# papers

title: Improving Personalized Search with Regularized Low-Rank Parameter Updates
authors: Fiona Ryan, Josef Sivic, Fabian Caba Heilbron, Judy Hoffman, James M. Rehg, Bryan Russell
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 19748-19757
affiliations: ["Georgia Tech", "Adobe Research", "CIIRC CTU", "UIUC"]
cities: ["Atlanta", "San Jose", "Prague", "Champaign"]
countries: ["USA", "USA", "Czech Republic", "USA"]
highlight: yes
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Ryan_Improving_Personalized_Search_with_Regularized_Low-Rank_Parameter_Updates_CVPR_2025_paper.html

abstract:
Personalized vision-language retrieval seeks to recognize new concepts (e.g. "my dog Fido") from only a few examples. This task is challenging because it requires not only learning a new concept from a few images, but also integrating the personal and general knowledge together to recognize the concept in different contexts. In this paper, we show how to effectively adapt the internal representation of a vision-language dual encoder model for personalized vision-language retrieval. We find that regularized low-rank adaption of a small set of parameters in the language encoder's final layer serves as a highly effective alternative to textual inversion for recognizing the personal concept while preserving general knowledge. Additionally, we explore strategies for combining parameters of multiple learned personal concepts, finding that parameter addition is effective. To evaluate how well general knowledge is preserved in a finetuned representation, we introduce a metric that measures image retrieval accuracy based on captions generated by a vision language model (VLM). Our approach achieves state-of-the-art accuracy on two benchmarks for personalized image retrieval with natural language queries - DeepFashion2 and ConConChi - outperforming the prior art by 4%-22% on personal retrievals.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Ryan_Improving_Personalized_Search_with_Regularized_Low-Rank_Parameter_Updates_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Ryan_Improving_Personalized_Search_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Ryan_2025_CVPR,
    author    = {Ryan, Fiona and Sivic, Josef and Heilbron, Fabian Caba and Hoffman, Judy and Rehg, James M. and Russell, Bryan},
    title     = {Improving Personalized Search with Regularized Low-Rank Parameter Updates},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {19748-19757}
}

raw_affiliation:
[{"affiliation": "Georgia Tech", "city": "Atlanta", "country": "USA"}, {"affiliation": "Adobe Research", "city": "San Jose", "country": "USA"}, {"affiliation": "CIIRC CTU", "city": "Prague", "country": "Czech Republic"}, {"affiliation": "UIUC", "city": "Champaign", "country": "USA"}]


title: A Focused Human Body Model for Accurate Anthropometric Measurements Extraction
authors: Shuhang Chen, Xianliang Huang, Zhizhou Zhong, Juhong Guan, Shuigeng Zhou
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 22658-22667
cities: ["Shanghai", "Shanghai"]
countries: ["China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Chen_A_Focused_Human_Body_Model_for_Accurate_Anthropometric_Measurements_Extraction_CVPR_2025_paper.html

abstract:
3D anthropometric measurements have a variety of applications in industrial design and architecture (e.g. vehicle seating and cockpits), Clothing (e.g. military uniforms), Ergonomics (e.g. seating) and Medicine (e.g. nutrition and diabetes) etc. Therefore, there is a need for systems that can accurately extract human body measurements. Current methods estimate human body measurements from 3D scans, resulting in a heavy data collection burden. Moreover, minor variations in camera angle, distance, and body postures may significantly affect the measurement accuracy. In response to these challenges, this paper introduces a focused human body model for accurately extracting anthropometric measurements. Concretely, we design a Bypass Network based on CNN and ResNet architectures, which augments the frozen backbone SMPLer-X with additional feature extraction capabilities. On the other hand, to boost the efficiency of training a large-scale model, we integrate a dynamical loss function that automatically recalibrates the weights to make the network focus on targeted anthropometric parts. In addition, we construct a multimodal body measurement benchmark dataset consisting of depth, point clouds, mesh and corresponding body measurements to support model evaluation and future anthropometric measurement research. Extensive experiments on both open-source and the proposed human body datasets demonstrate the superiority of our approach over existing counterparts, including the current mainstream commercial body measurement software.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Chen_A_Focused_Human_Body_Model_for_Accurate_Anthropometric_Measurements_Extraction_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Chen_A_Focused_Human_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Chen_2025_CVPR,
    author    = {Chen, Shuhang and Huang, Xianliang and Zhong, Zhizhou and Guan, Juhong and Zhou, Shuigeng},
    title     = {A Focused Human Body Model for Accurate Anthropometric Measurements Extraction},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {22658-22667}
}

raw_affiliation:
[{"affiliation": "Shanghai Key Lab of Intelligent Information Processing, and School of Computer Science, Fudan University", "city": "Shanghai", "country": "China"}, {"affiliation": "School of Computer Science Technology, Tongji University", "city": "Shanghai", "country": "China"}]

affiliations:
["Shanghai Key Lab of Intelligent Information Processing, and School of Computer Science, Fudan University", "School of Computer Science Technology, Tongji University"]


title: SnapGen-V: Generating a Five-Second Video within Five Seconds on a Mobile Device
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 2479-2490
affiliations: ["Snap Inc.", "Northeastern University", "Rutgers University"]
cities: ["Menlo Park", "Boston", "New Brunswick"]
countries: ["USA", "USA", "USA"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Wu_SnapGen-V_Generating_a_Five-Second_Video_within_Five_Seconds_on_a_CVPR_2025_paper.html

authors:
Yushu Wu, Zhixing Zhang, Yanyu Li, Yanwu Xu, Anil Kag, Yang Sui, Huseyin Coskun, Ke Ma, Aleksei Lebedev, Ju Hu, Dimitris N. Metaxas, Yanzhi Wang, Sergey Tulyakov, Jian Ren

abstract:
We have witnessed the unprecedented success of diffusion-based video generation over the past year. Recently proposed models from the community have wielded the power to generate cinematic and high-resolution videos with smooth motions from arbitrary input prompts. However, as a supertask of image generation, video generation models require more computation and are thus hosted mostly on cloud servers, limiting broader adoption among content creators. In this work, we propose a comprehensive acceleration framework to bring the power of the large-scale video diffusion model to the hands of edge users. From the network architecture scope, we initialize from a compact image backbone and search out the design and arrangement of temporal layers to maximize hardware efficiency. In addition, we propose a dedicated adversarial fine-tuning algorithm for our efficient model and reduce the denoising steps to 4. Our model, with only 0.6B parameters, can generate a 5-second video on an iPhone 16 PM within 5 seconds. Compared to server-side models that take minutes on powerful GPUs to generate a single video, we accelerate the generation by magnitudes while delivering on-par quality.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Wu_SnapGen-V_Generating_a_Five-Second_Video_within_Five_Seconds_on_a_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Wu_SnapGen-V_Generating_a_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Wu_2025_CVPR,
    author    = {Wu, Yushu and Zhang, Zhixing and Li, Yanyu and Xu, Yanwu and Kag, Anil and Sui, Yang and Coskun, Huseyin and Ma, Ke and Lebedev, Aleksei and Hu, Ju and Metaxas, Dimitris N. and Wang, Yanzhi and Tulyakov, Sergey and Ren, Jian},
    title     = {SnapGen-V: Generating a Five-Second Video within Five Seconds on a Mobile Device},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {2479-2490}
}

raw_affiliation:
[{"affiliation": "Snap Inc.", "city": "Menlo Park", "country": "USA"}, {"affiliation": "Northeastern University", "city": "Boston", "country": "USA"}, {"affiliation": "Rutgers University", "city": "New Brunswick", "country": "USA"}]


title: Adapting Dense Matching for Homography Estimation with Grid-based Acceleration
authors: Kaining Zhang, Yuxin Deng, Jiayi Ma, Paolo Favaro
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 6294-6303
affiliations: ["Wuhan University", "University of Bern"]
cities: ["Wuhan", "Bern"]
countries: ["China", "Switzerland"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Zhang_Adapting_Dense_Matching_for_Homography_Estimation_with_Grid-based_Acceleration_CVPR_2025_paper.html

abstract:
Current deep homography estimation methods are typically constrained to processing low-resolution image pairs due to network architecture and computational limitations. For high-resolution images, downsampling is often required, which can greatly degrade estimation accuracy. In contrast, image matching methods, which match pixels and compute homography from correspondences, provide greater resolution flexibility. So in this work, we revisit the traditional image matching paradigm for homography estimation and  propose GFNet, a Grid Flow regression Network that adapts the high-accuracy dense matching framework for homography estimation while enhancing efficiency through a grid-based strategy--estimating flow only over a coarse grid by leveraging homography's global smoothness. We demonstrate the effectiveness of GFNet on a wide range of experiments on multiple datasets, including the common scene MSCOCO, multimodal datasets VIS-IR and GoogleMap, and the dynamic scene VIRAT. Notably, on 448x448 GoogleMap, GFNet achieves an improvement of +13.5% in auc@3 while reducing MACs by ~47% compared to the SOTA dense matching method. Additionally, it shows a 1.8ximprovement in auc@3 over the SOTA deep homography method. Code is available at \textcolor[rgb] 0.95, 0.08, 0.58  https://github.com/KN-Zhang/GFNet .

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Zhang_Adapting_Dense_Matching_for_Homography_Estimation_with_Grid-based_Acceleration_CVPR_2025_paper.pdf

bibtex:
@InProceedings{Zhang_2025_CVPR,
    author    = {Zhang, Kaining and Deng, Yuxin and Ma, Jiayi and Favaro, Paolo},
    title     = {Adapting Dense Matching for Homography Estimation with Grid-based Acceleration},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {6294-6303}
}

raw_affiliation:
[{"affiliation": "Wuhan University", "city": "Wuhan", "country": "China"}, {"affiliation": "University of Bern", "city": "Bern", "country": "Switzerland"}]


title: HyperLoRA: Parameter-Efficient Adaptive Generation for Portrait Synthesis
authors: Mengtian Li, Jinshu Chen, Wanquan Feng, Bingchuan Li, Fei Dai, Songtao Zhao, Qian He
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 13114-13123
raw_affiliation: [{"affiliation": "Intelligent Creation, ByteDance", "city": "Beijing", "country": "China"}]
affiliations: ["Intelligent Creation, ByteDance"]
cities: ["Beijing"]
countries: ["China"]
highlight: yes
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Li_HyperLoRA_Parameter-Efficient_Adaptive_Generation_for_Portrait_Synthesis_CVPR_2025_paper.html

abstract:
Personalized portrait synthesis, essential in domains like social entertainment, has recently made significant progress. Person-wise fine-tuning based methods, such as LoRA and DreamBooth, can produce photorealistic outputs but need training on individual samples, consuming time and resources and posing an unstable risk. Adapter based techniques such as IP-Adapter freeze the foundational model parameters and employ a plug-in architecture to enable zero-shot inference, but they often exhibit a lack of naturalness and authenticity, which are not to be overlooked in portrait synthesis tasks. In this paper, we introduce a parameter-efficient adaptive generation method, namely HyperLoRA, that uses an adaptive plug-in network to generate LoRA weights, merging the superior performance of LoRA with the zero-shot capability of adapter scheme. Through our carefully designed network structure and training strategy, we achieve zero-shot personalized portrait generation (supporting both single and multiple image inputs) with high photorealism, fidelity, and editability.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Li_HyperLoRA_Parameter-Efficient_Adaptive_Generation_for_Portrait_Synthesis_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Li_HyperLoRA_Parameter-Efficient_Adaptive_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Li_2025_CVPR,
    author    = {Li, Mengtian and Chen, Jinshu and Feng, Wanquan and Li, Bingchuan and Dai, Fei and Zhao, Songtao and He, Qian},
    title     = {HyperLoRA: Parameter-Efficient Adaptive Generation for Portrait Synthesis},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {13114-13123}
}


title: ACE: Anti-Editing Concept Erasure in Text-to-Image Models
authors: Zihao Wang, Yuxiang Wei, Fan Li, Renjing Pei, Hang Xu, Wangmeng Zuo
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 23505-23515
raw_affiliation: [{"affiliation": "string", "city": "string", "country": "string"}]
affiliations: ["string"]
cities: ["string"]
countries: ["string"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Wang_ACE_Anti-Editing_Concept_Erasure_in_Text-to-Image_Models_CVPR_2025_paper.html

