, evaluating their real-world interactive capabilities in streaming video contexts remains a formidable challenge. In this work, we introduce OmniMMI, a comprehensive multi-modal interaction benchmark tailored for OmniLLMs in streaming video contexts. OmniMMI encompasses over 1,121 videos and 2,290 questions, addressing two critical yet underexplored challenges in existing video benchmarks: streaming video understanding and proactive reasoning, across six distinct subtasks.  Moreover, we propose a novel framework, Multi-modal Multiplexing Modeling (M4), designed to enable an inference-efficient streaming model that can see, listen while generating.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Wang_OmniMMI_A_Comprehensive_Multi-modal_Interaction_Benchmark_in_Streaming_Video_Contexts_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Wang_OmniMMI_A_Comprehensive_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Wang_2025_CVPR,
    author    = {Wang, Yuxuan and Wang, Yueqian and Chen, Bo and Wu, Tong and Zhao, Dongyan and Zheng, Zilong},
    title     = {OmniMMI: A Comprehensive Multi-modal Interaction Benchmark in Streaming Video Contexts},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {18925-18935}
}

raw_affiliation:
[{"affiliation": "Beijing Institute for General Arti\ufb01cial Intelligence", "city": "Beijing", "country": "China"}, {"affiliation": "State Key Laboratory of General Arti\ufb01cial Intelligence", "city": "Beijing", "country": "China"}, {"affiliation": "Wangxuan Institute of Computer Technology, Peking University", "city": "Beijing", "country": "China"}, {"affiliation": "X-LANCE Lab, Shanghai Jiao Tong University", "city": "Shanghai", "country": "China"}]

affiliations:
["Beijing Institute for General Arti\ufb01cial Intelligence", "State Key Laboratory of General Arti\ufb01cial Intelligence", "Wangxuan Institute of Computer Technology, Peking University", "X-LANCE Lab, Shanghai Jiao Tong University"]


authors: Hyo-Jun Lee, Yeong Jun Koh, Hanul Kim, Hyunseop Kim, Yonguk Lee, Jinu Lee
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 17145-17154
cities: ["Chuncheon", "Daejeon", "Seoul", "Seoul"]
countries: ["South Korea", "South Korea", "South Korea", "South Korea"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Lee_SOAP_Vision-Centric_3D_Semantic_Scene_Completion_with_Scene-Adaptive_Decoder_and_CVPR_2025_paper.html

title:
SOAP: Vision-Centric 3D Semantic Scene Completion with Scene-Adaptive Decoder and Occluded Region-Aware View Projection

abstract:
Existing view transformations in vision-centric 3D Semantic Scene Completion (SSC) inevitably experience erroneous feature duplication in the reconstructed voxel space due to occlusions, leading to a dilution of informative contexts. Furthermore, semantic classes exhibit high variability in their appearance in real-world driving scenarios. To address these issues, we introduce a novel 3D SSC method, called SOAP, including two key components: an occluded region-aware view projection and a scene-adaptive decoder. The occluded region-aware view projection effectively converts 2D image features into voxel space, refining the duplicated features of occluded regions using information gathered from previous observations. The scene-adaptive decoder guides query embeddings to learn diverse driving environments based on a comprehensive semantic repository. Extensive experiments validate that the proposed SOAP significantly outperforms existing methods for the vision-centric 3D SSC on automated driving datasets, SemanticKITTI and SSCBench. Code is available at  https://github.com/gywns6287/SOAP.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Lee_SOAP_Vision-Centric_3D_Semantic_Scene_Completion_with_Scene-Adaptive_Decoder_and_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Lee_SOAP_Vision-Centric_3D_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Lee_2025_CVPR,
    author    = {Lee, Hyo-Jun and Koh, Yeong Jun and Kim, Hanul and Kim, Hyunseop and Lee, Yonguk and Lee, Jinu},
    title     = {SOAP: Vision-Centric 3D Semantic Scene Completion with Scene-Adaptive Decoder and Occluded Region-Aware View Projection},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {17145-17154}
}

raw_affiliation:
[{"affiliation": "Kangwon National University", "city": "Chuncheon", "country": "South Korea"}, {"affiliation": "Chungnam National University", "city": "Daejeon", "country": "South Korea"}, {"affiliation": "Seoul National University of Science and Technology", "city": "Seoul", "country": "South Korea"}, {"affiliation": "42dot Inc.", "city": "Seoul", "country": "South Korea"}]

affiliations:
["Kangwon National University", "Chungnam National University", "Seoul National University of Science and Technology", "42dot Inc."]


title: Taxonomy-Aware Evaluation of Vision-Language Models
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 9109-9120
affiliations: ["University of Copenhagen", "ETH Z\bzurich"]
cities: ["Copenhagen", "Zurich"]
countries: ["Denmark", "Switzerland"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Snaebjarnarson_Taxonomy-Aware_Evaluation_of_Vision-Language_Models_CVPR_2025_paper.html

authors:
Vésteinn Snæbjarnarson, Kevin Du, Niklas Stoehr, Serge Belongie, Ryan Cotterell, Nico Lang, Stella Frank

abstract:
When a vision-language model (VLM) is prompted to identify an entity depicted in an image, it may answer "I see a conifer," rather than the specific label "Norway spruce". This raises two issues for evaluation: Firstly, the unconstrained generated text needs to be mapped to the evaluation label space (i.e., "conifer"). Secondly, a useful classification measure should give partial credit to less specific, but not incorrect, answers ("Norway spruce" being a type of "conifer"). To meet these requirements, we propose a framework for evaluating unconstrained text predictions such as those generated from a vision-language model against a taxonomy. Specifically, we propose the use of hierarchical precision and recall measures to assess the level of correctness and specificity of predictions with regard to a taxonomy. Experimentally, we first show that existing text similarity measures do not capture taxonomic similarity well. We then develop and compare different methods to map textual VLM predictions onto a taxonomy. This allows us to compute hierarchical similarity measures between the generated text and the ground truth labels. Finally, we analyze modern VLMs on fine-grained visual classification tasks based on our proposed taxonomic evaluation scheme.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Snaebjarnarson_Taxonomy-Aware_Evaluation_of_Vision-Language_Models_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Snaebjarnarson_Taxonomy-Aware_Evaluation_of_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Snaebjarnarson_2025_CVPR,
    author    = {Sn{\ae}bjarnarson, V\'esteinn and Du, Kevin and Stoehr, Niklas and Belongie, Serge and Cotterell, Ryan and Lang, Nico and Frank, Stella},
    title     = {Taxonomy-Aware Evaluation of Vision-Language Models},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {9109-9120}
}

raw_affiliation:
[{"affiliation": "University of Copenhagen", "city": "Copenhagen", "country": "Denmark"}, {"affiliation": "ETH Z\bzurich", "city": "Zurich", "country": "Switzerland"}]


title: Active Event-based Stereo Vision
authors: Jianing Li, Yunjian Zhang, Haiqian Han, Xiangyang Ji
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 971-981
raw_affiliation: [{"affiliation": "Tsinghua University", "city": "Beijing", "country": "China"}]
affiliations: ["Tsinghua University"]
cities: ["Beijing"]
countries: ["China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Li_Active_Event-based_Stereo_Vision_CVPR_2025_paper.html

abstract:
Conventional frame-based imaging for active stereo systems has encountered major challenges in fast-motion scenarios. However, how to design a novel paradigm for high-speed depth sensing still remains an open issue. In this paper, we propose a novel problem setting, namely active event-based stereo vision, which provides the first insight of integrating binocular event cameras and an infrared projector for high-speed depth sensing. Technically, we first build a stereo camera prototype system and present a real-world dataset with over 21.5k spatiotemporal synchronized labels at 15 Hz, while also creating a realistic synthetic dataset with stereo event streams and 23.8k synchronized labels at 20 Hz. Then, we propose ActiveEventNet, a lightweight yet effective active event-based stereo matching neural network that learns to generate high-quality dense disparity maps from stereo event streams with low latency. Experiments demonstrate that our ActiveEventNet outperforms state-of-the-art methods meanwhile significantly reducing computational complexity. Our solution offers superior depth sensing compared to conventional stereo cameras in high-speed scenes, while also achieving the inference speed of up to 150 FPS with our prototype. We believe that this novel paradigm will provide new insights into future depth sensing systems.  Our project can be available at https://github.com/jianing-li/active_event_based_stereo.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Li_Active_Event-based_Stereo_Vision_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Li_Active_Event-based_Stereo_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Li_2025_CVPR,
    author    = {Li, Jianing and Zhang, Yunjian and Han, Haiqian and Ji, Xiangyang},
    title     = {Active Event-based Stereo Vision},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {971-981}
}


authors: Gen Luo, Xue Yang, Wenhan Dou, Zhaokai Wang, Jiawen Liu, Jifeng Dai, Yu Qiao, Xizhou Zhu
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 24960-24971
cities: ["Shanghai", "Beijing", "Shanghai", "Baltimore"]
countries: ["China", "China", "China", "USA"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Luo_Mono-InternVL_Pushing_the_Boundaries_of_Monolithic_Multimodal_Large_Language_Models_CVPR_2025_paper.html

title:
Mono-InternVL: Pushing the Boundaries of Monolithic Multimodal Large Language Models with Endogenous Visual Pre-training

abstract:
In this paper, we focus on monolithic Multimodal Large Language Models (MLLMs) that integrate visual encoding and language decoding into a single LLM.  In particular, we identify that existing pre-training strategies for monolithic MLLMs often suffer from unstable optimization or catastrophic forgetting.  To address this issue, our core idea is to embed a new visual parameter space into a pre-trained LLM, thereby stably learning visual knowledge from noisy data while freezing the LLM.  Based on this principle, we present Mono-InternVL,  a novel monolithic MLLM  that seamlessly integrates a set of visual experts via a multimodal mixture-of-experts structure.  Moreover, we propose an innovative pre-training strategy to maximize the visual capability of Mono-InternVL, namely Endogenous Visual Pre-training (EViP).  In particular, EViP is designed as a progressive learning process for visual experts,   which aims to fully exploit the visual knowledge from noisy data to high-quality data.   To validate our approach, we conduct extensive experiments on 16 benchmarks.  Experimental results confirm the superior performance of Mono-InternVL over existing monolithic MLLMs on 13 of 16 multimodal benchmarks, e.g., +80 points over Emu3 on OCRBench.  Compared to the modular baseline, i.e., InternVL-1.5, Mono-InternVL still retains comparable multimodal performance while reducing up to   67% first token latency.  Our project is available at https://internvl.github.io/blog/2024-10-10-Mono-InternVL/.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Luo_Mono-InternVL_Pushing_the_Boundaries_of_Monolithic_Multimodal_Large_Language_Models_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Luo_Mono-InternVL_Pushing_the_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Luo_2025_CVPR,
    author    = {Luo, Gen and Yang, Xue and Dou, Wenhan and Wang, Zhaokai and Liu, Jiawen and Dai, Jifeng and Qiao, Yu and Zhu, Xizhou},
    title     = {Mono-InternVL: Pushing the Boundaries of Monolithic Multimodal Large Language Models with Endogenous Visual Pre-training},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {24960-24971}
}

raw_affiliation:
[{"affiliation": "OpenGVLab, Shanghai AI Laboratory", "city": "Shanghai", "country": "China"}, {"affiliation": "Tsinghua University", "city": "Beijing", "country": "China"}, {"affiliation": "Shanghai Jiao Tong University", "city": "Shanghai", "country": "China"}, {"affiliation": "Johns Hopkins University", "city": "Baltimore", "country": "USA"}]

affiliations:
["OpenGVLab, Shanghai AI Laboratory", "Tsinghua University", "Shanghai Jiao Tong University", "Johns Hopkins University"]


title: SimVS: Simulating World Inconsistencies for Robust View Synthesis
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 16464-16474
affiliations: ["UC San Diego", "Google DeepMind", "Google Research", "Columbia", "Univ . Maryland"]
cities: ["San Diego", "Mountain View", "Mountain View", "New York", "College Park"]
countries: ["USA", "USA", "USA", "USA", "USA"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Trevithick_SimVS_Simulating_World_Inconsistencies_for_Robust_View_Synthesis_CVPR_2025_paper.html

authors:
Alex Trevithick, Roni Paiss, Philipp Henzler, Dor Verbin, Rundi Wu, Hadi Alzayer, Ruiqi Gao, Ben Poole, Jonathan T. Barron, Aleksander Holynski, Ravi Ramamoorthi, Pratul P. Srinivasan

abstract:
Novel-view synthesis techniques achieve impressive results for static scenes but struggle when faced with the inconsistencies inherent to casual capture settings: varying illumination, scene motion, and other unintended effects that are difficult to model explicitly. We present an approach for leveraging generative video models to simulate the inconsistencies in the world that can occur during capture. We use this process, along with existing multi-view datasets, to create synthetic data for training a multi-view harmonization network that is able to reconcile inconsistent observations into a consistent 3D scene. We demonstrate that our world-simulation strategy significantly outperforms traditional augmentation methods in handling real-world scene variations, thereby enabling highly accurate static 3D reconstructions in the presence of a variety of challenging inconsistencies.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Trevithick_SimVS_Simulating_World_Inconsistencies_for_Robust_View_Synthesis_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Trevithick_SimVS_Simulating_World_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Trevithick_2025_CVPR,
    author    = {Trevithick, Alex and Paiss, Roni and Henzler, Philipp and Verbin, Dor and Wu, Rundi and Alzayer, Hadi and Gao, Ruiqi and Poole, Ben and Barron, Jonathan T. and Holynski, Aleksander and Ramamoorthi, Ravi and Srinivasan, Pratul P.},
    title     = {SimVS: Simulating World Inconsistencies for Robust View Synthesis},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {16464-16474}
}

raw_affiliation:
[{"affiliation": "UC San Diego", "city": "San Diego", "country": "USA"}, {"affiliation": "Google DeepMind", "city": "Mountain View", "country": "USA"}, {"affiliation": "Google Research", "city": "Mountain View", "country": "USA"}, {"affiliation": "Columbia", "city": "New York", "country": "USA"}, {"affiliation": "Univ . Maryland", "city": "College Park", "country": "USA"}]


title: FLAVC: Learned Video Compression with Feature Level Attention
authors: Chun Zhang, Heming Sun, Jiro Katto
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 28019-28028
affiliations: ["Waseda University", "Yokohama National University"]
cities: ["Tokyo", "Yokohama"]
countries: ["Japan", "Japan"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Zhang_FLAVC_Learned_Video_Compression_with_Feature_Level_Attention_CVPR_2025_paper.html

abstract:
Learned Video Compression (LVC) aims to reduce redundancy in sequential data through deep learning approaches. Recent advances have significantly boosted LVC performance by shifting compression operations to feature domain, often combining Motion Estimation and Motion Compensation module(MEMC) with CNN-based context extraction. However, reliance on motions and convolution-driven context models limits generalizability and global perception. To address these issues, we propose a Feature-level Attention (FLA) module within a Transformer-based framework that perceives full-frame explicitly, thus bypassing confined motion signatures. FLA accomplishes global perception by converting high-level local patch embeddings to one-dimensional batch-wise vectors and replacing traditional attention weights to a global context matrix. Amongst this, a dense overlapping patcher (DP) is introduced to retain local features before embedding projection. Furthermore, a Transformer-CNN mixed encoder is applied to alleviate the spatial feature bottleneck without expanding latent size. Experiments demonstrate excellent generalizability with universally efficient redundancy reduction in different scenarios. Extensive tests on four video compression datasets show that our method achieves state-of-the-art Rate-Distortion performance compared to existing LVC methods and traditional codecs. A down-scaled version of our model reduced computation overhead by a great margin while maintained great performance.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Zhang_FLAVC_Learned_Video_Compression_with_Feature_Level_Attention_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Zhang_FLAVC_Learned_Video_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Zhang_2025_CVPR,
    author    = {Zhang, Chun and Sun, Heming and Katto, Jiro},
    title     = {FLAVC: Learned Video Compression with Feature Level Attention},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {28019-28028}
}

raw_affiliation:
[{"affiliation": "Waseda University", "city": "Tokyo", "country": "Japan"}, {"affiliation": "Yokohama National University", "city": "Yokohama", "country": "Japan"}]


authors: Wentao Qu, Jing Wang, YongShun Gong, Xiaoshui Huang, Liang Xiao
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 27325-27335
affiliations: ["NJUST1", "THU2", "SDU3", "SJTU4"]
cities: ["Nanjing", "Beijing", "Jinan", "Shanghai"]
countries: ["China", "China", "China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Qu_An_End-to-End_Robust_Point_Cloud_Semantic_Segmentation_Network_with_Single-Step_CVPR_2025_paper.html

title:
An End-to-End Robust Point Cloud Semantic Segmentation Network with Single-Step Conditional Diffusion Models

abstract:
Existing conditional Denoising Diffusion Probabilistic Models (DDPMs) with a Noise-Conditional Framework (NCF) remain challenging for 3D scene understanding tasks, as the complex geometric details in scenes increase the difficulty of fitting the gradients of the data distribution (the scores) from semantic labels. This also results in longer training and inference time for DDPMs compared to non-DDPMs. From a different perspective, we delve deeply into the model paradigm dominated by the Conditional Network. In this paper, we propose an end-to-end robust semantic Segmentation Network based on a Conditional-Noise Framework (CNF) of DDPMs, named CDSegNet. Specifically, CDSegNet models the Noise Network (NN) as a learnable noise-feature generator. This enables the Conditional Network (CN) to understand 3D scene semantics under multi-level feature perturbations,  enhancing the generalization in unseen scenes. Meanwhile, benefiting from the noise system of DDPMs, CDSegNet exhibits  strong robustness for data noise and sparsity in experiments. Moreover, thanks to CNF, CDSegNet can generate the semantic labels in a single-step inference like non-DDPMs, due to avoiding directly fitting the scores from semantic labels in the dominant network of CDSegNet. On public indoor and outdoor benchmarks, CDSegNet significantly outperforms existing methods, achieving state-of-the-art performance.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Qu_An_End-to-End_Robust_Point_Cloud_Semantic_Segmentation_Network_with_Single-Step_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Qu_An_End-to-End_Robust_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Qu_2025_CVPR,
    author    = {Qu, Wentao and Wang, Jing and Gong, YongShun and Huang, Xiaoshui and Xiao, Liang},
    title     = {An End-to-End Robust Point Cloud Semantic Segmentation Network with Single-Step Conditional Diffusion Models},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {27325-27335}
}

raw_affiliation:
[{"affiliation": "NJUST1", "city": "Nanjing", "country": "China"}, {"affiliation": "THU2", "city": "Beijing", "country": "China"}, {"affiliation": "SDU3", "city": "Jinan", "country": "China"}, {"affiliation": "SJTU4", "city": "Shanghai", "country": "China"}]


authors: Chen Zhao, Zhizhou Chen, Yunzhe Xu, Enxuan Gu, Jian Li, Zili Yi, Qian Wang, Jian Yang, Ying Tai
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 17935-17946
cities: ["Nanjing", "Dalian", "Shenzhen", "Beijing"]
countries: ["China", "China", "China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Zhao_From_Zero_to_Detail_Deconstructing_Ultra-High-Definition_Image_Restoration_from_Progressive_CVPR_2025_paper.html

title:
From Zero to Detail: Deconstructing Ultra-High-Definition Image Restoration from Progressive Spectral Perspective

abstract:
Ultra-high-definition (UHD) image restoration faces significant challenges due to its high resolution, complex content, and intricate details. To cope with these challenges, we analyze the restoration process in depth through a progressive spectral perspective, and deconstruct the complex UHD restoration problem into three progressive stages: zero-frequency enhancement, low-frequency restoration, and high-frequency refinement. Building on this insight, we propose a novel framework, ERR, which comprises three collaborative  sub-networks: the zero-frequency enhancer (ZFE), the low-frequency restorer (LFR), and the high-frequency refiner (HFR). Specifically, the ZFE integrates global priors to learn global mapping, while the LFR restores low-frequency information, emphasizing reconstruction of coarse-grained content. Finally, the HFR employs our designed  frequency-windowed Kolmogorov-Arnold Networks (FW-KAN) to refine textures and details, producing high-quality image restoration. Our approach significantly outperforms previous UHD methods across various tasks, with extensive ablation studies validating the effectiveness of each component.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Zhao_From_Zero_to_Detail_Deconstructing_Ultra-High-Definition_Image_Restoration_from_Progressive_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Zhao_From_Zero_to_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Zhao_2025_CVPR,
    author    = {Zhao, Chen and Chen, Zhizhou and Xu, Yunzhe and Gu, Enxuan and Li, Jian and Yi, Zili and Wang, Qian and Yang, Jian and Tai, Ying},
    title     = {From Zero to Detail: Deconstructing Ultra-High-Definition Image Restoration from Progressive Spectral Perspective},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {17935-17946}
}

raw_affiliation:
[{"affiliation": "Nanjing University", "city": "Nanjing", "country": "China"}, {"affiliation": "Dalian University of Technology", "city": "Dalian", "country": "China"}, {"affiliation": "Tencent Youtu", "city": "Shenzhen", "country": "China"}, {"affiliation": "China Mobile Institute", "city": "Beijing", "country": "China"}]

affiliations:
["Nanjing University", "Dalian University of Technology", "Tencent Youtu", "China Mobile Institute"]


title: SMILE: Infusing Spatial and Motion Semantics in Masked Video Learning
authors: Fida Mohammad Thoker, Letian Jiang, Chen Zhao, Bernard Ghanem
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 8438-8449
affiliations: ["King Abdullah University of Science and Technology (KAUST)"]
cities: ["Thuwal"]
countries: ["Saudi Arabia"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Thoker_SMILE_Infusing_Spatial_and_Motion_Semantics_in_Masked_Video_Learning_CVPR_2025_paper.html

abstract:
Masked video modeling, such as VideoMAE, is an effective paradigm for video self-supervised learning (SSL). However, they are primarily based on reconstructing pixel level details on natural videos which have substantial temporal redundancy, limiting their capability for semantic representation and sufficient encoding of motion dynamics. To address these issues, this paper introduces a novel SSL approach for video representation learning, dubbed as SMILE, by infusing both spatial and motion semantics. In SMILE, we leverage image-language pretrained models, such as CLIP, to guide the learning process with their high-level spatial semantics. We enhance the representation of motion by introducing synthetic motion patterns in the training data, allowing the model to capture more complex and dynamic content. Furthermore, using SMILE, we establish a new self-supervised video learning paradigm capable of learning strong video representations without requiring any natural video data. We have carried out extensive experiments on 7 datasets with various downstream scenarios. SMILE surpasses current state-of-the-art SSL methods, showcasing its effectiveness in learning more discriminative and generalizable video representations.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Thoker_SMILE_Infusing_Spatial_and_Motion_Semantics_in_Masked_Video_Learning_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Thoker_SMILE_Infusing_Spatial_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Thoker_2025_CVPR,
    author    = {Thoker, Fida Mohammad and Jiang, Letian and Zhao, Chen and Ghanem, Bernard},
    title     = {SMILE: Infusing Spatial and Motion Semantics in Masked Video Learning},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {8438-8449}
}

raw_affiliation:
[{"affiliation": "King Abdullah University of Science and Technology (KAUST)", "city": "Thuwal", "country": "Saudi Arabia"}]


title: Video Language Model Pretraining with Spatio-temporal Masking
authors: Yue Wu, Zhaobo Qi, Junshu Sun, Yaowei Wang, Qingming Huang, Shuhui Wang
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 8557-8567
cities: ["Beijing", "Shenzhen", "Weihai", "Beijing", "Shenzhen"]
countries: ["China", "China", "China", "China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Wu_Video_Language_Model_Pretraining_with_Spatio-temporal_Masking_CVPR_2025_paper.html

abstract:
The development of self-supervised video-language models based on mask learning has significantly advanced downstream video tasks. These models leverage masked reconstruction to facilitate joint learning of visual and linguistic information. However, recent study reveals that reconstructing image features yields superior downstream performance compared to video feature reconstruction. We hypothesize that this performance gap stems from the way how masking strategies influence the model's attention to temporal dynamics. To validate this hypothesis, we performed two sets of experiments that demonstrate that alignment between the masked target and the reconstruction target is crucial for self-supervised video-language learning. Based on these findings, we propose a spatio-temporal masking strategy (STM) for video-language model pretraining that operates across adjacent frames, and a decoder leverages semantic information to enhance the spatio-temporal representations of masked tokens. Thanks to the combination of masking strategy and reconstruction decoder, STM enforces the model to learn spatio-temporal feature representation comprehensively. Experiments in three video understanding downstream tasks validate the superiority of our method.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Wu_Video_Language_Model_Pretraining_with_Spatio-temporal_Masking_CVPR_2025_paper.pdf

bibtex:
@InProceedings{Wu_2025_CVPR,
    author    = {Wu, Yue and Qi, Zhaobo and Sun, Junshu and Wang, Yaowei and Huang, Qingming and Wang, Shuhui},
    title     = {Video Language Model Pretraining with Spatio-temporal Masking},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {8557-8567}
}

raw_affiliation:
[{"affiliation": "Key Lab of Intell. Info. Process., Inst. of Comput. Tech., CAS", "city": "Beijing", "country": "China"}, {"affiliation": "Pengcheng Laboratory", "city": "Shenzhen", "country": "China"}, {"affiliation": "Harbin Institute of Technology, Weihai", "city": "Weihai", "country": "China"}, {"affiliation": "University of Chinese Academy of Sciences", "city": "Beijing", "country": "China"}, {"affiliation": "Harbin Institute of Technology, Shenzhen", "city": "Shenzhen", "country": "China"}]

affiliations:
["Key Lab of Intell. Info. Process., Inst. of Comput. Tech., CAS", "Pengcheng Laboratory", "Harbin Institute of Technology, Weihai", "University of Chinese Academy of Sciences", "Harbin Institute of Technology, Shenzhen"]


title: COSMOS: Cross-Modality Self-Distillation for Vision Language Pre-training
authors: Sanghwan Kim, Rui Xiao, Mariana-Iuliana Georgescu, Stephan Alaniz, Zeynep Akata
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 14690-14700
cities: ["Munich", "Munich", "Munich", "Munich"]
countries: ["Germany", "Germany", "Germany", "Germany"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Kim_COSMOS_Cross-Modality_Self-Distillation_for_Vision_Language_Pre-training_CVPR_2025_paper.html

abstract:
Vision-Language Models (VLMs) trained with contrastive loss have achieved significant advancements in various vision and language tasks. However, the global nature of the contrastive loss makes VLMs focus predominantly on foreground objects, neglecting other crucial information in the image, which limits their effectiveness in downstream tasks. To address these challenges, we propose COSMOS: CrOSs-MOdality Self-distillation for vision-language pre-training that integrates a novel text-cropping strategy and cross-attention module into a self-supervised learning framework. We create global and local views of images and texts (i.e., multi-modal augmentations), which are essential for self-distillation in VLMs. We further introduce a cross-attention module, enabling COSMOS to learn comprehensive cross-modal representations optimized via a cross-modality self-distillation loss. COSMOS consistently outperforms previous strong baselines on various zero-shot downstream tasks, including retrieval, classification, and semantic segmentation. Additionally, it surpasses CLIP-based models trained on larger datasets in visual perception and contextual understanding tasks. Code is available at https://github.com/ExplainableML/cosmos.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Kim_COSMOS_Cross-Modality_Self-Distillation_for_Vision_Language_Pre-training_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Kim_COSMOS_Cross-Modality_Self-Distillation_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Kim_2025_CVPR,
    author    = {Kim, Sanghwan and Xiao, Rui and Georgescu, Mariana-Iuliana and Alaniz, Stephan and Akata, Zeynep},
    title     = {COSMOS: Cross-Modality Self-Distillation for Vision Language Pre-training},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {14690-14700}
}

raw_affiliation:
[{"affiliation": "Technical University of Munich", "city": "Munich", "country": "Germany"}, {"affiliation": "Munich Center for Machine Learning (MCML)", "city": "Munich", "country": "Germany"}, {"affiliation": "Helmholtz Munich", "city": "Munich", "country": "Germany"}, {"affiliation": "Munich Data Science Institute (MDSI)", "city": "Munich", "country": "Germany"}]

affiliations:
["Technical University of Munich", "Munich Center for Machine Learning (MCML)", "Helmholtz Munich", "Munich Data Science Institute (MDSI)"]


title: Lifting Motion to the 3D World via 2D Diffusion
authors: Jiaman Li, C. Karen Liu, Jiajun Wu
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 17518-17528
raw_affiliation: [{"affiliation": "Stanford University", "city": "Stanford", "country": "USA"}]
affiliations: ["Stanford University"]
cities: ["Stanford"]
countries: ["USA"]
highlight: yes
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Li_Lifting_Motion_to_the_3D_World_via_2D_Diffusion_CVPR_2025_paper.html

abstract:
Estimating 3D motion from 2D observations is a long-standing research challenge. Prior work typically requires training on datasets containing ground truth 3D motions, limiting their applicability to activities well-represented in existing motion capture data. This dependency particularly hinders generalization to out-of-distribution scenarios or subjects where collecting 3D ground truth is challenging, such as complex athletic movements or animal motion. We introduce MVLift, a novel approach to predict global 3D motion---including both joint rotations and root trajectories in the world coordinate system---using only 2D pose sequences for training. Our multi-stage framework leverages 2D motion diffusion models to progressively generate consistent 2D pose sequences across multiple views, a key step in recovering accurate global 3D motion. MVLift generalizes across various domains, including human poses, human-object interactions, and animal poses. Despite not requiring 3D supervision, it outperforms prior work on five datasets, including those methods that require 3D supervision.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Li_Lifting_Motion_to_the_3D_World_via_2D_Diffusion_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Li_Lifting_Motion_to_CVPR_2025_supplemental.zip

bibtex:
@InProceedings{Li_2025_CVPR,
    author    = {Li, Jiaman and Liu, C. Karen and Wu, Jiajun},
    title     = {Lifting Motion to the 3D World via 2D Diffusion},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {17518-17528}
}


title: TAPT: Test-Time Adversarial Prompt Tuning for Robust Inference in Vision-Language Models
authors: Xin Wang, Kai Chen, Jiaming Zhang, Jingjing Chen, Xingjun Ma
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 19910-19920
cities: ["Shanghai", "Hong Kong"]
countries: ["China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Wang_TAPT_Test-Time_Adversarial_Prompt_Tuning_for_Robust_Inference_in_Vision-Language_CVPR_2025_paper.html

abstract:
Large pre-trained Vision-Language Models (VLMs) such as CLIP have demonstrated excellent zero-shot generalizability across various downstream tasks. However, recent studies have shown that the inference performance of CLIP can be greatly degraded by small adversarial perturbations, especially its visual modality, posing significant safety threats. To mitigate this vulnerability, in this paper, we propose a novel defense method called Test-Time Adversarial Prompt Tuning (TAPT) to enhance the inference robustness of CLIP against visual adversarial attacks. TAPT is a test-time defense method that learns defensive bimodal (textual and visual) prompts to robustify the inference process of CLIP. Specifically, it is an unsupervised method that optimizes the defensive prompts for each test sample by minimizing a multi-view entropy and aligning adversarial-clean distributions. We evaluate the effectiveness of TAPT on 11 benchmark datasets, including ImageNet and 10 other zero-shot datasets, demonstrating that it enhances the zero-shot adversarial robustness of the original CLIP by at least 48.9% against AutoAttack (AA), while largely maintaining performance on clean examples. Moreover, TAPT outperforms existing adversarial prompt tuning methods across various backbones, achieving an average robustness improvement of at least 36.6%. Code is available at https://github.com/xinwong/TAPT.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Wang_TAPT_Test-Time_Adversarial_Prompt_Tuning_for_Robust_Inference_in_Vision-Language_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Wang_TAPT_Test-Time_Adversarial_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Wang_2025_CVPR,
    author    = {Wang, Xin and Chen, Kai and Zhang, Jiaming and Chen, Jingjing and Ma, Xingjun},
    title     = {TAPT: Test-Time Adversarial Prompt Tuning for Robust Inference in Vision-Language Models},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {19910-19920}
}

raw_affiliation:
[{"affiliation": "Shanghai Key Lab of Intell. Info. Processing, School of CS, Fudan University", "city": "Shanghai", "country": "China"}, {"affiliation": "Hong Kong University of Science and Technology", "city": "Hong Kong", "country": "China"}]

affiliations:
["Shanghai Key Lab of Intell. Info. Processing, School of CS, Fudan University", "Hong Kong University of Science and Technology"]


title: Active Data Curation Effectively Distills Large-Scale Multimodal Models
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 14422-14437
cities: ["Mountain View", "London", "T\u00fcbingen", "Cambridge"]
countries: ["USA", "UK", "Germany", "UK"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Udandarao_Active_Data_Curation_Effectively_Distills_Large-Scale_Multimodal_Models_CVPR_2025_paper.html

authors:
Vishaal Udandarao, Nikhil Parthasarathy, Muhammad Ferjad Naeem, Talfan Evans, Samuel Albanie, Federico Tombari, Yongqin Xian, Alessio Tonioni, Olivier J. Henaff

abstract:
Knowledge distillation (KD) is the de facto standard for compressing large-scale models into smaller ones. Prior works have explored ever more complex KD strategies involving different objective functions, teacher-ensembles, and weight inheritance. In this work we explore an alternative, yet simple approach---active data curation as effective distillation for contrastive multimodal pretraining. Our simple online batch selection method, ACID, outperforms strong KD baselines across various model-, data- and compute-configurations. Further, we find such an active data curation strategy to in fact be complementary to standard KD, and can be effectively combined to train highly performant inference-efficient models. Our simple and scalable pretraining framework, ACED, achieves state-of-the-art results across 27 zero-shot classification and retrieval tasks with upto 11% less inference FLOPs. We further demonstrate that our ACED models yield strong vision-encoders for training generative multimodal models in the LiT-Decoder setting, outperforming larger vision encoders for image-captioning and visual question-answering tasks.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Udandarao_Active_Data_Curation_Effectively_Distills_Large-Scale_Multimodal_Models_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Udandarao_Active_Data_Curation_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Udandarao_2025_CVPR,
    author    = {Udandarao, Vishaal and Parthasarathy, Nikhil and Naeem, Muhammad Ferjad and Evans, Talfan and Albanie, Samuel and Tombari, Federico and Xian, Yongqin and Tonioni, Alessio and Henaff, Olivier J.},
    title     = {Active Data Curation Effectively Distills Large-Scale Multimodal Models},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {14422-14437}
}

raw_affiliation:
[{"affiliation": "Google", "city": "Mountain View", "country": "USA"}, {"affiliation": "Google DeepMind", "city": "London", "country": "UK"}, {"affiliation": "T\u00a8ubingen AI Center, University of T\u00a8ubingen", "city": "T\u00fcbingen", "country": "Germany"}, {"affiliation": "University of Cambridge", "city": "Cambridge", "country": "UK"}]

affiliations:
["Google", "Google DeepMind", "T\u00a8ubingen AI Center, University of T\u00a8ubingen", "University of Cambridge"]


title: PCDreamer: Point Cloud Completion Through Multi-view Diffusion Priors
authors: Guangshun Wei, Yuan Feng, Long Ma, Chen Wang, Yuanfeng Zhou, Changjian Li
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 27243-27253
affiliations: ["Shandong University", "University of Edinburgh"]
cities: ["Jinan", "Edinburgh"]
countries: ["China", "United Kingdom"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Wei_PCDreamer_Point_Cloud_Completion_Through_Multi-view_Diffusion_Priors_CVPR_2025_paper.html

abstract:
This paper presents PCDreamer, a novel method for point cloud completion. Traditional methods typically extract features from partial point clouds to predict missing regions, but the large solution space often leads to unsatisfactory results. More recent approaches have started to use images as extra guidance, effectively improving performance, but obtaining paired data of images and partial point clouds is challenging in practice. To overcome these limitations, we harness the relatively view-consistent multi-view diffusion priors within large models,  to generate novel views of the desired shape. The resulting image set encodes both global and local shape cues, which are especially beneficial for shape completion. To fully exploit the priors, we have designed a shape fusion module for producing an initial complete shape from multi-modality input (i.e., images and point clouds), and a follow-up shape consolidation module to obtain the final complete shape by discarding unreliable points introduced by the inconsistency from diffusion priors. Extensive experimental results demonstrate our superior performance, especially in recovering fine details.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Wei_PCDreamer_Point_Cloud_Completion_Through_Multi-view_Diffusion_Priors_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Wei_PCDreamer_Point_Cloud_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Wei_2025_CVPR,
    author    = {Wei, Guangshun and Feng, Yuan and Ma, Long and Wang, Chen and Zhou, Yuanfeng and Li, Changjian},
    title     = {PCDreamer: Point Cloud Completion Through Multi-view Diffusion Priors},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {27243-27253}
}