abstract:
Recent advance in text-to-image diffusion models have significantly facilitated the generation of high-quality images, but also raising concerns about the illegal creation of harmful content, such as copyrighted images. Existing concept erasure methods achieve superior results in preventing the production of erased concept from prompts, but typically perform poorly in preventing undesired editing. To address this issue, we propose an Anti-Editing Concept Erasure (ACE) method, which not only erases the target concept during generation but also filters out it during editing. Specifically, we propose to inject the erasure guidance into both conditional and the unconditional noise prediction, enabling the model to effectively prevent the creation of erasure concepts during both editing and generation. Furthermore, a stochastic correction guidance is introduced during training to address the erosion of unrelated concepts. We conducted erasure editing experiments with representative editing methods (i.e., LEDITS++ and MasaCtrl) to erase IP characters, and the results indicate that our ACE effectively filters out target concepts in both types of edits. Additional experiments on erasing explicit concepts and artistic styles further demonstrate that our ACE performs favorably against state-of-the-art methods. Our code will be publicly available at https://github.com/120L020904/ACE.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Wang_ACE_Anti-Editing_Concept_Erasure_in_Text-to-Image_Models_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Wang_ACE_Anti-Editing_Concept_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Wang_2025_CVPR,
    author    = {Wang, Zihao and Wei, Yuxiang and Li, Fan and Pei, Renjing and Xu, Hang and Zuo, Wangmeng},
    title     = {ACE: Anti-Editing Concept Erasure in Text-to-Image Models},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {23505-23515}
}


title: EchoMatch: Partial-to-Partial Shape Matching via Correspondence Reflection
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 11665-11675
affiliations: ["Technical University of Munich", "Munich Center for Machine Learning", "University of Bonn"]
cities: ["Munich", "Munich", "Bonn"]
countries: ["Germany", "Germany", "Germany"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Xie_EchoMatch_Partial-to-Partial_Shape_Matching_via_Correspondence_Reflection_CVPR_2025_paper.html

authors:
Yizheng Xie, Viktoria Ehm, Paul Roetzer, Nafie El Amrani, Maolin Gao, Florian Bernard, Daniel Cremers

abstract:
Finding correspondences between 3D shapes is a crucial problem in computer vision and graphics. While most research has focused on finding correspondences in settings where at least one of the shapes is complete, the realm of partial-to-partial shape matching remains under-explored. Yet, it is important since in many applications shapes are only observed partially due to occlusion or scanning. Finding correspondences between partial shapes comes with an additional challenge: We not only want to identify correspondences between points on either shape but also have to determine which points of each shape actually have a partner. To tackle this challenging problem, we present EchoMatch, a novel framework for partial-to-partial shape matching that incorporates the concept of correspondence reflection to enable an overlap prediction within a functional map framework. With this approach, we show that we can outperform current SOTA methods in challenging partial-to-partial shape matching problems. Our code is available at https://echo-match.github.io.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Xie_EchoMatch_Partial-to-Partial_Shape_Matching_via_Correspondence_Reflection_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Xie_EchoMatch_Partial-to-Partial_Shape_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Xie_2025_CVPR,
    author    = {Xie, Yizheng and Ehm, Viktoria and Roetzer, Paul and El Amrani, Nafie and Gao, Maolin and Bernard, Florian and Cremers, Daniel},
    title     = {EchoMatch: Partial-to-Partial Shape Matching via Correspondence Reflection},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {11665-11675}
}

raw_affiliation:
[{"affiliation": "Technical University of Munich", "city": "Munich", "country": "Germany"}, {"affiliation": "Munich Center for Machine Learning", "city": "Munich", "country": "Germany"}, {"affiliation": "University of Bonn", "city": "Bonn", "country": "Germany"}]


authors: Junhao Xu, Yanan Zhang, Zhi Cai, Di Huang
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 6834-6843
cities: ["Beijing", "Hefei"]
countries: ["China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Xu_CoSDH_Communication-Efficient_Collaborative_Perception_via_Supply-Demand_Awareness_and_Intermediate-Late_Hybridization_CVPR_2025_paper.html

title:
CoSDH: Communication-Efficient Collaborative Perception via Supply-Demand Awareness and Intermediate-Late Hybridization

abstract:
Multi-agent collaborative perception enhances perceptual capabilities by utilizing information from multiple agents and is considered a fundamental solution to the problem of weak single-vehicle perception in autonomous driving. However, existing collaborative perception methods face a dilemma between communication efficiency and perception accuracy. To address this issue, we propose a novel communication-efficient collaborative perception framework based on supply-demand awareness and intermediate-late hybridization, dubbed as CoSDH. By modeling the supply-demand relationship between agents, the framework refines the selection of collaboration regions, reducing unnecessary communication cost while maintaining accuracy. In addition, we innovatively introduce the intermediate-late hybrid collaboration mode, where late-stage collaboration compensates for the performance degradation in collaborative perception under low communication bandwidth. Extensive experiments on multiple datasets, including both simulated and real-world scenarios, demonstrate that CoSDH achieves state-of-the-art detection accuracy and optimal bandwidth trade-offs, delivering superior detection precision under real communication bandwidths, thus proving its effectiveness and practical applicability. The code will be released at https://github.com/Xu2729/CoSDH.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Xu_CoSDH_Communication-Efficient_Collaborative_Perception_via_Supply-Demand_Awareness_and_Intermediate-Late_Hybridization_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Xu_CoSDH_Communication-Efficient_Collaborative_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Xu_2025_CVPR,
    author    = {Xu, Junhao and Zhang, Yanan and Cai, Zhi and Huang, Di},
    title     = {CoSDH: Communication-Efficient Collaborative Perception via Supply-Demand Awareness and Intermediate-Late Hybridization},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {6834-6843}
}

raw_affiliation:
[{"affiliation": "State Key Laboratory of Complex and Critical Software Environment, Beihang University", "city": "Beijing", "country": "China"}, {"affiliation": "School of Computer Science and Information Engineering, Hefei University of Technology", "city": "Hefei", "country": "China"}]

affiliations:
["State Key Laboratory of Complex and Critical Software Environment, Beihang University", "School of Computer Science and Information Engineering, Hefei University of Technology"]


title: Stereo Anywhere: Robust Zero-Shot Deep Stereo Matching Even Where Either Stereo or Mono Fail
authors: Luca Bartolomei, Fabio Tosi, Matteo Poggi, Stefano Mattoccia
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 1013-1027
cities: ["Bologna", "Bologna"]
countries: ["Italy", "Italy"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Bartolomei_Stereo_Anywhere_Robust_Zero-Shot_Deep_Stereo_Matching_Even_Where_Either_CVPR_2025_paper.html

abstract:
We introduce Stereo Anywhere, a novel stereo-matching framework that combines geometric constraints with robust priors from monocular depth Vision Foundation Models (VFMs). By elegantly coupling these complementary worlds through a dual-branch architecture, we seamlessly integrate stereo matching with learned contextual cues. Following this design, our framework introduces novel cost volume fusion mechanisms that effectively handle critical challenges such as textureless regions, occlusions, and non-Lambertian surfaces. Through our novel optical illusion dataset, MonoTrap, and extensive evaluation across multiple benchmarks, we demonstrate that our synthetic-only trained model achieves state-of-the-art results in zero-shot generalization, significantly outperforming existing solutions while showing remarkable robustness to challenging cases such as mirrors and transparencies.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Bartolomei_Stereo_Anywhere_Robust_Zero-Shot_Deep_Stereo_Matching_Even_Where_Either_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Bartolomei_Stereo_Anywhere_Robust_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Bartolomei_2025_CVPR,
    author    = {Bartolomei, Luca and Tosi, Fabio and Poggi, Matteo and Mattoccia, Stefano},
    title     = {Stereo Anywhere: Robust Zero-Shot Deep Stereo Matching Even Where Either Stereo or Mono Fail},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {1013-1027}
}

raw_affiliation:
[{"affiliation": "Advanced Research Center on Electronic System (ARCES)", "city": "Bologna", "country": "Italy"}, {"affiliation": "Department of Computer Science and Engineering (DISI) University of Bologna", "city": "Bologna", "country": "Italy"}]

affiliations:
["Advanced Research Center on Electronic System (ARCES)", "Department of Computer Science and Engineering (DISI) University of Bologna"]


title: Order-Robust Class Incremental Learning: Graph-Driven Dynamic Similarity Grouping
authors: Guannan Lai, Yujie Li, Xiangkun Wang, Junbo Zhang, Tianrui Li, Xin Yang
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 4894-4904
cities: ["Chengdu", "Leiden", "Beijing", "Chengdu"]
countries: ["China", "Netherlands", "China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Lai_Order-Robust_Class_Incremental_Learning_Graph-Driven_Dynamic_Similarity_Grouping_CVPR_2025_paper.html

abstract:
Class Incremental Learning (CIL) aims to enable models to learn new classes sequentially while retaining knowledge of previous ones. Although current methods have alleviated catastrophic forgetting (CF), recent studies highlight that the performance of CIL models is highly sensitive to the order of class arrival, particularly when sequentially introduced classes exhibit high inter-class similarity. To address this critical yet understudied challenge of class order sensitivity, we first extend existing CIL frameworks through theoretical analysis, proving that grouping classes with lower pairwise similarity during incremental phases significantly improves model robustness to order variations. Building on this insight, we propose Graph-Driven Dynamic Similarity Grouping (GDDSG), a novel method that employs graph coloring algorithms to dynamically partition classes into similarity-constrained groups. Each group trains an isolated CIL sub-model and constructs meta-features for class group identification. Experimental results demonstrate that our method effectively addresses the issue of class order sensitivity while achieving optimal performance in both model accuracy and anti-forgetting capability. Our code is available at https://github.com/AIGNLAI/GDDSG.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Lai_Order-Robust_Class_Incremental_Learning_Graph-Driven_Dynamic_Similarity_Grouping_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Lai_Order-Robust_Class_Incremental_CVPR_2025_supplemental.zip

bibtex:
@InProceedings{Lai_2025_CVPR,
    author    = {Lai, Guannan and Li, Yujie and Wang, Xiangkun and Zhang, Junbo and Li, Tianrui and Yang, Xin},
    title     = {Order-Robust Class Incremental Learning: Graph-Driven Dynamic Similarity Grouping},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {4894-4904}
}

raw_affiliation:
[{"affiliation": "School of Computing and Artificial Intelligence, Southwestern University of Finance and Economics", "city": "Chengdu", "country": "China"}, {"affiliation": "LIACS, Leiden University", "city": "Leiden", "country": "Netherlands"}, {"affiliation": "JD Intelligent Cities Research", "city": "Beijing", "country": "China"}, {"affiliation": "Southwest Jiaotong University", "city": "Chengdu", "country": "China"}]

affiliations:
["School of Computing and Artificial Intelligence, Southwestern University of Finance and Economics", "LIACS, Leiden University", "JD Intelligent Cities Research", "Southwest Jiaotong University"]


title: Where the Devil Hides: Deepfake Detectors Can No Longer Be Trusted
authors: Shuaiwei Yuan, Junyu Dong, Yuezun Li
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 8764-8774
affiliations: ["School of Computer Science and Technology, Ocean University of China"]
cities: ["Qingdao"]
countries: ["China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Yuan_Where_the_Devil_Hides_Deepfake_Detectors_Can_No_Longer_Be_CVPR_2025_paper.html