raw_affiliation:
[{"affiliation": "Shandong University", "city": "Jinan", "country": "China"}, {"affiliation": "University of Edinburgh", "city": "Edinburgh", "country": "United Kingdom"}]


title: Your ViT is Secretly an Image Segmentation Model
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 25303-25313
affiliations: ["Eindhoven University of Technology", "Polytechnic of Turin", "RWTH Aachen University"]
cities: ["Eindhoven", "Turin", "Aachen"]
countries: ["Netherlands", "Italy", "Germany"]
highlight: yes
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Kerssies_Your_ViT_is_Secretly_an_Image_Segmentation_Model_CVPR_2025_paper.html

authors:
Tommie Kerssies, Niccolò Cavagnero, Alexander Hermans, Narges Norouzi, Giuseppe Averta, Bastian Leibe, Gijs Dubbelman, Daan de Geus

abstract:
Vision Transformers (ViTs) have shown remarkable performance and scalability across various computer vision tasks. To apply single-scale ViTs to image segmentation, existing methods adopt a convolutional adapter to generate multi-scale features, a pixel decoder to fuse these features, and a Transformer decoder that uses the fused features to make predictions. In this paper, we show that the inductive biases introduced by these task-specific components can instead be learned by the ViT itself, given sufficiently large models and extensive pre-training. Based on these findings, we introduce the Encoder-only Mask Transformer (EoMT), which repurposes the plain ViT architecture to conduct image segmentation. With large-scale models and pre-training, EoMT obtains a segmentation accuracy similar to state-of-the-art models that use task-specific components. At the same time, EoMT is significantly faster than these methods due to its architectural simplicity, e.g., up to 4x faster with ViT-L. Across a range of model sizes, EoMT demonstrates an optimal balance between segmentation accuracy and prediction speed, suggesting that compute resources are better spent on scaling the ViT itself rather than adding architectural complexity. Code: https://www.tue-mps.org/eomt/.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Kerssies_Your_ViT_is_Secretly_an_Image_Segmentation_Model_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Kerssies_Your_ViT_is_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Kerssies_2025_CVPR,
    author    = {Kerssies, Tommie and Cavagnero, Niccol\`o and Hermans, Alexander and Norouzi, Narges and Averta, Giuseppe and Leibe, Bastian and Dubbelman, Gijs and de Geus, Daan},
    title     = {Your ViT is Secretly an Image Segmentation Model},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {25303-25313}
}

raw_affiliation:
[{"affiliation": "Eindhoven University of Technology", "city": "Eindhoven", "country": "Netherlands"}, {"affiliation": "Polytechnic of Turin", "city": "Turin", "country": "Italy"}, {"affiliation": "RWTH Aachen University", "city": "Aachen", "country": "Germany"}]


title: Cross-Rejective Open-Set SAR Image Registration
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 23027-23036
cities: ["Xi'an", "Beijing", "Perth", "Shenzhen"]
countries: ["China", "China", "Australia", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Mao_Cross-Rejective_Open-Set_SAR_Image_Registration_CVPR_2025_paper.html

authors:
Shasha Mao, Shiming Lu, Zhaolong Du, Licheng Jiao, Shuiping Gou, Luntian Mou, Xuequan Lu, Lin Xiong, Yimeng Zhang

abstract:
Synthetic Aperture Radar (SAR) image registration is an essential upstream task in geoscience applications, in which pre-detected keypoints from two images are employed as observed objects to seek matched-point pairs. In general, the registration is regarded as a typical closed-set classification, which forces each keypoint to be classified into the given classes, but ignoring an essential issue that numerous redundant keypoints are beyond the given classes, which unavoidably results in capturing incorrect matched-point pairs. Based on this, we propose a Cross-Rejective Open-set SAR Image Registration (CroR-OSIR) method. In this work, these redundant keypoints are regarded as out-of-distribution (OOD) samples, and we formulate the registration as a special open-set task with two modules: supervised contrastive feature-tuning and cross-rejective open-set recognition (CroR-OSR). Unlike traditional open-set recognition, all samples, including OOD samples, are available in the CroR-OSR module. CroR-OSR conducts the closed-set classifications in individual open-set domains from two images, meanwhile employing the cross-domain rejection during training, to exclude these OOD samples based on confidence and consistency. Moreover, a new supervised contrastive tuning strategy is incorporated for feature-tuning. Especially, the cross-domain estimation labels obtained by CroR-OSR are fed back to the feature-tuning module for feature-tuning, to enhance feature discriminability. The experimental results illustrate that the proposed method achieves more precise registration than the state-of-the-art methods. The code is released at https://github.com/XDyaoshi/CroR-OSIR-main.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Mao_Cross-Rejective_Open-Set_SAR_Image_Registration_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Mao_Cross-Rejective_Open-Set_SAR_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Mao_2025_CVPR,
    author    = {Mao, Shasha and Lu, Shiming and Du, Zhaolong and Jiao, Licheng and Gou, Shuiping and Mou, Luntian and Lu, Xuequan and Xiong, Lin and Zhang, Yimeng},
    title     = {Cross-Rejective Open-Set SAR Image Registration},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {23027-23036}
}

raw_affiliation:
[{"affiliation": "Xidian University", "city": "Xi'an", "country": "China"}, {"affiliation": "Beijing University of Technology", "city": "Beijing", "country": "China"}, {"affiliation": "University of Western Australia", "city": "Perth", "country": "Australia"}, {"affiliation": "ShensiliCon", "city": "Shenzhen", "country": "China"}]

affiliations:
["Xidian University", "Beijing University of Technology", "University of Western Australia", "ShensiliCon"]


title: Synthetic Data is an Elegant GIFT for Continual Vision-Language Models
authors: Bin Wu, Wuxuan Shi, Jinqiao Wang, Mang Ye
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 2813-2823
cities: ["Wuhan", "Beijing", "Wuhan", "Wuhan"]
countries: ["China", "China", "China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Wu_Synthetic_Data_is_an_Elegant_GIFT_for_Continual_Vision-Language_Models_CVPR_2025_paper.html

abstract:
Pre-trained Vision-Language Models (VLMs) require Continual Learning (CL) to efficiently update their knowledge and adapt to various downstream tasks without retraining from scratch. However, for VLMs, in addition to the loss of knowledge previously learned from downstream tasks, pre-training knowledge is also corrupted during continual fine-tuning. This issue is exacerbated by the unavailability of original pre-training data, leaving VLM's generalization ability degrading. In this paper, we propose GIFT, a novel continual fine-tuning approach that utilizes synthetic data to overcome catastrophic forgetting in VLMs. Taking advantage of recent advances in text-to-image synthesis, we employ a pre-trained diffusion model to recreate both pre-training and learned downstream task data. In this way, the VLM can revisit previous knowledge through distillation on matching diffusion-generated images and corresponding text prompts. Leveraging the broad distribution and high alignment between synthetic image-text pairs in VLM's feature space, we propose a contrastive distillation loss along with an image-text alignment constraint. To further combat in-distribution overfitting and enhance distillation performance with limited amount of generated data, we incorporate adaptive weight consolidation, utilizing Fisher information from these synthetic image-text pairs and achieving a better stability-plasticity balance. Extensive experiments demonstrate that our method consistently outperforms previous state-of-the-art approaches across various settings.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Wu_Synthetic_Data_is_an_Elegant_GIFT_for_Continual_Vision-Language_Models_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Wu_Synthetic_Data_is_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Wu_2025_CVPR,
    author    = {Wu, Bin and Shi, Wuxuan and Wang, Jinqiao and Ye, Mang},
    title     = {Synthetic Data is an Elegant GIFT for Continual Vision-Language Models},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {2813-2823}
}

raw_affiliation:
[{"affiliation": "School of Computer Science, Wuhan University", "city": "Wuhan", "country": "China"}, {"affiliation": "Institute of Automation, Chinese Academy of Sciences", "city": "Beijing", "country": "China"}, {"affiliation": "Wuhan AI Research", "city": "Wuhan", "country": "China"}, {"affiliation": "Taikang Center for Life and Medical Sciences, Wuhan University", "city": "Wuhan", "country": "China"}]

affiliations:
["School of Computer Science, Wuhan University", "Institute of Automation, Chinese Academy of Sciences", "Wuhan AI Research", "Taikang Center for Life and Medical Sciences, Wuhan University"]


title: SplineGS: Robust Motion-Adaptive Spline for Real-Time Dynamic 3D Gaussians from Monocular Video
authors: Jongmin Park, Minh-Quan Viet Bui, Juan Luis Gonzalez Bello, Jaeho Moon, Jihyong Oh, Munchurl Kim
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 26866-26875
affiliations: ["Korea Advanced Institute of Science and Technology (KAIST)", "Chung-Ang University"]
cities: ["Daejeon", "Seoul"]
countries: ["South Korea", "South Korea"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Park_SplineGS_Robust_Motion-Adaptive_Spline_for_Real-Time_Dynamic_3D_Gaussians_from_CVPR_2025_paper.html

abstract:
Synthesizing novel views from in-the-wild monocular videos is challenging due to scene dynamics and the lack of multi-view cues. To address this, we propose SplineGS, a COLMAP-free dynamic 3D Gaussian Splatting (3DGS) framework for high-quality reconstruction and fast rendering from monocular videos. At its core is a novel Motion-Adaptive Spline (MAS) method, which represents continuous dynamic 3D Gaussian trajectories using cubic Hermite splines with a small number of control points. For MAS, we introduce a Motion-Adaptive Control points Pruning (MACP) method to model the deformation of each dynamic 3D Gaussian across varying motions, progressively pruning control points while maintaining dynamic modeling integrity. Additionally, we present a joint optimization strategy for camera parameter estimation and 3D Gaussian attributes, leveraging photometric and geometric consistency. This eliminates the need for Structure-from-Motion preprocessing and enhances SplineGS's robustness in real-world conditions. Experiments show that SplineGS significantly outperforms state-of-the-art methods in novel view synthesis quality for dynamic scenes from monocular videos, achieving thousands times faster rendering speed.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Park_SplineGS_Robust_Motion-Adaptive_Spline_for_Real-Time_Dynamic_3D_Gaussians_from_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Park_SplineGS_Robust_Motion-Adaptive_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Park_2025_CVPR,
    author    = {Park, Jongmin and Bui, Minh-Quan Viet and Bello, Juan Luis Gonzalez and Moon, Jaeho and Oh, Jihyong and Kim, Munchurl},
    title     = {SplineGS: Robust Motion-Adaptive Spline for Real-Time Dynamic 3D Gaussians from Monocular Video},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {26866-26875}
}

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


title: SCSA: A Plug-and-Play Semantic Continuous-Sparse Attention for Arbitrary Semantic Style Transfer
authors: Chunnan Shang, Zhizhong Wang, Hongwei Wang, Xiangming Meng
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 13051-13060
raw_affiliation: [{"affiliation": "Zhejiang University", "city": "Hangzhou", "country": "China"}]
affiliations: ["Zhejiang University"]
cities: ["Hangzhou"]
countries: ["China"]
highlight: yes
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Shang_SCSA_A_Plug-and-Play_Semantic_Continuous-Sparse_Attention_for_Arbitrary_Semantic_Style_CVPR_2025_paper.html

abstract:
Attention-based arbitrary style transfer methods, including CNN-based, Transformer-based, and Diffusion-based, have flourished and produced high-quality stylized images. However, they perform poorly on the content and style images with the same semantics, i.e., the style of the corresponding semantic region of the generated stylized image is inconsistent with that of the style image. We argue that the root cause lies in their failure to consider the relationship between local regions and semantic regions. To address this issue, we propose a plug-and-play semantic continuous-sparse attention, dubbed SCSA, for arbitrary semantic style transfer---each query point considers certain key points in the corresponding semantic region. Specifically, semantic continuous attention ensures each query point fully attends to all the continuous key points in the same semantic region that reflect the overall style characteristics of that region; Semantic sparse attention allows each query point to focus on the most similar sparse key point in the same semantic region that exhibits the specific stylistic texture of that region. By combining the two modules, the resulting SCSA aligns the overall style of the corresponding semantic regions while transferring the vivid textures of these regions. Qualitative and quantitative results demonstrate that SCSA enables attention-based arbitrary style transfer methods to produce high-quality semantic stylized images.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Shang_SCSA_A_Plug-and-Play_Semantic_Continuous-Sparse_Attention_for_Arbitrary_Semantic_Style_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Shang_SCSA_A_Plug-and-Play_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Shang_2025_CVPR,
    author    = {Shang, Chunnan and Wang, Zhizhong and Wang, Hongwei and Meng, Xiangming},
    title     = {SCSA: A Plug-and-Play Semantic Continuous-Sparse Attention for Arbitrary Semantic Style Transfer},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {13051-13060}
}


title: Timestep Embedding Tells: It's Time to Cache for Video Diffusion Model
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 7353-7363
cities: ["Beijing", "Hangzhou", "Beijing", "Shanghai", "Singapore"]
countries: ["China", "China", "China", "China", "Singapore"]
highlight: yes
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Liu_Timestep_Embedding_Tells_Its_Time_to_Cache_for_Video_Diffusion_CVPR_2025_paper.html

authors:
Feng Liu, Shiwei Zhang, Xiaofeng Wang, Yujie Wei, Haonan Qiu, Yuzhong Zhao, Yingya Zhang, Qixiang Ye, Fang Wan

abstract:
As a fundamental backbone for video generation, diffusion models are challenged by low inference speed due to the sequential nature of denoising.Previous methods speed up the models by caching and reusing model outputs at uniformly selected timesteps.However, such a strategy neglects the fact that differences among model outputs are not uniform across timesteps, which hinders selecting the appropriate model outputs to cache, leading to a poor balance between inference efficiency and visual quality.In this study, we introduce Timestep Embedding Aware Cache (TeaCache), a training-free caching approach that estimates and leverages the fluctuating differences among model outputs across timesteps.Rather than directly using the time-consuming model outputs, TeaCache focuses on model inputs, which have a strong correlation with the modeloutputs while incurring negligible computational cost.TeaCache first modulates the noisy inputs using the timestep embeddings to ensure their differences better approximating those of model outputs. TeaCache then introduces a rescaling strategy to refine the estimated differences and utilizes them to indicate output caching.Experiments show that TeaCache achieves up to 4.41xacceleration over Open-Sora-Plan with negligible (-0.07% Vbench score) degradation of visual quality.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Liu_Timestep_Embedding_Tells_Its_Time_to_Cache_for_Video_Diffusion_CVPR_2025_paper.pdf

bibtex:
@InProceedings{Liu_2025_CVPR,
    author    = {Liu, Feng and Zhang, Shiwei and Wang, Xiaofeng and Wei, Yujie and Qiu, Haonan and Zhao, Yuzhong and Zhang, Yingya and Ye, Qixiang and Wan, Fang},
    title     = {Timestep Embedding Tells: It's Time to Cache for Video Diffusion Model},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {7353-7363}
}

raw_affiliation:
[{"affiliation": "University of Chinese Academy of Sciences", "city": "Beijing", "country": "China"}, {"affiliation": "Alibaba Group", "city": "Hangzhou", "country": "China"}, {"affiliation": "Institute of Automation, Chinese Academy of Sciences", "city": "Beijing", "country": "China"}, {"affiliation": "Fudan University", "city": "Shanghai", "country": "China"}, {"affiliation": "Nanyang Technological University", "city": "Singapore", "country": "Singapore"}]

affiliations:
["University of Chinese Academy of Sciences", "Alibaba Group", "Institute of Automation, Chinese Academy of Sciences", "Fudan University", "Nanyang Technological University"]


authors: Tianyi Wang, Zichen Wang, Cong Wang, Yuanchao Shu, Ruilong Deng, Peng Cheng, Jiming Chen
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 19230-19240
raw_affiliation: [{"affiliation": "Zhejiang University", "city": "Hangzhou", "country": "China"}]
affiliations: ["Zhejiang University"]
cities: ["Hangzhou"]
countries: ["China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Wang_Cant_Slow_Me_Down_Learning_Robust_and_Hardware-Adaptive_Object_Detectors_CVPR_2025_paper.html

title:
Can't Slow Me Down: Learning Robust and Hardware-Adaptive Object Detectors against Latency Attacks for Edge Devices

abstract:
Object detection is a fundamental enabler for many real-time downstream applications such as autonomous driving, augmented reality and supply chain management. However, the algorithmic backbone of neural networks is brittle to imperceptible perturbations in the system inputs, which were generally known as misclassifying attacks. By targeting the real-time processing capability, a new class of latency attacks has been reported recently. They exploit new attack surfaces in object detectors by creating a computational bottleneck in the post-processing module, which leads to cascading failure and puts the real-time downstream tasks at risk. In this work, we take an initial attempt to defend against this attack via background-attentive adversarial training that is also cognizant of the underlying hardware capabilities. We first draw system-level connections between latency attacks and hardware capacity across heterogeneous GPU devices. Based on the particular adversarial behaviors, we utilize objectness loss as a proxy and build background attention into the adversarial training pipeline, and achieve a favorable balance between clean and robust accuracy. The extensive experiments demonstrate the effectiveness of the defense in restoring real-time processing capability from 13 FPS to 43 FPS on Jetson Orin NX, with a better trade-off between the clean and robust accuracy. The source code is available at: https://github.com/Hill-Wu-1998/underload.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Wang_Cant_Slow_Me_Down_Learning_Robust_and_Hardware-Adaptive_Object_Detectors_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Wang_Cant_Slow_Me_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Wang_2025_CVPR,
    author    = {Wang, Tianyi and Wang, Zichen and Wang, Cong and Shu, Yuanchao and Deng, Ruilong and Cheng, Peng and Chen, Jiming},
    title     = {Can't Slow Me Down: Learning Robust and Hardware-Adaptive Object Detectors against Latency Attacks for Edge Devices},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {19230-19240}
}


title: Multi-modal Knowledge Distillation-based Human Trajectory Forecasting
authors: Jaewoo Jeong, Seohee Lee, Daehee Park, Giwon Lee, Kuk-Jin Yoon
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 24222-24233
affiliations: ["Visual Intelligence Lab., KAIST, Korea", "Intelligent Systems and Learning Lab., DGIST, Korea"]
cities: ["Daejeon", "Daegu"]
countries: ["South Korea", "South Korea"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Jeong_Multi-modal_Knowledge_Distillation-based_Human_Trajectory_Forecasting_CVPR_2025_paper.html

abstract:
Pedestrian trajectory forecasting is crucial in various applications such as autonomous driving and mobile robot navigation. In such applications, camera-based perception enables the extraction of additional modalities (human pose, text) to enhance prediction accuracy. Indeed, we find that textual descriptions play a crucial role in integrating additional modalities into a unified understanding. However, online extraction of text requires the use of VLM, which may not be feasible for resource-constrained systems. To address this challenge, we propose a multi-modal knowledge distillation framework: a student model with limited modality is distilled from a teacher model trained with full range of modalities. The comprehensive knowledge of a teacher model trained with trajectory, human pose, and text is distilled into a student model using only trajectory or human pose as a sole supplement. In doing so, we separately distill the core locomotion insights from intra-agent multi-modality and inter-agent interaction. Our generalizable framework is validated with two state-of-the-art models across three datasets on both ego-view (JRDB, SIT) and BEV-view (ETH/UCY) setups, utilizing both annotated and VLM-generated text captions. Distilled student models show consistent improvement in all prediction metrics for both full and instantaneous observations, improving up to  13%. The code is available at github.com/Jaewoo97/KDTF.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Jeong_Multi-modal_Knowledge_Distillation-based_Human_Trajectory_Forecasting_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Jeong_Multi-modal_Knowledge_Distillation-based_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Jeong_2025_CVPR,
    author    = {Jeong, Jaewoo and Lee, Seohee and Park, Daehee and Lee, Giwon and Yoon, Kuk-Jin},
    title     = {Multi-modal Knowledge Distillation-based Human Trajectory Forecasting},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {24222-24233}
}

raw_affiliation:
[{"affiliation": "Visual Intelligence Lab., KAIST, Korea", "city": "Daejeon", "country": "South Korea"}, {"affiliation": "Intelligent Systems and Learning Lab., DGIST, Korea", "city": "Daegu", "country": "South Korea"}]


title: SAM2Object: Consolidating View Consistency via SAM2 for Zero-Shot 3D Instance Segmentation
authors: Jihuai Zhao, Junbao Zhuo, Jiansheng Chen, Huimin Ma
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 19325-19334
affiliations: ["University of Science and Technology Beijing"]
cities: ["Beijing"]
countries: ["China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Zhao_SAM2Object_Consolidating_View_Consistency_via_SAM2_for_Zero-Shot_3D_Instance_CVPR_2025_paper.html

abstract:
In the field of zero-shot 3D instance segmentation, existing 2D-to-3D lifting methods typically obtain 2D segmentation across multiple RGB frames using vision foundation models, which are then projected and merged into 3D space. However, since the inference of vision foundation models on a single frame is not integrated with adjacent frames, the masks of the same object may vary across different frames, leading to a lack of view consistency in the 2D segmentation. Furthermore, current lifting methods average the 2D segmentation from multiple views during the projection into 3D space, causing low-quality masks and high-quality masks to share the same weight. These factors can lead to fragmented 3D segmentation. In this paper, we present SAM2Object, a novel zero-shot 3D instance segmentation method that effectively utilizes the Segment Anything Model 2 to segment and track objects, consolidating view consistency across frames. Our approach combines these consistent 2D masks with 3D geometric priors, improving the robustness of 3D segmentation. Additionally, we introduce mask consolidation module to filter out low-quality masks across frames, which enables more precise 2D-to-3D matching. Comprehensive evaluations on ScanNetV2, ScanNet++ and ScanNet200 demonstrate the robustness and effectiveness of SAM2Object, showcasing its ability to outperform previous methods. Our project page is at https://jihuaizhaohd.github.io/SAM2Object.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Zhao_SAM2Object_Consolidating_View_Consistency_via_SAM2_for_Zero-Shot_3D_Instance_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Zhao_SAM2Object_Consolidating_View_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Zhao_2025_CVPR,
    author    = {Zhao, Jihuai and Zhuo, Junbao and Chen, Jiansheng and Ma, Huimin},
    title     = {SAM2Object: Consolidating View Consistency via SAM2 for Zero-Shot 3D Instance Segmentation},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {19325-19334}
}

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


title: CDI: Copyrighted Data Identification in Diffusion Models
authors: Jan Dubiński, Antoni Kowalczuk, Franziska Boenisch, Adam Dziedzic
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 18674-18684
cities: ["Warsaw", "Saarbr\u00fccken", "Saarbr\u00fccken", "Saarbr\u00fccken"]
countries: ["Poland", "Germany", "Germany", "Germany"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Dubinski_CDI_Copyrighted_Data_Identification_in_Diffusion_Models_CVPR_2025_paper.html

abstract:
Diffusion Models (DMs) benefit from large and diverse datasets for their training. Since this data is often scraped from the Internet without permission from the data owners, this raises concerns about copyright and intellectual property protections. While (illicit) use of data is easily detected for training samples perfectly re-created by a DM at inference time, it is much harder for data owners to verify if their data was used for training when the outputs from the suspect DM are not close replicas. Conceptually, membership inference attacks (MIAs), which detect if a given data point was used during training, present themselves as a suitable tool to address this challenge. However, we demonstrate that existing MIAs are not strong enough to reliably determine the membership of individual images in large, state-of-the-art DMs. To overcome this limitation, we propose Copyrighted Data Identification (CDI), a framework for data owners to identify whether their dataset was used to train a given DM. CDI relies on dataset inference techniques, i.e., instead of using the membership signal from a single data point, CDI leverages the fact that most data owners, such as providers of stock photography, visual media companies, or even individual artists, own datasets with multiple publicly exposed data points which might all be included in the training of a given DM. By selectively aggregating signals from existing MIAs and using new handcrafted methods to extract features from these datasets, feeding them to a scoring model, and applying rigorous statistical testing, CDI allows data owners with as little as 70 data points to identify with a confidence of more than 99% whether their data was used to train a given DM. Thereby, CDI represents a valuable tool for data owners to claim illegitimate use of their copyrighted data.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Dubinski_CDI_Copyrighted_Data_Identification_in_Diffusion_Models_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Dubinski_CDI_Copyrighted_Data_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Dubinski_2025_CVPR,
    author    = {Dubi\'nski, Jan and Kowalczuk, Antoni and Boenisch, Franziska and Dziedzic, Adam},
    title     = {CDI: Copyrighted Data Identification in Diffusion Models},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {18674-18684}
}

raw_affiliation:
[{"affiliation": "Warsaw University of Technology, IDEAS NCBR", "city": "Warsaw", "country": "Poland"}, {"affiliation": "CISPA Helmholtz Center for Information Security", "city": "Saarbr\u00fccken", "country": "Germany"}, {"affiliation": "CISPA Helmholtz Center for Information Security", "city": "Saarbr\u00fccken", "country": "Germany"}, {"affiliation": "CISPA Helmholtz Center for Information Security", "city": "Saarbr\u00fccken", "country": "Germany"}]

affiliations:
["Warsaw University of Technology, IDEAS NCBR", "CISPA Helmholtz Center for Information Security", "CISPA Helmholtz Center for Information Security", "CISPA Helmholtz Center for Information Security"]


title: Hypergraph Vision Transformers: Images are More than Nodes, More than Edges
authors: Joshua Fixelle
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 9751-9761
raw_affiliation: [{"affiliation": "University of Virginia", "city": "Charlottesville", "country": "USA"}]
affiliations: ["University of Virginia"]
cities: ["Charlottesville"]
countries: ["USA"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Fixelle_Hypergraph_Vision_Transformers_Images_are_More_than_Nodes_More_than_CVPR_2025_paper.html

abstract:
Recent advancements in computer vision have highlighted the scalability of Vision Transformers (ViTs) across various tasks, yet challenges remain in balancing adaptability, computational efficiency, and the ability to model higher-order relationships. Vision Graph Neural Networks (ViGs) offer an alternative by leveraging graph-based methodologies but are hindered by the computational bottlenecks of clustering algorithms used for edge generation. To address these issues, we propose the Hypergraph Vision Transformer (HgVT), which incorporates a hierarchical bipartite hypergraph structure into the vision transformer framework to capture higher-order semantic relationships while maintaining computational efficiency. HgVT leverages population and diversity regularization for dynamic hypergraph construction without clustering, and expert edge pooling to enhance semantic extraction and facilitate graph-based image retrieval. Empirical results demonstrate that HgVT achieves strong performance on image classification and retrieval, positioning it as an efficient framework for semantic-based vision tasks.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Fixelle_Hypergraph_Vision_Transformers_Images_are_More_than_Nodes_More_than_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Fixelle_Hypergraph_Vision_Transformers_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Fixelle_2025_CVPR,
    author    = {Fixelle, Joshua},
    title     = {Hypergraph Vision Transformers: Images are More than Nodes, More than Edges},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {9751-9761}
}


title: Binarized Neural Network for Multi-spectral Image Fusion
authors: Junming Hou, Xiaoyu Chen, Ran Ran, Xiaofeng Cong, Xinyang Liu, Jian Wei You, Liang-Jian Deng
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 2236-2245
cities: ["Nanjing", "Chengdu", "Hong Kong"]
countries: ["China", "China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Hou_Binarized_Neural_Network_for_Multi-spectral_Image_Fusion_CVPR_2025_paper.html

abstract:
Pan-sharpening technology refers to generating a high-resolution (HR) multi-spectral (MS) image with broad applications by fusing a low-resolution (LR) MS image and HR panchromatic (PAN) image. While deep learning approaches have shown impressive performance in pan-sharpening, they generally require extensive hardware with high memory and computational power, limiting their deployment on resource-constrained satellites. In this study, we investigate the use of binary neural networks (BNNs) for pan-sharpening and observe that binarization leads to distinct information degradation across different frequency components of an image. Building on this insight, we propose a novel binary pan-sharpening network, termed BNNPan, structured around the Prior-Integrated Binary Frequency (PIBF) module that features three key ingredients: Binary Wavelet Transform Convolution, Latent Diffusion Prior Compensation, and Channel-wise Distribution Calibration. Specifically, the first decomposes input features into distinct frequency components using Wavelet Transform, then applies a "divide-and-conquer" strategy to optimize binary feature learning for each component, informed by the corresponding full-precision residual statistics. The second integrates a latent diffusion prior to compensate for compromised information during binarization, while the third performs channel-wise calibration to further refine feature representation. Our BNNPan, developed with the proposed techniques, achieves promising pan-sharpening performance on multiple remote sensing datasets, surpassing state-of-the-art binarization algorithms.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Hou_Binarized_Neural_Network_for_Multi-spectral_Image_Fusion_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Hou_Binarized_Neural_Network_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Hou_2025_CVPR,
    author    = {Hou, Junming and Chen, Xiaoyu and Ran, Ran and Cong, Xiaofeng and Liu, Xinyang and You, Jian Wei and Deng, Liang-Jian},
    title     = {Binarized Neural Network for Multi-spectral Image Fusion},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {2236-2245}
}

raw_affiliation:
[{"affiliation": "Southeast University", "city": "Nanjing", "country": "China"}, {"affiliation": "University of Electronic Science and Technology of China", "city": "Chengdu", "country": "China"}, {"affiliation": "The Hong Kong Polytechnic University", "city": "Hong Kong", "country": "China"}]

affiliations:
["Southeast University", "University of Electronic Science and Technology of China", "The Hong Kong Polytechnic University"]


title: CRISP: Object Pose and Shape Estimation with Test-Time Adaptation
authors: Jingnan Shi, Rajat Talak, Harry Zhang, David Jin, Luca Carlone
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 11644-11653
affiliations: ["Laboratory for Information & Decision Systems (LIDS) Massachusetts Institute of Technology"]
cities: ["Cambridge"]
countries: ["USA"]
highlight: yes
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Shi_CRISP_Object_Pose_and_Shape_Estimation_with_Test-Time_Adaptation_CVPR_2025_paper.html

abstract:
We consider the problem of estimating object pose and shape from an RGB-D image. Our first contribution is to introduce CRISP, a category-agnostic object pose and shape estimation pipeline. The pipeline implements an encoder-decoder model for shape estimation. It uses FiLM-conditioning for implicit shape reconstruction and a DPT-based network for estimating pose-normalized points for pose estimation. As a second contribution, we propose an optimization-based pose and shape corrector that can correct estimation errors caused by a domain gap. Observing that the shape decoder is well behaved in the convex hull of known shapes, we approximate the shape decoder with an active shape model, and show that this reduces the shape correction problem to a constrained linear least squares problem, which can be solved efficiently by an interior point algorithm. Third, we introduce a self-training pipeline to perform self-supervised domain adaptation of CRISP. The self-training is based on a correct-and-certify approach, which leverages the corrector to generate pseudo-labels at test time, and uses them to self-train CRISP. We demonstrate CRISP (and the self-training) on YCBV, SPE3R, and NOCS datasets. CRISP shows high performance on all the datasets. Moreover, our self-training is capable of bridging a large domain gap. Finally, CRISP also shows an ability to generalize to unseen objects. Code, pre-trained models and videos of sample results are available on the project webpage https://web.mit.edu/sparklab/research/crisp_object_pose_shape/.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Shi_CRISP_Object_Pose_and_Shape_Estimation_with_Test-Time_Adaptation_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Shi_CRISP_Object_Pose_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Shi_2025_CVPR,
    author    = {Shi, Jingnan and Talak, Rajat and Zhang, Harry and Jin, David and Carlone, Luca},
    title     = {CRISP: Object Pose and Shape Estimation with Test-Time Adaptation},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {11644-11653}
}

raw_affiliation:
[{"affiliation": "Laboratory for Information & Decision Systems (LIDS) Massachusetts Institute of Technology", "city": "Cambridge", "country": "USA"}]


title: ShiftwiseConv: Small Convolutional Kernel with Large Kernel Effect
authors: Dachong Li, Li Li, Zhuangzhuang Chen, Jianqiang Li
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 25281-25291
cities: ["Shenzhen", "Shenzhen", "Shenzhen"]
countries: ["China", "China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Li_ShiftwiseConv_Small_Convolutional_Kernel_with_Large_Kernel_Effect_CVPR_2025_paper.html

abstract:
Large kernels make standard convolutional neural networks (CNNs) great again over transformer architectures in various vision tasks. Nonetheless, recent studies meticulously designed around increasing kernel size have shown diminishing returns or stagnation in performance. Thus, the hidden factors of large kernel convolution that affect model performance remain unexplored. In this paper, we reveal that the key hidden factors of large kernels can be summarized as two separate components: extracting features at a certain granularity and fusing features by multiple pathways. To this end, we leverage the multi-path long-distance sparse dependency relationship to enhance feature utilization via the proposed Shiftwise (SW) convolution operator with a pure CNN architecture. In a wide range of vision tasks such as classification, segmentation, and detection, SW surpasses state-of-the-art transformers and CNN architectures, including SLaK and UniRepLKNet. More importantly, our experiments demonstrate that 3 x3 convolutions can replace large convolutions in existing large kernel CNNs to achieve comparable effects, which may inspire follow-up works. Code and all the models at  https://github.com/lidc54/shift-wiseConv.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Li_ShiftwiseConv_Small_Convolutional_Kernel_with_Large_Kernel_Effect_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Li_ShiftwiseConv_Small_Convolutional_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Li_2025_CVPR,
    author    = {Li, Dachong and Li, Li and Chen, Zhuangzhuang and Li, Jianqiang},
    title     = {ShiftwiseConv: Small Convolutional Kernel with Large Kernel Effect},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {25281-25291}
}

raw_affiliation:
[{"affiliation": "College of Computer Science and Software Engineering, Shenzhen University", "city": "Shenzhen", "country": "China"}, {"affiliation": "School of Mathematical Sciences, Shenzhen University", "city": "Shenzhen", "country": "China"}, {"affiliation": "National Engineering Laboratory for Big Data System Computing Technology", "city": "Shenzhen", "country": "China"}]

affiliations:
["College of Computer Science and Software Engineering, Shenzhen University", "School of Mathematical Sciences, Shenzhen University", "National Engineering Laboratory for Big Data System Computing Technology"]


title: GaussianIP: Identity-Preserving Realistic 3D Human Generation via Human-Centric Diffusion Prior
authors: Zichen Tang, Yuan Yao, Miaomiao Cui, Liefeng Bo, Hongyu Yang
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 348-358
cities: ["Beijing", "Shanghai"]
countries: ["China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Tang_GaussianIP_Identity-Preserving_Realistic_3D_Human_Generation_via_Human-Centric_Diffusion_Prior_CVPR_2025_paper.html

abstract:
Text-guided 3D human generation has advanced with the development of efficient 3D representations and 2D-lifting methods like score distillation sampling (SDS). However, current methods suffer from prolonged training times and often produce results that lack fine facial and garment details. In this paper, we propose GaussianIP, an effective two-stage framework for generating identity-preserving realistic 3D humans from text and image prompts. Our core insight is to leverage human-centric knowledge to facilitate the generation process. In stage 1, we propose a novel Adaptive Human Distillation Sampling (AHDS) method to rapidly generate a 3D human that maintains high identity consistency with the image prompt and achieves a realistic appearance. Compared to traditional SDS methods, AHDS better aligns with the human-centric generation process, enhancing visual quality with notably fewer training steps. To further improve the visual quality of the face and clothes regions, we design a View-Consistent Refinement (VCR) strategy in stage 2. Specifically, it produces detail-enhanced results of the multi-view images from stage 1 iteratively, ensuring the 3D texture consistency across views via mutual attention and distance-guided attention fusion. Then a polished version of the 3D human can be achieved by directly perform reconstruction with the refined images. Extensive experiments demonstrate that GaussianIP outperforms existing methods in both visual quality and training efficiency, particularly in generating identity-preserving results.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Tang_GaussianIP_Identity-Preserving_Realistic_3D_Human_Generation_via_Human-Centric_Diffusion_Prior_CVPR_2025_paper.pdf

bibtex:
@InProceedings{Tang_2025_CVPR,
    author    = {Tang, Zichen and Yao, Yuan and Cui, Miaomiao and Bo, Liefeng and Yang, Hongyu},
    title     = {GaussianIP: Identity-Preserving Realistic 3D Human Generation via Human-Centric Diffusion Prior},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {348-358}
}