abstract:
With the advancement of AI generative techniques, Deepfake faces have become incredibly realistic and nearly indistinguishable to the human eye. To counter this, Deepfake detectors have been developed as reliable tools for assessing face authenticity. These detectors are typically developed on Deep Neural Networks (DNNs) and trained using third-party datasets. However, this protocol raises a new security risk that can seriously undermine the trustfulness of Deepfake detectors: Once the third-party data providers insert poisoned (corrupted) data maliciously, Deepfake detectors trained on these datasets will be injected "backdoors" that cause abnormal behavior when presented with samples containing specific triggers. This is a practical concern, as third-party providers may distribute or sell these triggers to malicious users, allowing them to manipulate detector performance and escape accountability.This paper investigates this risk in depth and describes a solution to stealthily infect Deepfake detectors. Specifically, we develop a trigger generator, that can synthesize passcode-controlled, semantic-suppression, adaptive, and invisible trigger patterns, ensuring both the stealthiness and effectiveness of these triggers. Then we discuss two poisoning scenarios, dirty-label poisoning and clean-label poisoning, to accomplish the injection of backdoors. Extensive experiments demonstrate the effectiveness, stealthiness, and practicality of our method compared to several baselines.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Yuan_Where_the_Devil_Hides_Deepfake_Detectors_Can_No_Longer_Be_CVPR_2025_paper.pdf

bibtex:
@InProceedings{Yuan_2025_CVPR,
    author    = {Yuan, Shuaiwei and Dong, Junyu and Li, Yuezun},
    title     = {Where the Devil Hides: Deepfake Detectors Can No Longer Be Trusted},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {8764-8774}
}

raw_affiliation:
[{"affiliation": "School of Computer Science and Technology, Ocean University of China", "city": "Qingdao", "country": "China"}]


authors: Weilong Yan, Ming Li, Haipeng Li, Shuwei Shao, Robby T. Tan
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 21880-21890
cities: ["Singapore", "Singapore", "Shenzhen", "Chengdu", "Jinan"]
countries: ["Singapore", "Singapore", "China", "China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Yan_Synthetic-to-Real_Self-supervised_Robust_Depth_Estimation_via_Learning_with_Motion_and_CVPR_2025_paper.html

title:
Synthetic-to-Real Self-supervised Robust Depth Estimation via Learning with Motion and Structure Priors

abstract:
Self-supervised depth estimation from monocular cameras in diverse outdoor conditions, such as daytime, rain, and nighttime, is challenging due to the difficulty of learning universal representations and the severe lack of labeled real-world adverse data.Previous methods either rely on synthetic inputs and pseudo-depth labels or directly apply daytime strategies to adverse conditions, resulting in suboptimal results.In this paper, we present the first synthetic-to-real robust depth estimation framework, incorporating motion and structure priors to capture real-world knowledge effectively. In the synthetic adaptation, we transfer motion-structure knowledge inside cost volumes for better robust representation, using a frozen daytime model to train a depth estimator in synthetic adverse conditions.In the innovative real adaptation which targets to fix synthetic-real gaps, models trained earlier identify the weather-insensitive regions with a designed consistency-reweighting strategy to emphasize valid pseudo-labels.We further introduce a new regularization by gathering explicit depth distribution prior to constrain the model facing real-world data.Experiments show that our method outperforms the state-of-the-art across diverse conditions in multi-frame and single-frame settings. We achieve improvements of 7.5% in AbsRel and 4.3% in RMSE on average for nuScenes and Robotcar datasets (daytime, nighttime, rain). In zero-shot evaluation on DrivingStereo (rain, fog), our method generalizes better than previous ones. Our code will be released soon.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Yan_Synthetic-to-Real_Self-supervised_Robust_Depth_Estimation_via_Learning_with_Motion_and_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Yan_Synthetic-to-Real_Self-supervised_Robust_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Yan_2025_CVPR,
    author    = {Yan, Weilong and Li, Ming and Li, Haipeng and Shao, Shuwei and Tan, Robby T.},
    title     = {Synthetic-to-Real Self-supervised Robust Depth Estimation via Learning with Motion and Structure Priors},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {21880-21890}
}

raw_affiliation:
[{"affiliation": "National University of Singapore", "city": "Singapore", "country": "Singapore"}, {"affiliation": "ASUS Intelligent Cloud Services", "city": "Singapore", "country": "Singapore"}, {"affiliation": "Guangdong Laboratory of Artificial Intelligence and Digital Economy (SZ)", "city": "Shenzhen", "country": "China"}, {"affiliation": "University of Electronic Science and Technology of China", "city": "Chengdu", "country": "China"}, {"affiliation": "School of Control Science and Engineering, Shandong University", "city": "Jinan", "country": "China"}]

affiliations:
["National University of Singapore", "ASUS Intelligent Cloud Services", "Guangdong Laboratory of Artificial Intelligence and Digital Economy (SZ)", "University of Electronic Science and Technology of China", "School of Control Science and Engineering, Shandong University"]


title: CaMuViD: Calibration-Free Multi-View Detection
authors: Amir Etefaghi Daryani, M. Usman Maqbool Bhutta, Byron Hernandez, Henry Medeiros
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 1220-1229
raw_affiliation: [{"affiliation": "University of Florida", "city": "Gainesville", "country": "USA"}]
affiliations: ["University of Florida"]
cities: ["Gainesville"]
countries: ["USA"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Daryani_CaMuViD_Calibration-Free_Multi-View_Detection_CVPR_2025_paper.html

abstract:
Multi-view object detection in crowded environments presents significant challenges, particularly for occlusion management across multiple camera views. This paper introduces a novel approach that extends conventional multi-view detection to operate directly within each camera's image space. Our method finds objects bounding boxes for images from various perspectives without resorting to a bird's eye view (BEV) representation. Thus, our approach removes the need for camera calibration by leveraging a learnable architecture that facilitates flexible transformations and improves feature fusion across perspectives to increase detection accuracy. Our model achieves Multi-Object Detection Accuracy (MODA) scores of 95.0% and 96.5% on the Wildtrack and MultiviewX datasets, respectively,  significantly advancing the state of the art in multi-view detection. Furthermore, it demonstrates robust performance even without ground truth annotations, highlighting its resilience and practicality in real-world applications. These results emphasize the effectiveness of our calibration-free, multi-view object detector.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Daryani_CaMuViD_Calibration-Free_Multi-View_Detection_CVPR_2025_paper.pdf

bibtex:
@InProceedings{Daryani_2025_CVPR,
    author    = {Daryani, Amir Etefaghi and Bhutta, M. Usman Maqbool and Hernandez, Byron and Medeiros, Henry},
    title     = {CaMuViD: Calibration-Free Multi-View Detection},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {1220-1229}
}


title: Prosody-Enhanced Acoustic Pre-training and Acoustic-Disentangled Prosody Adapting for Movie Dubbing
authors: Zhedong Zhang, Liang Li, Chenggang Yan, Chunshan Liu, Anton van den Hengel, Yuankai Qi
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 172-182
cities: ["Hangzhou", "Beijing", "Adelaide", "Sydney"]
countries: ["China", "China", "Australia", "Australia"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Zhang_Prosody-Enhanced_Acoustic_Pre-training_and_Acoustic-Disentangled_Prosody_Adapting_for_Movie_Dubbing_CVPR_2025_paper.html

abstract:
Movie dubbing describes the process of transforming a script into speech that aligns temporally and emotionally with a given movie clip while exemplifying the speaker's voice demonstrated in a short reference audio clip. This task demands the model bridge character performances and complicated prosody structures to build a high-quality video-synchronized dubbing track. The limited scale of movie dubbing datasets, along with the background noise inherent in audio data, hinder the acoustic modeling performance of trained models. To address these issues, we propose an acoustic-prosody disentangled two-stage method to achieve high-quality dubbing generation with precise prosody alignment. First, we propose a prosody-enhanced acoustic pre-training to develop robust acoustic modeling capabilities. Then, we freeze the pre-trained acoustic system and design a disentangled framework to model prosodic text features and dubbing style while maintaining acoustic quality. Additionally, we incorporate an in-domain emotion analysis module to reduce the impact of visual domain shifts across different movies, thereby enhancing emotion-prosody alignment. Extensive experiments show that our method performs favorably against the state-of-the-art models on two primary benchmarks. The project is available at https://zzdoog.github.io/ProDubber/.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Zhang_Prosody-Enhanced_Acoustic_Pre-training_and_Acoustic-Disentangled_Prosody_Adapting_for_Movie_Dubbing_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Zhang_Prosody-Enhanced_Acoustic_Pre-training_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Zhang_2025_CVPR,
    author    = {Zhang, Zhedong and Li, Liang and Yan, Chenggang and Liu, Chunshan and van den Hengel, Anton and Qi, Yuankai},
    title     = {Prosody-Enhanced Acoustic Pre-training and Acoustic-Disentangled Prosody Adapting for Movie Dubbing},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {172-182}
}

raw_affiliation:
[{"affiliation": "Hangzhou Dianzi University", "city": "Hangzhou", "country": "China"}, {"affiliation": "Institute of Computing Technology, Chinese Academy of Sciences", "city": "Beijing", "country": "China"}, {"affiliation": "University of Adelaide", "city": "Adelaide", "country": "Australia"}, {"affiliation": "Macquarie University", "city": "Sydney", "country": "Australia"}]

affiliations:
["Hangzhou Dianzi University", "Institute of Computing Technology, Chinese Academy of Sciences", "University of Adelaide", "Macquarie University"]


title: Hierarchical Knowledge Prompt Tuning for Multi-task Test-Time Adaptation
authors: Qiang Zhang, Mengsheng Zhao, Jiawei Liu, Fanrui Zhang, Yongchao Xu, Zheng-Jun Zha
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 30524-30533
cities: ["Hefei"]
countries: ["China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Zhang_Hierarchical_Knowledge_Prompt_Tuning_for_Multi-task_Test-Time_Adaptation_CVPR_2025_paper.html

abstract:
Test-time adaptation using vision-language models (such as CLIP) to quickly adjust to distributional shifts of downstream tasks has shown great potential. Despite significant progress, existing methods are still limited to single-task test-time adaptation scenarios and have not effectively explored the issue of multi-task adaptation. To address this practical problem, we propose a novel Hierarchical Knowledge Prompt Tuning (HKPT) method, which achieves joint adaptation to multiple target domains by mining more comprehensive source domain discriminative knowledge and hierarchically modeling task-specific and task-shared knowledge. Specifically, HKPT constructs a CLIP prompt distillation framework that utilizes the broader source domain knowledge of large teacher CLIP to guide prompt tuning for lightweight student CLIP from multiple views during testing. Meanwhile, HKPT establishes task-specific dual dynamic knowledge graph to capture fine-grained contextual knowledge from continuous test data. To fully exploit the complementarity among multiple target tasks, HKPT employs an adaptive task grouping strategy for achieving inter-task knowledge sharing. Furthermore, HKPT can seamlessly transfer to basic single-task test-time adaptation scenarios while maintaining robust performance. Extensive experimental results in both multi-task and single-task test-time adaptation settings demonstrate that our HKPT significantly outperforms state-of-the-art methods.

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

bibtex:
@InProceedings{Zhang_2025_CVPR,
    author    = {Zhang, Qiang and Zhao, Mengsheng and Liu, Jiawei and Zhang, Fanrui and Xu, Yongchao and Zha, Zheng-Jun},
    title     = {Hierarchical Knowledge Prompt Tuning for Multi-task Test-Time Adaptation},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {30524-30533}
}

raw_affiliation:
[{"affiliation": "MoE Key Laboratory of Brain-inspired Intelligent Perception and Cognition, University of Science and Technology of China, China", "city": "Hefei", "country": "China"}]

affiliations:
["MoE Key Laboratory of Brain-inspired Intelligent Perception and Cognition, University of Science and Technology of China, China"]


title: Cross-Modal Interactive Perception Network with Mamba for Lung Tumor Segmentation in PET-CT Images
authors: Jie Mei, Chenyu Lin, Yu Qiu, Yaonan Wang, Hui Zhang, Ziyang Wang, Dong Dai
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 15653-15662
cities: ["Changsha", "Tianjin", "Changsha", "Tianjin"]
countries: ["China", "China", "China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Mei_Cross-Modal_Interactive_Perception_Network_with_Mamba_for_Lung_Tumor_Segmentation_CVPR_2025_paper.html

abstract:
Lung cancer is a leading cause of cancer-related deaths globally. PET-CT is crucial for imaging lung tumors, providing essential metabolic and anatomical information, while it faces challenges such as poor image quality, motion artifacts, and complex tumor morphology. Deep learning-based models are expected to address these problems, however, existing small-scale and private datasets limit significant performance improvements for these methods. Hence, we introduce a large-scale PET-CT lung tumor segmentation dataset, termed PCLT20K, which comprises 21,930 pairs of PET-CT images from 605 patients. Furthermore, we propose a cross-modal interactive perception network with Mamba (CIPA) for lung tumor segmentation in PET-CT images. Specifically, we design a channel-wise rectification module (CRM) that implements a channel state space block across multi-modal features to learn correlated representations and helps filter out modality-specific noise. A dynamic cross-modality interaction module (DCIM) is designed to effectively integrate position and context information, which employs PET images to learn regional position information and serves as a bridge to assist in modeling the relationships between local features of CT images. Extensive experiments on a comprehensive benchmark demonstrate the effectiveness of our CIPA compared to the current state-of-the-art segmentation methods. We hope our research can provide more exploration opportunities for medical image segmentation. The dataset and code are available at https://github.com/mj129/CIPA.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Mei_Cross-Modal_Interactive_Perception_Network_with_Mamba_for_Lung_Tumor_Segmentation_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Mei_Cross-Modal_Interactive_Perception_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Mei_2025_CVPR,
    author    = {Mei, Jie and Lin, Chenyu and Qiu, Yu and Wang, Yaonan and Zhang, Hui and Wang, Ziyang and Dai, Dong},
    title     = {Cross-Modal Interactive Perception Network with Mamba for Lung Tumor Segmentation in PET-CT Images},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {15653-15662}
}

raw_affiliation:
[{"affiliation": "Hunan University", "city": "Changsha", "country": "China"}, {"affiliation": "Nankai University", "city": "Tianjin", "country": "China"}, {"affiliation": "Hunan Normal University", "city": "Changsha", "country": "China"}, {"affiliation": "Tianjin Medical University Cancer Institute and Hospital", "city": "Tianjin", "country": "China"}]

affiliations:
["Hunan University", "Nankai University", "Hunan Normal University", "Tianjin Medical University Cancer Institute and Hospital"]


title: LaTexBlend: Scaling Multi-concept Customized Generation with Latent Textual Blending
authors: Jian Jin, Zhenbo Yu, Yang Shen, Zhenyong Fu, Jian Yang
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 23585-23594
affiliations: ["PCA Lab\u2020, Nanjing University of Science and Technology"]
cities: ["Nanjing"]
countries: ["China"]
highlight: yes
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Jin_LaTexBlend_Scaling_Multi-concept_Customized_Generation_with_Latent_Textual_Blending_CVPR_2025_paper.html

abstract:
Customized text-to-image generation renders user-specified concepts into novel contexts based on textual prompts. Scaling the number of concepts in customized generation meets a broader demand for user creation, whereas existing methods face challenges with generation quality and computational efficiency. In this paper, we propose LaTexBlend, a novel framework for effectively and efficiently scaling multi-concept customized generation. The core idea of LaTexBlend is to represent single concepts and blend multiple concepts within a Latent Textual space, which is positioned after the text encoder and a linear projection. LaTexBlend customizes each concept individually, storing them in a concept bank with a compact representation of latent textual features that captures sufficient concept information to ensure high fidelity. At inference, concepts from the bank can be freely and seamlessly combined in the latent textual space, offering two key merits for multi-concept generation: 1) excellent scalability, and 2) significant reduction of denoising deviation, preserving coherent layouts. Extensive experiments demonstrate that LaTexBlend can flexibly integrate multiple customized concepts with harmonious structures and high subject fidelity, substantially outperforming baselines in both generation quality and computational efficiency. Our code will be publicly available.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Jin_LaTexBlend_Scaling_Multi-concept_Customized_Generation_with_Latent_Textual_Blending_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Jin_LaTexBlend_Scaling_Multi-concept_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Jin_2025_CVPR,
    author    = {Jin, Jian and Yu, Zhenbo and Shen, Yang and Fu, Zhenyong and Yang, Jian},
    title     = {LaTexBlend: Scaling Multi-concept Customized Generation with Latent Textual Blending},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {23585-23594}
}

raw_affiliation:
[{"affiliation": "PCA Lab\u2020, Nanjing University of Science and Technology", "city": "Nanjing", "country": "China"}]


title: DejaVid: Encoder-Agnostic Learned Temporal Matching for Video Classification
authors: Darryl Ho, Samuel Madden
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 24023-24032
affiliations: ["MIT CSAIL", "MIT CSAIL"]
cities: ["Cambridge", "Cambridge"]
countries: ["USA", "USA"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Ho_DejaVid_Encoder-Agnostic_Learned_Temporal_Matching_for_Video_Classification_CVPR_2025_paper.html

abstract:
In recent years, large transformer-based video encoder models have greatly advanced state-of-the-art performance on video classification tasks. However, these large models typically process videos by averaging embedding outputs from multiple clips over time to produce fixed-length representations. This approach fails to account for a variety of time-related features, such as variable video durations, chronological order of events, and temporal variance in feature significance. While methods for temporal modeling do exist, they often require significant architectural changes and expensive retraining, making them impractical for off-the-shelf, fine-tuned large encoders. To overcome these limitations, we propose DejaVid, an encoder-agnostic method that enhances model performance without the need for retraining or altering the architecture. Our framework converts a video into a variable-length temporal sequence of embeddings (TSE). A TSE naturally preserves temporal order and accommodates variable video durations. We then learn per-timestep, per-feature weights over the encoded TSE frames, allowing us to account for variations in feature importance over time. We introduce a new neural network architecture inspired by traditional time series alignment algorithms for this learning task. Our evaluation demonstrates that DejaVid substantially improves the performance of a state-of-the-art large encoder, achieving leading Top-1 accuracy of 77.2% on Something-Something V2, 89.1% on Kinetics-400, and 88.6% on HMDB51, while adding fewer than 1.8% additional learnable parameters and requiring less than 3 hours of training time. Our code is available at https://github.com/darrylho/DejaVid.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Ho_DejaVid_Encoder-Agnostic_Learned_Temporal_Matching_for_Video_Classification_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Ho_DejaVid_Encoder-Agnostic_Learned_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Ho_2025_CVPR,
    author    = {Ho, Darryl and Madden, Samuel},
    title     = {DejaVid: Encoder-Agnostic Learned Temporal Matching for Video Classification},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {24023-24032}
}

raw_affiliation:
[{"affiliation": "MIT CSAIL", "city": "Cambridge", "country": "USA"}, {"affiliation": "MIT CSAIL", "city": "Cambridge", "country": "USA"}]


title: HVI: A New Color Space for Low-light Image Enhancement
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 5678-5687
cities: ["Xi'an", "Singapore", "Xi'an"]
countries: ["China", "Singapore", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Yan_HVI_A_New_Color_Space_for_Low-light_Image_Enhancement_CVPR_2025_paper.html

authors:
Qingsen Yan, Yixu Feng, Cheng Zhang, Guansong Pang, Kangbiao Shi, Peng Wu, Wei Dong, Jinqiu Sun, Yanning Zhang

abstract:
Low-Light Image Enhancement (LLIE) is a crucial computer vision task that aims to restore detailed visual information from corrupted low-light images. Many existing LLIE methods are based on standard RGB (sRGB) space, which often produce color bias and brightness artifacts due to inherent high color sensitivity in sRGB. While converting the images using Hue, Saturation and Value (HSV) color space helps resolve the brightness issue, it introduces significant red and black noise artifacts. To address this issue, we propose a new color space for LLIE, namely Horizontal/Vertical-Intensity (HVI), defined by polarized HS maps and learnable intensity. The former enforces small distances for red coordinates to remove the red artifacts, while the latter compresses the low-light regions to remove the black artifacts. To fully leverage the chromatic and intensity information, a novel Color and Intensity Decoupling Network (CIDNet) is further introduced to learn accurate photometric mapping function under different lighting conditions in the HVI space. Comprehensive results from benchmark and ablation experiments show that the proposed HVI color space with CIDNet outperforms the state-of-the-art methods on 10 datasets. The code is available at https://github.com/Fediory/HVI-CIDNet.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Yan_HVI_A_New_Color_Space_for_Low-light_Image_Enhancement_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Yan_HVI_A_New_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Yan_2025_CVPR,
    author    = {Yan, Qingsen and Feng, Yixu and Zhang, Cheng and Pang, Guansong and Shi, Kangbiao and Wu, Peng and Dong, Wei and Sun, Jinqiu and Zhang, Yanning},
    title     = {HVI: A New Color Space for Low-light Image Enhancement},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {5678-5687}
}

raw_affiliation:
[{"affiliation": "Northwestern Polytechnical University", "city": "Xi'an", "country": "China"}, {"affiliation": "Singapore Management University", "city": "Singapore", "country": "Singapore"}, {"affiliation": "Xi\u2019an University of Architecture and Technology", "city": "Xi'an", "country": "China"}]

affiliations:
["Northwestern Polytechnical University", "Singapore Management University", "Xi\u2019an University of Architecture and Technology"]


title: DualPM: Dual Posed-Canonical Point Maps for 3D Shape and Pose Reconstruction
authors: Ben Kaye, Tomas Jakab, Shangzhe Wu, Christian Ruprecht, Andrea Vedaldi
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 6425-6435
affiliations: ["University of Oxford", "Stanford University", "University of Cambridge"]
cities: ["Oxford", "Stanford", "Cambridge"]
countries: ["UK", "USA", "UK"]
highlight: yes
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Kaye_DualPM_Dual_Posed-Canonical_Point_Maps_for_3D_Shape_and_Pose_CVPR_2025_paper.html

abstract:
The choice of data representation is a key factor in the success of deep learning in geometric tasks. For instance, DUSt3R has recently introduced the concept of viewpoint- invariant point maps, generalizing depth prediction, and showing that one can reduce all the key problems in the 3D reconstruction of static scenes to predicting such point maps. In this paper, we develop an analogous concept for a very different problem, namely, the reconstruction of the 3D shape and pose of deformable objects. To this end, we introduce the Dual Point Maps (DualPM), where a pair of point maps is extracted from the same image, one associating pixels to their 3D locations on the object, and the other to a canonical version of the object at rest pose. We also extend point maps to amodal reconstruction, seeing through self-occlusions to obtain the complete shape of the object. We show that 3D reconstruction and 3D pose estimation reduce to the prediction of the DualPMs. We demonstrate empirically that this representation is a good target for a deep network to predict; specifically, we consider modeling quadrupeds, showing that DualPMs can be trained purely on 3D synthetic data, consisting of one or two models per category, while generalizing very well to real images. With this, we improve by a large margin previous methods for the 3D analysis and reconstruction of this type of objects.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Kaye_DualPM_Dual_Posed-Canonical_Point_Maps_for_3D_Shape_and_Pose_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Kaye_DualPM_Dual_Posed-Canonical_CVPR_2025_supplemental.zip

bibtex:
@InProceedings{Kaye_2025_CVPR,
    author    = {Kaye, Ben and Jakab, Tomas and Wu, Shangzhe and Ruprecht, Christian and Vedaldi, Andrea},
    title     = {DualPM: Dual Posed-Canonical Point Maps for 3D Shape and Pose Reconstruction},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {6425-6435}
}

raw_affiliation:
[{"affiliation": "University of Oxford", "city": "Oxford", "country": "UK"}, {"affiliation": "Stanford University", "city": "Stanford", "country": "USA"}, {"affiliation": "University of Cambridge", "city": "Cambridge", "country": "UK"}]


authors: Yi Du, Zhipeng Zhao, Shaoshu Su, Sharath Golluri, Haoze Zheng, Runmao Yao, Chen Wang
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 16953-16964
affiliations: ["Spatial AI & Robotics (SAIR) Lab, University at Buffalo"]
cities: ["Buffalo"]
countries: ["USA"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Du_SuperPC_A_Single_Diffusion_Model_for_Point_Cloud_Completion_Upsampling_CVPR_2025_paper.html

title:
SuperPC: A Single Diffusion Model for Point Cloud Completion, Upsampling, Denoising, and Colorization