raw_affiliation:
[{"affiliation": "School of Artificial Intelligence, Beihang University", "city": "Beijing", "country": "China"}, {"affiliation": "Shanghai Artificial Intelligence Laboratory", "city": "Shanghai", "country": "China"}]

affiliations:
["School of Artificial Intelligence, Beihang University", "Shanghai Artificial Intelligence Laboratory"]


title: Creating Your Editable 3D Photorealistic Avatar with Tetrahedron-constrained Gaussian Splatting
authors: Hanxi Liu, Yifang Men, Zhouhui Lian
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 15976-15986
cities: ["Beijing", "Hangzhou"]
countries: ["China", "China"]
highlight: yes
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Liu_Creating_Your_Editable_3D_Photorealistic_Avatar_with_Tetrahedron-constrained_Gaussian_Splatting_CVPR_2025_paper.html

abstract:
Personalized 3D avatar editing holds significant promise due to its user-friendliness and availability to applications such as AR/VR and virtual try-ons. Previous studies have explored the feasibility of 3D editing, but often struggle to generate visually pleasing results, possibly due to the unstable representation learning under mixed optimization of geometry and texture in complicated reconstructed scenarios. In this paper, we aim to provide an accessible solution for ordinary users to create their editable 3D avatars with precise region localization, geometric adaptability, and photorealistic renderings. To tackle this challenge, we introduce a meticulously designed framework that decouples the editing process into local spatial adaptation and realistic appearance learning, utilizing a hybrid Tetrahedron-constrained Gaussian Splatting (TetGS) as the underlying representation. TetGS combines the controllable explicit structure of tetrahedral grids with the high-precision rendering capabilities of 3D Gaussian Splatting and is optimized in a progressive manner comprising three stages: 3D avatar instantiation from real-world monocular videos to provide accurate priors for TetGS initialization; localized spatial adaptation with explicitly partitioned tetrahedrons to guide the redistribution of Gaussian kernels; and geometry-based appearance generation with a coarse-to-fine activation strategy. Both qualitative and quantitative experiments demonstrate the effectiveness and superiority of our approach in generating photorealistic 3D editable avatars.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Liu_Creating_Your_Editable_3D_Photorealistic_Avatar_with_Tetrahedron-constrained_Gaussian_Splatting_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Liu_Creating_Your_Editable_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Liu_2025_CVPR,
    author    = {Liu, Hanxi and Men, Yifang and Lian, Zhouhui},
    title     = {Creating Your Editable 3D Photorealistic Avatar with Tetrahedron-constrained Gaussian Splatting},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {15976-15986}
}

raw_affiliation:
[{"affiliation": "Wangxuan Institute of Computer Technology, Peking University", "city": "Beijing", "country": "China"}, {"affiliation": "Institute for Intelligent Computing, Alibaba Group", "city": "Hangzhou", "country": "China"}]

affiliations:
["Wangxuan Institute of Computer Technology, Peking University", "Institute for Intelligent Computing, Alibaba Group"]


title: FineVQ: Fine-Grained User Generated Content Video Quality Assessment
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 3206-3217
affiliations: ["Shanghai Jiao Tong University", "Bilibili Inc."]
cities: ["Shanghai", "Shanghai"]
countries: ["China", "China"]
highlight: yes
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Duan_FineVQ_Fine-Grained_User_Generated_Content_Video_Quality_Assessment_CVPR_2025_paper.html

authors:
Huiyu Duan, Qiang Hu, Jiarui Wang, Liu Yang, Zitong Xu, Lu Liu, Xiongkuo Min, Chunlei Cai, Tianxiao Ye, Xiaoyun Zhang, Guangtao Zhai

abstract:
The rapid growth of user-generated content (UGC) videos has produced an urgent need for effective video quality assessment (VQA) algorithms to monitor video quality and guide optimization and recommendation procedures. However, current VQA models generally only give an overall rating for a UGC video, which lacks fine-grained labels for serving video processing and recommendation applications. To address the challenges and promote the development of UGC videos, we establish the first large-scale Fine-grained Video quality assessment Database, termed FineVD, which comprises 6104 UGC videos with fine-grained quality scores and descriptions across multiple dimensions. Based on this database, we propose a Fine-grained Video Quality assessment (FineVQ) model to learn the fine-grained quality of UGC videos, with the capabilities of quality rating, quality scoring, and quality attribution. Extensive experimental results demonstrate that our proposed FineVQ can produce fine-grained video-quality results and achieve state-of-the-art performance on FineVD and other commonly used UGC-VQA datasets. Both FineVD and FineVQ are publicly available at: https://github.com/IntMeGroup/FineVQ.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Duan_FineVQ_Fine-Grained_User_Generated_Content_Video_Quality_Assessment_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Duan_FineVQ_Fine-Grained_User_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Duan_2025_CVPR,
    author    = {Duan, Huiyu and Hu, Qiang and Wang, Jiarui and Yang, Liu and Xu, Zitong and Liu, Lu and Min, Xiongkuo and Cai, Chunlei and Ye, Tianxiao and Zhang, Xiaoyun and Zhai, Guangtao},
    title     = {FineVQ: Fine-Grained User Generated Content Video Quality Assessment},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {3206-3217}
}

raw_affiliation:
[{"affiliation": "Shanghai Jiao Tong University", "city": "Shanghai", "country": "China"}, {"affiliation": "Bilibili Inc.", "city": "Shanghai", "country": "China"}]


title: Unveiling the Ignorance of MLLMs: Seeing Clearly, Answering Incorrectly
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 9087-9097
cities: ["Hong Kong", "Shanghai", "Beijing", "Beijing", "Unknown", "Unknown", "Beijing", "Orlando"]
countries: ["Hong Kong", "China", "China", "China", "Unknown", "Unknown", "China", "USA"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Liu_Unveiling_the_Ignorance_of_MLLMs_Seeing_Clearly_Answering_Incorrectly_CVPR_2025_paper.html

authors:
Yexin Liu, Zhengyang Liang, Yueze Wang, Xianfeng Wu, Feilong Tang, Muyang He, Jian Li, Zheng Liu, Harry Yang, Sernam Lim, Bo Zhao

abstract:
**M**ultimodal **L**arge **L**anguage **M**odels (MLLMs) have displayed remarkable performance in multimodal tasks, particularly in visual comprehension. However, we reveal that MLLMs often generate incorrect answers even when they understand the visual content. To this end, we manually construct a benchmark with 12 categories and design evaluation metrics that assess the degree of error in MLLM responses even when the visual content is seemingly understood. Based on this benchmark, we test 15 leading MLLMs and analyze the distribution of attention maps and logits of some MLLMs. Our investigation identifies two primary issues: 1) most instruction tuning datasets predominantly feature questions that "directly" relate to the visual content, leading to a bias in MLLMs' responses to other indirect questions, and 2) MLLMs' attention to visual tokens is notably lower than to system and question tokens. We further observe that attention scores between questions and visual tokens as well as the model's confidence in the answers are lower in response to misleading questions than to straightforward ones. To address the first challenge, we introduce a paired positive and negative data construction pipeline to diversify the dataset. For the second challenge, we propose to enhance the model's focus on visual content during decoding by refining the text and visual prompt. For the text prompt, we propose a content-guided refinement strategy that performs preliminary visual content analysis to generate structured information before answering the question. Additionally, we employ a visual attention refinement strategy that highlights question-relevant visual tokens to increase the model's attention to visual content that aligns with the question. Extensive experiments demonstrate that these challenges can be significantly mitigated with our proposed dataset and techniques. The benchmark, training set, and code will be available.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Liu_Unveiling_the_Ignorance_of_MLLMs_Seeing_Clearly_Answering_Incorrectly_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Liu_Unveiling_the_Ignorance_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Liu_2025_CVPR,
    author    = {Liu, Yexin and Liang, Zhengyang and Wang, Yueze and Wu, Xianfeng and Tang, Feilong and He, Muyang and Li, Jian and Liu, Zheng and Yang, Harry and Lim, Sernam and Zhao, Bo},
    title     = {Unveiling the Ignorance of MLLMs: Seeing Clearly, Answering Incorrectly},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {9087-9097}
}

raw_affiliation:
[{"affiliation": "Hong Kong University of Science and Technology", "city": "Hong Kong", "country": "Hong Kong"}, {"affiliation": "School of AI, Shanghai Jiao Tong University", "city": "Shanghai", "country": "China"}, {"affiliation": "Beijing University of Posts and Telecommunications", "city": "Beijing", "country": "China"}, {"affiliation": "Peking University", "city": "Beijing", "country": "China"}, {"affiliation": "Everlyn AI", "city": "Unknown", "country": "Unknown"}, {"affiliation": "Youtu Lab, Tencent", "city": "Unknown", "country": "Unknown"}, {"affiliation": "Beijing Academy of Artificial Intelligence", "city": "Beijing", "country": "China"}, {"affiliation": "University of Central Florida", "city": "Orlando", "country": "USA"}]

affiliations:
["Hong Kong University of Science and Technology", "School of AI, Shanghai Jiao Tong University", "Beijing University of Posts and Telecommunications", "Peking University", "Everlyn AI", "Youtu Lab, Tencent", "Beijing Academy of Artificial Intelligence", "University of Central Florida"]


title: Object-Shot Enhanced Grounding Network for Egocentric Video
authors: Yisen Feng, Haoyu Zhang, Meng Liu, Weili Guan, Liqiang Nie
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 24190-24200
cities: ["Shenzhen", "Shenzhen", "Jinan"]
countries: ["China", "China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Feng_Object-Shot_Enhanced_Grounding_Network_for_Egocentric_Video_CVPR_2025_paper.html

abstract:
Egocentric video grounding is a crucial task for embodied intelligence applications, distinct from exocentric video moment localization. Existing methods primarily focus on the distributional differences between egocentric and exocentric videos but often neglect key characteristics of egocentric videos and the fine-grained information emphasized by question-type queries. To address these limitations, we propose OSGNet, an Object-Shot enhanced Grounding Network for egocentric video. Specifically, we extract object information from videos to enrich video representation, particularly for objects highlighted in the textual query but not directly captured in the video features. Additionally, we analyze the frequent shot movements inherent to egocentric videos, leveraging these features to extract the wearer's attention information, which enhances the model's ability to perform modality alignment. Experiments conducted on three datasets demonstrate that OSGNet achieves state-of-the-art performance, validating the effectiveness of our approach. Our code can be found at https://github.com/Yisen-Feng/OSGNet.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Feng_Object-Shot_Enhanced_Grounding_Network_for_Egocentric_Video_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Feng_Object-Shot_Enhanced_Grounding_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Feng_2025_CVPR,
    author    = {Feng, Yisen and Zhang, Haoyu and Liu, Meng and Guan, Weili and Nie, Liqiang},
    title     = {Object-Shot Enhanced Grounding Network for Egocentric Video},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {24190-24200}
}

raw_affiliation:
[{"affiliation": "Harbin Institute of Technology (Shenzhen)", "city": "Shenzhen", "country": "China"}, {"affiliation": "Pengcheng Laboratory", "city": "Shenzhen", "country": "China"}, {"affiliation": "Shandong Jianzhu University", "city": "Jinan", "country": "China"}]

affiliations:
["Harbin Institute of Technology (Shenzhen)", "Pengcheng Laboratory", "Shandong Jianzhu University"]


title: MANTA: Diffusion Mamba for Efficient and Effective Stochastic Long-Term Dense Action Anticipation
authors: Olga Zatsarynna, Emad Bahrami, Yazan Abu Farha, Gianpiero Francesca, Juergen Gall
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 3438-3448
affiliations: ["Computer Vision Foundation", "IEEE"]
cities: ["Los Alamitos", "New York"]
countries: ["USA", "USA"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Zatsarynna_MANTA_Diffusion_Mamba_for_Efficient_and_Effective_Stochastic_Long-Term_Dense_CVPR_2025_paper.html

abstract:
Long-term dense action anticipation is very challenging since it requires predicting actions and their durations several minutes into the future based on provided video observations. To model the uncertainty of future outcomes, stochastic models predict several potential future action sequences for the same observation. Recent work has further proposed to incorporate uncertainty modelling for observed frames by simultaneously predicting per-frame past and future actions in a unified manner. While such joint modelling of actions is beneficial, it requires long-range temporal capabilities to connect events across distant past and future time points. However, the previous work struggles to achieve such a long-range understanding due to its limited and/or sparse receptive field. To alleviate this issue, we propose a novel MANTA (MAmba for ANTicipation) network. Our model enables effective long-term temporal modelling even for very long sequences while maintaining linear complexity in sequence length. We demonstrate that our approach achieves state-of-the-art results on three datasets - Breakfast, 50Salads, and Assembly101 - while also significantly improving computational and memory efficiency. Our code is available at https://github.com/olga-zats/DIFF_MANTA.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Zatsarynna_MANTA_Diffusion_Mamba_for_Efficient_and_Effective_Stochastic_Long-Term_Dense_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Zatsarynna_MANTA_Diffusion_Mamba_CVPR_2025_supplemental.zip

bibtex:
@InProceedings{Zatsarynna_2025_CVPR,
    author    = {Zatsarynna, Olga and Bahrami, Emad and Abu Farha, Yazan and Francesca, Gianpiero and Gall, Juergen},
    title     = {MANTA: Diffusion Mamba for Efficient and Effective Stochastic Long-Term Dense Action Anticipation},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {3438-3448}
}

raw_affiliation:
[{"affiliation": "Computer Vision Foundation", "city": "Los Alamitos", "country": "USA"}, {"affiliation": "IEEE", "city": "New York", "country": "USA"}]


title: METASCENES: Towards Automated Replica Creation for Real-world 3D Scans
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 1667-1679
cities: ["Beijing", "Beijing", "Beijing", "Hefei"]
countries: ["China", "China", "China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Yu_METASCENES_Towards_Automated_Replica_Creation_for_Real-world_3D_Scans_CVPR_2025_paper.html

authors:
Huangyue Yu, Baoxiong Jia, Yixin Chen, Yandan Yang, Puhao Li, Rongpeng Su, Jiaxin Li, Qing Li, Wei Liang, Song-Chun Zhu, Tengyu Liu, Siyuan Huang

abstract:
Embodied AI (EAI) research requires high-quality, diverse 3D scenes to effectively support skill acquisition, sim-to-real transfer, and generalization. Achieving these quality standards, however, necessitates the precise replication of real-world object diversity. Existing datasets demonstrate that this process heavily relies on artist-driven designs, which demand substantial human effort and present significant scalability challenges. To scalably produce realistic and interactive 3D scenes, we first present MetaScenes, a large-scale simulatable 3D scene dataset constructed from real-world scans, which includes 15366 objects spanning 831 fine-grained categories. Then, we introduce Scan2Sim, a robust multi-modal alignment model, which enables the automated, high-quality replacement of assets, thereby eliminating the reliance on artist-driven designs for scaling 3D scenes. We further propose two benchmarks to evaluate MetaScenes: a detailed scene synthesis task focused on small item layouts for robotic manipulation and a domain transfer task in vision-and-language navigation (VLN) to validate cross-domain transfer. Results confirm MetaScenes's potential to enhance EAI by supporting more generalizable agent learning and sim-to-real applications, introducing new possibilities for EAI research.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Yu_METASCENES_Towards_Automated_Replica_Creation_for_Real-world_3D_Scans_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Yu_METASCENES_Towards_Automated_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Yu_2025_CVPR,
    author    = {Yu, Huangyue and Jia, Baoxiong and Chen, Yixin and Yang, Yandan and Li, Puhao and Su, Rongpeng and Li, Jiaxin and Li, Qing and Liang, Wei and Zhu, Song-Chun and Liu, Tengyu and Huang, Siyuan},
    title     = {METASCENES: Towards Automated Replica Creation for Real-world 3D Scans},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {1667-1679}
}

raw_affiliation:
[{"affiliation": "State Key Laboratory of General Artificial Intelligence, BIGAI", "city": "Beijing", "country": "China"}, {"affiliation": "Beijing Institute of Technology", "city": "Beijing", "country": "China"}, {"affiliation": "Tsinghua University", "city": "Beijing", "country": "China"}, {"affiliation": "University of Science and Technology of China", "city": "Hefei", "country": "China"}]

affiliations:
["State Key Laboratory of General Artificial Intelligence, BIGAI", "Beijing Institute of Technology", "Tsinghua University", "University of Science and Technology of China"]


authors: Junjie Zhou, Jiao Tang, Yingli Zuo, Peng Wan, Daoqiang Zhang, Wei Shao
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 10384-10393
cities: ["Nanjing", "Nanjing"]
countries: ["China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Zhou_Robust_Multimodal_Survival_Prediction_with_Conditional_Latent_Differentiation_Variational_AutoEncoder_CVPR_2025_paper.html

title:
Robust Multimodal Survival Prediction with Conditional Latent Differentiation Variational AutoEncoder

abstract:
The integrative analysis of histopathological images and genomic data has received increasing attention for survival prediction of human cancers. However, the existing studies always hold the assumption that full modalities are available. As a matter of fact, the cost for collecting genomic data is high, which sometimes makes genomic data unavailable in testing samples. A common way of tackling such incompleteness is to generate the genomic representations from the pathology images. Nevertheless, such strategy still faces the following two challenges: (1) The gigapixel whole slide images (WSIs) are huge and thus hard for representation. (2) It is difficult to generate the genomic embeddings with diverse function categories in a unified generative framework. To address the above challenges, we propose a Conditional Latent Differentiation Variational AutoEncoder (LD-CVAE) for robust multimodal survival prediction, even with missing genomic data. Specifically, a Variational Information Bottleneck Transformer (VIB-Trans) module is proposed to learn compressed pathological representations from the gigapixel WSIs. To generate different functional genomic features, we develop a novel Latent Differentiation Variational AutoEncoder (LD-VAE) to learn the genomic and function-specific posteriors for the genomic embeddings with diverse functions. Finally, we use the product-of-experts technique to integrate the genomic posterior and image posterior for the joint latent distribution estimation in LD-CVAE. We test the effectiveness of our method on five different cancer datasets, and the experimental results demonstrate its superiority in both complete and missing modality scenarios.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Zhou_Robust_Multimodal_Survival_Prediction_with_Conditional_Latent_Differentiation_Variational_AutoEncoder_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Zhou_Robust_Multimodal_Survival_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Zhou_2025_CVPR,
    author    = {Zhou, Junjie and Tang, Jiao and Zuo, Yingli and Wan, Peng and Zhang, Daoqiang and Shao, Wei},
    title     = {Robust Multimodal Survival Prediction with Conditional Latent Differentiation Variational AutoEncoder},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {10384-10393}
}

raw_affiliation:
[{"affiliation": "The College of Arti\ufb01cial Intelligence, Nanjing University of Aeronautics and Astronautics", "city": "Nanjing", "country": "China"}, {"affiliation": "The Key Laboratory of Brain-Machine Intelligence Technology, Ministry of Education", "city": "Nanjing", "country": "China"}]

affiliations:
["The College of Arti\ufb01cial Intelligence, Nanjing University of Aeronautics and Astronautics", "The Key Laboratory of Brain-Machine Intelligence Technology, Ministry of Education"]


authors: Ahmad Rahimi, Po-Chien Luan, Yuejiang Liu, Frano Rajič, Alexandre Alahi
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 17271-17281
cities: ["Lausanne", "Stanford", "Zurich"]
countries: ["Switzerland", "USA", "Switzerland"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Rahimi_Sim-to-Real_Causal_Transfer_A_Metric_Learning_Approach_to_Causally-Aware_Interaction_CVPR_2025_paper.html

title:
Sim-to-Real Causal Transfer: A Metric Learning Approach to Causally-Aware Interaction Representations

abstract:
Modeling spatial-temporal interactions among neighboring agents is at the heart of multi-agent problems such as motion forecasting and crowd navigation. Despite notable progress, it remains unclear to which extent modern representations can capture the causal relationships behind agent interactions. In this work, we take an in-depth look at the causal awareness of these representations, from computational formalism to real-world practice. First, we revisit the notion of non-causal robustness studied in the recent CausalAgents benchmark. We show that existing representations are already partially resilient to perturbations of non-causal agents, and yet modeling indirect causal effects involving mediator agents remains challenging. To address this challenge, we introduce a metric learning approach that regularizes latent representations with causal annotations. Our controlled experiments show that this approach not only leads to higher degrees of causal awareness but also yields stronger out-of-distribution robustness. To further operationalize it in practice, we propose a sim-to-real causal transfer method via cross-domain multi-task learning. Experiments on trajectory prediction datasets show that our method can significantly boost generalization, even in the absence of real-world causal annotations, where we acquire higher prediction accuracy by only using 25% of real-world data. We hope our work provides a new perspective on the challenges and potential pathways toward causally-aware representations of multi-agent interactions. Our code is available in supplementary materials. Our code is available at https://github.com/vita-epfl/CausalSim2Real.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Rahimi_Sim-to-Real_Causal_Transfer_A_Metric_Learning_Approach_to_Causally-Aware_Interaction_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Rahimi_Sim-to-Real_Causal_Transfer_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Rahimi_2025_CVPR,
    author    = {Rahimi, Ahmad and Luan, Po-Chien and Liu, Yuejiang and Raji\v{c}, Frano and Alahi, Alexandre},
    title     = {Sim-to-Real Causal Transfer: A Metric Learning Approach to Causally-Aware Interaction Representations},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {17271-17281}
}

raw_affiliation:
[{"affiliation": "\u00b4Ecole Polytechnique F\u00b4ed\u00b4erale de Lausanne (EPFL)", "city": "Lausanne", "country": "Switzerland"}, {"affiliation": "Stanford University", "city": "Stanford", "country": "USA"}, {"affiliation": "ETH Zurich", "city": "Zurich", "country": "Switzerland"}]

affiliations:
["\u00b4Ecole Polytechnique F\u00b4ed\u00b4erale de Lausanne (EPFL)", "Stanford University", "ETH Zurich"]


title: Ev-3DOD: Pushing the Temporal Boundaries of 3D Object Detection with Event Cameras
authors: Hoonhee Cho, Jae-Young Kang, Youngho Kim, Kuk-Jin Yoon
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 27197-27210
raw_affiliation: [{"affiliation": "KAIST", "city": "Daejeon", "country": "South Korea"}]
affiliations: ["KAIST"]
cities: ["Daejeon"]
countries: ["South Korea"]
highlight: yes
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Cho_Ev-3DOD_Pushing_the_Temporal_Boundaries_of_3D_Object_Detection_with_CVPR_2025_paper.html

abstract:
Detecting 3D objects in point clouds plays a crucial role in autonomous driving systems. Recently, advanced multi-modal methods incorporating camera information have achieved notable performance. For a safe and effective autonomous driving system, algorithms that excel not only in accuracy but also in speed and low latency are essential. However, existing algorithms fail to meet these requirements due to the latency and bandwidth limitations of fixed frame rate sensors, e.g., LiDAR and camera. To address this limitation, we introduce asynchronous event cameras into 3D object detection for the first time. We leverage their high temporal resolution and low bandwidth to enable high-speed 3D object detection. Our method enables detection even during inter-frame intervals when synchronized data is unavailable, by retrieving previous 3D information through the event camera. Furthermore, we introduce the first event-based 3D object detection datasets, Ev-Waymo and DSEC-3DOD, both of which include ground truth 3D bounding boxes at 100 FPS, establishing the first benchmarks for event-based 3D detectors. The code and dataset are available at https://github.com/mickeykang16/Ev3DOD.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Cho_Ev-3DOD_Pushing_the_Temporal_Boundaries_of_3D_Object_Detection_with_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Cho_Ev-3DOD_Pushing_the_CVPR_2025_supplemental.zip

bibtex:
@InProceedings{Cho_2025_CVPR,
    author    = {Cho, Hoonhee and Kang, Jae-Young and Kim, Youngho and Yoon, Kuk-Jin},
    title     = {Ev-3DOD: Pushing the Temporal Boundaries of 3D Object Detection with Event Cameras},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {27197-27210}
}


title: Zero-Shot Blind-spot Image Denoising via Implicit Neural Sampling
authors: Yuhui Quan, Tianxiang Zheng, Zhiyuan Ma, Hui Ji
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 7502-7512
cities: ["Guangzhou", "Singapore"]
countries: ["China", "Singapore"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Quan_Zero-Shot_Blind-spot_Image_Denoising_via_Implicit_Neural_Sampling_CVPR_2025_paper.html

abstract:
The blind-spot principle has been a widely used tool in zero-shot image denoising but faces challenges with real-world noise that exhibits strong local correlations. Existing methods focus on reducing noise correlation, which also weaken the pixel correlations needed for accurately estimating missing pixels. In this paper, we first present a rigorous analysis of how noise correlation and pixel correlation impact the statistical risk of a linear blind-spot denoiser. We then propose using an implicit neural representation to resample noisy pixels, effectively reducing noise correlation while preserving the essential pixel correlations for successful blind-spot denoising. Extensive experiments show our method surpasses existing zero-shot denoising techniques on real-world noisy images.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Quan_Zero-Shot_Blind-spot_Image_Denoising_via_Implicit_Neural_Sampling_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Quan_Zero-Shot_Blind-spot_Image_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Quan_2025_CVPR,
    author    = {Quan, Yuhui and Zheng, Tianxiang and Ma, Zhiyuan and Ji, Hui},
    title     = {Zero-Shot Blind-spot Image Denoising via Implicit Neural Sampling},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {7502-7512}
}

raw_affiliation:
[{"affiliation": "School of Computer Science and Engineering, South China University of Technology", "city": "Guangzhou", "country": "China"}, {"affiliation": "Department of Mathematics, National University of Singapore", "city": "Singapore", "country": "Singapore"}]

affiliations:
["School of Computer Science and Engineering, South China University of Technology", "Department of Mathematics, National University of Singapore"]


title: Nearly Zero-Cost Protection Against Mimicry by Personalized Diffusion Models
authors: Namhyuk Ahn, KiYoon Yoo, Wonhyuk Ahn, Daesik Kim, Seung-Hun Nam
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 28801-28810
affiliations: ["Inha University", "NA VER WEBTOON AI", "KRAFTON"]
cities: ["Incheon", "Seoul", "Seongnam-si"]
countries: ["South Korea", "South Korea", "South Korea"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Ahn_Nearly_Zero-Cost_Protection_Against_Mimicry_by_Personalized_Diffusion_Models_CVPR_2025_paper.html

abstract:
Recent advancements in diffusion models revolutionize image generation but pose risks of misuse, such as replicating artworks or generating deepfakes. Existing image protection methods, though effective, struggle to balance protection efficacy, invisibility, and latency, thus limiting practical use. We introduce perturbation pre-training to reduce latency and propose a mixture-of-perturbations approach that dynamically adapts to input images to minimize performance degradation. Our novel training strategy computes protection loss across multiple VAE feature spaces, while adaptive targeted protection at inference enhances robustness and invisibility. Experiments show comparable protection performance with improved invisibility and drastically reduced inference time.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Ahn_Nearly_Zero-Cost_Protection_Against_Mimicry_by_Personalized_Diffusion_Models_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Ahn_Nearly_Zero-Cost_Protection_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Ahn_2025_CVPR,
    author    = {Ahn, Namhyuk and Yoo, KiYoon and Ahn, Wonhyuk and Kim, Daesik and Nam, Seung-Hun},
    title     = {Nearly Zero-Cost Protection Against Mimicry by Personalized Diffusion Models},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {28801-28810}
}

raw_affiliation:
[{"affiliation": "Inha University", "city": "Incheon", "country": "South Korea"}, {"affiliation": "NA VER WEBTOON AI", "city": "Seoul", "country": "South Korea"}, {"affiliation": "KRAFTON", "city": "Seongnam-si", "country": "South Korea"}]


title: Tripartite Weight-Space Ensemble for Few-Shot Class-Incremental Learning
authors: Juntae Lee, Munawar Hayat, Sungrack Yun
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 15329-15338
raw_affiliation: [{"affiliation": "Qualcomm AI Research\u2020", "city": "San Diego", "country": "USA"}]
affiliations: ["Qualcomm AI Research\u2020"]
cities: ["San Diego"]
countries: ["USA"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Lee_Tripartite_Weight-Space_Ensemble_for_Few-Shot_Class-Incremental_Learning_CVPR_2025_paper.html

abstract:
Few-shot class incremental learning (FSCIL) enables the continual learning of new concepts with only a few training examples. In FSCIL, the model undergoes substantial updates, making it prone to forgetting previous concepts and overfitting to the limited new examples. Most recent trend is typically to disentangle the learning of the representation from the classification head of the model. A well-generalized feature extractor on the base classes (many examples and many classes) is learned, and then fixed during incremental learning. Arguing that the fixed feature extractor restricts the model's adaptability to new classes, we introduce a novel FSCIL method to effectively address catastrophic forgetting and overfitting issues. Our method enables to seamlessly update the entire model with a few examples. We mainly propose a tripartite weight-space ensemble (Tri-WE). Tri-WE interpolates the base, immediately previous, and current models in weight-space, especially for the classification heads of the models. Then, it collaboratively maintains knowledge from the base and previous models. In addition, we recognize the challenges of distilling generalized representations from the previous model from scarce data. Hence, we suggest a regularization loss term using amplified data knowledge distillation. Simply intermixing the few-shot data, we can produce richer data enabling the distillation of critical knowledge from the previous model. Consequently, we attain state-of-the-art results on the miniImageNet, CUB200, and CIFAR100 datasets.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Lee_Tripartite_Weight-Space_Ensemble_for_Few-Shot_Class-Incremental_Learning_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Lee_Tripartite_Weight-Space_Ensemble_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Lee_2025_CVPR,
    author    = {Lee, Juntae and Hayat, Munawar and Yun, Sungrack},
    title     = {Tripartite Weight-Space Ensemble for Few-Shot Class-Incremental Learning},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {15329-15338}
}


title: The Devil is in Temporal Token: High Quality Video Reasoning Segmentation
authors: Sitong Gong, Yunzhi Zhuge, Lu Zhang, Zongxin Yang, Pingping Zhang, Huchuan Lu
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 29183-29192
affiliations: ["IIAU, Dalian University of Technology", "Havard University"]
cities: ["Dalian", "Cambridge"]
countries: ["China", "USA"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Gong_The_Devil_is_in_Temporal_Token_High_Quality_Video_Reasoning_CVPR_2025_paper.html

abstract:
Existing methods for Video Reasoning Segmentation rely heavily on a single special token to represent the object in the keyframe or the entire video, inadequately capturing spatial complexity and inter-frame motion. To overcome these challenges, we propose VRS-HQ, an end-to-end video reasoning segmentation approach that leverages Multimodal Large Language Models (MLLMs) to inject rich spatiotemporal features into hierarchical tokens. Our key innovations include a Temporal Dynamic Aggregation (TDA) and a Token-driven Keyframe Selection (TKS). Specifically, we design frame-level <SEG> and temporal-level <TAK> tokens that utilize MLLM's autoregressive learning to effectively capture both local and global information. Subsequently, we apply a similarity-based weighted fusion and frame selection strategy, then utilize SAM2 to perform keyframe segmentation and propagation. To enhance keyframe localization accuracy, the TKS filters keyframes based on SAM2's occlusion scores during inference. VRS-HQ achieves state-of-the-art performance on ReVOS, surpassing VISA  by 5.9%/12.5%/9.1% in J&F scores across the three subsets. These results highlight the strong temporal reasoning and segmentation capabilities of our method.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Gong_The_Devil_is_in_Temporal_Token_High_Quality_Video_Reasoning_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Gong_The_Devil_is_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Gong_2025_CVPR,
    author    = {Gong, Sitong and Zhuge, Yunzhi and Zhang, Lu and Yang, Zongxin and Zhang, Pingping and Lu, Huchuan},
    title     = {The Devil is in Temporal Token: High Quality Video Reasoning Segmentation},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {29183-29192}
}

raw_affiliation:
[{"affiliation": "IIAU, Dalian University of Technology", "city": "Dalian", "country": "China"}, {"affiliation": "Havard University", "city": "Cambridge", "country": "USA"}]


title: PerLA: Perceptive 3D Language Assistant
authors: Guofeng Mei, Wei Lin, Luigi Riz, Yujiao Wu, Fabio Poiesi, Yiming Wang
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 14369-14379
affiliations: ["Fondazione Bruno Kessler", "JKU Linz", "CSIRO"]
cities: ["Trento", "Linz", "Canberra"]
countries: ["Italy", "Austria", "Australia"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Mei_PerLA_Perceptive_3D_Language_Assistant_CVPR_2025_paper.html

abstract:
Enabling Large Language Models (LLMs) to understand the 3D physical world is an emerging yet challenging research direction.  Current strategies for processing point clouds typically downsample the scene or divide it into smaller parts for separate analysis. However, both approaches risk losing key local details or global contextual information. In this paper, we introduce PerLA, a 3D language assistant designed to be more perceptive to both details and context, making visual representations more informative for the LLM. PerLA captures high-resolution (local) details in parallel from different point cloud areas and integrates them with (global) context obtained from a lower-resolution whole point cloud. We present a novel algorithm that preserves point cloud locality through the Hilbert curve and effectively aggregates local-to-global information via cross-attention and a graph neural network. Lastly, we introduce a novel loss for local representation consensus to promote training stability. PerLA outperforms state-of-the-art 3D language assistants, with gains of up to +1.34 CiDEr on ScanQA for question answering, and +4.22 on ScanRefer and +3.88 on Nr3D for dense captioning.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Mei_PerLA_Perceptive_3D_Language_Assistant_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Mei_PerLA_Perceptive_3D_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Mei_2025_CVPR,
    author    = {Mei, Guofeng and Lin, Wei and Riz, Luigi and Wu, Yujiao and Poiesi, Fabio and Wang, Yiming},
    title     = {PerLA: Perceptive 3D Language Assistant},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {14369-14379}
}

raw_affiliation:
[{"affiliation": "Fondazione Bruno Kessler", "city": "Trento", "country": "Italy"}, {"affiliation": "JKU Linz", "city": "Linz", "country": "Austria"}, {"affiliation": "CSIRO", "city": "Canberra", "country": "Australia"}]


authors: Han Zhou, Wei Dong, Jun Chen
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 21580-21589
raw_affiliation: [{"affiliation": "McMaster University", "city": "Hamilton", "country": "Canada"}]
affiliations: ["McMaster University"]
cities: ["Hamilton"]
countries: ["Canada"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Zhou_LITA-GS_Illumination-Agnostic_Novel_View_Synthesis_via_Reference-Free_3D_Gaussian_Splatting_CVPR_2025_paper.html

title:
LITA-GS: Illumination-Agnostic Novel View Synthesis via Reference-Free 3D Gaussian Splatting and Physical Priors

abstract:
Directly employing 3D Gaussian Splatting (3DGS) on images with adverse illumination conditions exhibits considerable difficulty in achieving high-quality normally-exposed representation due to: (1) The limited Structure from Motion (SfM) points estimated in adverse illumination scenarios fail to capture sufficient scene details; (2) Without ground-truth references, the intensive information loss, huge noise, and color distortion poses substantial challenges for 3DGS to produce high-quality results; (3) Combining existing exposure correction methods with 3DGS can not achieve satisfactory performance due to their individual enhancement process, which leads to the illumination inconsistency between enhanced images from different viewpoints. To address these issues, we propose LITA-GS, a novel illumination-agnostic novel view synthesis method via reference-free 3DGS and physical priors. Firstly, we introduce an illumination-invariant physical prior extraction pipeline. Secondly, based on the extracted robust spatial structure prior, we develop the lighting-agnostic structure rendering strategy, which facilitates the optimization of the scene structure and object appearance. Moreover, a progressive denoising module is introduced to effectively surpass the noise within the light-invariant representation. We adopt the unsupervised strategy for the training of LITA-GS and extensive experiments demonstrate that LITA-GS surpass the state-of-the-art (SOTA) NeRF-based method by 1.7 dB in PSNR and 0.09 in SSIM while enjoying faster inference speed and costing reduced training time. The code will be released upon acceptance.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Zhou_LITA-GS_Illumination-Agnostic_Novel_View_Synthesis_via_Reference-Free_3D_Gaussian_Splatting_CVPR_2025_paper.pdf

bibtex:
@InProceedings{Zhou_2025_CVPR,
    author    = {Zhou, Han and Dong, Wei and Chen, Jun},
    title     = {LITA-GS: Illumination-Agnostic Novel View Synthesis via Reference-Free 3D Gaussian Splatting and Physical Priors},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {21580-21589}
}


title: PhyT2V: LLM-Guided Iterative Self-Refinement for Physics-Grounded Text-to-Video Generation
authors: Qiyao Xue, Xiangyu Yin, Boyuan Yang, Wei Gao
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 18826-18836
raw_affiliation: [{"affiliation": "University of Pittsburgh", "city": "Pittsburgh", "country": "USA"}]
affiliations: ["University of Pittsburgh"]
cities: ["Pittsburgh"]
countries: ["USA"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Xue_PhyT2V_LLM-Guided_Iterative_Self-Refinement_for_Physics-Grounded_Text-to-Video_Generation_CVPR_2025_paper.html

abstract:
Text-to-video (T2V) generation has been recently enabled by transformer-based diffusion models, but current T2V models lack capabilities in adhering to the real-world common knowledge and physical rules, due to their limited understanding of physical realism and deficiency in temporal modeling. Existing solutions are either data-driven or require extra model inputs, but cannot be generalizable to out-of-distribution domains. In this paper, we present PhyT2V, a new data-independent T2V technique that expands the current T2V model's capability of video generation to out-of-distribution domains, by enabling chain-of-thought and step-back reasoning in T2V prompting. Our experiments show that PhyT2V improves existing T2V models' adherence to real-world physical rules by 2.3x, and achieves 35% improvement compared to T2V prompt enhancers.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Xue_PhyT2V_LLM-Guided_Iterative_Self-Refinement_for_Physics-Grounded_Text-to-Video_Generation_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Xue_PhyT2V_LLM-Guided_Iterative_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Xue_2025_CVPR,
    author    = {Xue, Qiyao and Yin, Xiangyu and Yang, Boyuan and Gao, Wei},
    title     = {PhyT2V: LLM-Guided Iterative Self-Refinement for Physics-Grounded Text-to-Video Generation},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {18826-18836}
}


title: Track4Gen: Teaching Video Diffusion Models to Track Points Improves Video Generation
authors: Hyeonho Jeong, Chun-Hao P. Huang, Jong Chul Ye, Niloy J. Mitra, Duygu Ceylan
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 7276-7287
affiliations: ["Adobe Research", "KAIST", "University College London"]
cities: ["San Jose", "Daejeon", "London"]
countries: ["USA", "South Korea", "UK"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Jeong_Track4Gen_Teaching_Video_Diffusion_Models_to_Track_Points_Improves_Video_CVPR_2025_paper.html

abstract:
While recent foundational video generators produce visually rich output, they still struggle with appearance drift, where objects gradually degrade or change inconsistently across frames, breaking visual coherence. We hypothesize that this is because there is no explicit supervision in terms of spatial tracking at the feature level. We propose Track4Gen, a spatially aware video generator that combines video diffusion loss with point tracking across frames, providing enhanced spatial supervision on the diffusion features. Track4Gen merges the video generation and point tracking tasks into a single network by making minimal changes to existing video generation architectures. Using Stable Video Diffusion as a backbone, Track4Gen demonstrates that it is possible to unify video generation and point tracking, which are typically handled as separate tasks. Our extensive evaluations show that Track4Gen effectively reduces appearance drift, resulting in temporally stable and visually coherent video generation. Project page: https://hyeonho99.github.io/track4gen/