abstract:
Point cloud (PC) processing tasks--such as completion, upsampling, denoising, and colorization--are crucial in applications like autonomous driving and 3D reconstruction. Despite substantial advancements, prior approaches often address each of these tasks independently, with separate models focused on individual issues. However, this isolated approach fails to account for the fact that defects like incompleteness, low resolution, noise, and lack of color frequently coexist, with each defect influencing and correlating with the others.Simply applying these models sequentially can lead to error accumulation from each model, along with increased computational costs. To address these challenges, we introduce SuperPC, the first unified diffusion model capable of concurrently handling all four tasks. Our approach employs a three-level-conditioned diffusion framework, enhanced by a novel spatial-mix-fusion strategy, to leverage the correlations among these four defects for simultaneous, efficient processing.We show that SuperPC outperforms the state-of-the-art specialized models as well as their combination on all four individual tasks.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Du_SuperPC_A_Single_Diffusion_Model_for_Point_Cloud_Completion_Upsampling_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Du_SuperPC_A_Single_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Du_2025_CVPR,
    author    = {Du, Yi and Zhao, Zhipeng and Su, Shaoshu and Golluri, Sharath and Zheng, Haoze and Yao, Runmao and Wang, Chen},
    title     = {SuperPC: A Single Diffusion Model for Point Cloud Completion, Upsampling, Denoising, and Colorization},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {16953-16964}
}

raw_affiliation:
[{"affiliation": "Spatial AI & Robotics (SAIR) Lab, University at Buffalo", "city": "Buffalo", "country": "USA"}]


title: One Diffusion to Generate Them All
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 2671-2682
affiliations: ["Allen Institute for AI", "University of California, Irvine", "University of Washington"]
cities: ["Seattle", "Irvine", "Seattle"]
countries: ["USA", "USA", "USA"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Le_One_Diffusion_to_Generate_Them_All_CVPR_2025_paper.html

authors:
Duong H. Le, Tuan Pham, Sangho Lee, Christopher Clark, Aniruddha Kembhavi, Stephan Mandt, Ranjay Krishna, Jiasen Lu

abstract:
We introduce \texttt OneDiffusion  - a single large-scale diffusion model designed to tackle a wide range of image synthesis and understanding tasks.  It can generate images conditioned on text, depth, pose, layout, or semantic maps. It also handles super-resolution, multi-view generation, instant personalization, and text-guided image editing. Additionally, the same model is also effective for image understanding tasks such as depth estimation, pose estimation, open-vocab segmentation and camera pose estimation, etc.Our unified model is trained with simple but effective recipe: we casting all tasks as modeling a sequence of frames where we inject different noise scales during training to each frame. During inference, we can set any frame to a clean image for tasks like conditional image generation, camera pose estimation, or generating paired depth and images from a caption. Experiment results shows our proposed method achieve competitive performance on wide range of tasks.  By eliminating the need for specialized architectures or finetuning, OneDiffusion provides enhanced flexibility and scalability, reflecting the emerging trend towards general-purpose diffusion models in the field.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Le_One_Diffusion_to_Generate_Them_All_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Le_One_Diffusion_to_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Le_2025_CVPR,
    author    = {Le, Duong H. and Pham, Tuan and Lee, Sangho and Clark, Christopher and Kembhavi, Aniruddha and Mandt, Stephan and Krishna, Ranjay and Lu, Jiasen},
    title     = {One Diffusion to Generate Them All},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {2671-2682}
}

raw_affiliation:
[{"affiliation": "Allen Institute for AI", "city": "Seattle", "country": "USA"}, {"affiliation": "University of California, Irvine", "city": "Irvine", "country": "USA"}, {"affiliation": "University of Washington", "city": "Seattle", "country": "USA"}]


title: Let's Verify and Reinforce Image Generation Step by Step
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 28662-28672
affiliations: ["CUHK 1MMLab", "2MiuLar Lab", "Peking University", "Shanghai AI Lab", "SIAT", "CPII under InnoHK"]
cities: ["Hong Kong", "Hong Kong", "Beijing", "Shanghai", "Shenzhen", "Hong Kong"]
countries: ["China", "China", "China", "China", "China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Zhang_Lets_Verify_and_Reinforce_Image_Generation_Step_by_Step_CVPR_2025_paper.html

authors:
Renrui Zhang, Chengzhuo Tong, Zhizheng Zhao, Ziyu Guo, Haoquan Zhang, Manyuan Zhang, Jiaming Liu, Peng Gao, Hongsheng Li

abstract:
Chain-of-Thought (CoT) reasoning has been extensively explored in large models to tackle complex understanding tasks. However, it still remains an open question whether such strategies can be applied to verifying and reinforcing image generation scenarios. In this paper, we provide the first comprehensive investigation in the potential of CoT reasoning to enhance autoregressive image generation. We focus on three techniques: scaling test-time computation for verification, aligning model preferences with Direct Preference Optimization (DPO), and integrating these techniques for complementary effects. Our results demonstrate that these approaches can be effectively adapted and combined to significantly improve image generation performance. Furthermore, given the pivotal role of reward models in our findings, we propose the Potential Assessment Reward Model (PARM) specialized for autoregressive image generation. PARM adaptively assesses each generation step through a potential assessment mechanism, merging the strengths of existing reward models. Using our investigated reasoning strategies, we enhance a baseline model, Show-o, to achieve superior results, with a significant +24% improvement on the GenEval benchmark, surpassing Stable Diffusion 3 by +15%. We hope our study provides unique insights and paves a new path for integrating CoT reasoning with autoregressive image generation. Code is released at https://github.com/ZiyuGuo99/Image-Generation-CoT.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Zhang_Lets_Verify_and_Reinforce_Image_Generation_Step_by_Step_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Zhang_Lets_Verify_and_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Zhang_2025_CVPR,
    author    = {Zhang, Renrui and Tong, Chengzhuo and Zhao, Zhizheng and Guo, Ziyu and Zhang, Haoquan and Zhang, Manyuan and Liu, Jiaming and Gao, Peng and Li, Hongsheng},
    title     = {Let's Verify and Reinforce Image Generation Step by Step},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {28662-28672}
}

raw_affiliation:
[{"affiliation": "CUHK 1MMLab", "city": "Hong Kong", "country": "China"}, {"affiliation": "2MiuLar Lab", "city": "Hong Kong", "country": "China"}, {"affiliation": "Peking University", "city": "Beijing", "country": "China"}, {"affiliation": "Shanghai AI Lab", "city": "Shanghai", "country": "China"}, {"affiliation": "SIAT", "city": "Shenzhen", "country": "China"}, {"affiliation": "CPII under InnoHK", "city": "Hong Kong", "country": "China"}]


title: All-Optical Nonlinear Diffractive Deep Network for Ultrafast Image Denoising
authors: Xiaoling Zhou, Zhemg Lee, Wei Ye, Rui Xie, Wenbo Zhang, Guanju Peng, Zongze Li, Shikun Zhang
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 28221-28231
affiliations: ["Peking University", "Tianjin University", "Pengcheng Laboratory"]
cities: ["Beijing", "Tianjin", "Shenzhen"]
countries: ["China", "China", "China"]
highlight: yes
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Zhou_All-Optical_Nonlinear_Diffractive_Deep_Network_for_Ultrafast_Image_Denoising_CVPR_2025_paper.html

abstract:
Image denoising poses a significant challenge in image processing, aiming to remove noise and artifacts from input images. However, current denoising algorithms implemented on electronic chips frequently encounter latency issues and demand substantial computational resources. In this paper, we introduce an all-optical Nonlinear Diffractive Denoising Deep Network (N3DNet) for image denoising at the speed of light. Initially, we incorporate an image encoding and pre-denoising module into the Diffractive Deep Neural Network and integrate a nonlinear activation function, termed the phase exponential linear function, after each diffractive layer, thereby boosting the network's nonlinear modeling and denoising capabilities. Subsequently, we devise a new reinforcement learning algorithm called regularization-assisted deep Q-network to optimize N3DNet. Finally, leveraging 3D printing techniques, we fabricate N3DNet using the trained parameters and construct a physical experimental system for real-world applications. A new benchmark dataset, termed MIDD, is constructed for mode image denoising, comprising 120K pairs of noisy/noise-free images captured from real fiber communication systems across various transmission lengths. Through extensive simulation and real experiments, we validate that N3DNet outperforms both traditional and deep learning-based denoising approaches across various datasets. Remarkably, its processing speed is nearly 3,800 times faster than electronic chip-based methods.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Zhou_All-Optical_Nonlinear_Diffractive_Deep_Network_for_Ultrafast_Image_Denoising_CVPR_2025_paper.pdf

bibtex:
@InProceedings{Zhou_2025_CVPR,
    author    = {Zhou, Xiaoling and Lee, Zhemg and Ye, Wei and Xie, Rui and Zhang, Wenbo and Peng, Guanju and Li, Zongze and Zhang, Shikun},
    title     = {All-Optical Nonlinear Diffractive Deep Network for Ultrafast Image Denoising},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {28221-28231}
}

raw_affiliation:
[{"affiliation": "Peking University", "city": "Beijing", "country": "China"}, {"affiliation": "Tianjin University", "city": "Tianjin", "country": "China"}, {"affiliation": "Pengcheng Laboratory", "city": "Shenzhen", "country": "China"}]


title: Maintaining Consistent Inter-Class Topology in Continual Test-Time Adaptation
authors: Chenggong Ni, Fan Lyu, Jiayao Tan, Fuyuan Hu, Rui Yao, Tao Zhou
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 15319-15328
cities: ["Suzhou", "Suzhou", "Suzhou", "Beijing", "Xuzhou", "Yinchuan"]
countries: ["China", "China", "China", "China", "China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Ni_Maintaining_Consistent_Inter-Class_Topology_in_Continual_Test-Time_Adaptation_CVPR_2025_paper.html

abstract:
This paper introduces Topological Consistency Adaptation (TCA), a novel approach to Continual Test-time Adaptation (CTTA) that addresses the challenges of domain shifts and error accumulation in testing scenarios. TCA ensures the stability of inter-class relationships by enforcing a class topological consistency constraint, which minimizes the distortion of class centroids and preserves the topological structure during continuous adaptation. Additionally, we propose an intra-class compactness loss to maintain compactness within classes, indirectly supporting inter-class stability. To further enhance model adaptation, we introduce a batch imbalance topology weighting mechanism that accounts for class distribution imbalances within each batch, optimizing centroid distances and stabilizing the inter-class topology. Experiments show that our method demonstrates improvements in handling continuous domain shifts, ensuring stable feature distributions and boosting predictive performance.