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Jeong_Track4Gen_Teaching_Video_Diffusion_Models_to_Track_Points_Improves_Video_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Jeong_Track4Gen_Teaching_Video_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Jeong_2025_CVPR,
    author    = {Jeong, Hyeonho and Huang, Chun-Hao P. and Ye, Jong Chul and Mitra, Niloy J. and Ceylan, Duygu},
    title     = {Track4Gen: Teaching Video Diffusion Models to Track Points Improves Video Generation},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {7276-7287}
}

raw_affiliation:
[{"affiliation": "Adobe Research", "city": "San Jose", "country": "USA"}, {"affiliation": "KAIST", "city": "Daejeon", "country": "South Korea"}, {"affiliation": "University College London", "city": "London", "country": "UK"}]


title: Mask^2DiT: Dual Mask-based Diffusion Transformer for Multi-Scene Long Video Generation
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 18837-18846
affiliations: ["University of Science and Technology of China", "Bytedance Intelligent Creation", "Yuanshi Inc."]
cities: ["Hefei", "Beijing", "Beijing"]
countries: ["China", "China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Qi_Mask2DiT_Dual_Mask-based_Diffusion_Transformer_for_Multi-Scene_Long_Video_Generation_CVPR_2025_paper.html

authors:
Tianhao Qi, Jianlong Yuan, Wanquan Feng, Shancheng Fang, Jiawei Liu, SiYu Zhou, Qian He, Hongtao Xie, Yongdong Zhang

abstract:
Sora has unveiled the immense potential of the Diffusion Transformer (DiT) architecture in single-scene video generation. However, the more challenging task of multi-scene video generation, which offers broader applications, remains relatively underexplored. To bridge this gap, we propose Mask^2DiT, a novel approach that establishes fine-grained, one-to-one alignment between video segments and their corresponding text annotations. Specifically, we introduce a symmetric binary mask at each attention layer within the DiT architecture, ensuring that each text annotation applies exclusively to its respective video segment while preserving temporal coherence across visual tokens. This attention mechanism enables precise segment-level textual-to-visual alignment, allowing the DiT architecture to effectively handle video generation tasks with a fixed number of scenes. To further equip the DiT architecture with the ability to generate additional scenes based on existing ones, we incorporate a segment-level conditional mask, which conditions each newly generated segment on the preceding video segments, thereby enabling auto-regressive scene extension. Both qualitative and quantitative experiments confirm that Mask^2DiT excels in maintaining visual consistency across segments while ensuring semantic alignment between each segment and its corresponding text description. Our project page is \href https://tianhao-qi.github.io/Mask2DiTProject/  https://tianhao-qi.github.io/Mask2DiTProject/ .

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Qi_Mask2DiT_Dual_Mask-based_Diffusion_Transformer_for_Multi-Scene_Long_Video_Generation_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Qi_Mask2DiT_Dual_Mask-based_CVPR_2025_supplemental.zip

bibtex:
@InProceedings{Qi_2025_CVPR,
    author    = {Qi, Tianhao and Yuan, Jianlong and Feng, Wanquan and Fang, Shancheng and Liu, Jiawei and Zhou, SiYu and He, Qian and Xie, Hongtao and Zhang, Yongdong},
    title     = {Mask{\textasciicircum}2DiT: Dual Mask-based Diffusion Transformer for Multi-Scene Long Video Generation},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {18837-18846}
}

raw_affiliation:
[{"affiliation": "University of Science and Technology of China", "city": "Hefei", "country": "China"}, {"affiliation": "Bytedance Intelligent Creation", "city": "Beijing", "country": "China"}, {"affiliation": "Yuanshi Inc.", "city": "Beijing", "country": "China"}]


title: JamMa: Ultra-lightweight Local Feature Matching with Joint Mamba
authors: Xiaoyong Lu, Songlin Du
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 14934-14943
cities: ["Nanjing", "Shenzhen"]
countries: ["China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Lu_JamMa_Ultra-lightweight_Local_Feature_Matching_with_Joint_Mamba_CVPR_2025_paper.html

abstract:
Existing state-of-the-art feature matchers capture long-range dependencies with Transformers but are hindered by high spatial complexity,leading to demanding training and high-latency inference.Striking a better balance between performance and efficiency remains a critical challenge in feature matching.Inspired by the linear complexity \mathcal O (N) of Mamba, we propose an ultra-lightweight Mamba-based matcher, named JamMa, which converges on a single GPU and achieves an impressive performance-efficiency balance in inference.To unlock the potential of Mamba for feature matching,we propose Joint Mamba with a scan-merge strategy named JEGO, which enables:(1) Joint scan of two images to achieve high-frequency mutual interaction, (2) Efficient scan with skip steps to reduce sequence length, (3) Global receptive field, and (4) Omnidirectional feature representation.With the above properties, the JEGO strategy significantly outperforms the scan-merge strategies proposed in VMamba and EVMamba in the feature matching task.Compared to attention-based sparse and semi-dense matchers, JamMa demonstrates a notably superior balance between performance and efficiency,delivering better performance with less than 50% of the parameters and FLOPs.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Lu_JamMa_Ultra-lightweight_Local_Feature_Matching_with_Joint_Mamba_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Lu_JamMa_Ultra-lightweight_Local_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Lu_2025_CVPR,
    author    = {Lu, Xiaoyong and Du, Songlin},
    title     = {JamMa: Ultra-lightweight Local Feature Matching with Joint Mamba},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {14934-14943}
}

raw_affiliation:
[{"affiliation": "School of Automation, Southeast University", "city": "Nanjing", "country": "China"}, {"affiliation": "Shenzhen Research Institute of Southeast University", "city": "Shenzhen", "country": "China"}]

affiliations:
["School of Automation, Southeast University", "Shenzhen Research Institute of Southeast University"]


title: DyCoke: Dynamic Compression of Tokens for Fast Video Large Language Models
authors: Keda Tao, Can Qin, Haoxuan You, Yang Sui, Huan Wang
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 18992-19001
cities: ["Hangzhou", "Xi'an", "San Francisco", "New York", "Houston"]
countries: ["China", "China", "USA", "USA", "USA"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Tao_DyCoke_Dynamic_Compression_of_Tokens_for_Fast_Video_Large_Language_CVPR_2025_paper.html

abstract:
Video large language models (VLLMs) have significantly advanced recently in processing complex video content. Yet, their inference efficiency remains constrained because of the high computational cost stemming from the thousands of visual tokens generated from the video inputs. We empirically observe that, unlike single image inputs, VLLMs typically attend visual tokens from different frames at different decoding iterations. This makes a one-shot pruning strategy prone to removing important tokens by mistake. Motivated by this, we present DyCoke, a training-free token compression method to optimize token representation and accelerate VLLMs. DyCoke incorporates a plug-and-play temporal compression module to minimize temporal redundancy by merging redundant tokens across frames and applying dynamic KV cache reduction to prune spatially redundant tokens selectively. It ensures high-quality inference by dynamically retaining the critical tokens at each decoding step. Extensive experimental results demonstrate that DyCoke can outperform the prior SoTA counterparts, achieving 1.5x inference speedup, and 1.4x memory reduction against the baseline VLLM, while still improving the performance, with no training.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Tao_DyCoke_Dynamic_Compression_of_Tokens_for_Fast_Video_Large_Language_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Tao_DyCoke_Dynamic_Compression_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Tao_2025_CVPR,
    author    = {Tao, Keda and Qin, Can and You, Haoxuan and Sui, Yang and Wang, Huan},
    title     = {DyCoke: Dynamic Compression of Tokens for Fast Video Large Language Models},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {18992-19001}
}

raw_affiliation:
[{"affiliation": "Westlake University", "city": "Hangzhou", "country": "China"}, {"affiliation": "Xidian University", "city": "Xi'an", "country": "China"}, {"affiliation": "Salesforce AI Research", "city": "San Francisco", "country": "USA"}, {"affiliation": "Columbia University", "city": "New York", "country": "USA"}, {"affiliation": "Rice University", "city": "Houston", "country": "USA"}]

affiliations:
["Westlake University", "Xidian University", "Salesforce AI Research", "Columbia University", "Rice University"]


title: T-FAKE: Synthesizing Thermal Images for Facial Landmarking
authors: Philipp Flotho, Moritz Piening, Anna Kukleva, Gabriele Steidl
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 26356-26366
cities: ["Saarbr\u00fccken", "Berlin", "Saarbr\u00fccken", "Berlin"]
countries: ["Germany", "Germany", "Germany", "Germany"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Flotho_T-FAKE_Synthesizing_Thermal_Images_for_Facial_Landmarking_CVPR_2025_paper.html

abstract:
Facial analysis is a key component in a wide range of applications such as security, autonomous driving, entertainment, and healthcare. Despite the availability of various facial RGB datasets, the thermal modality, which plays a crucial role in life sciences, medicine, and biometrics, has been largely overlooked. To address this gap, we introduce the T-FAKE dataset, a new large-scale synthetic thermal dataset with sparse and dense landmarks. To facilitate the creation of the dataset, we propose a novel RGB2Thermal loss function, which enables the domain-adaptive transfer of thermal style to RGB faces. By utilizing the Wasserstein distance between thermal and RGB patches and the statistical analysis of clinical temperature distributions on faces, we ensure that the generated thermal images closely resemble real samples. Using RGB2Thermal style transfer based on our RGB2Thermal loss function, we create the large-scale synthetic thermal T-FAKE dataset. Leveraging our novel T-FAKE dataset, probabilistic landmark prediction, and label adaptation networks, we demonstrate significant improvements in landmark detection methods on thermal images across different landmark conventions. Our models show excellent performance with both sparse 70-point landmarks and dense 478-point landmark annotations

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Flotho_T-FAKE_Synthesizing_Thermal_Images_for_Facial_Landmarking_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Flotho_T-FAKE_Synthesizing_Thermal_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Flotho_2025_CVPR,
    author    = {Flotho, Philipp and Piening, Moritz and Kukleva, Anna and Steidl, Gabriele},
    title     = {T-FAKE: Synthesizing Thermal Images for Facial Landmarking},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {26356-26366}
}

raw_affiliation:
[{"affiliation": "Saarland University", "city": "Saarbr\u00fccken", "country": "Germany"}, {"affiliation": "Technical University of Berlin", "city": "Berlin", "country": "Germany"}, {"affiliation": "MPI for Informatics, SIC", "city": "Saarbr\u00fccken", "country": "Germany"}, {"affiliation": "Technical University of Berlin", "city": "Berlin", "country": "Germany"}]

affiliations:
["Saarland University", "Technical University of Berlin", "MPI for Informatics, SIC", "Technical University of Berlin"]


title: Multi-Resolution Pathology-Language Pre-training Model with Text-Guided Visual Representation
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 25907-25919
cities: ["Abu Dhabi", "Lahore", "Perth"]
countries: ["UAE", "Pakistan", "Australia"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Albastaki_Multi-Resolution_Pathology-Language_Pre-training_Model_with_Text-Guided_Visual_Representation_CVPR_2025_paper.html

authors:
Shahad Albastaki, Anabia Sohail, Iyyakutti Iyappan Ganapathi, Basit Alawode, Asim Khan, Sajid Javed, Naoufel Werghi, Mohammed Bennamoun, Arif Mahmood

abstract:
In Computational Pathology (CPath), the introduction of Vision-Language Models (VLMs) has opened new avenues for research, focusing primarily on aligning image-text pairs at a single magnification level. However, this approach might not be sufficient for tasks like cancer subtype classification, tissue phenotyping, and survival analysis due to the limited level of detail that a single-resolution image can provide. Addressing this, we propose a novel multi-resolution paradigm leveraging Whole Slide Images (WSIs) to extract histology patches at multiple resolutions and generate corresponding textual descriptions through advanced CPath VLM. We introduce visual-textual alignment at multiple resolutions as well as cross-resolution alignment to establish more effective text-guided visual representations. Cross-resolution alignment using a multi-modal encoder enhances the model's ability to capture context from multiple resolutions in histology images. Our model aims to capture a broader range of information, supported by novel loss functions, enriches feature representation, improves discriminative ability, and enhances generalization across different resolutions. Pre-trained on a comprehensive TCGA dataset with 34 million image-language pairs at various resolutions, our fine-tuned model outperforms State-Of-The-Art (SOTA) counterparts across multiple datasets and tasks, demonstrating its effectiveness in CPath. The code is available on GitHub at: https://github.com/BasitAlawode/MR-PLIP.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Albastaki_Multi-Resolution_Pathology-Language_Pre-training_Model_with_Text-Guided_Visual_Representation_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Albastaki_Multi-Resolution_Pathology-Language_Pre-training_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Albastaki_2025_CVPR,
    author    = {Albastaki, Shahad and Sohail, Anabia and Ganapathi, Iyyakutti Iyappan and Alawode, Basit and Khan, Asim and Javed, Sajid and Werghi, Naoufel and Bennamoun, Mohammed and Mahmood, Arif},
    title     = {Multi-Resolution Pathology-Language Pre-training Model with Text-Guided Visual Representation},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {25907-25919}
}

raw_affiliation:
[{"affiliation": "Department of Computer Science, \u2217ARIC, Khalifa University of Science and Technology, UAE", "city": "Abu Dhabi", "country": "UAE"}, {"affiliation": "Information Technology University of the Punjab, Pakistan", "city": "Lahore", "country": "Pakistan"}, {"affiliation": "University of the Western Australia", "city": "Perth", "country": "Australia"}]

affiliations:
["Department of Computer Science, \u2217ARIC, Khalifa University of Science and Technology, UAE", "Information Technology University of the Punjab, Pakistan", "University of the Western Australia"]


authors: Jinlu Zhang, Yixin Chen, Zan Wang, Jie Yang, Yizhou Wang, Siyuan Huang
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 7015-7025
cities: ["Beijing", "Beijing", "Beijing", "Shenzhen", "Beijing", "Beijing", "Beijing"]
countries: ["China", "China", "China", "China", "China", "China", "China"]
highlight: yes
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Zhang_InteractAnything_Zero-shot_Human_Object_Interaction_Synthesis_via_LLM_Feedback_and_CVPR_2025_paper.html

title:
InteractAnything: Zero-shot Human Object Interaction Synthesis via LLM Feedback and Object Affordance Parsing

abstract:
Recent advances in 3D human-aware generation have made significant progress. However, existing methods still struggle with generating novel Human Object Interaction (HOI) from text, particularly for open-set objects. We identify three main challenges of this task: precise human-object relation reasoning, affordance parsing for any object, and detailed human interaction pose synthesis aligning description and object geometry. In this work, we propose a novel zero-shot 3D HOI generation framework without training on specific datasets, leveraging the knowledge from large-scale pre-trained models. Specifically, the human-object relations are inferred from large language models (LLMs) to initialize object properties and guide the optimization process. Then we utilize a pre-trained 2D image diffusion model to parse unseen objects and extract contact points, avoiding the limitations imposed by existing 3D asset knowledge. The initial human pose is generated by sampling multiple hypotheses through multi-view SDS based on the input text and object geometry. Finally, we introduce a detailed optimization to generate fine-grained, precise, and natural interaction, enforcing realistic 3D contact between the 3D object and the involved body parts, including hands in grasping. This is achieved by distilling human-level feedback from LLMs to capture detailed human-object relations from the text instruction. Extensive experiments validate the effectiveness of our approach compared to prior works, particularly in terms of the fine-grained nature of interactions and the ability to handle open-set 3D objects. Project page: jinluzhang.site/projects/interactanything.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Zhang_InteractAnything_Zero-shot_Human_Object_Interaction_Synthesis_via_LLM_Feedback_and_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Zhang_InteractAnything_Zero-shot_Human_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Zhang_2025_CVPR,
    author    = {Zhang, Jinlu and Chen, Yixin and Wang, Zan and Yang, Jie and Wang, Yizhou and Huang, Siyuan},
    title     = {InteractAnything: Zero-shot Human Object Interaction Synthesis via LLM Feedback and Object Affordance Parsing},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {7015-7025}
}

raw_affiliation:
[{"affiliation": "Center on Frontiers of Computing Studies, School of Computer Science, Peking University", "city": "Beijing", "country": "China"}, {"affiliation": "State Key Laboratory of General Artificial Intelligence, BIGAI", "city": "Beijing", "country": "China"}, {"affiliation": "Beijing Institute of Technology", "city": "Beijing", "country": "China"}, {"affiliation": "The Chinese University of Hong Kong, Shenzhen", "city": "Shenzhen", "country": "China"}, {"affiliation": "Nat\u2019l Eng. Research Center of Visual Technology, Peking University", "city": "Beijing", "country": "China"}, {"affiliation": "Institute for AI, Peking University", "city": "Beijing", "country": "China"}, {"affiliation": "State Key Laboratory of General Artificial Intelligence, Peking University", "city": "Beijing", "country": "China"}]

affiliations:
["Center on Frontiers of Computing Studies, School of Computer Science, Peking University", "State Key Laboratory of General Artificial Intelligence, BIGAI", "Beijing Institute of Technology", "The Chinese University of Hong Kong, Shenzhen", "Nat\u2019l Eng. Research Center of Visual Technology, Peking University", "Institute for AI, Peking University", "State Key Laboratory of General Artificial Intelligence, Peking University"]


title: MammAlps: A Multi-view Video Behavior Monitoring Dataset of Wild Mammals in the Swiss Alps
authors: Valentin Gabeff, Haozhe Qi, Brendan Flaherty, Gencer Sumbul, Alexander Mathis, Devis Tuia
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 13854-13864
affiliations: ["Ecole Polytechnique F\u00b4ed\u00b4erale de Lausanne (EPFL), Switzerland"]
cities: ["Lausanne"]
countries: ["Switzerland"]
highlight: yes
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Gabeff_MammAlps_A_Multi-view_Video_Behavior_Monitoring_Dataset_of_Wild_Mammals_CVPR_2025_paper.html

abstract:
Monitoring wildlife is essential for ecology and ethology, especially in light of the increasing human impact on ecosystems. Camera traps have emerged as habitat-centric sensors enabling the study of wildlife populations at scale with minimal disturbance. However, the lack of annotated video datasets limits the development of powerful video understanding models needed to process the vast amount of fieldwork data collected. To advance research in wild animal behavior monitoring we present MammAlps, a multimodal and multi-view dataset of wildlife behavior monitoring from 9 camera-traps in the Swiss National Park. MammAlps contains over 14 hours of video with audio, 2D segmentation maps and 8.5 hours of individual tracks densely labeled for species and behavior. Based on 6135 single animal clips, we propose the first hierarchical and multimodal animal behavior recognition benchmark using audio, video and reference scene segmentation maps as inputs. Furthermore, we also propose a second ecology-oriented benchmark aiming at identifying activities, species, number of individuals and meteorological conditions from 397 multi-view and long-term ecological events, including false positive triggers. We advocate that both tasks are complementary and contribute to bridging the gap between machine learning and ecology. Code and data are available at: https://github.com/eceo-epfl/MammAlps

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Gabeff_MammAlps_A_Multi-view_Video_Behavior_Monitoring_Dataset_of_Wild_Mammals_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Gabeff_MammAlps_A_Multi-view_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Gabeff_2025_CVPR,
    author    = {Gabeff, Valentin and Qi, Haozhe and Flaherty, Brendan and Sumbul, Gencer and Mathis, Alexander and Tuia, Devis},
    title     = {MammAlps: A Multi-view Video Behavior Monitoring Dataset of Wild Mammals in the Swiss Alps},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {13854-13864}
}

raw_affiliation:
[{"affiliation": "Ecole Polytechnique F\u00b4ed\u00b4erale de Lausanne (EPFL), Switzerland", "city": "Lausanne", "country": "Switzerland"}]


title: Diffusion-based Realistic Listening Head Generation via Hybrid Motion Modeling
authors: Yinuo Wang, Yanbo Fan, Xuan Wang, Guo Yu, Fei Wang
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 15885-15895
cities: ["Xi'an", "Hangzhou"]
countries: ["China", "China"]
highlight: yes
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Wang_Diffusion-based_Realistic_Listening_Head_Generation_via_Hybrid_Motion_Modeling_CVPR_2025_paper.html

abstract:
Listening head generation aims to synthesize non-verbal responsive listening head videos that naturally react to a certain speaker, for which, both realistic head movements, expressive facial expressions, and high visual qualities are expected. Previous approaches typically follow a two-stage pipeline that first generates intermediate 3D motion signals such as 3DMM coefficients, and then synthesizes the videos by deterministic rendering, suffering from limited motion expressiveness and low visual quality (eg, 256 x256). In this work, we propose a novel listening head generation method that harnesses the generative capabilities of the diffusion model for both motion generation and high-quality rendering. Crucially, we propose an effective hybrid motion modeling module that addresses training difficulties caused by the scarcity of listening head data while preserving the intricate details that may be lost in explicit motion representations. We further develop a tailored control guidance for head pose and facial expression, by integrating their intrinsic motion characteristics. Our method enables high-fidelity video generation with 512x512 resolution and delivers vivid listener motion feedback. We conduct comprehensive experiments and obtain superior performance in terms of both visual quality and motion expressiveness compared with existing methods.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Wang_Diffusion-based_Realistic_Listening_Head_Generation_via_Hybrid_Motion_Modeling_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Wang_Diffusion-based_Realistic_Listening_CVPR_2025_supplemental.zip

bibtex:
@InProceedings{Wang_2025_CVPR,
    author    = {Wang, Yinuo and Fan, Yanbo and Wang, Xuan and Yu, Guo and Wang, Fei},
    title     = {Diffusion-based Realistic Listening Head Generation via Hybrid Motion Modeling},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {15885-15895}
}

raw_affiliation:
[{"affiliation": "National Key Laboratory of Human-Machine Hybrid Augmented Intelligence, National Engineering Research Center for Visual Information and Applications, and Institute of Artificial Intelligence and Robotics, Xi\u2019an Jiaotong University", "city": "Xi'an", "country": "China"}, {"affiliation": "Ant Group", "city": "Hangzhou", "country": "China"}]

affiliations:
["National Key Laboratory of Human-Machine Hybrid Augmented Intelligence, National Engineering Research Center for Visual Information and Applications, and Institute of Artificial Intelligence and Robotics, Xi\u2019an Jiaotong University", "Ant Group"]


title: SAT-HMR: Real-Time Multi-Person 3D Mesh Estimation via Scale-Adaptive Tokens
authors: Chi Su, Xiaoxuan Ma, Jiajun Su, Yizhou Wang
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 16796-16806
cities: ["Beijing", "Beijing", "Beijing", "Beijing", "Shenzhen"]
countries: ["China", "China", "China", "China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Su_SAT-HMR_Real-Time_Multi-Person_3D_Mesh_Estimation_via_Scale-Adaptive_Tokens_CVPR_2025_paper.html

abstract:
We propose a one-stage framework for real-time multi-person 3D human mesh estimation from a single RGB image. While current one-stage methods, which follow a DETR-style pipeline, achieve state-of-the-art (SOTA) performance with high-resolution inputs, we observe that this particularly benefits the estimation of individuals in smaller scales of the image (e.g., those far from the camera), but at the cost of significantly increased computation overhead. To address this, we introduce scale-adaptive tokens that are dynamically adjusted based on the relative scale of each individual in the image within the DETR framework. Specifically, individuals in smaller scales are processed at higher resolutions, larger ones at lower resolutions, and background regions are further distilled. These scale-adaptive tokens more efficiently encode the image features, facilitating subsequent decoding to regress the human mesh, while allowing the model to allocate computational resources more effectively and focus on more challenging cases. Experiments show that our method preserves the accuracy benefits of high-resolution processing while substantially reducing computational cost, achieving real-time inference with performance comparable to SOTA methods. Code and models are available at https://ChiSu001.github.io/SAT-HMR/.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Su_SAT-HMR_Real-Time_Multi-Person_3D_Mesh_Estimation_via_Scale-Adaptive_Tokens_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Su_SAT-HMR_Real-Time_Multi-Person_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Su_2025_CVPR,
    author    = {Su, Chi and Ma, Xiaoxuan and Su, Jiajun and Wang, Yizhou},
    title     = {SAT-HMR: Real-Time Multi-Person 3D Mesh Estimation via Scale-Adaptive Tokens},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {16796-16806}
}

raw_affiliation:
[{"affiliation": "Center on Frontiers of Computing Studies, School of Computer Science, Peking University", "city": "Beijing", "country": "China"}, {"affiliation": "Inst. for Artificial Intelligence, Peking University", "city": "Beijing", "country": "China"}, {"affiliation": "Nat\u2019l Eng. Research Center of Visual Technology, Peking University", "city": "Beijing", "country": "China"}, {"affiliation": "State Key Laboratory of General Artificial Intelligence, Peking University", "city": "Beijing", "country": "China"}, {"affiliation": "International Digital Economy Academy (IDEA)", "city": "Shenzhen", "country": "China"}]

affiliations:
["Center on Frontiers of Computing Studies, School of Computer Science, Peking University", "Inst. for Artificial Intelligence, Peking University", "Nat\u2019l Eng. Research Center of Visual Technology, Peking University", "State Key Laboratory of General Artificial Intelligence, Peking University", "International Digital Economy Academy (IDEA)"]


title: PICD: Versatile Perceptual Image Compression with Diffusion Rendering
authors: Tongda Xu, Jiahao Li, Bin Li, Yan Wang, Ya-Qin Zhang, Yan Lu
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 28436-28445
affiliations: ["AIR, Tsinghua University", "Microsoft Research Asia"]
cities: ["Beijing", "Beijing"]
countries: ["China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Xu_PICD_Versatile_Perceptual_Image_Compression_with_Diffusion_Rendering_CVPR_2025_paper.html

abstract:
Recently, perceptual image compression has achieved significant advancements, delivering high visual quality at low bitrates for natural images. However, for screen content, existing methods often produce noticeable artifacts when compressing text. To tackle this challenge, we propose versatile perceptual screen image compression with diffusion rendering (PICD), a codec that works well for both screen and natural images. More specifically, we propose a compression framework that encodes the text and image separately, and renders them into one image using diffusion model. For this diffusion rendering, we integrate conditional information into diffusion models at three distinct levels: 1). Domain level: We fine-tune the base diffusion model using text content prompts with screen content. 2). Adaptor level: We develop an efficient adaptor to control the diffusion model using compressed image and text as input. 3). Instance level: We apply instance-wise guidance to further enhance the decoding process. Empirically, our PICD surpasses existing perceptual codecs in terms of both text accuracy and perceptual quality. Additionally, without text conditions, our approach serves effectively as a perceptual codec for natural images.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Xu_PICD_Versatile_Perceptual_Image_Compression_with_Diffusion_Rendering_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Xu_PICD_Versatile_Perceptual_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Xu_2025_CVPR,
    author    = {Xu, Tongda and Li, Jiahao and Li, Bin and Wang, Yan and Zhang, Ya-Qin and Lu, Yan},
    title     = {PICD: Versatile Perceptual Image Compression with Diffusion Rendering},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {28436-28445}
}

raw_affiliation:
[{"affiliation": "AIR, Tsinghua University", "city": "Beijing", "country": "China"}, {"affiliation": "Microsoft Research Asia", "city": "Beijing", "country": "China"}]


title: UniScene: Unified Occupancy-centric Driving Scene Generation
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 11971-11981
cities: ["Shanghai", "Ningbo", "Beijing", "Beijing", "Unknown", "Shanghai", "Hong Kong"]
countries: ["China", "China", "China", "China", "Unknown", "China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Li_UniScene_Unified_Occupancy-centric_Driving_Scene_Generation_CVPR_2025_paper.html

authors:
Bohan Li, Jiazhe Guo, Hongsi Liu, Yingshuang Zou, Yikang Ding, Xiwu Chen, Hu Zhu, Feiyang Tan, Chi Zhang, Tiancai Wang, Shuchang Zhou, Li Zhang, Xiaojuan Qi, Hao Zhao, Mu Yang, Wenjun Zeng, Xin Jin

abstract:
Generating high-fidelity, controllable, and annotated training data is critical for autonomous driving. Existing methods typically generate a single data form directly from a coarse scene layout, which not only fails to output rich data forms required for diverse downstream tasks but also struggles to model the direct layout-to-data distribution. In this paper, we introduce UniScene, the first unified framework for generating three key data forms - semantic occupancy, video, and LiDAR - in driving scenes. UniScene employs a progressive generation process that decomposes the complex task of scene generation into two hierarchical steps: (a) first generating semantic occupancy from a customized scene layout as a meta scene representation rich in both semantic and geometric information, and then (b) conditioned on occupancy, generating video and LiDAR data, respectively, with two novel transfer strategies of Gaussian-based Joint Rendering and Prior-guided Sparse Modeling. This occupancy-centric approach reduces the generation burden, especially for intricate scenes, while providing detailed intermediate representations for the subsequent generation stages. Extensive experiments demonstrate that UniScene outperforms previous SOTAs in the occupancy, video, and LiDAR generation, which also indeed benefits downstream driving tasks. The Project is available at https://arlo0o.github.io/uniscene/.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Li_UniScene_Unified_Occupancy-centric_Driving_Scene_Generation_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Li_UniScene_Unified_Occupancy-centric_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Li_2025_CVPR,
    author    = {Li, Bohan and Guo, Jiazhe and Liu, Hongsi and Zou, Yingshuang and Ding, Yikang and Chen, Xiwu and Zhu, Hu and Tan, Feiyang and Zhang, Chi and Wang, Tiancai and Zhou, Shuchang and Zhang, Li and Qi, Xiaojuan and Zhao, Hao and Yang, Mu and Zeng, Wenjun and Jin, Xin},
    title     = {UniScene: Unified Occupancy-centric Driving Scene Generation},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {11971-11981}
}

raw_affiliation:
[{"affiliation": "Shanghai Jiao Tong University", "city": "Shanghai", "country": "China"}, {"affiliation": "Ningbo Institute of Digital Twin, Eastern Institute of Technology", "city": "Ningbo", "country": "China"}, {"affiliation": "Tsinghua University", "city": "Beijing", "country": "China"}, {"affiliation": "MEGVII Technology", "city": "Beijing", "country": "China"}, {"affiliation": "Mach Drive", "city": "Unknown", "country": "Unknown"}, {"affiliation": "Fudan University", "city": "Shanghai", "country": "China"}, {"affiliation": "University of Hong Kong", "city": "Hong Kong", "country": "China"}]

affiliations:
["Shanghai Jiao Tong University", "Ningbo Institute of Digital Twin, Eastern Institute of Technology", "Tsinghua University", "MEGVII Technology", "Mach Drive", "Fudan University", "University of Hong Kong"]


title: Wonderland: Navigating 3D Scenes from a Single Image
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 798-810
affiliations: ["University of Toronto", "Snap Inc.", "University of California, Los Angeles"]
cities: ["Toronto", "Santa Monica", "Los Angeles"]
countries: ["Canada", "USA", "USA"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Liang_Wonderland_Navigating_3D_Scenes_from_a_Single_Image_CVPR_2025_paper.html

authors:
Hanwen Liang, Junli Cao, Vidit Goel, Guocheng Qian, Sergei Korolev, Demetri Terzopoulos, Konstantinos N. Plataniotis, Sergey Tulyakov, Jian Ren

abstract:
This paper addresses a challenging question: how can we efficiently create high-quality, wide-scope 3D scenes from a single arbitrary image?Existing methods face several constraints, such as requiring multi-view data, time-consuming per-scene optimization, low visual quality, and distorted reconstructions for unseen areas. We propose a novel pipeline to overcome these limitations.Specifically, we introduce a large-scale reconstruction model that uses latents from a video diffusion model to predict 3D Gaussian Splatting, even from a single-condition image, in a feed-forward manner. The video diffusion model is designed to precisely follow a specified camera trajectory, allowing it to generate compressed latents that contain multi-view information while maintaining 3D consistency.We further train the 3D reconstruction model to operate on the video latent space with a progressive training strategy, enabling the generation of high-quality, wide-scope, and generic 3D scenes.Extensive evaluations on various datasets show that our model significantly outperforms existing methods for single-view 3D rendering, particularly with out-of-domain images. For the first time, we demonstrate that a 3D reconstruction model can be effectively built upon the latent space of a diffusion model to realize efficient 3D scene generation.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Liang_Wonderland_Navigating_3D_Scenes_from_a_Single_Image_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Liang_Wonderland_Navigating_3D_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Liang_2025_CVPR,
    author    = {Liang, Hanwen and Cao, Junli and Goel, Vidit and Qian, Guocheng and Korolev, Sergei and Terzopoulos, Demetri and Plataniotis, Konstantinos N. and Tulyakov, Sergey and Ren, Jian},
    title     = {Wonderland: Navigating 3D Scenes from a Single Image},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {798-810}
}

raw_affiliation:
[{"affiliation": "University of Toronto", "city": "Toronto", "country": "Canada"}, {"affiliation": "Snap Inc.", "city": "Santa Monica", "country": "USA"}, {"affiliation": "University of California, Los Angeles", "city": "Los Angeles", "country": "USA"}]


title: Learning from Streaming Video with Orthogonal Gradients
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 13651-13660
affiliations: ["Google DeepMind", "University of Bristol", "University of Oxford"]
cities: ["Mountain View", "Bristol", "Oxford"]
countries: ["USA", "UK", "UK"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Han_Learning_from_Streaming_Video_with_Orthogonal_Gradients_CVPR_2025_paper.html

authors:
Tengda Han, Dilara Gokay, Joseph Heyward, Chuhan Zhang, Daniel Zoran, Viorica Patraucean, Joao Carreira, Dima Damen, Andrew Zisserman