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

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Ni_Maintaining_Consistent_Inter-Class_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Ni_2025_CVPR,
    author    = {Ni, Chenggong and Lyu, Fan and Tan, Jiayao and Hu, Fuyuan and Yao, Rui and Zhou, Tao},
    title     = {Maintaining Consistent Inter-Class Topology in Continual Test-Time Adaptation},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {15319-15328}
}

raw_affiliation:
[{"affiliation": "School of Electronics and Information Engineering, Suzhou University of Science and Technology", "city": "Suzhou", "country": "China"}, {"affiliation": "Jiangsu Industrial Intelligent and Low-carbon Technology Engineering Center", "city": "Suzhou", "country": "China"}, {"affiliation": "Suzhou Key Laboratory of Intelligent and Low-carbon Technology Application", "city": "Suzhou", "country": "China"}, {"affiliation": "New Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences", "city": "Beijing", "country": "China"}, {"affiliation": "China University of Mining and Technology", "city": "Xuzhou", "country": "China"}, {"affiliation": "North Minzu University", "city": "Yinchuan", "country": "China"}]

affiliations:
["School of Electronics and Information Engineering, Suzhou University of Science and Technology", "Jiangsu Industrial Intelligent and Low-carbon Technology Engineering Center", "Suzhou Key Laboratory of Intelligent and Low-carbon Technology Application", "New Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences", "China University of Mining and Technology", "North Minzu University"]


title: UNOPose: Unseen Object Pose Estimation with an Unposed RGB-D Reference Image
authors: Xingyu Liu, Gu Wang, Ruida Zhang, Chenyangguang Zhang, Federico Tombari, Xiangyang Ji
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 22023-22034
affiliations: ["Tsinghua University, BNRist", "Technical University of Munich", "Google"]
cities: ["Beijing", "Munich", "Mountain View"]
countries: ["China", "Germany", "USA"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Liu_UNOPose_Unseen_Object_Pose_Estimation_with_an_Unposed_RGB-D_Reference_CVPR_2025_paper.html

abstract:
Unseen object pose estimation methods often rely on CAD models or multiple reference views, making the onboarding stage costly. To simplify reference acquisition, we aim to estimate the unseen object's pose through a single unposed RGB-D reference image. While previous works leverage reference images as pose anchors to limit the range of relative pose, our scenario presents significant challenges since the relative transformation could vary across the entire SE(3) space. Moreover, factors like occlusion, sensor noise, and extreme geometry could result in low viewpoint overlap. To address these challenges, we present a novel approach and benchmark, termed UNOPose, for UNseen One-reference-based object Pose estimation. Building upon a coarse-to-fine paradigm,  UNOPose constructs an SE(3)-invariant reference frame to standardize object representation despite pose and size variations. To alleviate small overlap across viewpoints, we recalibrate the weight of each correspondence based on its predicted likelihood of being within the overlapping region. Evaluated on our proposed benchmark based on the BOP Challenge, UNOPose demonstrates superior performance, significantly outperforming traditional and learning-based methods in the one-reference setting and remaining competitive with CAD-model-based methods. The code and dataset are available at github.com/shanice-l/UNOPose.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Liu_UNOPose_Unseen_Object_Pose_Estimation_with_an_Unposed_RGB-D_Reference_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Liu_UNOPose_Unseen_Object_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Liu_2025_CVPR,
    author    = {Liu, Xingyu and Wang, Gu and Zhang, Ruida and Zhang, Chenyangguang and Tombari, Federico and Ji, Xiangyang},
    title     = {UNOPose: Unseen Object Pose Estimation with an Unposed RGB-D Reference Image},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {22023-22034}
}

raw_affiliation:
[{"affiliation": "Tsinghua University, BNRist", "city": "Beijing", "country": "China"}, {"affiliation": "Technical University of Munich", "city": "Munich", "country": "Germany"}, {"affiliation": "Google", "city": "Mountain View", "country": "USA"}]


title: CoSER: Towards Consistent Dense Multiview Text-to-Image Generator for 3D Creation
authors: Bonan Li, Zicheng Zhang, Xingyi Yang, Xinchao Wang
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 2880-2890
affiliations: ["University of Chinese Academy of Sciences", "National University of Singapore"]
cities: ["Beijing", "Singapore"]
countries: ["China", "Singapore"]
highlight: yes
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Li_CoSER_Towards_Consistent_Dense_Multiview_Text-to-Image_Generator_for_3D_Creation_CVPR_2025_paper.html

abstract:
Generating dense multiview images from text prompts is crucial for creating high-fidelity 3D assets. Nevertheless, existing methods struggle with space-view correspondences, resulting in sparse and low-quality outputs. In this paper, we introduce CoSER, a novel consistent dense Multiview Text-to-Image Generator for Text-to-3D, achieving both efficiency and quality by meticulously learning neighbor-view coherence and further alleviating ambiguity through the swift traversal of all views. For achieving neighbor-view consistency, each viewpoint densely interacts with adjacent viewpoints to perceive the global spatial structure, and aggregates information along motion paths explicitly defined by physical principles to refine details. To further enhance cross-view consistency and alleviate content drift, CoSER rapidly scan all views in spiral bidirectional manner to aware holistic information and then scores each point based on semantic material. Subsequently, we conduct weighted down-sampling along the spatial dimension based on scores, thereby facilitating prominent information fusion across all views with lightweight computation. Technically, the core module is built by integrating the attention mechanism with a selective state space model, exploiting the robust learning capabilities of the former and the low overhead of the latter. Extensive evaluation shows that CoSER is capable of producing dense, high-fidelity, content-consistent multiview images that can be flexibly integrated into various 3D generation models.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Li_CoSER_Towards_Consistent_Dense_Multiview_Text-to-Image_Generator_for_3D_Creation_CVPR_2025_paper.pdf

bibtex:
@InProceedings{Li_2025_CVPR,
    author    = {Li, Bonan and Zhang, Zicheng and Yang, Xingyi and Wang, Xinchao},
    title     = {CoSER: Towards Consistent Dense Multiview Text-to-Image Generator for 3D Creation},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {2880-2890}
}

raw_affiliation:
[{"affiliation": "University of Chinese Academy of Sciences", "city": "Beijing", "country": "China"}, {"affiliation": "National University of Singapore", "city": "Singapore", "country": "Singapore"}]


title: HybridMQA: Exploring Geometry-Texture Interactions for Colored Mesh Quality Assessment
authors: Armin Shafiee Sarvestani, Sheyang Tang, Zhou Wang
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 21414-21424
affiliations: ["Department of Electrical and Computer Engineering, University of Waterloo"]
cities: ["Waterloo"]
countries: ["Canada"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Sarvestani_HybridMQA_Exploring_Geometry-Texture_Interactions_for_Colored_Mesh_Quality_Assessment_CVPR_2025_paper.html

abstract:
Mesh quality assessment (MQA) models play a critical role in the design, optimization, and evaluation of mesh operation systems in a wide variety of applications. Current MQA models, whether model-based methods using topology-aware features or projection-based approaches working on rendered 2D projections, often fail to capture the intricate interactions between texture and 3D geometry. We introduce HybridMQA, a first-of-its-kind hybrid full-reference colored MQA framework that integrates model-based and projection-based approaches, capturing complex interactions between textural information and 3D structures for enriched quality representations. Our method employs graph learning to extract detailed 3D representations, which are then projected to 2D using a novel feature rendering process that precisely aligns them with colored projections. This enables the exploration of geometry-texture interactions via cross-attention, producing comprehensive mesh quality representations. Extensive experiments demonstrate HybridMQA's superior performance across diverse datasets, highlighting its ability to effectively leverage geometry-texture interactions for a thorough understanding of mesh quality. Our project website is available at https://arshafiee.github.io/hybridmqa/.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Sarvestani_HybridMQA_Exploring_Geometry-Texture_Interactions_for_Colored_Mesh_Quality_Assessment_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Sarvestani_HybridMQA_Exploring_Geometry-Texture_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Sarvestani_2025_CVPR,
    author    = {Sarvestani, Armin Shafiee and Tang, Sheyang and Wang, Zhou},
    title     = {HybridMQA: Exploring Geometry-Texture Interactions for Colored Mesh Quality Assessment},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {21414-21424}
}

raw_affiliation:
[{"affiliation": "Department of Electrical and Computer Engineering, University of Waterloo", "city": "Waterloo", "country": "Canada"}]


authors: Changhao Peng
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 11779-11788
raw_affiliation: [{"affiliation": "Peking University", "city": "Beijing", "country": "China"}]
affiliations: ["Peking University"]
cities: ["Beijing"]
countries: ["China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Peng_Generalized_Gaussian_Entropy_Model_for_Point_Cloud_Attribute_Compression_with_CVPR_2025_paper.html

title:
Generalized Gaussian Entropy Model for Point Cloud Attribute Compression with Dynamic Likelihood Intervals

abstract:
Gaussian and Laplacian entropy models are proved effective in learned point cloud attribute compression, as they assist in arithmetic coding of latents. However, we demonstrate through experiments that there is still unutilized information in entropy parameters estimated by neural networks in current methods, which can be used for more accurate probability estimation. Thus we introduce generalized Gaussian entropy model, which controls the tail shape through shape parameter to more accurately estimate the probability of latents. Meanwhile, to the best of our knowledge, existing methods use fixed likelihood intervals for each integer during arithmetic coding, which limits model performance. We propose Mean Error Discriminator (MED) to determine whether the entropy parameter estimation is accurate and then dynamically adjust likelihood intervals. Experiments show that our method significantly improves rate-distortion (RD) performance on three VAE-based models for point cloud attribute compression, and our method can be applied to other compression tasks, such as image and video compression.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Peng_Generalized_Gaussian_Entropy_Model_for_Point_Cloud_Attribute_Compression_with_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Peng_Generalized_Gaussian_Entropy_CVPR_2025_supplemental.zip

bibtex:
@InProceedings{Peng_2025_CVPR,
    author    = {Peng, Changhao},
    title     = {Generalized Gaussian Entropy Model for Point Cloud Attribute Compression with Dynamic Likelihood Intervals},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {11779-11788}
}


authors: Jian Zhu, He Wang, Yang Xu, Zebin Wu, Zhihui Wei
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 17862-17871
affiliations: ["School of Computer Science and Engineering, Nanjing University of Science and Technology"]
cities: ["Nanjing"]
countries: ["China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Zhu_Self-Learning_Hyperspectral_and_Multispectral_Image_Fusion_via_Adaptive_Residual_Guided_CVPR_2025_paper.html

title:
Self-Learning Hyperspectral and Multispectral Image Fusion via Adaptive Residual Guided Subspace Diffusion Model

abstract:
Hyperspectral and multispectral image (HSI-MSI) fusion involves combining a low-resolution hyperspectral image (LR-HSI) with a high-resolution multispectral image (HR-MSI) to generate a high-resolution hyperspectral image (HR-HSI). Most deep learning-based methods for HSI-MSI fusion rely on large amounts of hyperspectral data for supervised training, which is often scarce in practical applications. In this paper, we propose a self-learning Adaptive Residual Guided Subspace Diffusion Model (ARGS-Diff), which only utilizes the observed images without any extra training data. Specifically, as the LR-HSI contains spectral information and the HR-MSI contains spatial information, we design two lightweight spectral and spatial diffusion models to separately learn the spectral and spatial distributions from them. Then, we use these two models to reconstruct HR-HSI from two low-dimensional components, i.e, the spectral basis and the reduced coefficient, during the reverse diffusion process. Furthermore, we introduce an Adaptive Residual Guided Module (ARGM), which refines the two components through a residual guided function at each sampling step, thereby stabilizing the sampling process. Extensive experimental results demonstrate that ARGS-Diff outperforms existing state-of-the-art methods in terms of both performance and computational efficiency in the field of HSI-MSI fusion.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Zhu_Self-Learning_Hyperspectral_and_Multispectral_Image_Fusion_via_Adaptive_Residual_Guided_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Zhu_Self-Learning_Hyperspectral_and_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Zhu_2025_CVPR,
    author    = {Zhu, Jian and Wang, He and Xu, Yang and Wu, Zebin and Wei, Zhihui},
    title     = {Self-Learning Hyperspectral and Multispectral Image Fusion via Adaptive Residual Guided Subspace Diffusion Model},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {17862-17871}
}

raw_affiliation:
[{"affiliation": "School of Computer Science and Engineering, Nanjing University of Science and Technology", "city": "Nanjing", "country": "China"}]


title: SIR-DIFF: Sparse Image Sets Restoration with Multi-View Diffusion Model
authors: Yucheng Mao, Boyang Wang, Nilesh Kulkarni, Jeong Joon Park
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 21620-21630
affiliations: ["University of Michigan", "Net\ufb02ix"]
cities: ["Ann Arbor", "Unknown"]
countries: ["USA", "USA"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Mao_SIR-DIFF_Sparse_Image_Sets_Restoration_with_Multi-View_Diffusion_Model_CVPR_2025_paper.html

abstract:
The computer vision community has developed numerous techniques for digitally restoring true scene information from single-view degraded photographs, an important yet extremely ill-posed task. In this work, we tackle image restoration from a different perspective by jointly denoising multiple photographs of the same scene. Our core hypothesis is that degraded images capturing a shared scene contain complementary information that, when combined, better constrains the restoration problem. To this end, we implement a powerful multi-view diffusion model that jointly generates uncorrupted views by extracting rich information from multi-view relationships. Our experiments show that our multi-view approach outperforms existing single-view image and even video-based methods on image deblurring and super-resolution tasks. Critically, our model is trained to output 3D consistent images, making it a promising tool for applications requiring robust multi-view integration, such as 3D reconstruction or pose estimation.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Mao_SIR-DIFF_Sparse_Image_Sets_Restoration_with_Multi-View_Diffusion_Model_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Mao_SIR-DIFF_Sparse_Image_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Mao_2025_CVPR,
    author    = {Mao, Yucheng and Wang, Boyang and Kulkarni, Nilesh and Park, Jeong Joon},
    title     = {SIR-DIFF: Sparse Image Sets Restoration with Multi-View Diffusion Model},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {21620-21630}
}