abstract:
We address the challenge of representation learning from a continuous stream of video as input, in a self-supervised manner. This differs from the standard approaches to video learning where videos are chopped and shuffled during training in order to create a non-redundant batch that satisfies the independently and identically distributed (IID) sample assumption expected by conventional training paradigms. When videos are only available as a continuous stream of input, the IID assumption is evidently broken, leading to poor performance. We demonstrate the drop in performance when moving from shuffled to sequential learning on three tasks: the one-video representation learning method DoRA, standard VideoMAE on multi-video datasets, and the task of future video prediction. To address this drop, we propose a geometric modification to standard optimizers, to decorrelate batches by utilising orthogonal gradients during training. The proposed modification can be applied to any optimizer -- we demonstrate it with Stochastic Gradient Descent (SGD) and AdamW. Our proposed orthogonal optimizer allows models trained from streaming videos to alleviate the drop in representation learning performance, as evaluated on downstream tasks. On three scenarios (DoRA, VideoMAE, future prediction), we show our orthogonal optimizer outperforms the strong AdamW in all three scenarios.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Han_Learning_from_Streaming_Video_with_Orthogonal_Gradients_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Han_Learning_from_Streaming_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Han_2025_CVPR,
    author    = {Han, Tengda and Gokay, Dilara and Heyward, Joseph and Zhang, Chuhan and Zoran, Daniel and Patraucean, Viorica and Carreira, Joao and Damen, Dima and Zisserman, Andrew},
    title     = {Learning from Streaming Video with Orthogonal Gradients},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {13651-13660}
}

raw_affiliation:
[{"affiliation": "Google DeepMind", "city": "Mountain View", "country": "USA"}, {"affiliation": "University of Bristol", "city": "Bristol", "country": "UK"}, {"affiliation": "University of Oxford", "city": "Oxford", "country": "UK"}]


title: Towards Satellite Image Road Graph Extraction: A Global-Scale Dataset and A Novel Method
authors: Pan Yin, Kaiyu Li, Xiangyong Cao, Jing Yao, Lei Liu, Xueru Bai, Feng Zhou, Deyu Meng
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 1527-1537
cities: ["Xi'an", "Xi'an", "Xi'an", "Xi'an", "Unknown", "Unknown", "Unknown"]
countries: ["China", "China", "China", "China", "China", "China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Yin_Towards_Satellite_Image_Road_Graph_Extraction_A_Global-Scale_Dataset_and_CVPR_2025_paper.html

abstract:
Recently, road graph extraction has garnered increasing attention due to its crucial role in autonomous driving, navigation, etc. However, accurately and efficiently extracting road graphs remains a persistent challenge, primarily due to the severe scarcity of labeled data. To address this limitation, we collect a global-scale satellite road graph extraction dataset, i.e. Global-Scale dataset. Specifically, the Global-Scale dataset is  20x larger than the largest existing public road extraction dataset and spans over 13,800 km^2 globally. Additionally, we develop a novel road graph extraction model, i.e. SAM-Road++, which adopts a node-guided resampling method to alleviate the mismatch issue between training and inference in SAM-Road, a pioneering state-of-the-art road graph extraction model. Furthermore, we propose a simple yet effective "extended-line" strategy in SAM-Road++ to mitigate the occlusion issue on the road. Extensive experiments demonstrate the validity of the collected Global-Scale dataset and the proposed SAM-Road++ method, particularly highlighting its superior predictive power in unseen regions.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Yin_Towards_Satellite_Image_Road_Graph_Extraction_A_Global-Scale_Dataset_and_CVPR_2025_paper.pdf

bibtex:
@InProceedings{Yin_2025_CVPR,
    author    = {Yin, Pan and Li, Kaiyu and Cao, Xiangyong and Yao, Jing and Liu, Lei and Bai, Xueru and Zhou, Feng and Meng, Deyu},
    title     = {Towards Satellite Image Road Graph Extraction: A Global-Scale Dataset and A Novel Method},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {1527-1537}
}

raw_affiliation:
[{"affiliation": "School of Computer Science and Technology, Xi\u2019an Jiaotong University", "city": "Xi'an", "country": "China"}, {"affiliation": "Ministry of Education Key Laboratory of Intelligent Networks and Network Security, Xi\u2019an Jiaotong University", "city": "Xi'an", "country": "China"}, {"affiliation": "School of Mathematics and Statistics, Xi\u2019an Jiaotong University", "city": "Xi'an", "country": "China"}, {"affiliation": "School of Software Engineering, Xi\u2019an Jiaotong University", "city": "Xi'an", "country": "China"}, {"affiliation": "Pengcheng Laboratory", "city": "Unknown", "country": "China"}, {"affiliation": "Chinese Academy of Sciences", "city": "Unknown", "country": "China"}, {"affiliation": "Xidian University", "city": "Unknown", "country": "China"}]

affiliations:
["School of Computer Science and Technology, Xi\u2019an Jiaotong University", "Ministry of Education Key Laboratory of Intelligent Networks and Network Security, Xi\u2019an Jiaotong University", "School of Mathematics and Statistics, Xi\u2019an Jiaotong University", "School of Software Engineering, Xi\u2019an Jiaotong University", "Pengcheng Laboratory", "Chinese Academy of Sciences", "Xidian University"]


title: SuperLightNet: Lightweight Parameter Aggregation Network for Multimodal Brain Tumor Segmentation
authors: Feng Yu, Jiacheng Cao, Li Liu, Minghua Jiang
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 5197-5206
cities: ["Wuhan", "Singapore", "Wuhan"]
countries: ["China", "Singapore", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Yu_SuperLightNet_Lightweight_Parameter_Aggregation_Network_for_Multimodal_Brain_Tumor_Segmentation_CVPR_2025_paper.html

abstract:
Multimodal 3D segmentation involves a significant number of 3D convolution operations, which requires substantial computational resources and high-performance computing devices in MRI multimodal brain tumor segmentation. The key challenge in multimodal 3D segmentation is how to minimize network computational load while maintaining high accuracy. To address the issue, a novel lightweight parameter aggregation network (SuperLightNet) is proposed to realize the efficient encoder and decoder for the high accurate and low computation. A random multiview drop encoder is designed to learn the spatial structure of multimodal images through a random multi-view approach for solving the high computational time complexity that has arisen in recent years with methods relying on transformers and Mamba. A learnable residual skip decoder is designed to incorporate learnable residual and group skip weights for addressing the reduced computational efficiency caused by the use of overly heavy convolution and deconvolution decoders. Experimental results demonstrate that the proposed method achieves a leading reduction in parameter count by 95.59%, the 96.78% improvement in computational efficiency, the 96.86% enhancement in memory access performance, and the average performance gain of 0.21% on the BraTS2019 and BraTS2021 datasets in comparison with the state-of-the-art methods. Code is available at https://github.com/WTU1020-Medical-Segmentation/SuperLightNet.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Yu_SuperLightNet_Lightweight_Parameter_Aggregation_Network_for_Multimodal_Brain_Tumor_Segmentation_CVPR_2025_paper.pdf

bibtex:
@InProceedings{Yu_2025_CVPR,
    author    = {Yu, Feng and Cao, Jiacheng and Liu, Li and Jiang, Minghua},
    title     = {SuperLightNet: Lightweight Parameter Aggregation Network for Multimodal Brain Tumor Segmentation},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {5197-5206}
}

raw_affiliation:
[{"affiliation": "Wuhan Textile University, China", "city": "Wuhan", "country": "China"}, {"affiliation": "Nanyang Technological University, Singapore", "city": "Singapore", "country": "Singapore"}, {"affiliation": "Engineering Research Center of Hubei Province for Clothing Information, China", "city": "Wuhan", "country": "China"}]

affiliations:
["Wuhan Textile University, China", "Nanyang Technological University, Singapore", "Engineering Research Center of Hubei Province for Clothing Information, China"]


authors: Juan Luis Gonzalez, Xu Yao, Alex Whelan, Kyle Olszewski, Hyeongwoo Kim, Pablo Garrido
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 22901-22910
affiliations: ["Flawless AI", "Imperial College London"]
cities: ["Unknown", "London"]
countries: ["Unknown", "UK"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Gonzalez_VideoSPatS_Video_SPatiotemporal_Splines_for_Disentangled_Occlusion_Appearance_and_Motion_CVPR_2025_paper.html

title:
VideoSPatS: Video SPatiotemporal Splines for Disentangled Occlusion, Appearance and Motion Modeling and Editing

abstract:
We present an implicit video representation for occlusions, appearance, and motion disentanglement from monocular videos, which we refer to as Video Spatiotemporal Splines (VideoSPatS).Unlike previous methods that map time and coordinates to deformation and canonical colors, our VideoSPatS maps input coordinates into Spatial and Color Spline deformation fields Ds and Dc, which disentangle motion and appearance in videos. With spline-based parametrization, our method naturally generates temporally consistent flow and guarantees long-term temporal consistency, which is crucial for convincing video editing.Aided by additional prediction blocks, our VideoSPatS also performs layer separation between the latent video and the selected occluder. By disentangling occlusions, appearance, and motion, our method allows for better spatiotemporal modeling and editing of diverse videos, including in-the-wild talking head videos with challenging occlusions, shadows, and specularities while maintaining a reasonable canonical space for editing.We also present general video modeling results on the DAVIS, and CoDeF datasets, as well as our own talking head video dataset collected from open-source web videos.  Extensive ablations show the combination of Ds and Dc under neural splines can overcome motion and appearance ambiguities, paving the way to more advanced video editing models.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Gonzalez_VideoSPatS_Video_SPatiotemporal_Splines_for_Disentangled_Occlusion_Appearance_and_Motion_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Gonzalez_VideoSPatS_Video_SPatiotemporal_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Gonzalez_2025_CVPR,
    author    = {Gonzalez, Juan Luis and Yao, Xu and Whelan, Alex and Olszewski, Kyle and Kim, Hyeongwoo and Garrido, Pablo},
    title     = {VideoSPatS: Video SPatiotemporal Splines for Disentangled Occlusion, Appearance and Motion Modeling and Editing},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {22901-22910}
}

raw_affiliation:
[{"affiliation": "Flawless AI", "city": "Unknown", "country": "Unknown"}, {"affiliation": "Imperial College London", "city": "London", "country": "UK"}]


title: Classifier-to-Bias: Toward Unsupervised Automatic Bias Detection for Visual Classifiers
authors: Quentin Guimard, Moreno D'Incà, Massimiliano Mancini, Elisa Ricci
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 15151-15161
affiliations: ["University of Trento", "Fondazione Bruno Kessler"]
cities: ["Trento", "Trento"]
countries: ["Italy", "Italy"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Guimard_Classifier-to-Bias_Toward_Unsupervised_Automatic_Bias_Detection_for_Visual_Classifiers_CVPR_2025_paper.html

abstract:
A person downloading a pre-trained model from the web should be aware of its biases. Existing approaches for bias identification rely on datasets containing labels for the task of interest, something that a non-expert may not have access to, or may not have the necessary resources to collect: this greatly limits the number of tasks where model biases can be identified. In this work, we present Classifier-to-Bias (C2B), the first bias discovery framework that works without access to any labeled data: it only relies on a textual description of the classification task to identify biases in the target classification model. This description is fed to a large language model to generate bias proposals and corresponding captions depicting biases together with task-specific target labels. A retrieval model collects images for those captions, which are then used to assess the accuracy of the model w.r.t. the given biases. C2B is training-free, does not require any annotations, has no constraints on the list of biases, and can be applied to any pre-trained model on any classification task. Experiments on two publicly available datasets show that C2B discovers biases beyond those of the original datasets and outperforms a recent state-of-the-art bias detection baseline that relies on task-specific annotations, being a promising first step toward addressing task-agnostic unsupervised bias detection.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Guimard_Classifier-to-Bias_Toward_Unsupervised_Automatic_Bias_Detection_for_Visual_Classifiers_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Guimard_Classifier-to-Bias_Toward_Unsupervised_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Guimard_2025_CVPR,
    author    = {Guimard, Quentin and D'Inc\`a, Moreno and Mancini, Massimiliano and Ricci, Elisa},
    title     = {Classifier-to-Bias: Toward Unsupervised Automatic Bias Detection for Visual Classifiers},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {15151-15161}
}

raw_affiliation:
[{"affiliation": "University of Trento", "city": "Trento", "country": "Italy"}, {"affiliation": "Fondazione Bruno Kessler", "city": "Trento", "country": "Italy"}]


title: Self-Supervised Spatial Correspondence Across Modalities
authors: Ayush Shrivastava, Andrew Owens
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 6383-6393
raw_affiliation: [{"affiliation": "University of Michigan", "city": "Ann Arbor", "country": "USA"}]
affiliations: ["University of Michigan"]
cities: ["Ann Arbor"]
countries: ["USA"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Shrivastava_Self-Supervised_Spatial_Correspondence_Across_Modalities_CVPR_2025_paper.html

abstract:
We present a method for finding cross-modal space-time correspondences.  Given two images from different visual modalities, such as an RGB image and a depth map, our model identifies which pairs of pixels correspond to the same physical points in the scene. To solve this problem, we extend the contrastive random walk framework to simultaneously learn cycle-consistent feature representations for both cross-modal and intra-modal matching. The resulting model is simple and has no explicit photo-consistency assumptions. It can be trained entirely using unlabeled data, without the need for any spatially aligned multimodal image pairs. We evaluate our method on both geometric and semantic correspondence tasks. For geometric matching, we consider challenging tasks such as RGB-to-depth and RGB-to-thermal matching (and vice versa); for semantic matching, we evaluate on photo-sketch and cross-style image alignment. Our method achieves strong performance across all benchmarks.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Shrivastava_Self-Supervised_Spatial_Correspondence_Across_Modalities_CVPR_2025_paper.pdf

bibtex:
@InProceedings{Shrivastava_2025_CVPR,
    author    = {Shrivastava, Ayush and Owens, Andrew},
    title     = {Self-Supervised Spatial Correspondence Across Modalities},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {6383-6393}
}


title: MOS-Attack: A Scalable Multi-objective Adversarial Attack Framework
authors: Ping Guo, Cheng Gong, Xi Lin, Fei Liu, Zhichao Lu, Qingfu Zhang, Zhenkun Wang
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 5041-5051
cities: ["Hong Kong", "Shenzhen", "Shenzhen"]
countries: ["China", "China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Guo_MOS-Attack_A_Scalable_Multi-objective_Adversarial_Attack_Framework_CVPR_2025_paper.html

abstract:
Crafting adversarial examples is crucial for evaluating and enhancing the robustness of Deep Neural Networks (DNNs), presenting a challenge equivalent to maximizing a non-differentiable 0-1 loss function.    However, existing single objective methods, namely adversarial attacks focus on a surrogate loss function, do not fully harness the benefits of engaging multiple loss functions, as a result of insufficient understanding of their synergistic and conflicting nature.    To overcome these limitations, we propose the Multi-Objective Set-based Attack (MOS Attack), a novel adversarial attack framework leveraging multiple loss functions and automatically uncovering their interrelations.    The MOS Attack adopts a set-based multi-objective optimization strategy, enabling the incorporation of numerous loss functions without additional parameters.    It also automatically mines synergistic patterns among various losses, facilitating the generation of potent adversarial attacks with fewer objectives.    Extensive experiments have shown that our MOS Attack outperforms single-objective attacks. Furthermore, by harnessing the identified synergistic patterns, MOS Attack continues to show superior results with a reduced number of loss functions.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Guo_MOS-Attack_A_Scalable_Multi-objective_Adversarial_Attack_Framework_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Guo_MOS-Attack_A_Scalable_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Guo_2025_CVPR,
    author    = {Guo, Ping and Gong, Cheng and Lin, Xi and Liu, Fei and Lu, Zhichao and Zhang, Qingfu and Wang, Zhenkun},
    title     = {MOS-Attack: A Scalable Multi-objective Adversarial Attack Framework},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {5041-5051}
}

raw_affiliation:
[{"affiliation": "City University of Hong Kong", "city": "Hong Kong", "country": "China"}, {"affiliation": "CityU Shenzhen Research Institute", "city": "Shenzhen", "country": "China"}, {"affiliation": "Southern University of Science and Technology", "city": "Shenzhen", "country": "China"}]

affiliations:
["City University of Hong Kong", "CityU Shenzhen Research Institute", "Southern University of Science and Technology"]


title: Six-CD: Benchmarking Concept Removals for Text-to-image Diffusion Models
authors: Jie Ren, Kangrui Chen, Yingqian Cui, Shenglai Zeng, Hui Liu, Yue Xing, Jiliang Tang, Lingjuan Lyu
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 28769-28778
affiliations: ["Michigan State University", "Sony AI"]
cities: ["East Lansing", "Tokyo"]
countries: ["USA", "Japan"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Ren_Six-CD_Benchmarking_Concept_Removals_for_Text-to-image_Diffusion_Models_CVPR_2025_paper.html

abstract:
Text-to-image (T2I) diffusion models have shown exceptional capabilities in generating images that closely correspond to textual prompts. However, the advancement of T2I diffusion models presents significant risks, as the models could be exploited for malicious purposes, such as generating images with violence or nudity, or creating unauthorized portraits of public figures in inappropriate contexts. To mitigate these risks, concept removal methods have been proposed. These methods aim to modify diffusion models to prevent the generation of malicious and unwanted concepts. Despite these efforts, existing research faces several challenges: (1) a lack of consistent comparisons on a comprehensive dataset, (2) ineffective prompts in harmful and nudity concepts, (3) overlooked evaluation of the ability to generate the benign part within prompts containing malicious concepts. To address these gaps, we propose to benchmark the concept removal methods by introducing a new dataset, Six-CD, along with a novel evaluation metric. In this benchmark, we conduct a thorough evaluation of concept removals, with the experimental observations and discussions offering valuable insights in the field.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Ren_Six-CD_Benchmarking_Concept_Removals_for_Text-to-image_Diffusion_Models_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Ren_Six-CD_Benchmarking_Concept_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Ren_2025_CVPR,
    author    = {Ren, Jie and Chen, Kangrui and Cui, Yingqian and Zeng, Shenglai and Liu, Hui and Xing, Yue and Tang, Jiliang and Lyu, Lingjuan},
    title     = {Six-CD: Benchmarking Concept Removals for Text-to-image Diffusion Models},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {28769-28778}
}

raw_affiliation:
[{"affiliation": "Michigan State University", "city": "East Lansing", "country": "USA"}, {"affiliation": "Sony AI", "city": "Tokyo", "country": "Japan"}]


title: Motion Modes: What Could Happen Next?
authors: Karran Pandey, Yannick Hold-Geoffroy, Matheus Gadelha, Niloy J. Mitra, Karan Singh, Paul Guerrero
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 2030-2039
affiliations: ["University of Toronto", "Adobe Research", "UCL"]
cities: ["Toronto", "San Jose", "London"]
countries: ["Canada", "USA", "UK"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Pandey_Motion_Modes_What_Could_Happen_Next_CVPR_2025_paper.html

abstract:
Predicting diverse object motions from a single static image remains challenging, as current video generation models often entangle object movement with camera motion and other scene changes. While recent methods can predict specific motions from motion arrow input, they rely on synthetic data and predefined motions, limiting their application to complex scenes. We introduce Motion Modes, a training-free approach that explores a pre-trained image-to-video generator's latent distribution to discover various distinct and plausible motions focused on selected objects in static images. We achieve this by employing a flow generator guided by energy functions designed to disentangle object and camera motion. Additionally, we use an energy inspired by particle guidance to diversify the generated motions, without requiring explicit training data. Experimental results demonstrate that Motion Modes generates realistic and varied object animations, surpassing previous methods and even human predictions regarding plausibility and diversity.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Pandey_Motion_Modes_What_Could_Happen_Next_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Pandey_Motion_Modes_What_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Pandey_2025_CVPR,
    author    = {Pandey, Karran and Hold-Geoffroy, Yannick and Gadelha, Matheus and Mitra, Niloy J. and Singh, Karan and Guerrero, Paul},
    title     = {Motion Modes: What Could Happen Next?},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {2030-2039}
}

raw_affiliation:
[{"affiliation": "University of Toronto", "city": "Toronto", "country": "Canada"}, {"affiliation": "Adobe Research", "city": "San Jose", "country": "USA"}, {"affiliation": "UCL", "city": "London", "country": "UK"}]


title: Finer-CAM: Spotting the Difference Reveals Finer Details for Visual Explanation
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 9611-9620
raw_affiliation: [{"affiliation": "The Ohio State University", "city": "Columbus", "country": "USA"}]
affiliations: ["The Ohio State University"]
cities: ["Columbus"]
countries: ["USA"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Zhang_Finer-CAM_Spotting_the_Difference_Reveals_Finer_Details_for_Visual_Explanation_CVPR_2025_paper.html

authors:
Ziheng Zhang, Jianyang Gu, Arpita Chowdhury, Zheda Mai, David Carlyn, Tanya Berger-Wolf, Yu Su, Wei-Lun Chao

abstract:
Class activation map (CAM) has been widely used to highlight image regions that contribute to class predictions. Despite its simplicity and computational efficiency, CAM often struggles to identify discriminative regions that distinguish visually similar fine-grained classes. Prior efforts address this limitation by introducing more sophisticated explanation processes, but at the cost of extra complexity. In this paper, we propose Finer-CAM, a method that retains CAM's efficiency while achieving precise localization of discriminative regions. Our key insight is that the deficiency of CAM lies not in "how" it explains, but in "what" it explains. Specifically, previous methods attempt to identify all cues contributing to the target class's logit value, which inadvertently also activates regions predictive of visually similar classes. By explicitly comparing the target class with similar classes and spotting their differences, Finer-CAM suppresses features shared with other classes and emphasizes the unique, discriminative details of the target class. Finer-CAM is easy to implement, compatible with various CAM methods, and can be extended to multi-modal models for accurate localization of specific concepts. Additionally, Finer-CAM allows adjustable comparison strength, enabling users to selectively highlight coarse object contours or fine discriminative details. Quantitatively, we show that masking out the top 5% of activated pixels by Finer-CAM results in a larger relative confidence drop compared to baselines. The source code and demo are available at https://github.com/Imageomics/Finer-CAM.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Zhang_Finer-CAM_Spotting_the_Difference_Reveals_Finer_Details_for_Visual_Explanation_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Zhang_Finer-CAM_Spotting_the_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Zhang_2025_CVPR,
    author    = {Zhang, Ziheng and Gu, Jianyang and Chowdhury, Arpita and Mai, Zheda and Carlyn, David and Berger-Wolf, Tanya and Su, Yu and Chao, Wei-Lun},
    title     = {Finer-CAM: Spotting the Difference Reveals Finer Details for Visual Explanation},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {9611-9620}
}


authors: Yanis Benidir, Nicolas Gonthier, Clement Mallet
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 2204-2214
raw_affiliation: [{"affiliation": "Univ Gustave Eiffel, ENSG, IGN, LASTIG", "city": "Paris", "country": "France"}]
affiliations: ["Univ Gustave Eiffel, ENSG, IGN, LASTIG"]
cities: ["Paris"]
countries: ["France"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Benidir_The_Change_You_Want_To_Detect_Semantic_Change_Detection_In_CVPR_2025_paper.html

title:
The Change You Want To Detect: Semantic Change Detection In Earth Observation With Hybrid Data Generationf

abstract:
Bi-temporal change detection at scale based on Very High Resolution (VHR) images is crucial for Earth monitoring. Such task remains poorly addressed even in the deep learning era: it either requires large volumes of annotated data - in the semantic case - or is limited to restricted datasets for binary set-ups. Most approaches do not exhibit the versatility required for temporal and spatial adaptation: simplicity in architecture design and pretraining on realistic and comprehensive datasets. Synthetic datasets is the key solution but still fails handling complex and diverse scenes. In this paper, we present HySCDG a generative pipeline for creating a large hybrid semantic change detection dataset that contains both real VHR images and inpainted ones, along with land cover semantic map at both dates and the change map. Being semantically and spatially guided, HySCDG generates realistic images, leading to a comprehensive and hybrid transfer-proof dataset FSC-180k. We evaluate FSC-180k on five change detection cases (binary and semantic), from zero-shot to mixed and sequential training, and also under low data regime training. Experiments demonstrate that pretraining on our hybrid dataset leads to a significant performance boost, outperforming SyntheWorld, a fully synthetic dataset, in every configuration. All codes, models, and data will be made available.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Benidir_The_Change_You_Want_To_Detect_Semantic_Change_Detection_In_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Benidir_The_Change_You_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Benidir_2025_CVPR,
    author    = {Benidir, Yanis and Gonthier, Nicolas and Mallet, Clement},
    title     = {The Change You Want To Detect: Semantic Change Detection In Earth Observation With Hybrid Data Generationf},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {2204-2214}
}


title: Black-Box Forgery Attacks on Semantic Watermarks for Diffusion Models
authors: Andreas Müller, Denis Lukovnikov, Jonas Thietke, Asja Fischer, Erwin Quiring
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 20937-20946
raw_affiliation: [{"affiliation": "Ruhr University Bochum", "city": "Bochum", "country": "Germany"}]
affiliations: ["Ruhr University Bochum"]
cities: ["Bochum"]
countries: ["Germany"]
oral: yes
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Muller_Black-Box_Forgery_Attacks_on_Semantic_Watermarks_for_Diffusion_Models_CVPR_2025_paper.html

abstract:
Integrating watermarking into the generation process of latent diffusion models (LDMs) simplifies detection and attribution of generated content. Semantic watermarks, such as Tree-Rings and Gaussian Shading, represent a novel class of watermarking techniques that are easy to implement and highly robust against various perturbations. However, our work demonstrates a fundamental security vulnerability of semantic watermarks. We show that attackers can leverage unrelated models, even with different latent spaces and architectures (UNet vs DiT), to perform powerful and realistic forgery attacks. Specifically, we design two watermark forgery attacks. The first \imprints a targeted watermark into real images by manipulating the latent representation of an arbitrary image in an unrelated LDM to get closer to the latent representation of a watermarked image. We also show that this technique can be used for watermark removal. The second attack generates new images with the target watermark by inverting a watermarked image and re-generating it with an arbitrary prompt. Both attacks just need a single reference image with the target watermark. Overall, our findings question the applicability of semantic watermarks by revealing that attackers can easily forge or remove these watermarks under realistic conditions.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Muller_Black-Box_Forgery_Attacks_on_Semantic_Watermarks_for_Diffusion_Models_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Muller_Black-Box_Forgery_Attacks_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Muller_2025_CVPR,
    author    = {M\"uller, Andreas and Lukovnikov, Denis and Thietke, Jonas and Fischer, Asja and Quiring, Erwin},
    title     = {Black-Box Forgery Attacks on Semantic Watermarks for Diffusion Models},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {20937-20946}
}


title: An Image-like Diffusion Method for Human-Object Interaction Detection
authors: Xiaofei Hui, Haoxuan Qu, Hossein Rahmani, Jun Liu
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 14002-14012
raw_affiliation: [{"affiliation": "Lancaster University", "city": "Lancaster", "country": "United Kingdom"}]
affiliations: ["Lancaster University"]
cities: ["Lancaster"]
countries: ["United Kingdom"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Hui_An_Image-like_Diffusion_Method_for_Human-Object_Interaction_Detection_CVPR_2025_paper.html

abstract:
Human-object interaction (HOI) detection often faces high levels of ambiguity and indeterminacy, as the same interaction can appear vastly different across different human-object pairs. Additionally, the indeterminacy can be further exacerbated by issues such as occlusions and cluttered backgrounds. To handle such a challenging task, in this work, we begin with a key observation: the output of HOI detection for each human-object pair can be recast as an image. Thus, inspired by the strong image generation capabilities of image diffusion models, we propose a new framework, HOI-IDiff. In HOI-IDiff, we tackle HOI detection from a novel perspective, using an Image-like Diffusion process to generate HOI detection outputs as images. Furthermore, recognizing that our recast images differ in certain properties from natural images, we enhance our framework with a customized HOI diffusion process and a slice patchification model architecture, which are specifically tailored to generate our recast "HOI images". Extensive experiments demonstrate the efficacy of our framework.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Hui_An_Image-like_Diffusion_Method_for_Human-Object_Interaction_Detection_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Hui_An_Image-like_Diffusion_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Hui_2025_CVPR,
    author    = {Hui, Xiaofei and Qu, Haoxuan and Rahmani, Hossein and Liu, Jun},
    title     = {An Image-like Diffusion Method for Human-Object Interaction Detection},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {14002-14012}
}


title: VidSeg: Training-free Video Semantic Segmentation based on Diffusion Models
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 22985-22994
affiliations: ["KAUST", "Max Planck Institute for Informatics"]
cities: ["Thuwal", "Saarbr\u00fccken"]
countries: ["Saudi Arabia", "Germany"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Wang_VidSeg_Training-free_Video_Semantic_Segmentation_based_on_Diffusion_Models_CVPR_2025_paper.html

authors:
Qian Wang, Abdelrahman Eldesokey, Mohit Mendiratta, Fangneng Zhan, Adam Kortylewski, Christian Theobalt, Peter Wonka

abstract:
We introduce the first training-free approach for Video Semantic Segmentation (VSS) based on pre-trained diffusion models. A growing research direction attempts to employ diffusion models to perform downstream vision tasks by exploiting their deep understanding of image semantics. Yet, the majority of these approaches have focused on image-related tasks like semantic segmentation, with less emphasis on video tasks such as VSS. Ideally, diffusion-based image semantic segmentation approaches can be applied to videos in a frame-by-frame manner. However, we find their performance on videos to be subpar due to the absence of any modeling of temporal information inherent in the video data. To this end, we tackle this problem and introduce a framework tailored for VSS based on pre-trained image and video diffusion models. We propose building a scene context model based on the diffusion features, where the model is autoregressively updated to adapt to scene changes. This context model predicts per-frame coarse segmentation maps that are temporally consistent. To refine these maps further, we propose a correspondence-based refinement strategy that aggregates predictions temporally, resulting in more confident predictions. Finally, we introduce a masked modulation approach to upsample the coarse maps to a high-quality full resolution. Experiments show that our proposed approach significantly outperforms existing training-free image semantic segmentation approaches on various VSS benchmarks without any training or fine-tuning. Moreover, it rivals supervised VSS approaches on the VSPW dataset despite not being explicitly trained for VSS.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Wang_VidSeg_Training-free_Video_Semantic_Segmentation_based_on_Diffusion_Models_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Wang_VidSeg_Training-free_Video_CVPR_2025_supplemental.zip

bibtex:
@InProceedings{Wang_2025_CVPR,
    author    = {Wang, Qian and Eldesokey, Abdelrahman and Mendiratta, Mohit and Zhan, Fangneng and Kortylewski, Adam and Theobalt, Christian and Wonka, Peter},
    title     = {VidSeg: Training-free Video Semantic Segmentation based on Diffusion Models},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {22985-22994}
}

raw_affiliation:
[{"affiliation": "KAUST", "city": "Thuwal", "country": "Saudi Arabia"}, {"affiliation": "Max Planck Institute for Informatics", "city": "Saarbr\u00fccken", "country": "Germany"}]


title: COB-GS: Clear Object Boundaries in 3DGS Segmentation Based on Boundary-Adaptive Gaussian Splitting
authors: Jiaxin Zhang, Junjun Jiang, Youyu Chen, Kui Jiang, Xianming Liu
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 19335-19344
raw_affiliation: [{"affiliation": "Harbin Institute of Technology", "city": "Harbin", "country": "China"}]
affiliations: ["Harbin Institute of Technology"]
cities: ["Harbin"]
countries: ["China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Zhang_COB-GS_Clear_Object_Boundaries_in_3DGS_Segmentation_Based_on_Boundary-Adaptive_CVPR_2025_paper.html

abstract:
Accurate object segmentation is crucial for high-quality scene understanding in the 3D vision domain. However, 3D segmentation based on 3D Gaussian Splatting (3DGS) struggles with accurately delineating object boundaries, as Gaussian primitives often span across object edges due to their inherent volume and the lack of semantic guidance during training. In order to tackle these challenges, we introduce Clear Object Boundaries for 3DGS Segmentation (COB-GS), which aims to improve segmentation accuracy by clearly delineating blurry boundaries of interwoven Gaussian primitives within the scene. Unlike existing approaches that remove ambiguous Gaussians and sacrifice visual quality, COB-GS, as a 3DGS refinement method, jointly optimizes semantic and visual information, allowing the two different levels to cooperate with each other effectively. Specifically, for the semantic guidance, we introduce a boundary-adaptive Gaussian splitting technique that leverages semantic gradient statistics to identify and split ambiguous Gaussians, aligning them closely with object boundaries. For the visual optimization, we rectify the degraded suboptimal texture of the 3DGS scene, particularly along the refined boundary structures. Experimental results show that COB-GS substantially improves segmentation accuracy and robustness against inaccurate masks from pre-trained model, yielding clear boundaries while preserving high visual quality. Code is available at https://github.com/ZestfulJX/COB-GS.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Zhang_COB-GS_Clear_Object_Boundaries_in_3DGS_Segmentation_Based_on_Boundary-Adaptive_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Zhang_COB-GS_Clear_Object_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Zhang_2025_CVPR,
    author    = {Zhang, Jiaxin and Jiang, Junjun and Chen, Youyu and Jiang, Kui and Liu, Xianming},
    title     = {COB-GS: Clear Object Boundaries in 3DGS Segmentation Based on Boundary-Adaptive Gaussian Splitting},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {19335-19344}
}


title: Weakly Supervised Semantic Segmentation via Progressive Confidence Region Expansion
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 9829-9838
cities: ["Shanghai", "Xiamen", "Shenzhen", "Shanghai"]
countries: ["China", "China", "China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Xu_Weakly_Supervised_Semantic_Segmentation_via_Progressive_Confidence_Region_Expansion_CVPR_2025_paper.html

authors:
Xiangfeng Xu, Pinyi Zhang, Wenxuan Huang, Yunhang Shen, Haosheng Chen, Jingzhong Lin, Wei Li, Gaoqi He, Jiao Xie, Shaohui Lin

abstract:
Weakly supervised semantic segmentation (WSSS) has garnered considerable attention due to its effective reduction of annotation costs. Most approaches utilize Class Activation Maps (CAM) to produce pseudo-labels, thereby localizing target regions using only image-level annotations. However, the prevalent methods relying on vision transformers (ViT) encounter an "over-expansion" issue, i.e., CAM incorrectly expands high activation value from the target object to the background regions, as it is difficult to learn pixel-level local intrinsic inductive bias in ViT from weak supervisions. To solve this problem, we propose a Progressive Confidence Region Expansion (PCRE) framework for WSSS, it gradually learns a faithful mask over the target region and utilizes this mask to correct the confusion in CAM. PCRE has two key components: Confidence Region Mask Expansion (CRME) and Class-Prototype Enhancement (CPE). CRME progressively expands the mask in the small region with the highest confidence, eventually encompassing the entire target, thereby avoiding unintended CPE aims to enhance mask generation in CRME by leveraging the similarity between the learned, dataset-level class prototypes and patch features as supervision to optimize the mask output from CRME. Extensive experiments demonstrate that our method outperforms the existing single-stage and multi-stage approaches on the PASCAL VOC and MS COCO benchmark datasets. Our code is available at https://github.com/xxf011/WSSS-PCRE .

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Xu_Weakly_Supervised_Semantic_Segmentation_via_Progressive_Confidence_Region_Expansion_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Xu_Weakly_Supervised_Semantic_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Xu_2025_CVPR,
    author    = {Xu, Xiangfeng and Zhang, Pinyi and Huang, Wenxuan and Shen, Yunhang and Chen, Haosheng and Lin, Jingzhong and Li, Wei and He, Gaoqi and Xie, Jiao and Lin, Shaohui},
    title     = {Weakly Supervised Semantic Segmentation via Progressive Confidence Region Expansion},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {9829-9838}
}

raw_affiliation:
[{"affiliation": "East China Normal University", "city": "Shanghai", "country": "China"}, {"affiliation": "Xiamen University", "city": "Xiamen", "country": "China"}, {"affiliation": "Huawei Noah\u2019s Ark Lab", "city": "Shenzhen", "country": "China"}, {"affiliation": "Key Laboratory of Advanced Theory and Application in Statistics and Data ScienceMinistry of Education, China", "city": "Shanghai", "country": "China"}]

affiliations:
["East China Normal University", "Xiamen University", "Huawei Noah\u2019s Ark Lab", "Key Laboratory of Advanced Theory and Application in Statistics and Data ScienceMinistry of Education, China"]


authors: Savya Khosla, Sethuraman T V, Alexander Schwing, Derek Hoiem
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 3697-3706
raw_affiliation: [{"affiliation": "University of Illinois Urbana-Champaign", "city": "Urbana", "country": "USA"}]
affiliations: ["University of Illinois Urbana-Champaign"]
cities: ["Urbana"]
countries: ["USA"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Khosla_RELOCATE_A_Simple_Training-Free_Baseline_for_Visual_Query_Localization_Using_CVPR_2025_paper.html

title:
RELOCATE: A Simple Training-Free Baseline for Visual Query Localization Using Region-Based Representations

abstract:
We present RELOCATE, a simple training-free baseline designed to perform the challenging task of visual query localization in long videos. To eliminate the need for task-specific training and efficiently handle long videos, RELOCATE leverages a region-based representation derived from pretrained vision models. At a high level, it follows the classic object localization approach: (1) identify all objects in each video frame, (2) compare the objects with the given query and select the most similar ones, and (3) perform bidirectional tracking to get a spatio-temporal response. However, we propose some key enhancements to handle small objects, cluttered scenes, partial visibility, and varying appearances. Notably, we refine the selected objects for accurate localization and generate additional visual queries to capture visual variations. We evaluate RELOCATE on the challenging Ego4D Visual Query 2D Localization dataset, establishing a new baseline that outperforms prior task-specific methods by 49% (relative improvement) in spatio-temporal average precision.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Khosla_RELOCATE_A_Simple_Training-Free_Baseline_for_Visual_Query_Localization_Using_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Khosla_RELOCATE_A_Simple_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Khosla_2025_CVPR,
    author    = {Khosla, Savya and V, Sethuraman T and Schwing, Alexander and Hoiem, Derek},
    title     = {RELOCATE: A Simple Training-Free Baseline for Visual Query Localization Using Region-Based Representations},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {3697-3706}
}


title: PEER Pressure: Model-to-Model Regularization for Single Source Domain Generalization
authors: Dong Kyu Cho, Inwoo Hwang, Sanghack Lee
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 15360-15370
affiliations: ["New York University", "Columbia University", "Seoul National University"]
cities: ["New York", "New York", "Seoul"]
countries: ["USA", "USA", "South Korea"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Cho_PEER_Pressure_Model-to-Model_Regularization_for_Single_Source_Domain_Generalization_CVPR_2025_paper.html

abstract:
Data augmentation is a popular tool for single source domain generalization, which expands the source domain by generating simulated ones, improving generalization on unseen target domains. In this work, we show that the performance of such augmentation-based methods in the target domains universally fluctuates during training, posing challenges in model selection under realistic scenarios. We argue that the fluctuation stems from the inability of the model to accumulate the knowledge learned from diverse augmentations, exacerbating feature distortion during training. Based on this observation, we propose a novel generalization method, coined Parameter-Space Ensemble with Entropy Regularization (PEER), that uses a proxy model to learn the augmented data on behalf of the main model. The main model is updated by averaging its parameters with the proxy model, progressively accumulating knowledge over the training steps. Maximizing the mutual information between the output representations of the two models guides the learning process of the proxy model, mitigating feature distortion during training. Experimental results demonstrate the effectiveness of PEER in reducing the OOD performance fluctuation and enhancing generalization across various datasets, including PACS, Digits, Office-Home, and VLCS. Notably, our method with simple random augmentation achieves state-of-the-art performance, surpassing prior approaches on sDG that utilize complex data augmentation strategies.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Cho_PEER_Pressure_Model-to-Model_Regularization_for_Single_Source_Domain_Generalization_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Cho_PEER_Pressure_Model-to-Model_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Cho_2025_CVPR,
    author    = {Cho, Dong Kyu and Hwang, Inwoo and Lee, Sanghack},
    title     = {PEER Pressure: Model-to-Model Regularization for Single Source Domain Generalization},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {15360-15370}
}

raw_affiliation:
[{"affiliation": "New York University", "city": "New York", "country": "USA"}, {"affiliation": "Columbia University", "city": "New York", "country": "USA"}, {"affiliation": "Seoul National University", "city": "Seoul", "country": "South Korea"}]


authors: Ethan Griffiths, Maryam Haghighat, Simon Denman, Clinton Fookes, Milad Ramezani
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 6648-6658
affiliations: ["Queensland University of Technology (QUT)", "CSIRO Robotics, Data61, CSIRO"]
cities: ["Brisbane", "Canberra"]
countries: ["Australia", "Australia"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Griffiths_HOTFormerLoc_Hierarchical_Octree_Transformer_for_Versatile_Lidar_Place_Recognition_Across_CVPR_2025_paper.html

title:
HOTFormerLoc: Hierarchical Octree Transformer for Versatile Lidar Place Recognition Across Ground and Aerial Views

abstract:
We present HOTFormerLoc, a novel and versatile Hierarchical Octree-based TransFormer, for large-scale 3D place recognition in both ground-to-ground and ground-to-aerial scenarios across urban and forest environments. We propose an octree-based multi-scale attention mechanism that captures spatial and semantic features across granularities. To address the variable density of point distributions from spinning lidar, we present cylindrical octree attention windows to reflect the underlying distribution during attention.We introduce relay tokens to enable efficient global-local interactions and multi-scale representation learning at reduced computational cost. Our pyramid attentional pooling then synthesises a robust global descriptor for end-to-end place recognition in challenging environments. In addition, we introduce CS-Wild-Places, a novel 3D cross-source dataset featuring point cloud data from aerial and ground lidar scans captured in dense forests. Point clouds in CS-Wild-Places contain representational gaps and distinctive attributes such as varying point densities and noise patterns, making it a challenging benchmark for cross-view localisation in the wild. HOTFormerLoc achieves a top-1 average recall improvement of 5.5% - 11.5% on the CS-Wild-Places benchmark. Furthermore, it consistently outperforms SOTA 3D place recognition methods, with an average performance gain of 4.9% on well-established urban and forest datasets. The code and CS-Wild-Places benchmark is available at https://csiro-robotics.github.io/HOTFormerLoc.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Griffiths_HOTFormerLoc_Hierarchical_Octree_Transformer_for_Versatile_Lidar_Place_Recognition_Across_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Griffiths_HOTFormerLoc_Hierarchical_Octree_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Griffiths_2025_CVPR,
    author    = {Griffiths, Ethan and Haghighat, Maryam and Denman, Simon and Fookes, Clinton and Ramezani, Milad},
    title     = {HOTFormerLoc: Hierarchical Octree Transformer for Versatile Lidar Place Recognition Across Ground and Aerial Views},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {6648-6658}
}

raw_affiliation:
[{"affiliation": "Queensland University of Technology (QUT)", "city": "Brisbane", "country": "Australia"}, {"affiliation": "CSIRO Robotics, Data61, CSIRO", "city": "Canberra", "country": "Australia"}]


title: Revisiting Fairness in Multitask Learning: A Performance-Driven Approach for Variance Reduction
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. 20492-20501
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_Revisiting_Fairness_in_Multitask_Learning_A_Performance-Driven_Approach_for_Variance_CVPR_2025_paper.html

abstract:
Multi-task learning (MTL) can leverage shared knowledge across tasks to improve data efficiency and generalization performance, and has been applied in various scenarios. However, task imbalance remains a major challenge for existing MTL methods. While the prior works have attempted to mitigate inter-task unfairness through loss-based and gradient-based strategies, they still exhibit imbalanced performance across tasks on common benchmarks.This key observation motivates us to consider performance-level information as an explicit fairness indicator, which can precisely reflect the current optimization status of each task, and accordingly help to adjust the gradient aggregation process.Specifically, we utilize the performance variance among tasks as the fairness indicator and introduce a dynamic weighting strategy to gradually reduce the performance variance. Based on this, we propose PIVRG, a novel performance-informed variance reduction gradient aggregation approach.Extensive experiments show that PIVRG achieves SOTA performance across various benchmarks, spanning both supervised learning and reinforcement learning tasks with task numbers ranging from 2 to 40. Results from the ablation study also show that our approach can be integrated into existing methods, significantly enhancing their performance while reducing the performance variance among tasks, thus achieving fairer optimization.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Qin_Revisiting_Fairness_in_Multitask_Learning_A_Performance-Driven_Approach_for_Variance_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Qin_Revisiting_Fairness_in_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Qin_2025_CVPR,
    author    = {Qin, Xiaohan and Wang, Xiaoxing and Yan, Junchi},
    title     = {Revisiting Fairness in Multitask Learning: A Performance-Driven Approach for Variance Reduction},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {20492-20501}
}

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


title: UniK3D: Universal Camera Monocular 3D Estimation
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 1028-1039
affiliations: ["ETH Z\n\u00a8urich", "Toyota Motor Europe", "INSAIT, So\ufb01a University St. Kliment Ohridski"]
cities: ["Zurich", "Brussels", "Sofia"]
countries: ["Switzerland", "Belgium", "Bulgaria"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Piccinelli_UniK3D_Universal_Camera_Monocular_3D_Estimation_CVPR_2025_paper.html

authors:
Luigi Piccinelli, Christos Sakaridis, Mattia Segu, Yung-Hsu Yang, Siyuan Li, Wim Abbeloos, Luc Van Gool

abstract:
Monocular 3D estimation is crucial for visual perception. However, current methods fall short by relying on oversimplified assumptions, such as pinhole camera models or rectified images. These limitations severely restrict their general applicability, causing poor performance in real-world scenarios with fisheye or panoramic images and resulting in substantial context loss. To address this, we present UniK3D, the first generalizable method for monocular 3D estimation able to model any camera. Our method introduces a spherical 3D representation which allows for better disentanglement of camera and scene geometry and enables accurate metric 3D reconstruction for unconstrained camera models. Our camera component features a novel, model-independent representation of the pencil of rays, achieved through a learned superposition of spherical harmonics. We also introduce an angular loss, which, together with the camera module design, prevents the contraction of the 3D outputs for wide-view cameras. A comprehensive zero-shot evaluation on 13 diverse datasets demonstrates the state-of-the-art performance of UniK3D across 3D, depth, and camera metrics, with substantial gains in challenging large-field-of-view and panoramic settings, while maintaining top accuracy in conventional pinhole small-field-of-view domains. Code and models are available at github.com/lpiccinelli-eth/unik3d

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Piccinelli_UniK3D_Universal_Camera_Monocular_3D_Estimation_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Piccinelli_UniK3D_Universal_Camera_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Piccinelli_2025_CVPR,
    author    = {Piccinelli, Luigi and Sakaridis, Christos and Segu, Mattia and Yang, Yung-Hsu and Li, Siyuan and Abbeloos, Wim and Van Gool, Luc},
    title     = {UniK3D: Universal Camera Monocular 3D Estimation},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {1028-1039}
}

raw_affiliation:
[{"affiliation": "ETH Z\n\u00a8urich", "city": "Zurich", "country": "Switzerland"}, {"affiliation": "Toyota Motor Europe", "city": "Brussels", "country": "Belgium"}, {"affiliation": "INSAIT, So\ufb01a University St. Kliment Ohridski", "city": "Sofia", "country": "Bulgaria"}]


title: ConMo: Controllable Motion Disentanglement and Recomposition for Zero-Shot Motion Transfer
authors: Jiayi Gao, Zijin Yin, Changcheng Hua, Yuxin Peng, Kongming Liang, Zhanyu Ma, Jun Guo, Yang Liu
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 7191-7200
cities: ["Beijing", "Beijing"]
countries: ["China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Gao_ConMo_Controllable_Motion_Disentanglement_and_Recomposition_for_Zero-Shot_Motion_Transfer_CVPR_2025_paper.html

abstract:
The development of Text-to-Video (T2V) generation has made motion transfer possible, enabling the control of video motion based on existing footage. However, current methods have two limitations: 1) struggle to handle multi-subjects videos, failing to transfer specific subject motion; 2) struggle to preserve the diversity and accuracy of motion as transferring to subjects with varying shapes. To overcome these, we introduce ConMo, a zero-shot framework that disentangle and recompose the motions of subjects and camera movements. ConMo isolates individual subject and background motion cues from complex trajectories in source videos using only subject masks, and reassembles them for target video generation. This approach enables more accurate motion control across diverse subjects and improves performance in multi-subject scenarios. Additionally, we propose soft guidance in the recomposition stage which controls the retention of original motion to adjust shape constraints, aiding subject shape adaptation and semantic transformation. Unlike previous methods, ConMo unlocks a wide range of applications, including subject size and position editing, subject removal, semantic modifications, and camera motion simulation. Extensive experiments demonstrate that ConMo significantly outperforms state-of-the-art methods in motion fidelity and semantic consistency. The code is available at https://github.com/Andyplus1/ConMo.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Gao_ConMo_Controllable_Motion_Disentanglement_and_Recomposition_for_Zero-Shot_Motion_Transfer_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Gao_ConMo_Controllable_Motion_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Gao_2025_CVPR,
    author    = {Gao, Jiayi and Yin, Zijin and Hua, Changcheng and Peng, Yuxin and Liang, Kongming and Ma, Zhanyu and Guo, Jun and Liu, Yang},
    title     = {ConMo: Controllable Motion Disentanglement and Recomposition for Zero-Shot Motion Transfer},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {7191-7200}
}

raw_affiliation:
[{"affiliation": "Wangxuan Institute of Computer Technology, Peking University", "city": "Beijing", "country": "China"}, {"affiliation": "Beijing University of Posts and Telecommunications", "city": "Beijing", "country": "China"}]

affiliations:
["Wangxuan Institute of Computer Technology, Peking University", "Beijing University of Posts and Telecommunications"]


title: VideoMage: Multi-Subject and Motion Customization of Text-to-Video Diffusion Models
authors: Chi-Pin Huang, Yen-Siang Wu, Hung-Kai Chung, Kai-Po Chang, Fu-En Yang, Yu-Chiang Frank Wang
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 17603-17612
cities: ["Taipei", "Taipei", "Santa Clara"]
countries: ["Taiwan", "Taiwan", "USA"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Huang_VideoMage_Multi-Subject_and_Motion_Customization_of_Text-to-Video_Diffusion_Models_CVPR_2025_paper.html

abstract:
Customized text-to-video generation aims to produce high-quality videos that incorporate user-specified subject identities or motion patterns. However, existing methods mainly focus on personalizing a single concept, either subject identity or motion pattern, limiting their effectiveness for multiple subjects with the desired motion patterns. To tackle this challenge, we propose a unified framework VideoMage for video customization over both multiple subjects and their interactive motions. VideoMage employs subject and motion LoRAs to capture personalized content from user-provided images and videos, along with an appearance-agnostic motion learning approach to disentangle motion patterns from visual appearance. Furthermore, we develop a spatial-temporal composition scheme to guide interactions among subjects within the desired motion patterns. Extensive experiments demonstrate that VideoMage outperforms existing methods, generating coherent, user-controlled videos with consistent subject identities and interactions.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Huang_VideoMage_Multi-Subject_and_Motion_Customization_of_Text-to-Video_Diffusion_Models_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Huang_VideoMage_Multi-Subject_and_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Huang_2025_CVPR,
    author    = {Huang, Chi-Pin and Wu, Yen-Siang and Chung, Hung-Kai and Chang, Kai-Po and Yang, Fu-En and Wang, Yu-Chiang Frank},
    title     = {VideoMage: Multi-Subject and Motion Customization of Text-to-Video Diffusion Models},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {17603-17612}
}

raw_affiliation:
[{"affiliation": "Graduate Institute of Communication Engineering, National Taiwan University", "city": "Taipei", "country": "Taiwan"}, {"affiliation": "National Taiwan University", "city": "Taipei", "country": "Taiwan"}, {"affiliation": "NVIDIA", "city": "Santa Clara", "country": "USA"}]

affiliations:
["Graduate Institute of Communication Engineering, National Taiwan University", "National Taiwan University", "NVIDIA"]


authors: Huy Nguyen, Kien Nguyen, Akila Pemasiri, Feng Liu, Sridha Sridharan, Clinton Fookes
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 1241-1251
cities: ["Brisbane", "Philadelphia"]
countries: ["Australia", "USA"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Nguyen_AG-VPReID_A_Challenging_Large-Scale_Benchmark_for_Aerial-Ground_Video-based_Person_Re-Identification_CVPR_2025_paper.html

title:
AG-VPReID: A Challenging Large-Scale Benchmark for Aerial-Ground Video-based Person Re-Identification

abstract:
We introduce AG-VPReID, a new large-scale dataset for aerial-ground video-based person re-identification (ReID) that comprises 6,632 subjects, 32,321 tracklets and over 9.6 million frames captured by drones (altitudes ranging from 15-120m), CCTV, and wearable cameras. This dataset offers a real-world benchmark for evaluating the robustness to significant viewpoint changes, scale variations, and resolution differences in cross-platform aerial-ground settings. In addition, to address these challenges, we propose AG-VPReID-Net, an end-to-end framework composed of three complementary streams: (1) an Adapted Temporal-Spatial Stream addressing motion pattern inconsistencies and facilitating temporal feature learning, (2) a Normalized Appearance Stream leveraging physics-informed techniques to tackle resolution and appearance changes, and (3) a Multi-Scale Attention Stream handling scale variations across drone altitudes. We integrate visual-semantic cues from all streams to form a robust, viewpoint-invariant whole-body representation. Extensive experiments demonstrate that AG-VPReID-Net outperforms state-of-the-art approaches on both our new dataset and existing video-based ReID benchmarks, showcasing its effectiveness and generalizability. Nevertheless, the performance gap observed on AG-VPReID across all methods underscores the dataset's challenging nature. The dataset, code and trained models are available at https://github.com/agvpreid25/AG-VPReID-Net.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Nguyen_AG-VPReID_A_Challenging_Large-Scale_Benchmark_for_Aerial-Ground_Video-based_Person_Re-Identification_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Nguyen_AG-VPReID_A_Challenging_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Nguyen_2025_CVPR,
    author    = {Nguyen, Huy and Nguyen, Kien and Pemasiri, Akila and Liu, Feng and Sridharan, Sridha and Fookes, Clinton},
    title     = {AG-VPReID: A Challenging Large-Scale Benchmark for Aerial-Ground Video-based Person Re-Identification},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {1241-1251}
}

raw_affiliation:
[{"affiliation": "School of Electrical Engineering and Robotics, Queensland University of Technology", "city": "Brisbane", "country": "Australia"}, {"affiliation": "Department of Computer Science, Drexel University", "city": "Philadelphia", "country": "USA"}]

affiliations:
["School of Electrical Engineering and Robotics, Queensland University of Technology", "Department of Computer Science, Drexel University"]


title: EBS-EKF: Accurate and High Frequency Event-based Star Tracking
authors: Albert W. Reed, Connor Hashemi, Dennis Melamed, Nitesh Menon, Keigo Hirakawa, Scott McCloskey
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 6510-6519
affiliations: ["Kitware", "University of Dayton"]
cities: ["Clifton Park", "Dayton"]
countries: ["USA", "USA"]
highlight: yes
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Reed_EBS-EKF_Accurate_and_High_Frequency_Event-based_Star_Tracking_CVPR_2025_paper.html

abstract:
Event-based sensors (EBS) are a promising new technology for star tracking due to their low latency and power efficiency, but prior work has thus far been evaluated exclusively in simulation with simplified signal models.  We propose a novel algorithm for event-based star tracking, grounded in an analysis of the EBS circuit and an extended Kalman filter (EKF). We quantitatively evaluate our method using real night sky data, comparing its results with those from a space-ready active-pixel sensor (APS) star tracker. We demonstrate that our method is an order-of-magnitude more accurate than existing methods due to improved signal modeling and state estimation, while providing more frequent updates and greater motion tolerance than conventional APS trackers. We provide all code and the first dataset of events synchronized with APS solutions.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Reed_EBS-EKF_Accurate_and_High_Frequency_Event-based_Star_Tracking_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Reed_EBS-EKF_Accurate_and_CVPR_2025_supplemental.zip

bibtex:
@InProceedings{Reed_2025_CVPR,
    author    = {Reed, Albert W. and Hashemi, Connor and Melamed, Dennis and Menon, Nitesh and Hirakawa, Keigo and McCloskey, Scott},
    title     = {EBS-EKF: Accurate and High Frequency Event-based Star Tracking},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {6510-6519}
}

raw_affiliation:
[{"affiliation": "Kitware", "city": "Clifton Park", "country": "USA"}, {"affiliation": "University of Dayton", "city": "Dayton", "country": "USA"}]


title: PersonaBooth: Personalized Text-to-Motion Generation
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 22756-22765
cities: ["Birmingham", "Seongnam-si", "Yongin", "Seoul"]
countries: ["United Kingdom", "South Korea", "South Korea", "South Korea"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Kim_PersonaBooth_Personalized_Text-to-Motion_Generation_CVPR_2025_paper.html

authors:
Boeun Kim, Hea In Jeong, JungHoon Sung, Yihua Cheng, Jeongmin Lee, Ju Yong Chang, Sang-Il Choi, Younggeun Choi, Saim Shin, Jungho Kim, Hyung Jin Chang

abstract:
This paper introduces Motion Personalization, a new task that generates personalized motions aligned with text descriptions using several basic motions containing Persona. To support this novel task, we introduce a new large-scale motion dataset called PerMo (PersonaMotion), which captures the unique personas of multiple actors. We also propose a multi-modal finetuning method of a pretrained motion diffusion model called PersonaBooth. PersonaBooth addresses two main challenges: i) A significant distribution gap between the persona-focused PerMo dataset and the pretraining datasets, which lack persona-specific data, and ii) the difficulty of capturing a consistent persona from the motions vary in content (action type). To tackle the dataset distribution gap, we introduce a persona token to accept new persona features and perform multi-modal adaptation for both text and visuals during finetuning. To capture a consistent persona, we incorporate a contrastive learning technique to enhance intra-cohesion among samples with the same persona. Furthermore, we introduce a context-aware fusion mechanism to maximize the integration of persona cues from multiple input motions. PersonaBooth outperforms state-of-the-art motion style transfer methods, establishing a new benchmark for motion personalization.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Kim_PersonaBooth_Personalized_Text-to-Motion_Generation_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Kim_PersonaBooth_Personalized_Text-to-Motion_CVPR_2025_supplemental.zip

bibtex:
@InProceedings{Kim_2025_CVPR,
    author    = {Kim, Boeun and Jeong, Hea In and Sung, JungHoon and Cheng, Yihua and Lee, Jeongmin and Chang, Ju Yong and Choi, Sang-Il and Choi, Younggeun and Shin, Saim and Kim, Jungho and Chang, Hyung Jin},
    title     = {PersonaBooth: Personalized Text-to-Motion Generation},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {22756-22765}
}

raw_affiliation:
[{"affiliation": "University of Birmingham", "city": "Birmingham", "country": "United Kingdom"}, {"affiliation": "Korea Electronics Technology Institute", "city": "Seongnam-si", "country": "South Korea"}, {"affiliation": "Dankook University", "city": "Yongin", "country": "South Korea"}, {"affiliation": "Kwangwoon University", "city": "Seoul", "country": "South Korea"}]

affiliations:
["University of Birmingham", "Korea Electronics Technology Institute", "Dankook University", "Kwangwoon University"]


title: Benchmarking Object Detectors under Real-World Distribution Shifts in Satellite Imagery
authors: Sara A. Al-Emadi, Yin Yang, Ferda Ofli
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 8299-8309
affiliations: ["Qatar Computing Research Institute, HBKU", "College of Science and Engineering, HBKU"]
cities: ["Doha", "Doha"]
countries: ["Qatar", "Qatar"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Al-Emadi_Benchmarking_Object_Detectors_under_Real-World_Distribution_Shifts_in_Satellite_Imagery_CVPR_2025_paper.html

abstract:
Object detectors have achieved remarkable performance in many applications; however, these deep learning models are typically designed under the i.i.d. assumption, meaning they are trained and evaluated on data sampled from the same (source) distribution. In real-world deployment, however, target distributions often differ from source data, leading to substantial performance degradation. Domain Generalisation (DG) seeks to bridge this gap by enabling models to generalise to Out-Of-Distribution (OOD) data without access to target distributions during training, enhancing robustness to unseen conditions. In this work, we examine the generalisability and robustness of state-of-the-art object detectors under real-world distribution shifts, focusing particularly on spatial domain shifts. Despite the need, a standardised benchmark dataset specifically designed for assessing object detection under realistic DG scenarios is currently lacking. To address this, we introduce Real-World Distribution Shifts (RWDS), a suite of three novel DG benchmarking datasets that focus on humanitarian and climate change applications. These datasets enable the investigation of domain shifts across (i) climate zones and (ii) various disasters and geographic regions. To our knowledge, these are the first DG benchmarking datasets tailored for object detection in real-world, high-impact contexts. We aim for these datasets to serve as valuable resources for evaluating the robustness and generalisation of future object detection models. Our datasets and code are available at https://github.com/RWGAI/RWDS.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Al-Emadi_Benchmarking_Object_Detectors_under_Real-World_Distribution_Shifts_in_Satellite_Imagery_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Al-Emadi_Benchmarking_Object_Detectors_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Al-Emadi_2025_CVPR,
    author    = {Al-Emadi, Sara A. and Yang, Yin and Ofli, Ferda},
    title     = {Benchmarking Object Detectors under Real-World Distribution Shifts in Satellite Imagery},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {8299-8309}
}

raw_affiliation:
[{"affiliation": "Qatar Computing Research Institute, HBKU", "city": "Doha", "country": "Qatar"}, {"affiliation": "College of Science and Engineering, HBKU", "city": "Doha", "country": "Qatar"}]


title: SAIST: Segment Any Infrared Small Target Model Guided by Contrastive Language-Image Pretraining
authors: Mingjin Zhang, Xiaolong Li, Fei Gao, Jie Guo, Xinbo Gao, Jing Zhang
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 9549-9558
cities: ["Xi'an", "Chongqing", "Wuhan"]
countries: ["China", "China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Zhang_SAIST_Segment_Any_Infrared_Small_Target_Model_Guided_by_Contrastive_CVPR_2025_paper.html

abstract:
Infrared Small Target Detection (IRSTD) aims to identify low signal-to-noise ratio small targets in infrared images with complex backgrounds, which is crucial for various applications. However, existing IRSTD methods typically rely solely on image modalities for processing, which fail to fully capture contextual information, leading to limited detection accuracy and adaptability in complex environments. Inspired by vision-language models, this paper proposes a novel framework, SAIST, which integrates textual information with image modalities to enhance IRSTD performance. The framework consists of two main components: Scene Recognition Contrastive Language-Image Pretraining (SR-CLIP) and CLIP-guided Segment Anything Model (CG-SAM).  SR-CLIP generates a set of visual descriptions through object-object similarity and object-scene relevance, embedding them into learnable prompts to refine the textual description set. This reduces the domain gap between vision and language, generating precise textual and visual prompts. CG-SAM utilizes the prompts generated by SR-CLIP to accurately guide the Mask Decoder in learning prior knowledge of background features, while incorporating infrared imaging equations to improve small target recognition in complex backgrounds and significantly reduce the false alarm rate. Additionally, this paper introduces the first multimodal IRSTD dataset, MIRSTD, which contains abundant image-text pairs. Experimental results demonstrate that the proposed SAIST method outperforms existing state-of-the-art approaches.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Zhang_SAIST_Segment_Any_Infrared_Small_Target_Model_Guided_by_Contrastive_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Zhang_SAIST_Segment_Any_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Zhang_2025_CVPR,
    author    = {Zhang, Mingjin and Li, Xiaolong and Gao, Fei and Guo, Jie and Gao, Xinbo and Zhang, Jing},
    title     = {SAIST: Segment Any Infrared Small Target Model Guided by Contrastive Language-Image Pretraining},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {9549-9558}
}

raw_affiliation:
[{"affiliation": "Xidian University", "city": "Xi'an", "country": "China"}, {"affiliation": "Chongqing University of Posts and Telecommunications", "city": "Chongqing", "country": "China"}, {"affiliation": "School of Computer Science & School of Artificial Intelligence, Wuhan University", "city": "Wuhan", "country": "China"}]

affiliations:
["Xidian University", "Chongqing University of Posts and Telecommunications", "School of Computer Science & School of Artificial Intelligence, Wuhan University"]


title: Star with Bilinear Mapping
authors: Zelin Peng, Yu Huang, Zhengqin Xu, Feilong Tang, Ming Hu, Xiaokang Yang, Wei Shen
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 25292-25302
cities: ["Shanghai", "Abu Dhabi", "Melbourne"]
countries: ["China", "UAE", "Australia"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Peng_Star_with_Bilinear_Mapping_CVPR_2025_paper.html

abstract:
Contextual modeling is crucial for robust visual representation learning, especially in computer vision. Although Transformers have become a leading architecture for vision tasks due to their attention mechanism, the quadratic complexity of full attention operations presents substantial computational challenges. To address this, we introduce Star with Bilinear Mapping (SBM), a Transformer-like architecture that achieves global contextual modeling with linear complexity. SBM employs a bilinear mapping module (BM) with low-rank decomposition strategy and star operations (element-wise multiplication) to efficiently capture global contextual information. Our model demonstrates competitive performance on image classification and semantic segmentation tasks, delivering significant computational efficiency gains compared to traditional attention-based models.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Peng_Star_with_Bilinear_Mapping_CVPR_2025_paper.pdf

bibtex:
@InProceedings{Peng_2025_CVPR,
    author    = {Peng, Zelin and Huang, Yu and Xu, Zhengqin and Tang, Feilong and Hu, Ming and Yang, Xiaokang and Shen, Wei},
    title     = {Star with Bilinear Mapping},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {25292-25302}
}

raw_affiliation:
[{"affiliation": "MoE Key Lab of Arti\ufb01cial Intelligence, AI Institute, Shanghai Jiaotong University", "city": "Shanghai", "country": "China"}, {"affiliation": "MBZUAI", "city": "Abu Dhabi", "country": "UAE"}, {"affiliation": "Monash University", "city": "Melbourne", "country": "Australia"}]

affiliations:
["MoE Key Lab of Arti\ufb01cial Intelligence, AI Institute, Shanghai Jiaotong University", "MBZUAI", "Monash University"]


title: Closed-Loop Supervised Fine-Tuning of Tokenized Traffic Models
authors: Zhejun Zhang, Peter Karkus, Maximilian Igl, Wenhao Ding, Yuxiao Chen, Boris Ivanovic, Marco Pavone
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 5422-5432
affiliations: ["NVIDIA Research", "Stanford University"]
cities: ["Santa Clara", "Stanford"]
countries: ["USA", "USA"]
oral: yes
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Zhang_Closed-Loop_Supervised_Fine-Tuning_of_Tokenized_Traffic_Models_CVPR_2025_paper.html

abstract:
Traffic simulation aims to learn a policy for traffic agents that, when unrolled in closed-loop, faithfully recovers the joint distribution of trajectories observed in the real world. Inspired by large language models, tokenized multi-agent policies have recently become the state-of-the-art in traffic simulation. However, they are typically trained through open-loop behavior cloning, and thus suffer from covariate shift when executed in closed-loop during simulation. In this work, we present Closest Among Top-K (CAT-K) rollouts, a simple yet effective closed-loop fine-tuning strategy to mitigate covariate shift. CAT-K fine-tuning only requires existing trajectory data, without reinforcement learning or generative adversarial imitation. Concretely, CAT-K fine-tuning enables a small 7M-parameter tokenized traffic simulation policy to outperform a 102M-parameter model from the same model family, achieving the top spot on the Waymo Sim Agent Challenge leaderboard at the time of submission. The code is available at https://github.com/NVlabs/catk.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Zhang_Closed-Loop_Supervised_Fine-Tuning_of_Tokenized_Traffic_Models_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Zhang_Closed-Loop_Supervised_Fine-Tuning_CVPR_2025_supplemental.zip

bibtex:
@InProceedings{Zhang_2025_CVPR,
    author    = {Zhang, Zhejun and Karkus, Peter and Igl, Maximilian and Ding, Wenhao and Chen, Yuxiao and Ivanovic, Boris and Pavone, Marco},
    title     = {Closed-Loop Supervised Fine-Tuning of Tokenized Traffic Models},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {5422-5432}
}

raw_affiliation:
[{"affiliation": "NVIDIA Research", "city": "Santa Clara", "country": "USA"}, {"affiliation": "Stanford University", "city": "Stanford", "country": "USA"}]


title: DIFIX3D+: Improving 3D Reconstructions with Single-Step Diffusion Models
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 26024-26035
cities: ["Toronto", "Singapore", "Toronto", "Toronto"]
countries: ["Canada", "Singapore", "Canada", "Canada"]
oral: yes
award_candidate: yes
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Wu_DIFIX3D_Improving_3D_Reconstructions_with_Single-Step_Diffusion_Models_CVPR_2025_paper.html

authors:
Jay Zhangjie Wu, Yuxuan Zhang, Haithem Turki, Xuanchi Ren, Jun Gao, Mike Zheng Shou, Sanja Fidler, Zan Gojcic, Huan Ling

abstract:
Neural Radiance Fields and 3D Gaussian Splatting have revolutionized 3D reconstruction and novel-view synthesis task. However, achieving photorealistic rendering from extreme novel viewpoints remains challenging, as artifacts persist across representations. In this work, we introduce Difix3D+, a novel pipeline designed to enhance 3D reconstruction and novel-view synthesis through single-step diffusion models. At the core of our approach is Difix, a single-step image diffusion model trained to enhance and remove artifacts in rendered novel views caused by underconstrained regions of the 3D representation.Difix serves two critical roles in our pipeline. First, it is used during the reconstruction phase to clean up pseudo-training views that are rendered from the reconstruction and then distilled back into 3D. This greatly enhances underconstrained regions and improves the overall 3D representation quality. More importantly, Difix also acts as a neural enhancer during inference, effectively removing residual artifacts arising from imperfect 3D supervision and the limited capacity of current reconstruction models. Difix3D+ is a general solution, a single model compatible with both NeRF and 3DGS representations, and it achieves an average 2x improvement in FID score over baselines while maintaining 3D consistency.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Wu_DIFIX3D_Improving_3D_Reconstructions_with_Single-Step_Diffusion_Models_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Wu_DIFIX3D_Improving_3D_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Wu_2025_CVPR,
    author    = {Wu, Jay Zhangjie and Zhang, Yuxuan and Turki, Haithem and Ren, Xuanchi and Gao, Jun and Shou, Mike Zheng and Fidler, Sanja and Gojcic, Zan and Ling, Huan},
    title     = {DIFIX3D+: Improving 3D Reconstructions with Single-Step Diffusion Models},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {26024-26035}
}

raw_affiliation:
[{"affiliation": "NVIDIA", "city": "Toronto", "country": "Canada"}, {"affiliation": "Show Lab, National University of Singapore", "city": "Singapore", "country": "Singapore"}, {"affiliation": "University of Toronto", "city": "Toronto", "country": "Canada"}, {"affiliation": "Vector Institute", "city": "Toronto", "country": "Canada"}]

affiliations:
["NVIDIA", "Show Lab, National University of Singapore", "University of Toronto", "Vector Institute"]


title: Time of the Flight of the Gaussians: Optimizing Depth Indirectly in Dynamic Radiance Fields
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 21021-21030
affiliations: ["Brown University", "Meta Reality Labs", "Carnegie Mellon University"]
cities: ["Providence", "Menlo Park", "Pittsburgh"]
countries: ["USA", "USA", "USA"]
oral: yes
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Li_Time_of_the_Flight_of_the_Gaussians_Optimizing_Depth_Indirectly_CVPR_2025_paper.html

authors:
Runfeng Li, Mikhail Okunev, Zixuan Guo, Anh Ha Duong, Christian Richardt, Matthew O'Toole, James Tompkin

abstract:
We present a method to reconstruct dynamic scenes from monocular continuous-wave time-of-flight (C-ToF) cameras using raw sensor samples that achieves similar or better accuracy than neural volumetric approaches and is 100xfaster. Quickly achieving high-fidelity dynamic 3D reconstruction from a single viewpoint is a significant challenge in computer vision. In C-ToF radiance field reconstruction, the property of interest---depth---is not directly measured, causing an additional challenge. This problem has a large and underappreciated impact upon the optimization when using a fast primitive-based scene representation like 3D Gaussian splatting, which is commonly used with multi-view data to produce satisfactory results and is brittle in its optimization otherwise. We incorporate two heuristics into the optimization to improve the accuracy of scene geometry represented by Gaussians. Experimental results show that our approach produces accurate reconstructions under constrained C-ToF sensing conditions, including for fast motions like swinging baseball bats.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Li_Time_of_the_Flight_of_the_Gaussians_Optimizing_Depth_Indirectly_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Li_Time_of_the_CVPR_2025_supplemental.zip