raw_affiliation:
[{"affiliation": "University of Michigan", "city": "Ann Arbor", "country": "USA"}, {"affiliation": "Net\ufb02ix", "city": "Unknown", "country": "USA"}]


title: StickMotion: Generating 3D Human Motions by Drawing a Stickman
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 12370-12379
cities: ["Beijing", "Hefei", "Shenyang", "Suzhou", "Singapore", "Beijing", "Beijing", "Xi'an"]
countries: ["China", "China", "China", "China", "Singapore", "China", "China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Wang_StickMotion_Generating_3D_Human_Motions_by_Drawing_a_Stickman_CVPR_2025_paper.html

authors:
Tao Wang, Zhihua Wu, Qiaozhi He, Jiaming Chu, Ling Qian, Yu Cheng, Junliang Xing, Jian Zhao, Lei Jin

abstract:
Text-to-motion generation, which translates textual descriptions into human motions, has been challenging in accurately capturing detailed user-imagined motions from simple text inputs. This paper introduces StickMotion, an efficient diffusion-based network designed for multi-condition scenarios, which generates desired motions based on traditional text and our proposed stickman conditions for global and local control of these motions, respectively. We address the challenges introduced by the user-friendly stickman from three perspectives: 1) Data generation. We develop an algorithm to generate hand-drawn stickmen automatically across different dataset formats. 2) Multi-condition fusion. We propose a multi-condition module that integrates into the diffusion process and obtains outputs of all possible condition combinations, reducing computational complexity and enhancing StickMotion's performance compared to conventional approaches with the self-attention module. 3) Dynamic supervision. We empower StickMotion to make minor adjustments to the stickman's position within the output sequences, generating more natural movements through our proposed dynamic supervision strategy. Through quantitative experiments and user studies, sketching stickmen saves users about 51.5% of their time generating motions consistent with their imagination. Our codes, demos, and relevant data will be released to facilitate further research and validation within the scientific community.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Wang_StickMotion_Generating_3D_Human_Motions_by_Drawing_a_Stickman_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Wang_StickMotion_Generating_3D_CVPR_2025_supplemental.zip

bibtex:
@InProceedings{Wang_2025_CVPR,
    author    = {Wang, Tao and Wu, Zhihua and He, Qiaozhi and Chu, Jiaming and Qian, Ling and Cheng, Yu and Xing, Junliang and Zhao, Jian and Jin, Lei},
    title     = {StickMotion: Generating 3D Human Motions by Drawing a Stickman},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {12370-12379}
}

raw_affiliation:
[{"affiliation": "Beijing University of Posts and Telecommunications", "city": "Beijing", "country": "China"}, {"affiliation": "University of Science and Technology of China", "city": "Hefei", "country": "China"}, {"affiliation": "Northeastern University", "city": "Shenyang", "country": "China"}, {"affiliation": "China Mobile (Suzhou) Software Technology Co, Ltd.", "city": "Suzhou", "country": "China"}, {"affiliation": "National University of Singapore", "city": "Singapore", "country": "Singapore"}, {"affiliation": "Tsinghua University", "city": "Beijing", "country": "China"}, {"affiliation": "The Institute of AI (TeleAI), China Telecom", "city": "Beijing", "country": "China"}, {"affiliation": "Northwestern Polytechnical University", "city": "Xi'an", "country": "China"}]

affiliations:
["Beijing University of Posts and Telecommunications", "University of Science and Technology of China", "Northeastern University", "China Mobile (Suzhou) Software Technology Co, Ltd.", "National University of Singapore", "Tsinghua University", "The Institute of AI (TeleAI), China Telecom", "Northwestern Polytechnical University"]


title: Reversible Decoupling Network for Single Image Reflection Removal
authors: Hao Zhao, Mingjia Li, Qiming Hu, Xiaojie Guo
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 26430-26439
affiliations: ["College of Intelligence and Computing, Tianjin University"]
cities: ["Tianjin"]
countries: ["China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Zhao_Reversible_Decoupling_Network_for_Single_Image_Reflection_Removal_CVPR_2025_paper.html

abstract:
Recent deep-learning-based approaches to single-image reflection removal have shown promising advances, primarily for two reasons: 1) the utilization of recognition-pretrained features as inputs, and 2) the design of dual-stream interaction networks. However, according to the Information Bottleneck principle, high-level semantic clues tend to be compressed or discarded during layer-by-layer propagation. Additionally, interactions in dual-stream networks follow a fixed pattern across different layers, limiting overall performance. To address these limitations, we propose a novel architecture called Reversible Decoupling Network (RDNet), which employs a reversible encoder to secure valuable information while flexibly decoupling transmission- and reflection-relevant features during the forward pass. Furthermore, we customize a transmission-rate-aware prompt generator to dynamically calibrate features, further boosting performance. Extensive experiments demonstrate the superiority of RDNet over existing SOTA methods on five widely-adopted benchmark datasets. Our code will be made publicly available.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Zhao_Reversible_Decoupling_Network_for_Single_Image_Reflection_Removal_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Zhao_Reversible_Decoupling_Network_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Zhao_2025_CVPR,
    author    = {Zhao, Hao and Li, Mingjia and Hu, Qiming and Guo, Xiaojie},
    title     = {Reversible Decoupling Network for Single Image Reflection Removal},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {26430-26439}
}

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


authors: Xinhao Zhong, Hao Fang, Bin Chen, Xulin Gu, Meikang Qiu, Shuhan Qi, Shu-Tao Xia
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 30462-30471
cities: ["Shenzhen", "Shenzhen", "Shenzhen", "Augusta"]
countries: ["China", "China", "China", "USA"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Zhong_Hierarchical_Features_Matter_A_Deep_Exploration_of_Progressive_Parameterization_Method_CVPR_2025_paper.html

title:
Hierarchical Features Matter: A Deep Exploration of Progressive Parameterization Method for Dataset Distillation

abstract:
Dataset distillation is an emerging dataset reduction method, which condenses large-scale datasets while maintaining task accuracy. Current parameterization methods achieve enhanced performance under extremely high compression ratio by optimizing determined synthetic dataset in informative feature domain. However, they limit themselves to a fixed optimization space for distillation, neglecting the diverse guidance across different informative latent spaces. To overcome this limitation, we propose a novel parameterization method dubbed Hierarchical Parameterization Distillation (H-PD), to systematically explore hierarchical feature within provided feature space (e.g., layers within pre-trained generative adversarial networks). We verify the correctness of our insights by applying the hierarchical optimization strategy on GAN-based parameterization method. In addition, we introduce a novel class-relevant feature distance metric to alleviate the computational burden associated with synthetic dataset evaluation, bridging the gap between synthetic and original datasets. Experimental results demonstrate that the proposed H-PD achieves a significant performance improvement under various settings with equivalent time consumption, and even surpasses current generative distillation using diffusion models under extreme compression ratios IPC=1 and IPC=10.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Zhong_Hierarchical_Features_Matter_A_Deep_Exploration_of_Progressive_Parameterization_Method_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Zhong_Hierarchical_Features_Matter_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Zhong_2025_CVPR,
    author    = {Zhong, Xinhao and Fang, Hao and Chen, Bin and Gu, Xulin and Qiu, Meikang and Qi, Shuhan and Xia, Shu-Tao},
    title     = {Hierarchical Features Matter: A Deep Exploration of Progressive Parameterization Method for Dataset Distillation},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {30462-30471}
}

raw_affiliation:
[{"affiliation": "Harbin Institute of Technology, Shenzhen", "city": "Shenzhen", "country": "China"}, {"affiliation": "Peng Cheng Laboratory", "city": "Shenzhen", "country": "China"}, {"affiliation": "Tsinghua Shenzhen International Graduate School, Tsinghua University", "city": "Shenzhen", "country": "China"}, {"affiliation": "Augusta University", "city": "Augusta", "country": "USA"}]

affiliations:
["Harbin Institute of Technology, Shenzhen", "Peng Cheng Laboratory", "Tsinghua Shenzhen International Graduate School, Tsinghua University", "Augusta University"]


authors: Yi Yu, Weizhen Han, Libing Wu, Bingyi Liu, Enshu Wang, Zhuangzhuang Zhang
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 17229-17238
cities: ["Wuhan", "Wuhan", "Hong Kong"]
countries: ["China", "China", "China"]
highlight: yes
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Yu_Enduring_Efficient_and_Robust_Trajectory_Prediction_Attack_in_Autonomous_Driving_CVPR_2025_paper.html

title:
Enduring, Efficient and Robust Trajectory Prediction Attack in Autonomous Driving via Optimization-Driven Multi-Frame Perturbation Framework

abstract:
Trajectory prediction plays a crucial role in autonomous driving systems, and exploring its vulnerability has garnered widespread attention. However, existing trajectory prediction attack methods often rely on single-point attacks to make efficient perturbations. This limits their applications in real-world scenarios due to the transient nature of single-point attacks, their susceptibility to filtration, and the uncertainty regarding the deployment environment. To address these challenges, this paper proposes a novel LiDAR-induced attack framework to impose multi-frame attacks by optimization-driven adversarial location search, achieving endurance, efficiency, and robustness. This framework strategically places objects near the adversarial vehicle to implement an attack and introduces three key innovations. First, successive state perturbations are generated using a multi-frame single-point attack strategy, effectively misleading trajectory predictions over extended time horizons. Second, we efficiently optimize adversarial objects' locations through three specialized loss functions to achieve desired perturbations. Lastly, we improve robustness by treating the adversarial object as a point without size constraints during the location search phase and reduce dependence on both the specific attack point and the adversarial object's properties. Extensive experiments confirm the superior performance and robustness of our framework.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Yu_Enduring_Efficient_and_Robust_Trajectory_Prediction_Attack_in_Autonomous_Driving_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Yu_Enduring_Efficient_and_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Yu_2025_CVPR,
    author    = {Yu, Yi and Han, Weizhen and Wu, Libing and Liu, Bingyi and Wang, Enshu and Zhang, Zhuangzhuang},
    title     = {Enduring, Efficient and Robust Trajectory Prediction Attack in Autonomous Driving via Optimization-Driven Multi-Frame Perturbation Framework},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {17229-17238}
}

raw_affiliation:
[{"affiliation": "Key Laboratory of Aerospace Information Security and Trusted Computing, Ministry of Education, School of Cyber Science and Engineering, Wuhan University", "city": "Wuhan", "country": "China"}, {"affiliation": "School of Computer Science and Artificial Intelligence, Wuhan University of Technology", "city": "Wuhan", "country": "China"}, {"affiliation": "Department of Computer Science, City University of Hong Kong", "city": "Hong Kong", "country": "China"}]

affiliations:
["Key Laboratory of Aerospace Information Security and Trusted Computing, Ministry of Education, School of Cyber Science and Engineering, Wuhan University", "School of Computer Science and Artificial Intelligence, Wuhan University of Technology", "Department of Computer Science, City University of Hong Kong"]


title: GLASS: Guided Latent Slot Diffusion for Object-Centric Learning
authors: Krishnakant Singh, Simone Schaub-Meyer, Stefan Roth
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 28673-28683
affiliations: ["Department of Computer Science, TU Darmstadt", "hessian.AI"]
cities: ["Darmstadt", "Darmstadt"]
countries: ["Germany", "Germany"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Singh_GLASS_Guided_Latent_Slot_Diffusion_for_Object-Centric_Learning_CVPR_2025_paper.html