bibtex:
@InProceedings{Li_2025_CVPR,
    author    = {Li, Runfeng and Okunev, Mikhail and Guo, Zixuan and Duong, Anh Ha and Richardt, Christian and O'Toole, Matthew and Tompkin, James},
    title     = {Time of the Flight of the Gaussians: Optimizing Depth Indirectly in Dynamic Radiance Fields},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {21021-21030}
}

raw_affiliation:
[{"affiliation": "Brown University", "city": "Providence", "country": "USA"}, {"affiliation": "Meta Reality Labs", "city": "Menlo Park", "country": "USA"}, {"affiliation": "Carnegie Mellon University", "city": "Pittsburgh", "country": "USA"}]


title: Align3R: Aligned Monocular Depth Estimation for Dynamic Videos
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 22820-22830
affiliations: ["HKUST", "CUHK", "HKU", "ShanghaiTech", "WHU", "TAMU", "NTU"]
cities: ["Hong Kong", "Hong Kong", "Hong Kong", "Shanghai", "Wuhan", "College Station", "Singapore"]
countries: ["China", "China", "China", "China", "China", "USA", "Singapore"]
highlight: yes
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Lu_Align3R_Aligned_Monocular_Depth_Estimation_for_Dynamic_Videos_CVPR_2025_paper.html

authors:
Jiahao Lu, Tianyu Huang, Peng Li, Zhiyang Dou, Cheng Lin, Zhiming Cui, Zhen Dong, Sai-Kit Yeung, Wenping Wang, Yuan Liu

abstract:
Recent developments in monocular depth estimation methods enable high-quality depth estimation of single-view images but fail to estimate consistent video depth across different frames. Recent works address this problem by applying a video diffusion model to generate video depth conditioned on the input video, which is training-expensive and can only produce scale-invariant depth values without camera poses. In this paper, we propose a novel video-depth estimation method called Align3R to estimate temporal consistent depth maps for a dynamic video. Our key idea is to utilize the recent DUSt3R model to align estimated monocular depth maps of different timesteps. First, we fine-tune the DUSt3R model with additional estimated monocular depth as inputs for the dynamic scenes. Then, we apply optimization to reconstruct both depth maps and camera poses. Extensive experiments demonstrate that Align3R estimates consistent video depth and camera poses for a monocular video with superior performance than baseline methods.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Lu_Align3R_Aligned_Monocular_Depth_Estimation_for_Dynamic_Videos_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Lu_Align3R_Aligned_Monocular_CVPR_2025_supplemental.zip

bibtex:
@InProceedings{Lu_2025_CVPR,
    author    = {Lu, Jiahao and Huang, Tianyu and Li, Peng and Dou, Zhiyang and Lin, Cheng and Cui, Zhiming and Dong, Zhen and Yeung, Sai-Kit and Wang, Wenping and Liu, Yuan},
    title     = {Align3R: Aligned Monocular Depth Estimation for Dynamic Videos},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {22820-22830}
}

raw_affiliation:
[{"affiliation": "HKUST", "city": "Hong Kong", "country": "China"}, {"affiliation": "CUHK", "city": "Hong Kong", "country": "China"}, {"affiliation": "HKU", "city": "Hong Kong", "country": "China"}, {"affiliation": "ShanghaiTech", "city": "Shanghai", "country": "China"}, {"affiliation": "WHU", "city": "Wuhan", "country": "China"}, {"affiliation": "TAMU", "city": "College Station", "country": "USA"}, {"affiliation": "NTU", "city": "Singapore", "country": "Singapore"}]


title: Compositional Caching for Training-free Open-vocabulary Attribute Detection
authors: Marco Garosi, Alessandro Conti, Gaowen Liu, Elisa Ricci, Massimiliano Mancini
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 15098-15107
affiliations: ["University of Trento", "Cisco Research", "Fondazione Bruno Kessler"]
cities: ["Trento", "San Jose", "Trento"]
countries: ["Italy", "USA", "Italy"]
highlight: yes
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Garosi_Compositional_Caching_for_Training-free_Open-vocabulary_Attribute_Detection_CVPR_2025_paper.html

abstract:
Attribute detection is crucial for many computer vision tasks, as it enables systems to describe properties such as color, texture, and material. Current approaches often rely on labor-intensive annotation processes which are inherently limited: objects can be described at an arbitrary level of detail (e.g., color vs. color shades), leading to ambiguities when the annotators are not instructed carefully. Furthermore, they operate within a predefined set of attributes, reducing scalability and adaptability to unforeseen downstream applications. We present Compositional Caching (ComCa), a training-free method for open-vocabulary attribute detection that overcomes these constraints. ComCa requires only the list of target attributes and objects as input, using them to populate an auxiliary cache of images by leveraging web-scale databases and Large Language Models to determine attribute-object compatibility. To account for the compositional nature of attributes, cache images receive soft attribute labels. Those are aggregated at inference time based on the similarity between the input and cache images, refining the predictions of underlying Vision-Language Models (VLMs). Importantly, our approach is model-agnostic, compatible with various VLMs. Experiments on public datasets demonstrate that ComCa significantly outperforms zero-shot and cache-based baselines, competing with recent training-based methods, proving that a carefully designed training-free approach can successfully address open-vocabulary attribute detection.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Garosi_Compositional_Caching_for_Training-free_Open-vocabulary_Attribute_Detection_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Garosi_Compositional_Caching_for_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Garosi_2025_CVPR,
    author    = {Garosi, Marco and Conti, Alessandro and Liu, Gaowen and Ricci, Elisa and Mancini, Massimiliano},
    title     = {Compositional Caching for Training-free Open-vocabulary Attribute Detection},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {15098-15107}
}

raw_affiliation:
[{"affiliation": "University of Trento", "city": "Trento", "country": "Italy"}, {"affiliation": "Cisco Research", "city": "San Jose", "country": "USA"}, {"affiliation": "Fondazione Bruno Kessler", "city": "Trento", "country": "Italy"}]


title: Seek Common Ground While Reserving Differences: Semi-Supervised Image-Text Sentiment Recognition
authors: Wuyou Xia, Guoli Jia, Sicheng Zhao, Jufeng Yang
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 29601-29611
cities: ["Tianjin", "Shenzhen", "Shenzhen", "Beijing", "Beijing"]
countries: ["China", "China", "China", "China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Xia_Seek_Common_Ground_While_Reserving_Differences_Semi-Supervised_Image-Text_Sentiment_Recognition_CVPR_2025_paper.html

abstract:
Multimodal sentiment analysis has attracted extensive research attention as increasing users share images and texts to express their emotions and opinions on social media. Collecting large amounts of labeled sentiment data is an expensive and challenging task due to the high cost of labeling and unavoidable label ambiguity. Semi-supervised learning (SSL) is explored to utilize the extensive unlabeled data to alleviate the demand for annotation. However, different from typical multimodal tasks, the inconsistent sentiment between image and text leads to the sub-optimal performance of SSL algorithms. To address this issue, we propose SCDR, the first semi-supervised image-text sentiment recognition framework. To better utilize the discriminative features of each modality, we decouple features into common and private parts and then use the private features to train unimodal classifiers for enhanced modality-specific sentiment representation. Considering the complex relation between modalities, we devise a modal selection-based attention module that adaptively assesses the dominant sentiment modality at the sample level to guide the fusion of multimodal representations. Furthermore, to prevent the model predictions from overly relying on common features under the guidance of multimodal labels, we design a pseudo-label filtering strategy based on the matching degree of prediction and dominant modality. Extensive experiments and comparisons on five publicly available datasets demonstrate that SCDR outperforms state-of-the-art methods. The code is provided in the supplementary material and will be released to the public.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Xia_Seek_Common_Ground_While_Reserving_Differences_Semi-Supervised_Image-Text_Sentiment_Recognition_CVPR_2025_paper.pdf

bibtex:
@InProceedings{Xia_2025_CVPR,
    author    = {Xia, Wuyou and Jia, Guoli and Zhao, Sicheng and Yang, Jufeng},
    title     = {Seek Common Ground While Reserving Differences: Semi-Supervised Image-Text Sentiment Recognition},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {29601-29611}
}

raw_affiliation:
[{"affiliation": "VCIP & TMCC & DISSec, College of Computer Science, Nankai University", "city": "Tianjin", "country": "China"}, {"affiliation": "Pengcheng Laboratory", "city": "Shenzhen", "country": "China"}, {"affiliation": "Nankai International Advanced Research Institute (SHENZHEN\u00b7 FUTIAN)", "city": "Shenzhen", "country": "China"}, {"affiliation": "Department of Electronic Engineering, Tsinghua University", "city": "Beijing", "country": "China"}, {"affiliation": "BNRist, Tsinghua University", "city": "Beijing", "country": "China"}]

affiliations:
["VCIP & TMCC & DISSec, College of Computer Science, Nankai University", "Pengcheng Laboratory", "Nankai International Advanced Research Institute (SHENZHEN\u00b7 FUTIAN)", "Department of Electronic Engineering, Tsinghua University", "BNRist, Tsinghua University"]


title: CoLLM: A Large Language Model for Composed Image Retrieval
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 3994-4004
cities: ["College Park", "Seattle", "Orlando"]
countries: ["USA", "USA", "USA"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Huynh_CoLLM_A_Large_Language_Model_for_Composed_Image_Retrieval_CVPR_2025_paper.html

authors:
Chuong Huynh, Jinyu Yang, Ashish Tawari, Mubarak Shah, Son Tran, Raffay Hamid, Trishul Chilimbi, Abhinav Shrivastava

abstract:
Composed Image Retrieval (CIR) is a complex task that aims to retrieve images based on a multimodal query. Typical training data consists of triplets containing a reference image, a textual description of desired modifications, and the target image, which are expensive and time-consuming to acquire. The scarcity of CIR datasets has led to zero-shot approaches utilizing synthetic triplets or leveraging vision-language models (VLMs) with ubiquitous web-crawled image-caption pairs. However, these methods have significant limitations: synthetic triplets suffer from limited scale, lack of diversity, and unnatural modification text, while image-caption pairs hinder joint embedding learning of the multimodal query due to the absence of triplet data. Moreover, existing approaches struggle with complex and nuanced modification texts that demand sophisticated fusion and understanding of vision and language modalities. We present CoLLM, a one-stop framework that effectively addresses these limitations. Our approach generates triplets on-the-fly from image-caption pairs, enabling supervised training without manual annotation. We leverage Large Language Models (LLMs) to generate joint embeddings of reference images and modification texts, facilitating deeper multimodal fusion. Additionally, we introduce Multi-Text CIR (MTCIR), a large-scale dataset comprising 3.4M samples, and refine existing CIR benchmarks (CIRR and Fashion-IQ) to enhance evaluation reliability. Experimental results demonstrate that CoLLM achieves state-of-the-art performance across multiple CIR benchmarks and settings. MTCIR yields competitive results, with up to 15% performance improvement. Our refined benchmarks provide more reliable evaluation metrics for CIR models, contributing to the advancement of this important field. Project page is at collm-cvpr25.github.io

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Huynh_CoLLM_A_Large_Language_Model_for_Composed_Image_Retrieval_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Huynh_CoLLM_A_Large_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Huynh_2025_CVPR,
    author    = {Huynh, Chuong and Yang, Jinyu and Tawari, Ashish and Shah, Mubarak and Tran, Son and Hamid, Raffay and Chilimbi, Trishul and Shrivastava, Abhinav},
    title     = {CoLLM: A Large Language Model for Composed Image Retrieval},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {3994-4004}
}

raw_affiliation:
[{"affiliation": "University of Maryland, College Park", "city": "College Park", "country": "USA"}, {"affiliation": "Amazon", "city": "Seattle", "country": "USA"}, {"affiliation": "Center for Research in Computer Vision, University of Central Florida", "city": "Orlando", "country": "USA"}]

affiliations:
["University of Maryland, College Park", "Amazon", "Center for Research in Computer Vision, University of Central Florida"]


title: Anomize: Better Open Vocabulary Video Anomaly Detection
authors: Fei Li, Wenxuan Liu, Jingjing Chen, Ruixu Zhang, Yuran Wang, Xian Zhong, Zheng Wang
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 29203-29212
cities: ["Wuhan", "Shanghai", "Beijing", "Wuhan"]
countries: ["China", "China", "China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Li_Anomize_Better_Open_Vocabulary_Video_Anomaly_Detection_CVPR_2025_paper.html

abstract:
Open Vocabulary Video Anomaly Detection (OVVAD) seeks to detect and classify both base and novel anomalies. However, existing methods face two specific challenges related to novel anomalies. The first challenge is detection ambiguity, where the model struggles to assign accurate anomaly scores to unfamiliar anomalies. The second challenge is categorization confusion, where novel anomalies are often misclassified as visually similar base instances. To address these challenges, we explore supplementary information from multiple sources to mitigate detection ambiguity by leveraging multiple levels of visual data alongside matching textual information. Furthermore, we propose incorporating label relations to guide the encoding of new labels, thereby improving alignment between novel videos and their corresponding labels, which helps reduce categorization confusion. The resulting Anomize framework effectively tackles these issues, achieving superior performance on UCF-Crime and XD-Violence datasets, demonstrating its effectiveness in OVVAD.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Li_Anomize_Better_Open_Vocabulary_Video_Anomaly_Detection_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Li_Anomize_Better_Open_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Li_2025_CVPR,
    author    = {Li, Fei and Liu, Wenxuan and Chen, Jingjing and Zhang, Ruixu and Wang, Yuran and Zhong, Xian and Wang, Zheng},
    title     = {Anomize: Better Open Vocabulary Video Anomaly Detection},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {29203-29212}
}

raw_affiliation:
[{"affiliation": "National Engineering Research Center for Multimedia Software, School of Computer Science, Wuhan University", "city": "Wuhan", "country": "China"}, {"affiliation": "Shanghai Key Lab of Intelligent Information Processing, School of Computer Science, Fudan University", "city": "Shanghai", "country": "China"}, {"affiliation": "State Key Laboratory for Multimedia Information Processing, School of Computer Science, Peking University", "city": "Beijing", "country": "China"}, {"affiliation": "Hubei Key Laboratory of Transportation Internet of Things, Wuhan University of Technology", "city": "Wuhan", "country": "China"}]

affiliations:
["National Engineering Research Center for Multimedia Software, School of Computer Science, Wuhan University", "Shanghai Key Lab of Intelligent Information Processing, School of Computer Science, Fudan University", "State Key Laboratory for Multimedia Information Processing, School of Computer Science, Peking University", "Hubei Key Laboratory of Transportation Internet of Things, Wuhan University of Technology"]


title: Efficient Diffusion as Low Light Enhancer
authors: Guanzhou Lan, Qianli Ma, Yuqi Yang, Zhigang Wang, Dong Wang, Xuelong Li, Bin Zhao
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 21277-21286
cities: ["Xi'an", "Shanghai", "Shanghai", "Unknown"]
countries: ["China", "China", "China", "Unknown"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Lan_Efficient_Diffusion_as_Low_Light_Enhancer_CVPR_2025_paper.html

abstract:
The computational burden of the iterative sampling process remains a major challenge in diffusion-based Low-Light Image Enhancement (LLIE). Current acceleration methods, whether training-based or training-free, often lead to significant performance degradation, highlighting the trade-off between performance and efficiency.In this paper, we identify two primary factors contributing to performance degradation: fitting errors and the inference gap. Our key insight is that fitting errors can be mitigated by linearly extrapolating the incorrect score functions, while the inference gap can be reduced by shifting the Gaussian flow to a reflectance-aware residual space.Based on the above insights, we design Reflectance-Aware Trajectory Refinement (RATR) module, a simple yet effective module to refine the teacher trajectory using the reflectance component of images. Following this, we introduce Reflectance-aware Diffusion with Distilled Trajectory ReDDiT, an efficient and flexible distillation framework tailored for LLIE. Our framework achieves comparable performance to previous diffusion-based methods with redundant steps in just 2 steps while establishing new state-of-the-art (SOTA) results with 8 or 4 steps. Comprehensive experimental evaluations on 10 benchmark datasets validate the effectiveness of our method, consistently outperforming existing SOTA methods.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Lan_Efficient_Diffusion_as_Low_Light_Enhancer_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Lan_Efficient_Diffusion_as_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Lan_2025_CVPR,
    author    = {Lan, Guanzhou and Ma, Qianli and Yang, Yuqi and Wang, Zhigang and Wang, Dong and Li, Xuelong and Zhao, Bin},
    title     = {Efficient Diffusion as Low Light Enhancer},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {21277-21286}
}

raw_affiliation:
[{"affiliation": "Northwestern Polytechnical University", "city": "Xi'an", "country": "China"}, {"affiliation": "Shanghai Jiao Tong University", "city": "Shanghai", "country": "China"}, {"affiliation": "Shanghai AI Lab", "city": "Shanghai", "country": "China"}, {"affiliation": "TeleAI", "city": "Unknown", "country": "Unknown"}]

affiliations:
["Northwestern Polytechnical University", "Shanghai Jiao Tong University", "Shanghai AI Lab", "TeleAI"]


title: GraphMimic: Graph-to-Graphs Generative Modeling from Videos for Policy Learning
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 1756-1768
affiliations: ["Beijing Institute of Technology", "The University of Hong Kong"]
cities: ["Beijing", "Hong Kong"]
countries: ["China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Chen_GraphMimic_Graph-to-Graphs_Generative_Modeling_from_Videos_for_Policy_Learning_CVPR_2025_paper.html

authors:
Guangyan Chen, Te Cui, Meiling Wang, Chengcai Yang, Mengxiao Hu, Haoyang Lu, Yao Mu, Zicai Peng, Tianxing Zhou, Xinran Jiang, Yi Yang, Yufeng Yue

abstract:
Learning from demonstration is a powerful method for robotic skill acquisition. However, the significant expense of collecting such action-labeled robot data presents a major bottleneck. Video data, a rich data source encompassing diverse behavioral and physical knowledge, emerges as a promising alternative. In this paper, we present GraphMimic, a novel paradigm that leverages video data via graph-to-graphs generative modeling, which pre-trains models to generate future graphs conditioned on the graph within a video frame. Specifically, GraphMimic abstracts video frames into object and visual action vertices, and constructs graphs for state representations. The graph generative modeling network then effectively models internal structures and spatial relationships within the constructed graphs, aiming to generate future graphs. The generated graphs serve as conditions for the control policy, mapping to robot actions. Our concise approach captures important spatial relations and enhances future graph generation accuracy, enabling the acquisition of robust policies from limited action-labeled data. Furthermore, the transferable graph representations facilitate the effective learning of manipulation skills from cross-embodiment videos. Our experiments exhibit that GraphMimic achieves superior performance using merely 20% action-labeled data. Moreover, our method outperforms the state-of-the-art method by over 17% and 23% in simulation and real-world experiments, and delivers improvements of over 33% in cross-embodiment transfer experiments.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Chen_GraphMimic_Graph-to-Graphs_Generative_Modeling_from_Videos_for_Policy_Learning_CVPR_2025_paper.pdf

bibtex:
@InProceedings{Chen_2025_CVPR,
    author    = {Chen, Guangyan and Cui, Te and Wang, Meiling and Yang, Chengcai and Hu, Mengxiao and Lu, Haoyang and Mu, Yao and Peng, Zicai and Zhou, Tianxing and Jiang, Xinran and Yang, Yi and Yue, Yufeng},
    title     = {GraphMimic: Graph-to-Graphs Generative Modeling from Videos for Policy Learning},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {1756-1768}
}

raw_affiliation:
[{"affiliation": "Beijing Institute of Technology", "city": "Beijing", "country": "China"}, {"affiliation": "The University of Hong Kong", "city": "Hong Kong", "country": "China"}]


title: VI^3NR: Variance Informed Initialization for Implicit Neural Representations
authors: Chamin Hewa Koneputugodage, Yizhak Ben-Shabat, Sameera Ramasinghe, Stephen Gould
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 13477-13486
affiliations: ["The Australian National University", "Roblox", "Pluralis AI"]
cities: ["Canberra", "San Mateo", "Unknown"]
countries: ["Australia", "USA", "Unknown"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Koneputugodage_VI3NR_Variance_Informed_Initialization_for_Implicit_Neural_Representations_CVPR_2025_paper.html

abstract:
Implicit Neural Representations (INRs) are a versatile and powerful tool for encoding various forms of data, including images, videos, sound, and 3D shapes. A critical factor in the success of INRs is the initialization of the network, which can significantly impact the convergence and accuracy of the learned model. Unfortunately, commonly used neural network initializations are not widely applicable for many activation functions, especially those used by INRs. In this paper, we improve upon previous initialization methods by deriving an initialization that has stable variance across layers, and applies to any activation function. We show that this generalizes many previous initialization methods, and has even better stability for well studied activations. We also show that our initialization leads to improved results with INR activation functions in multiple signal modalities. Our approach is particularly effective for Gaussian INRs, where we demonstrate that the theory of our initialization matches with task performance in multiple experiments, allowing us to achieve improvements in image, audio, and 3D surface reconstruction.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Koneputugodage_VI3NR_Variance_Informed_Initialization_for_Implicit_Neural_Representations_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Koneputugodage_VI3NR_Variance_Informed_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Koneputugodage_2025_CVPR,
    author    = {Koneputugodage, Chamin Hewa and Ben-Shabat, Yizhak and Ramasinghe, Sameera and Gould, Stephen},
    title     = {VI{\textasciicircum}3NR: Variance Informed Initialization for Implicit Neural Representations},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {13477-13486}
}

raw_affiliation:
[{"affiliation": "The Australian National University", "city": "Canberra", "country": "Australia"}, {"affiliation": "Roblox", "city": "San Mateo", "country": "USA"}, {"affiliation": "Pluralis AI", "city": "Unknown", "country": "Unknown"}]


title: MMVU: Measuring Expert-Level Multi-Discipline Video Understanding
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 8475-8489
raw_affiliation: [{"affiliation": "Yale NLP MMVU Team", "city": "New Haven", "country": "USA"}]
affiliations: ["Yale NLP MMVU Team"]
cities: ["New Haven"]
countries: ["USA"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Zhao_MMVU_Measuring_Expert-Level_Multi-Discipline_Video_Understanding_CVPR_2025_paper.html

authors:
Yilun Zhao, Haowei Zhang, Lujing Xie, Tongyan Hu, Guo Gan, Yitao Long, Zhiyuan Hu, Weiyuan Chen, Chuhan Li, Zhijian Xu, Chengye Wang, Ziyao Shangguan, Zhenwen Liang, Yixin Liu, Chen Zhao, Arman Cohan

abstract:
We introduce MMVU, a comprehensive expert-level, multi-discipline benchmark for evaluating foundation models in video understanding. MMVU includes 3,000 expert-annotated questions spanning 27 subjects across four core disciplines: Science, Healthcare, Humanities & Social Sciences, and Engineering. Compared to prior benchmarks, MMVU features three key advancements. First, it challenges models to apply domain-specific knowledge and perform expert-level reasoning to analyze specialized-domain videos, moving beyond the basic visual perception typically assessed in current video benchmarks. Second, each example is annotated by human experts from scratch. We implement strict data quality controls to ensure the high quality of the dataset. Finally, each example is enriched with expert-annotated reasoning rationals and relevant domain knowledge, facilitating in-depth analysis. We conduct an extensive evaluation of 36 frontier multimodal foundation models on MMVU. The latest System-2-capable models, o1 and Gemini 2.0 Flash Thinking, achieve the highest performance among the tested models. However, they still fall short of matching human expertise. Through in-depth error analyses and case studies, we offer actionable insights for future advancements in expert-level, knowledge-intensive video understanding for specialized domains.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Zhao_MMVU_Measuring_Expert-Level_Multi-Discipline_Video_Understanding_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Zhao_MMVU_Measuring_Expert-Level_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Zhao_2025_CVPR,
    author    = {Zhao, Yilun and Zhang, Haowei and Xie, Lujing and Hu, Tongyan and Gan, Guo and Long, Yitao and Hu, Zhiyuan and Chen, Weiyuan and Li, Chuhan and Xu, Zhijian and Wang, Chengye and Shangguan, Ziyao and Liang, Zhenwen and Liu, Yixin and Zhao, Chen and Cohan, Arman},
    title     = {MMVU: Measuring Expert-Level Multi-Discipline Video Understanding},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {8475-8489}
}


title: M-LLM Based Video Frame Selection for Efficient Video Understanding
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 13702-13712
affiliations: ["Carnegie Mellon University", "University of Central Florida", "Amazon"]
cities: ["Pittsburgh", "Orlando", "Seattle"]
countries: ["USA", "USA", "USA"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Hu_M-LLM_Based_Video_Frame_Selection_for_Efficient_Video_Understanding_CVPR_2025_paper.html

authors:
Kai Hu, Feng Gao, Xiaohan Nie, Peng Zhou, Son Tran, Tal Neiman, Lingyun Wang, Mubarak Shah, Raffay Hamid, Bing Yin, Trishul Chilimbi

abstract:
Recent advances in Multi-Modal Large Language Models (M-LLMs) show promising results in video reasoning. Popular Multi-Modal Large Language Model (M-LLM) frameworks usually apply naive uniform sampling to reduce the number of video frames that are fed into an M-LLM, particularly for long context videos. However, it could lose crucial context in certain periods of a video, so that the downstream M-LLM may not have sufficient visual information to answer a question. To attack this pain point, we propose a light-weight M-LLM -based frame selection method that adaptively select frames that are more relevant to users' queries. In order to train the proposed frame selector, we introduce two supervision signals (i) Spatial signal, where single frame importance score by prompting an M-LLM; (ii) Temporal signal, in which multiple frames selection by prompting Large Language Model ( LLM ) using the captions of all frame candidates. The selected frames are then digested by a frozen downstream video M-LLM for visual reasoning and question answering. Empirical results show that the proposed M-LLM video frame selector improves the performances various downstream video Large Language Model (video-LLM) across medium (ActivityNet, NExT-QA) and long (EgoSchema, LongVideoBench) context video question answering benchmarks.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Hu_M-LLM_Based_Video_Frame_Selection_for_Efficient_Video_Understanding_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Hu_M-LLM_Based_Video_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Hu_2025_CVPR,
    author    = {Hu, Kai and Gao, Feng and Nie, Xiaohan and Zhou, Peng and Tran, Son and Neiman, Tal and Wang, Lingyun and Shah, Mubarak and Hamid, Raffay and Yin, Bing and Chilimbi, Trishul},
    title     = {M-LLM Based Video Frame Selection for Efficient Video Understanding},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {13702-13712}
}

raw_affiliation:
[{"affiliation": "Carnegie Mellon University", "city": "Pittsburgh", "country": "USA"}, {"affiliation": "University of Central Florida", "city": "Orlando", "country": "USA"}, {"affiliation": "Amazon", "city": "Seattle", "country": "USA"}]


title: Search and Detect: Training-Free Long Tail Object Detection via Web-Image Retrieval
authors: Mankeerat Sidhu, Hetarth Chopra, Ansel Blume, Jeonghwan Kim, Revanth Gangi Reddy, Heng Ji
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 15129-15138
raw_affiliation: [{"affiliation": "University of Illinois Urbana Champaign", "city": "Urbana", "country": "USA"}]
affiliations: ["University of Illinois Urbana Champaign"]
cities: ["Urbana"]
countries: ["USA"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Sidhu_Search_and_Detect_Training-Free_Long_Tail_Object_Detection_via_Web-Image_CVPR_2025_paper.html

abstract:
In this paper, we introduce SearchDet, a training-free long-tail object detection framework that significantly enhances open-vocabulary object detection performance. SearchDet retrieves a set of positive and negative images of an object to ground, embeds these images, and computes an input image--weighted query which is used to detect the desired concept in the image. Our proposed method is simple and training-free, yet achieves over 16.81% mAP improvement on ODinW and 59.85% mAP improvement on LVIS compared to state-of-the-art models such as GroundingDINO. We further show that our approach of basing object detection on a set of Web-retrieved exemplars is stable with respect to variations in the exemplars, suggesting a path towards eliminating costly data annotation and training procedures.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Sidhu_Search_and_Detect_Training-Free_Long_Tail_Object_Detection_via_Web-Image_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Sidhu_Search_and_Detect_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Sidhu_2025_CVPR,
    author    = {Sidhu, Mankeerat and Chopra, Hetarth and Blume, Ansel and Kim, Jeonghwan and Reddy, Revanth Gangi and Ji, Heng},
    title     = {Search and Detect: Training-Free Long Tail Object Detection via Web-Image Retrieval},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {15129-15138}
}


title: EgoLM: Multi-Modal Language Model of Egocentric Motions
authors: Fangzhou Hong, Vladimir Guzov, Hyo Jin Kim, Yuting Ye, Richard Newcombe, Ziwei Liu, Lingni Ma
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 5344-5354
cities: ["Menlo Park", "Singapore", "T\u00fcbingen", "Saarbr\u00fccken"]
countries: ["USA", "Singapore", "Germany", "Germany"]
oral: yes
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Hong_EgoLM_Multi-Modal_Language_Model_of_Egocentric_Motions_CVPR_2025_paper.html

abstract:
As wearable devices become more prevalent, understanding the user's motion is crucial for improving contextual AI systems. We introduce EgoLM, a versatile framework designed for egocentric motion understanding using multi-modal data. EgoLM integrates the rich contextual information from egocentric videos and motion sensors afforded by wearable devices. It also combines dense supervision signals from motion and language, leveraging the vast knowledge encoded in pre-trained large language models (LLMs). EgoLM models the joint distribution of egocentric motions and natural language using LLMs, conditioned on observations from egocentric videos and motion sensors. It unifies a range of motion understanding tasks, including motion narration from video or motion data, as well as motion generation from text or sparse sensor data. Unique to wearable devices, it also enables a novel task to generate text descriptions from sparse sensors. Through extensive experiments, we validate the effectiveness of EgoLM in addressing the challenges of under-constrained egocentric motion learning, and demonstrate its capability as a generalist model through a variety of applications.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Hong_EgoLM_Multi-Modal_Language_Model_of_Egocentric_Motions_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Hong_EgoLM_Multi-Modal_Language_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Hong_2025_CVPR,
    author    = {Hong, Fangzhou and Guzov, Vladimir and Kim, Hyo Jin and Ye, Yuting and Newcombe, Richard and Liu, Ziwei and Ma, Lingni},
    title     = {EgoLM: Multi-Modal Language Model of Egocentric Motions},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {5344-5354}
}

raw_affiliation:
[{"affiliation": "Meta Reality Labs Research", "city": "Menlo Park", "country": "USA"}, {"affiliation": "S-Lab, Nanyang Technological University", "city": "Singapore", "country": "Singapore"}, {"affiliation": "T\u00a8ubingen AI Center, University of T\u00a8ubingen", "city": "T\u00fcbingen", "country": "Germany"}, {"affiliation": "MPI for Informatics, Saarland Informatics Campus", "city": "Saarbr\u00fccken", "country": "Germany"}]

affiliations:
["Meta Reality Labs Research", "S-Lab, Nanyang Technological University", "T\u00a8ubingen AI Center, University of T\u00a8ubingen", "MPI for Informatics, Saarland Informatics Campus"]


authors: Zhuoman Liu, Weicai Ye, Yan Luximon, Pengfei Wan, Di Zhang
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 11016-11025
affiliations: ["The Hong Kong Polytechnic University", "Kuaishou Technology"]
cities: ["Hong Kong", "Beijing"]
countries: ["China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Liu_Unleashing_the_Potential_of_Multi-modal_Foundation_Models_and_Video_Diffusion_CVPR_2025_paper.html

title:
Unleashing the Potential of Multi-modal Foundation Models and Video Diffusion for 4D Dynamic Physical Scene Simulation

abstract:
Realistic simulation of dynamic scenes requires accurately capturing diverse material properties and modeling complex object interactions grounded in physical principles. However, existing methods are constrained to basic material types with limited predictable parameters, making them insufficient to represent the complexity of real-world materials. We introduce PhysFlow, a novel approach that leverages multi-modal foundation models and video diffusion to achieve enhanced 4D dynamic scene simulation. Our method utilizes multi-modal models to identify material types and initialize material parameters through image queries, while simultaneously inferring 3D Gaussian splats for detailed scene representation. We further refine these material parameters using video diffusion with a differentiable Material Point Method (MPM) and optical flow guidance rather than render loss or Score Distillation Sampling (SDS) loss. This integrated framework enables accurate prediction and realistic simulation of dynamic interactions in real-world scenarios, advancing both accuracy and flexibility in physics-based simulations.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Liu_Unleashing_the_Potential_of_Multi-modal_Foundation_Models_and_Video_Diffusion_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Liu_Unleashing_the_Potential_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Liu_2025_CVPR,
    author    = {Liu, Zhuoman and Ye, Weicai and Luximon, Yan and Wan, Pengfei and Zhang, Di},
    title     = {Unleashing the Potential of Multi-modal Foundation Models and Video Diffusion for 4D Dynamic Physical Scene Simulation},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {11016-11025}
}

raw_affiliation:
[{"affiliation": "The Hong Kong Polytechnic University", "city": "Hong Kong", "country": "China"}, {"affiliation": "Kuaishou Technology", "city": "Beijing", "country": "China"}]


title: Diffusion Model is Effectively Its Own Teacher
authors: Xinyin Ma, Runpeng Yu, Songhua Liu, Gongfan Fang, Xinchao Wang
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 12901-12911
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/Ma_Diffusion_Model_is_Effectively_Its_Own_Teacher_CVPR_2025_paper.html

abstract:
In this paper, we introduce a novel self-distillation paradigm for improving the performance of diffusion models. Previous studies have shown that introducing a teacher to distill the diffusion model can enhance its sampling efficiency. We raise an intriguing question: can the diffusion model itself serve as its teacher to further improve the performance of itself? To this end, we propose a new paradigm called Self Step-Distillation (SSD). The core idea of SSD is to integrate the predictions or the intermediate activations of the diffusion model at each timestep with its preceding timestep through a fusion mechanism. We propose two forms, explicit SSD and implicit SSD (iSSD), to perform N-step to N-step distillation from the diffusion model itself to achieve improved image quality. We further elucidate the relationship between SSD and high-order solver, highlighting their underlying relationship. The effectiveness of SSD is validated through extensive experiments on diffusion transformers of various sizes and across different sampling steps. Our results show that this novel self-distillation paradigm can significantly enhance performance. Additionally, our method is compatible with the distillation method designed for few-step inference. Notably, with iSSD trained less than one epoch, we obtain a 32-step DiT-XL/2 achieving an FID of 1.99, outperforming the original 250-step DiT-XL/2 with an FID of 2.26. We further validate the effectiveness of our method on text-to-image diffusion models, such as Stable Diffusion, and also observe notable improvement in image quality.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Ma_Diffusion_Model_is_Effectively_Its_Own_Teacher_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Ma_Diffusion_Model_is_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Ma_2025_CVPR,
    author    = {Ma, Xinyin and Yu, Runpeng and Liu, Songhua and Fang, Gongfan and Wang, Xinchao},
    title     = {Diffusion Model is Effectively Its Own Teacher},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {12901-12911}
}


title: Long Video Diffusion Generation with Segmented Cross-Attention and Content-Rich Video Data Curation
authors: Xin Yan, Yuxuan Cai, Qiuyue Wang, Yuan Zhou, Wenhao Huang, Huan Yang
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 3184-3194
raw_affiliation: [{"affiliation": "01.AI", "city": "Beijing", "country": "China"}]
affiliations: ["01.AI"]
cities: ["Beijing"]
countries: ["China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Yan_Long_Video_Diffusion_Generation_with_Segmented_Cross-Attention_and_Content-Rich_Video_CVPR_2025_paper.html

abstract:
We introduce Presto, a novel video diffusion model designed to generate 15-second videos with long-range coherence and rich content. Extending video generation to maintain scenario diversity over long durations presents significant challenges. To address this, we propose a Segmented Cross-Attention (SCA) strategy, which splits hidden states into segments along the temporal dimension, allowing each segment to cross-attend to a corresponding sub-caption. SCA requires no additional parameters, enabling seamless incorporation into current DiT-based architectures. To facilitate high-quality long video generation, we build the LongTake-HD dataset, consisting of 261k content-rich videos with scenario coherence, annotated with an overall video caption and five progressive sub-captions.  Experiments show that our Presto achieves 78.5% on the VBench Semantic Score and 100% on the Dynamic Degree, outperforming existing state-of-the-art video generation methods. This demonstrates that our proposed Presto significantly enhances the content richness, maintains long-range coherence, and captures intricate textual details. All code and model weights will be made publicly available.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Yan_Long_Video_Diffusion_Generation_with_Segmented_Cross-Attention_and_Content-Rich_Video_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Yan_Long_Video_Diffusion_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Yan_2025_CVPR,
    author    = {Yan, Xin and Cai, Yuxuan and Wang, Qiuyue and Zhou, Yuan and Huang, Wenhao and Yang, Huan},
    title     = {Long Video Diffusion Generation with Segmented Cross-Attention and Content-Rich Video Data Curation},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {3184-3194}
}


title: Learning Heterogeneous Tissues with Mixture of Experts for Gigapixel Whole Slide Images
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 5144-5153
cities: ["Nanjing", "Beijing", "Beijing", "Chongqing", "Beijing", "Hefei"]
countries: ["China", "China", "China", "China", "China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Wu_Learning_Heterogeneous_Tissues_with_Mixture_of_Experts_for_Gigapixel_Whole_CVPR_2025_paper.html

authors:
Junxian Wu, Minheng Chen, Xinyi Ke, Tianwang Xun, Xiaoming Jiang, Hongyu Zhou, Lizhi Shao, Youyong Kong

abstract:
Analyzing gigapixel Whole Slide Images (WSIs) is challenging due to the complex pathological tissue environment and the absence of target-driven domain knowledge. Previous methods incorporated pathological priors to mitigate this issue but relied on additional inference steps and specialized workflows, restricting scalability and the model's capacity to identify novel outcome-related factors. To address these challenges, we propose a plug-and-play Pathology-Aware Mixture-of-Experts (PAMoE) module, which based on mixture of experts to learn pathology-related knowledge and extract useful information. We train the experts to become 'specialists' in specific intratumoral tissues by learning to route each tissue to its mapped expert. In addition, to reduce the impact of irrelevant content on the model, we introduce a new routing rule that discards patches in which none of the experts express interest, which helps the model better capture the relationships between relevant patches. Through a comprehensive evaluation of PAMoE on survival task, we demonstrate that 1) Our module enhances the performance of baseline models in most cases, and 2) The sparse expert processing across different tissues enhances the learning of patch representations by addressing tissue heterogeneity.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Wu_Learning_Heterogeneous_Tissues_with_Mixture_of_Experts_for_Gigapixel_Whole_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Wu_Learning_Heterogeneous_Tissues_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Wu_2025_CVPR,
    author    = {Wu, Junxian and Chen, Minheng and Ke, Xinyi and Xun, Tianwang and Jiang, Xiaoming and Zhou, Hongyu and Shao, Lizhi and Kong, Youyong},
    title     = {Learning Heterogeneous Tissues with Mixture of Experts for Gigapixel Whole Slide Images},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {5144-5153}
}

raw_affiliation:
[{"affiliation": "Southeast University", "city": "Nanjing", "country": "China"}, {"affiliation": "Peking Union Medical College Hospital", "city": "Beijing", "country": "China"}, {"affiliation": "Institute of Automation, Chinese Academy of Sciences", "city": "Beijing", "country": "China"}, {"affiliation": "Chongqing University of Post and Telecommunications", "city": "Chongqing", "country": "China"}, {"affiliation": "Tsinghua University", "city": "Beijing", "country": "China"}, {"affiliation": "Anhui University", "city": "Hefei", "country": "China"}]

affiliations:
["Southeast University", "Peking Union Medical College Hospital", "Institute of Automation, Chinese Academy of Sciences", "Chongqing University of Post and Telecommunications", "Tsinghua University", "Anhui University"]


title: HyperNVD: Accelerating Neural Video Decomposition via Hypernetworks
authors: Maria Pilligua, Danna Xue, Javier Vazquez-Corral
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 22933-22942
affiliations: ["Universitat Aut`onoma de Barcelona", "Computer Vision Center"]
cities: ["Barcelona", "Barcelona"]
countries: ["Spain", "Spain"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Pilligua_HyperNVD_Accelerating_Neural_Video_Decomposition_via_Hypernetworks_CVPR_2025_paper.html

abstract:
Decomposing a video into a layer-based representation is crucial for easy video editing for the creative industries, as it enables independent editing of specific layers. Existing video-layer decomposition models rely on implicit neural representations (INRs) trained independently for each video, making the process time-consuming when applied to new videos. Noticing this limitation, we propose a meta-learning strategy to learn a generic video decomposition model to speed up the training on new videos. Our model is based on a hypernetwork architecture which, given a video-encoder embedding, generates the parameters for a compact INR-based neural video decomposition model. Our strategy mitigates the problem of single-video overfitting and, importantly, shortens the convergence of video decomposition on new, unseen videos. Our code is available at: https://hypernvd.github.io/

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Pilligua_HyperNVD_Accelerating_Neural_Video_Decomposition_via_Hypernetworks_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Pilligua_HyperNVD_Accelerating_Neural_CVPR_2025_supplemental.zip

bibtex:
@InProceedings{Pilligua_2025_CVPR,
    author    = {Pilligua, Maria and Xue, Danna and Vazquez-Corral, Javier},
    title     = {HyperNVD: Accelerating Neural Video Decomposition via Hypernetworks},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {22933-22942}
}

raw_affiliation:
[{"affiliation": "Universitat Aut`onoma de Barcelona", "city": "Barcelona", "country": "Spain"}, {"affiliation": "Computer Vision Center", "city": "Barcelona", "country": "Spain"}]


html_link: https://openaccess.thecvf.com/content/CVPR2025/html/Liu_UnCommon_Objects_in_3D_CVPR_2025_paper.html
title: UnCommon Objects in 3D
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 14102-14113
affiliations: ["Meta AI", "NKUA", "KAUST", "Carnegie Mellon University"]
cities: ["Menlo Park", "Athens", "Thuwal", "Pittsburgh"]
countries: ["USA", "Greece", "Saudi Arabia", "USA"]
authors:
Xingchen Liu, Piyush Tayal, Jianyuan Wang, Jesus Zarzar, Tom Monnier, Konstantinos Tertikas, Jiali Duan, Antoine Toisoul, Jason Y. Zhang, Natalia Neverova, Andrea Vedaldi, Roman Shapovalov, David Novotny

abstract:
We introduce Uncommon Objects in 3D (uCO3D), a new object-centric dataset for 3D deep learning and 3D generative AI. uCO3D is the largest publicly-available collection of high-resolution videos of objects with 3D annotations that ensures full-360 degree coverage. uCO3D is significantly more diverse than MVImgNet and CO3Dv2, covering more than 1,000 object categories. It is also of higher quality, due to extensive quality checks of both the collected videos and the 3D annotations. Similar to analogous datasets, uCO3D contains annotations for 3D camera poses, depth maps and sparse point clouds. In addition, each object is equipped with a caption and a 3D Gaussian Splat reconstruction. We train several large 3D models on MVImgNet, CO3Dv2, and uCO3D and obtain superior results using the latter, showing that uCO3D is better for learning applications.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Liu_UnCommon_Objects_in_3D_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Liu_UnCommon_Objects_in_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Liu_2025_CVPR,
    author    = {Liu, Xingchen and Tayal, Piyush and Wang, Jianyuan and Zarzar, Jesus and Monnier, Tom and Tertikas, Konstantinos and Duan, Jiali and Toisoul, Antoine and Zhang, Jason Y. and Neverova, Natalia and Vedaldi, Andrea and Shapovalov, Roman and Novotny, David},
    title     = {UnCommon Objects in 3D},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {14102-14113}
}

raw_affiliation:
[{"affiliation": "Meta AI", "city": "Menlo Park", "country": "USA"}, {"affiliation": "NKUA", "city": "Athens", "country": "Greece"}, {"affiliation": "KAUST", "city": "Thuwal", "country": "Saudi Arabia"}, {"affiliation": "Carnegie Mellon University", "city": "Pittsburgh", "country": "USA"}]


title: Disentangled Pose and Appearance Guidance for Multi-Pose Generation
authors: Tengfei Xiao, Yue Wu, Yuelong Li, Can Qin, Maoguo Gong, Qiguang Miao, Wenping Ma
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 5646-5655
cities: ["Xi'an", "Tianjin", "Shenyang", "Xi'an", "Tianjin"]
countries: ["China", "China", "China", "China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Xiao_Disentangled_Pose_and_Appearance_Guidance_for_Multi-Pose_Generation_CVPR_2025_paper.html

abstract:
Human pose generation is a complex task due to the non-rigid and highly variable nature of human body structures and appearances. However, existing methods often overlook the fundamental differences between spatial transformations of poses and texture generation for appearance, which makes them prone to overfitting. To address this issue, we propose a multi-pose generation framework driven by disentangled pose and appearance guidance. Our approach includes a Global-aware Pose Generation module that iteratively generates pose embeddings, enabling effective control over non-rigid body deformations. Additionally, we introduce the Global-aware Transformer Decoder, which leverages similarity queries and attention mechanisms to achieve spatial transformations and enhance pose consistency through a Global-aware block. In the appearance generation phase, we condition a diffusion model on pose embeddings produced in the initial stage and introduce an Appearance Adapter that extracts high-level contextual semantic information from multi-scale features, enabling further refinement of pose appearance textures and providing appearance guidance. Extensive experiments on the UBC Fashion and TikTok datasets demonstrate that our framework achieves state-of-the-art results in both quality and fidelity, establishing it as a powerful approach for complex pose generation tasks.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Xiao_Disentangled_Pose_and_Appearance_Guidance_for_Multi-Pose_Generation_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Xiao_Disentangled_Pose_and_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Xiao_2025_CVPR,
    author    = {Xiao, Tengfei and Wu, Yue and Li, Yuelong and Qin, Can and Gong, Maoguo and Miao, Qiguang and Ma, Wenping},
    title     = {Disentangled Pose and Appearance Guidance for Multi-Pose Generation},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {5646-5655}
}

raw_affiliation:
[{"affiliation": "Xidian University", "city": "Xi'an", "country": "China"}, {"affiliation": "Tiangong University", "city": "Tianjin", "country": "China"}, {"affiliation": "Northeastern University", "city": "Shenyang", "country": "China"}, {"affiliation": "MoE Key Lab of Collaborative Intelligence Systems, Xidian University", "city": "Xi'an", "country": "China"}, {"affiliation": "Tianjin Key Laboratory of Autonomous Intelligence Technology and Systems, Tiangong University", "city": "Tianjin", "country": "China"}]

affiliations:
["Xidian University", "Tiangong University", "Northeastern University", "MoE Key Lab of Collaborative Intelligence Systems, Xidian University", "Tianjin Key Laboratory of Autonomous Intelligence Technology and Systems, Tiangong University"]


authors: Yijie Liu, Xinyi Shang, Yiqun Zhang, Yang Lu, Chen Gong, Jing-Hao Xue, Hanzi Wang
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 10173-10182
cities: ["Xiamen", "Xiamen", "London", "Guangzhou", "Shanghai"]
countries: ["China", "China", "United Kingdom", "China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Liu_Mind_the_Gap_Confidence_Discrepancy_Can_Guide_Federated_Semi-Supervised_Learning_CVPR_2025_paper.html

title:
Mind the Gap: Confidence Discrepancy Can Guide Federated Semi-Supervised Learning Across Pseudo-Mismatch

abstract:
Federated Semi-Supervised Learning (FSSL) aims to leverage unlabeled data across clients with limited labeled data to train a global model with strong generalization ability. Most FSSL methods rely on consistency regularization with pseudo-labels, converting predictions from local or global models into hard pseudo-labels as supervisory signals. However, we discover that the quality of pseudo-label is largely deteriorated by data heterogeneity, an intrinsic facet of federated learning. In this paper, we study the problem of FSSL in-depth and show that (1) heterogeneity exacerbates pseudo-label mismatches, further degrading model performance and convergence, and (2) local and global models' predictive tendencies diverge as heterogeneity increases. Motivated by these findings, we propose a simple and effective method called Semi-supervised Aggregation for Globally-Enhanced Ensemble (SAGE), that can flexibly correct pseudo-labels based on confidence discrepancies. This strategy effectively mitigates performance degradation caused by incorrect pseudo-labels and enhances consensus between local and global models. Experimental results demonstrate that SAGE outperforms existing FSSL methods in both performance and convergence. Our code is available at \href https://github.com/Jay-Codeman/SAGE  https://github.com/Jay-Codeman/SAGE .

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Liu_Mind_the_Gap_Confidence_Discrepancy_Can_Guide_Federated_Semi-Supervised_Learning_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Liu_Mind_the_Gap_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Liu_2025_CVPR,
    author    = {Liu, Yijie and Shang, Xinyi and Zhang, Yiqun and Lu, Yang and Gong, Chen and Xue, Jing-Hao and Wang, Hanzi},
    title     = {Mind the Gap: Confidence Discrepancy Can Guide Federated Semi-Supervised Learning Across Pseudo-Mismatch},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {10173-10182}
}

raw_affiliation:
[{"affiliation": "Key Laboratory of Multimedia Trusted Perception and Efficient Computing, Ministry of Education of China, Xiamen University, Xiamen, China", "city": "Xiamen", "country": "China"}, {"affiliation": "Fujian Key Laboratory of Sensing and Computing for Smart City, School of Informatics, Xiamen University, Xiamen, China", "city": "Xiamen", "country": "China"}, {"affiliation": "Department of Statistical Science, University College London, United Kingdom", "city": "London", "country": "United Kingdom"}, {"affiliation": "School of Computer Science and Technology, Guangdong University of Technology, Guangzhou, China", "city": "Guangzhou", "country": "China"}, {"affiliation": "Department of Automation, Shanghai Jiao Tong University, China", "city": "Shanghai", "country": "China"}]

affiliations:
["Key Laboratory of Multimedia Trusted Perception and Efficient Computing, Ministry of Education of China, Xiamen University, Xiamen, China", "Fujian Key Laboratory of Sensing and Computing for Smart City, School of Informatics, Xiamen University, Xiamen, China", "Department of Statistical Science, University College London, United Kingdom", "School of Computer Science and Technology, Guangdong University of Technology, Guangzhou, China", "Department of Automation, Shanghai Jiao Tong University, China"]


title: Instant Adversarial Purification with Adversarial Consistency Distillation
authors: Chun Tong Lei, Hon Ming Yam, Zhongliang Guo, Yifei Qian, Chun Pong Lau
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 24331-24340
affiliations: ["City University of Hong Kong", "University of St Andrews", "University of Nottingham"]
cities: ["Hong Kong", "St Andrews", "Nottingham"]
countries: ["China", "United Kingdom", "United Kingdom"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Lei_Instant_Adversarial_Purification_with_Adversarial_Consistency_Distillation_CVPR_2025_paper.html

abstract:
Neural networks have revolutionized numerous fields with their exceptional performance, yet they remain susceptible to adversarial attacks through subtle perturbations. While diffusion-based purification methods like DiffPure offer promising defense mechanisms, their computational overhead presents a significant practical limitation. In this paper, we introduce One Step Control Purification (OSCP), a novel defense framework that achieves robust adversarial purification in a single Neural Function Evaluation (NFE) within diffusion models. We propose Gaussian Adversarial Noise Distillation (GAND) as the distillation objective and Controlled Adversarial Purification (CAP) as the inference pipeline, which makes OSCP demonstrate remarkable efficiency while maintaining defense efficacy. Our proposed GAND addresses a fundamental tension between consistency distillation and adversarial perturbation, bridging the gap between natural and adversarial manifolds in the latent space, while remaining computationally efficient through Parameter-Efficient Fine-Tuning (PEFT) methods such as LoRA, eliminating the high computational budget request from full parameter fine-tuning. The CAP guides the purification process through the unlearnable edge detection operator calculated by the input image as an extra prompt, effectively preventing the purified images from deviating from their original appearance when using large purification steps. Our experimental results on ImageNet showcase OSCP's superior performance, achieving a 74.19% defense success rate with merely 0.1s per purification --- a 100-fold speedup compared to conventional approaches.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Lei_Instant_Adversarial_Purification_with_Adversarial_Consistency_Distillation_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Lei_Instant_Adversarial_Purification_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Lei_2025_CVPR,
    author    = {Lei, Chun Tong and Yam, Hon Ming and Guo, Zhongliang and Qian, Yifei and Lau, Chun Pong},
    title     = {Instant Adversarial Purification with Adversarial Consistency Distillation},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {24331-24340}
}

raw_affiliation:
[{"affiliation": "City University of Hong Kong", "city": "Hong Kong", "country": "China"}, {"affiliation": "University of St Andrews", "city": "St Andrews", "country": "United Kingdom"}, {"affiliation": "University of Nottingham", "city": "Nottingham", "country": "United Kingdom"}]


title: Learning Textual Prompts for Open-World Semi-Supervised Learning
authors: Yuxin Fan, Junbiao Cui, Jiye Liang
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 14756-14765
cities: ["Taiyuan"]
countries: ["China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Fan_Learning_Textual_Prompts_for_Open-World_Semi-Supervised_Learning_CVPR_2025_paper.html

abstract:
Traditional semi-supervised learning achieves significant success in closed-world scenarios. To better align with the openness of the real world, researchers propose open-world semi-supervised learning (OWSSL), which enables models to effectively recognize known and unknown classes even without labels for unknown classes. Recently, researchers have attempted to enhance the model performance in recognizing visually similar classes by integrating textual information. However, these attempts do not effectively align images with text, resulting in limited improvements in model performance. In response to this challenge, we propose a novel OWSSL method. By adopting a global-and-local textual prompt learning strategy to enhance image-text alignment effectiveness, and implementing a forward-and-backward strategy to reduce noise in image-text matching for unlabeled samples, we ultimately enhance the model's ability to extract and recognize discriminative features across different classes. Experimental results on multiple fine-grained datasets demonstrate that our method achieves significant performance improvements compared to state-of-the-art methods.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Fan_Learning_Textual_Prompts_for_Open-World_Semi-Supervised_Learning_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Fan_Learning_Textual_Prompts_CVPR_2025_supplemental.zip

bibtex:
@InProceedings{Fan_2025_CVPR,
    author    = {Fan, Yuxin and Cui, Junbiao and Liang, Jiye},
    title     = {Learning Textual Prompts for Open-World Semi-Supervised Learning},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {14756-14765}
}

raw_affiliation:
[{"affiliation": "Key Laboratory of Computational Intelligence and Chinese Information Processing of Ministry of Education, School of Computer and Information Technology, Shanxi University, Taiyuan 030006, Shanxi, China", "city": "Taiyuan", "country": "China"}]

affiliations:
["Key Laboratory of Computational Intelligence and Chinese Information Processing of Ministry of Education, School of Computer and Information Technology, Shanxi University, Taiyuan 030006, Shanxi, China"]


authors: Tim Büchner, Christoph Anders, Orlando Guntinas-Lichius, Joachim Denzler
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 215-227
affiliations: ["Computer Vision Group Friedrich Schiller University Jena", "Jena University Hospital"]
cities: ["Jena", "Jena"]
countries: ["Germany", "Germany"]
highlight: yes
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Buchner_Electromyography-Informed_Facial_Expression_Reconstruction_for_Physiological-Based_Synthesis_and_Analysis_CVPR_2025_paper.html

title:
Electromyography-Informed Facial Expression Reconstruction for Physiological-Based Synthesis and Analysis

abstract:
The relationship between muscle activity and resulting facial expressions is crucial for various fields, including psychology, medicine, and entertainment. The synchronous recording of facial mimicry and muscular activity via surface electromyography (sEMG) provides a unique window into these complex dynamics. Unfortunately, existing methods for facial analysis cannot handle electrode occlusion, rendering them ineffective.  Even with occlusion-free reference images of the same person, variations in expression intensity and execution are unmatchable. Our electromyography-informed facial expression reconstruction (EIFER) approach is a novel method to restore faces under sEMG occlusion faithfully in an adversarial manner. We decouple facial geometry and visual appearance (e.g., skin texture, lighting, electrodes) by combining a 3D Morphable Model (3DMM) with neural unpaired image-to-image translation via reference recordings. Then, EIFER learns a bidirectional mapping between 3DMM expression parameters and muscle activity, establishing correspondence between the two domains. We validate the effectiveness of our approach through experiments on a dataset of synchronized sEMG recordings and facial mimicry, demonstrating faithful geometry and appearance reconstruction. Further, we synthesize expressions based on muscle activity and how observed expressions can predict dynamic muscle activity. Consequently, EIFER introduces a new paradigm for facial electromyography, which could be extended to other forms of multi-modal face recordings.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Buchner_Electromyography-Informed_Facial_Expression_Reconstruction_for_Physiological-Based_Synthesis_and_Analysis_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Buchner_Electromyography-Informed_Facial_Expression_CVPR_2025_supplemental.zip

bibtex:
@InProceedings{Buchner_2025_CVPR,
    author    = {B\"uchner, Tim and Anders, Christoph and Guntinas-Lichius, Orlando and Denzler, Joachim},
    title     = {Electromyography-Informed Facial Expression Reconstruction for Physiological-Based Synthesis and Analysis},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {215-227}
}

raw_affiliation:
[{"affiliation": "Computer Vision Group Friedrich Schiller University Jena", "city": "Jena", "country": "Germany"}, {"affiliation": "Jena University Hospital", "city": "Jena", "country": "Germany"}]


title: LongDiff: Training-Free Long Video Generation in One Go
authors: Zhuoling Li, Hossein Rahmani, Qiuhong Ke, Jun Liu
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 17789-17798
affiliations: ["Lancaster University", "Monash University"]
cities: ["Lancaster", "Melbourne"]
countries: ["United Kingdom", "Australia"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Li_LongDiff_Training-Free_Long_Video_Generation_in_One_Go_CVPR_2025_paper.html

abstract:
Video diffusion models have recently achieved remarkable results in video generation. Despite their encouraging performance, most of these models are mainly designed and trained for short video generation, leading to challenges in maintaining temporal consistency and visual details in long video generation. In this paper, through theoretical analysis of the mechanisms behind video generation, we identify two key challenges that hinder short-to-long generalization, namely, temporal position ambiguity and information dilution. To address these challenges, we propose LongDiff, a novel training-free method that unlocks the potential of the off-the-shelf  video diffusion models to achieve high-quality long video generation in one go.  Extensive experiments demonstrate the efficacy of our method.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Li_LongDiff_Training-Free_Long_Video_Generation_in_One_Go_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Li_LongDiff_Training-Free_Long_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Li_2025_CVPR,
    author    = {Li, Zhuoling and Rahmani, Hossein and Ke, Qiuhong and Liu, Jun},
    title     = {LongDiff: Training-Free Long Video Generation in One Go},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {17789-17798}
}

raw_affiliation:
[{"affiliation": "Lancaster University", "city": "Lancaster", "country": "United Kingdom"}, {"affiliation": "Monash University", "city": "Melbourne", "country": "Australia"}]


title: Feature Selection for Latent Factor Models
authors: Rittwika Kansabanik, Adrian Barbu
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 30742-30751
affiliations: ["Department of Statistics, Florida State University"]
cities: ["Tallahassee"]
countries: ["USA"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Kansabanik_Feature_Selection_for_Latent_Factor_Models_CVPR_2025_paper.html

abstract:
Feature selection is crucial for pinpointing relevant features in high-dimensional datasets, mitigating the 'curse of dimensionality,' and enhancing machine learning performance. Traditional feature selection methods for classification use data from all classes to select features for each class.This paper explores feature selection methods that select features for each class separately, using class models based on low-rank generative methods and introducing a signal-to-noise ratio (SNR) feature selection criterion. This novel approach theoretically guarantees true feature recovery under certain assumptions and is shown to outperform some existing feature selection methods on standard classification datasets.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Kansabanik_Feature_Selection_for_Latent_Factor_Models_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Kansabanik_Feature_Selection_for_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Kansabanik_2025_CVPR,
    author    = {Kansabanik, Rittwika and Barbu, Adrian},
    title     = {Feature Selection for Latent Factor Models},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {30742-30751}
}

raw_affiliation:
[{"affiliation": "Department of Statistics, Florida State University", "city": "Tallahassee", "country": "USA"}]


proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 23474-23483
affiliations: ["KAIST", "Republic of Korea Naval Academy", "AITRICS"]
cities: ["Daejeon", "Changwon", "Seoul"]
countries: ["South Korea", "South Korea", "South Korea"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Kim_Preserve_or_Modify_Context-Aware_Evaluation_for_Balancing_Preservation_and_Modification_CVPR_2025_paper.html

title:
Preserve or Modify? Context-Aware Evaluation for Balancing Preservation and Modification in Text-Guided Image Editing

authors:
Yoonjeon Kim, Soohyun Ryu, Yeonsung Jung, Hyunkoo Lee, Joowon Kim, June Yong Yang, Jaeryong Hwang, Eunho Yang

abstract:
The development of vision-language and generative models has significantly advanced text-guided image editing, which seeks the preservation of core elements in the source image while implementing modifications based on the target text. However, existing metrics have a context-blindness problem, indiscriminately applying the same evaluation criteria on completely different pairs of source image and target text, biasing towards either modification or preservation. Directional CLIP similarity, the only metric that considers both source image and target text, is also biased towards modification aspects and attends to irrelevant editing regions of the image. We propose AugCLIP, a context-aware metric that adaptively coordinates preservation and modification aspects, depending on the specific context of a given source image and target text. This is done by deriving the CLIP representation of an ideally edited image, that preserves the source image with necessary modifications to align with target text. More specifically, using a multi-modal large language model, AugCLIP augments the textual descriptions of the source and target, then calculates a modification vector through a hyperplane that separates source and target attributes in CLIP space. Extensive experiments on five benchmark datasets, encompassing a diverse range of editing scenarios, show that AugCLIP aligns remarkably well with human evaluation standards, outperforming existing metrics. The code is available at https://github.com/augclip/augclip_eval.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Kim_Preserve_or_Modify_Context-Aware_Evaluation_for_Balancing_Preservation_and_Modification_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Kim_Preserve_or_Modify_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Kim_2025_CVPR,
    author    = {Kim, Yoonjeon and Ryu, Soohyun and Jung, Yeonsung and Lee, Hyunkoo and Kim, Joowon and Yang, June Yong and Hwang, Jaeryong and Yang, Eunho},
    title     = {Preserve or Modify? Context-Aware Evaluation for Balancing Preservation and Modification in Text-Guided Image Editing},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {23474-23483}
}

raw_affiliation:
[{"affiliation": "KAIST", "city": "Daejeon", "country": "South Korea"}, {"affiliation": "Republic of Korea Naval Academy", "city": "Changwon", "country": "South Korea"}, {"affiliation": "AITRICS", "city": "Seoul", "country": "South Korea"}]


title: Mask-Adapter: The Devil is in the Masks for Open-Vocabulary Segmentation
authors: Yongkang Li, Tianheng Cheng, Bin Feng, Wenyu Liu, Xinggang Wang
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 14998-15008
affiliations: ["School of EIC, Huazhong University of Science & Technology"]
cities: ["Wuhan"]
countries: ["China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Li_Mask-Adapter_The_Devil_is_in_the_Masks_for_Open-Vocabulary_Segmentation_CVPR_2025_paper.html

abstract:
Recent open-vocabulary segmentation methods adopt mask generators to predict segmentation masks and leverage pre-trained vision-language models, *e.g.*, CLIP, to classify these masks via mask pooling.Although these approaches show promising results, it is counterintuitive that accurate masks often fail to yield accurate classification results through pooling CLIP image embeddings within the mask regions.In this paper, we reveal the performance limitations of mask pooling and introduce **Mask-Adapter**, a simple yet effective method to address these challenges in open-vocabulary segmentation.Compared to directly using proposal masks, our proposed Mask-Adapter extracts *semantic activation maps* from proposal masks, providing richer contextual information and ensuring alignment between masks and CLIP.Additionally, we propose a *mask consistency loss* that encourages proposal masks with similar IoUs to obtain similar CLIP embeddings to enhance models' robustness to varying predicted masks.Mask-Adapter integrates seamlessly into open-vocabulary segmentation methods based on mask pooling in a plug-and-play manner, delivering more accurate classification results. Extensive experiments across several zero-shot benchmarks demonstrate significant performance gains for the proposed Mask-Adapter on several well-established methods.Notably, Mask-Adapter also extends effectively to SAM and achieves impressive results on several open-vocabulary segmentation datasets. Code and models are available at https://github.com/hustvl/MaskAdapter.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Li_Mask-Adapter_The_Devil_is_in_the_Masks_for_Open-Vocabulary_Segmentation_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Li_Mask-Adapter_The_Devil_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Li_2025_CVPR,
    author    = {Li, Yongkang and Cheng, Tianheng and Feng, Bin and Liu, Wenyu and Wang, Xinggang},
    title     = {Mask-Adapter: The Devil is in the Masks for Open-Vocabulary Segmentation},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {14998-15008}
}

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


title: MPDrive: Improving Spatial Understanding with Marker-Based Prompt Learning for Autonomous Driving
authors: Zhiyuan Zhang, Xiaofan Li, Zhihao Xu, Wenjie Peng, Zijian Zhou, Miaojing Shi, Shuangping Huang
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 12089-12099
cities: ["Guangzhou", "Beijing", "London", "Shanghai", "Guangzhou"]
countries: ["China", "China", "UK", "China", "China"]
highlight: yes
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Zhang_MPDrive_Improving_Spatial_Understanding_with_Marker-Based_Prompt_Learning_for_Autonomous_CVPR_2025_paper.html

abstract:
Autonomous driving visual question answering (AD-VQA) aims to answer questions related to perception, prediction, and planning based on given driving scene images, heavily relying on the model's spatial understanding capabilities. Prior works typically express spatial information through textual representations of coordinates, resulting in semantic gaps between visual coordinate representations and textual descriptions. This oversight hinders the accurate transmission of spatial information and increases the expressive burden. To address this, we propose a novel Marker-based Prompt learning framework (MPDrive), which represents spatial coordinates by concise visual markers, ensuring linguistic expressive consistency and enhancing the accuracy of both visual perception and spatial expression in AD-VQA. Specifically, we create marker images by employing a detection expert to overlay object regions with numerical labels, converting complex textual coordinate generation into straightforward text-based visual marker predictions. Moreover, we fuse original and marker images as scene-level features and integrate them with detection priors to derive instance-level features. By combining these features, we construct dual-granularity visual prompts that stimulate the LLM's spatial perception capabilities. Extensive experiments on the DriveLM and CODA-LM datasets show that MPDrive achieves state-of-the-art performance, particularly in cases requiring sophisticated spatial understanding.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Zhang_MPDrive_Improving_Spatial_Understanding_with_Marker-Based_Prompt_Learning_for_Autonomous_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Zhang_MPDrive_Improving_Spatial_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Zhang_2025_CVPR,
    author    = {Zhang, Zhiyuan and Li, Xiaofan and Xu, Zhihao and Peng, Wenjie and Zhou, Zijian and Shi, Miaojing and Huang, Shuangping},
    title     = {MPDrive: Improving Spatial Understanding with Marker-Based Prompt Learning for Autonomous Driving},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {12089-12099}
}

raw_affiliation:
[{"affiliation": "South China University of Technology", "city": "Guangzhou", "country": "China"}, {"affiliation": "Baidu Inc.", "city": "Beijing", "country": "China"}, {"affiliation": "King\u2019s College London", "city": "London", "country": "UK"}, {"affiliation": "Tongji University", "city": "Shanghai", "country": "China"}, {"affiliation": "Pazhou Laboratory", "city": "Guangzhou", "country": "China"}]

affiliations:
["South China University of Technology", "Baidu Inc.", "King\u2019s College London", "Tongji University", "Pazhou Laboratory"]


authors: Fengfan Zhou, Bangjie Yin, Hefei Ling, Qianyu Zhou, Wenxuan Wang
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 3516-3527
cities: ["Wuhan", "Shanghai", "Shanghai", "Xi'an"]
countries: ["China", "China", "China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Zhou_Improving_the_Transferability_of_Adversarial_Attacks_on_Face_Recognition_with_CVPR_2025_paper.html

title:
Improving the Transferability of Adversarial Attacks on Face Recognition with Diverse Parameters Augmentation

abstract:
Face Recognition (FR) models are vulnerable to adversarial examples that subtly manipulate benign face images, underscoring the urgent need to improve the transferability of adversarial attacks in order to expose the blind spots of these systems. Existing adversarial attack methods often overlook the potential benefits of augmenting the surrogate model with diverse initializations, which limits the transferability of the generated adversarial examples. To address this gap, we propose a novel method called Diverse Parameters Augmentation (DPA) attack method, which enhances surrogate models by incorporating diverse parameter initializations, resulting in a broader and more diverse set of surrogate models. Specifically, DPA consists of two key stages: Diverse Parameters Optimization (DPO) and Hard Model Aggregation (HMA). In the DPO stage, we initialize the parameters of the surrogate model using both pre-trained and random parameters. Subsequently, we save the models in the intermediate training process to obtain a diverse set of surrogate models. During the HMA stage, we enhance the feature maps of the diversified surrogate models by incorporating beneficial perturbations, thereby further improving the transferability. Experimental results demonstrate that our proposed attack method can effectively enhance the transferability of the crafted adversarial face examples.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Zhou_Improving_the_Transferability_of_Adversarial_Attacks_on_Face_Recognition_with_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Zhou_Improving_the_Transferability_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Zhou_2025_CVPR,
    author    = {Zhou, Fengfan and Yin, Bangjie and Ling, Hefei and Zhou, Qianyu and Wang, Wenxuan},
    title     = {Improving the Transferability of Adversarial Attacks on Face Recognition with Diverse Parameters Augmentation},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {3516-3527}
}

raw_affiliation:
[{"affiliation": "School of Computer Science and Technology, Huazhong University of Science and Technology", "city": "Wuhan", "country": "China"}, {"affiliation": "Shanghai Shizhuang Information Technology Co., Ltd", "city": "Shanghai", "country": "China"}, {"affiliation": "Department of Computer Science and Engineering, Shanghai Jiao Tong University", "city": "Shanghai", "country": "China"}, {"affiliation": "School of Computer Science, Northwestern Polytechnical University", "city": "Xi'an", "country": "China"}]

affiliations:
["School of Computer Science and Technology, Huazhong University of Science and Technology", "Shanghai Shizhuang Information Technology Co., Ltd", "Department of Computer Science and Engineering, Shanghai Jiao Tong University", "School of Computer Science, Northwestern Polytechnical University"]


title: Adapting to Observation Length of Trajectory Prediction via Contrastive Learning
authors: Ruiqi Qiu, Jun Gong, Xinyu Zhang, Siqi Luo, Bowen Zhang, Yi Cen
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 1645-1654
raw_affiliation: [{"affiliation": "Northeastern University", "city": "Shenyang", "country": "China"}]
affiliations: ["Northeastern University"]
cities: ["Shenyang"]
countries: ["China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Qiu_Adapting_to_Observation_Length_of_Trajectory_Prediction_via_Contrastive_Learning_CVPR_2025_paper.html

abstract:
The ability to adapt to varying observation lengths is crucial for human trajectory prediction tasks, particularly in scenarios with limited observation lengths or missing data. Existing approaches mainly focus on introducing novel architectures or additional structural components, which substantially increase model complexity and present challenges for integration into existing models. We argue that current network architectures are sufficiently sophisticated to handle the Observation Length Shift problem, with the key challenge lying in improving feature representation for trajectories with limited lengths. To tackle this issue, we introduce a general and effective contrastive learning approach, called Contrastive Learning for Length Shift (CLLS). By incorporating contrastive learning during the training phase, our method encourages the model to extract length-invariant features, thus mitigating the impact of observation length variations. Furthermore, to better accommodate length adaptation tasks, we introduce a lightweight RNN network that, combined with CLLS, achieves state-of-the-art performance in both general prediction and observation length shift tasks. Experimental results demonstrate that our approach outperforms existing methods across multiple widely-used trajectory prediction datasets.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Qiu_Adapting_to_Observation_Length_of_Trajectory_Prediction_via_Contrastive_Learning_CVPR_2025_paper.pdf

bibtex:
@InProceedings{Qiu_2025_CVPR,
    author    = {Qiu, Ruiqi and Gong, Jun and Zhang, Xinyu and Luo, Siqi and Zhang, Bowen and Cen, Yi},
    title     = {Adapting to Observation Length of Trajectory Prediction via Contrastive Learning},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {1645-1654}
}