abstract:
Object-centric learning aims to decompose an input image into a set of meaningful object files (slots). These latent object representations enable a variety of downstream tasks. Yet, object-centric learning struggles on real-world datasets, which contain multiple objects of complex textures and shapes in natural everyday scenes. To address this, we introduce Guided Latent Slot Diffusion (GLASS), a novel slot attention model that learns in the space of generated images and uses semantic and instance guidance modules to learn better slot embeddings for various downstream tasks. Our experiments show that GLASS surpasses state-of-the-art slot attention methods by a wide margin on tasks such as (zero-shot) object discovery and conditional image generation for real-world scenes. Moreover, GLASS enables the first application of slot attention to compositional generation of complex, realistic scenes.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Singh_GLASS_Guided_Latent_Slot_Diffusion_for_Object-Centric_Learning_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Singh_GLASS_Guided_Latent_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Singh_2025_CVPR,
    author    = {Singh, Krishnakant and Schaub-Meyer, Simone and Roth, Stefan},
    title     = {GLASS: Guided Latent Slot Diffusion for Object-Centric Learning},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {28673-28683}
}

raw_affiliation:
[{"affiliation": "Department of Computer Science, TU Darmstadt", "city": "Darmstadt", "country": "Germany"}, {"affiliation": "hessian.AI", "city": "Darmstadt", "country": "Germany"}]


title: UNEM: UNrolled Generalized EM for Transductive Few-Shot Learning
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 9665-9675
cities: ["Milan", "Montreal", "Paris", "Paris"]
countries: ["Italy", "Canada", "France", "France"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Zhou_UNEM_UNrolled_Generalized_EM_for_Transductive_Few-Shot_Learning_CVPR_2025_paper.html

authors:
Long Zhou, Fereshteh Shakeri, Aymen Sadraoui, Mounir Kaaniche, Jean-Christophe Pesquet, Ismail Ben Ayed

abstract:
Transductive few-shot learning has recently triggered wide attention in computer vision. Yet, current methods introduce key hyper-parameters, which control the pre-diction statistics of the test batches, such as the level of class balance, affecting performances significantly. Such hyper-parameters are empirically grid-searched over validation data, and their configurations may vary substantially with the target dataset and pre-training model, making such empirical searches both sub-optimal and computationally intractable. In this work, we advocate and introduce the unrolling paradigm, also referred to as "learning to optimize", in the context of few-shot learning, thereby learning efficiently and effectively a set of optimized hyperparameters. Specifically, we unroll a generalization of the ubiquitous Expectation-Maximization (EM) optimizer into a neural network architecture, mapping each of its iterates to a layer and learning a set of key hyper-parameters over validation data. Our unrolling approach covers various statistical feature distributions and pre-training paradigms,  including recent foundational vision-language models and standard vision-only classifiers. We report comprehensive experiments, which cover a breadth of fine-grained downstream image classification tasks, showing significant gains brought by the proposed unrolled EM algorithm over iterative variants. The achieved improvements reach up to 10% and 7.5% on vision-only and vision-language benchmarks, respectively. The source code and learned parameters are available at https://github.com/ZhouLong0/UNEM-Transductive.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Zhou_UNEM_UNrolled_Generalized_EM_for_Transductive_Few-Shot_Learning_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Zhou_UNEM_UNrolled_Generalized_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Zhou_2025_CVPR,
    author    = {Zhou, Long and Shakeri, Fereshteh and Sadraoui, Aymen and Kaaniche, Mounir and Pesquet, Jean-Christophe and Ben Ayed, Ismail},
    title     = {UNEM: UNrolled Generalized EM for Transductive Few-Shot Learning},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {9665-9675}
}

raw_affiliation:
[{"affiliation": "Politecnico di Milano", "city": "Milan", "country": "Italy"}, {"affiliation": "\u00b4ETS Montr\u00b4eal", "city": "Montreal", "country": "Canada"}, {"affiliation": "Universit\u00b4e Paris-Saclay, Inria, CentraleSup\u00b4elec, CVN", "city": "Paris", "country": "France"}, {"affiliation": "Universit\u00b4e Sorbonne Paris Nord, L2TI, UR 3043", "city": "Paris", "country": "France"}]

affiliations:
["Politecnico di Milano", "\u00b4ETS Montr\u00b4eal", "Universit\u00b4e Paris-Saclay, Inria, CentraleSup\u00b4elec, CVN", "Universit\u00b4e Sorbonne Paris Nord, L2TI, UR 3043"]


authors: Jinfeng Xu, Xianzhi Li, Yuan Tang, Xu Han, Qiao Yu, Yixue Hao, Long Hu, Min Chen
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 27295-27304
cities: ["Wuhan", "Guangzhou", "Guangzhou", "Guangzhou"]
countries: ["China", "China", "China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Xu_SASep_Saliency-Aware_Structured_Separation_of_Geometry_and_Feature_for_Open_CVPR_2025_paper.html

title:
SASep: Saliency-Aware Structured Separation of Geometry and Feature for Open Set Learning on Point Clouds

abstract:
Recent advancements in deep learning have greatly enhanced 3D object recognition, but most models are limited to closed-set scenarios, unable to handle unknown samples in real-world applications. Open-set recognition (OSR) addresses this limitation by enabling models to both classify known classes and identify novel classes.However, current OSR methods rely on global features to differentiate known and unknown classes, treating the entire object uniformly and overlooking the varying semantic importance of its different parts.To address this gap, we propose Salience-Aware Structured Separation (SASep), which includes (i) a tunable semantic decomposition (TSD) module to semantically decompose objects into important and unimportant parts, (ii) a geometric synthesis strategy (GSS) to generate pseudo-unknown objects by combining these unimportant parts, and (iii) a synth-aided margin separation (SMS) module to enhance feature-level separation by expanding the feature distributions between classes.Together, these components improve both geometric and feature representations, enhancing the model's ability to effectively distinguish known and unknown classes.Experimental results show that SASep achieves superior performance in 3D OSR, outperforming existing state-of-the-art methods. The codes are available at https://github.com/JinfengX/SASep.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Xu_SASep_Saliency-Aware_Structured_Separation_of_Geometry_and_Feature_for_Open_CVPR_2025_paper.pdf

bibtex:
@InProceedings{Xu_2025_CVPR,
    author    = {Xu, Jinfeng and Li, Xianzhi and Tang, Yuan and Han, Xu and Yu, Qiao and Hao, Yixue and Hu, Long and Chen, Min},
    title     = {SASep: Saliency-Aware Structured Separation of Geometry and Feature for Open Set Learning on Point Clouds},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {27295-27304}
}

raw_affiliation:
[{"affiliation": "Huazhong University of Science and Technology", "city": "Wuhan", "country": "China"}, {"affiliation": "Guangdong Intelligent Robotics Institute", "city": "Guangzhou", "country": "China"}, {"affiliation": "South China University of Technology", "city": "Guangzhou", "country": "China"}, {"affiliation": "Pazhou Laboratory", "city": "Guangzhou", "country": "China"}]

affiliations:
["Huazhong University of Science and Technology", "Guangdong Intelligent Robotics Institute", "South China University of Technology", "Pazhou Laboratory"]


title: Low-Biased General Annotated Dataset Generation
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 25113-25123
cities: ["Xi'an", "Beijing", "Hangzhou", "Beijing"]
countries: ["China", "China", "China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Jiang_Low-Biased_General_Annotated_Dataset_Generation_CVPR_2025_paper.html

authors:
Dengyang Jiang, Haoyu Wang, Lei Zhang, Wei Wei, Guang Dai, Mengmeng Wang, Jingdong Wang, Yanning Zhang

abstract:
Pre-training backbone networks on a general annotated dataset (e.g., ImageNet) that comprises numerous manually collected images with category annotations has proven to be indispensable for enhancing the generalization capacity of downstream visual tasks. However, those manually collected images often exhibit bias, which is non-transferable across either categories or domains, thus causing the model's generalization capacity degeneration. To mitigate this problem, we present a low-biased general annotated dataset generation framework (lbGen). Instead of expensive manual collection, we aim at directly generating low-biased images with category annotations. To achieve this goal, we propose to leverage the advantage of a multimodal foundation model (e.g., CLIP), in terms of aligning images in a low-biased semantic space defined by language. Specifically, we develop a bi-level semantic alignment loss, which not only forces all generated images to be consistent with the semantic distribution of all categories belonging to the target dataset in an adversarial learning manner, but also requires each generated image to match the semantic description of its category name. In addition, we further cast an existing image quality scoring model into a quality assurance loss to preserve the quality of the generated image. By leveraging these two loss functions, we can obtain a low-biased image generation model by simply fine-tuning a pre-trained diffusion model using only all category names in the target dataset as input. Experimental results confirm that, compared with the manually labeled dataset or other synthetic datasets, the utilization of our generated low-biased dataset leads to stable generalization capacity enhancement of different backbone networks across various tasks, especially in tasks where the manually labeled samples are scarce.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Jiang_Low-Biased_General_Annotated_Dataset_Generation_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Jiang_Low-Biased_General_Annotated_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Jiang_2025_CVPR,
    author    = {Jiang, Dengyang and Wang, Haoyu and Zhang, Lei and Wei, Wei and Dai, Guang and Wang, Mengmeng and Wang, Jingdong and Zhang, Yanning},
    title     = {Low-Biased General Annotated Dataset Generation},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {25113-25123}
}

raw_affiliation:
[{"affiliation": "Northwestern Polytechnical University", "city": "Xi'an", "country": "China"}, {"affiliation": "SGIT AI Lab, State Grid Corporation of China", "city": "Beijing", "country": "China"}, {"affiliation": "Zhejiang University of Technology", "city": "Hangzhou", "country": "China"}, {"affiliation": "Baidu Inc.", "city": "Beijing", "country": "China"}]

affiliations:
["Northwestern Polytechnical University", "SGIT AI Lab, State Grid Corporation of China", "Zhejiang University of Technology", "Baidu Inc."]


title: G3Flow: Generative 3D Semantic Flow for Pose-aware and Generalizable Object Manipulation
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 1735-1744
cities: ["Hong Kong", "Beijing", "Shenzhen", "Shenzhen", "Guangzhou", "Shanghai"]
countries: ["China", "China", "China", "China", "China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Chen_G3Flow_Generative_3D_Semantic_Flow_for_Pose-aware_and_Generalizable_Object_CVPR_2025_paper.html

authors:
Tianxing Chen, Yao Mu, Zhixuan Liang, Zanxin Chen, Shijia Peng, Qiangyu Chen, Mingkun Xu, Ruizhen Hu, Hongyuan Zhang, Xuelong Li, Ping Luo

abstract:
Recent advances in imitation learning for 3D robotic manipulation have shown promising results with diffusion-based policies. However, achieving human-level dexterity requires seamless integration of geometric precision and semantic understanding. We present G3Flow, a novel framework that constructs real-time semantic flow, a dynamic, object-centric 3D semantic representation by leveraging foundation models. Our approach uniquely combines 3D generative models for digital twin creation, vision foundation models for semantic feature extraction, and robust pose tracking for continuous semantic flow updates. This integration enables complete semantic understanding even under occlusions while eliminating manual annotation requirements. By incorporating semantic flow into diffusion policies, we demonstrate significant improvements in both terminal-constrained manipulation and cross-object generalization. Extensive experiments across five simulation tasks show that G3Flow consistently outperforms existing approaches, achieving up to 68.3% and 50.1% average success rates on terminal-constrained manipulation and cross-object generalization tasks respectively. Our results demonstrate the effectiveness of G3Flow in enhancing real-time dynamic semantic feature understanding for robotic manipulation policies.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Chen_G3Flow_Generative_3D_Semantic_Flow_for_Pose-aware_and_Generalizable_Object_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Chen_G3Flow_Generative_3D_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Chen_2025_CVPR,
    author    = {Chen, Tianxing and Mu, Yao and Liang, Zhixuan and Chen, Zanxin and Peng, Shijia and Chen, Qiangyu and Xu, Mingkun and Hu, Ruizhen and Zhang, Hongyuan and Li, Xuelong and Luo, Ping},
    title     = {G3Flow: Generative 3D Semantic Flow for Pose-aware and Generalizable Object Manipulation},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {1735-1744}
}

raw_affiliation:
[{"affiliation": "The University of Hong Kong", "city": "Hong Kong", "country": "China"}, {"affiliation": "Institute of Arti\ufb01cial Intelligence (TeleAI), China Telecom", "city": "Beijing", "country": "China"}, {"affiliation": "Shenzhen University", "city": "Shenzhen", "country": "China"}, {"affiliation": "AgileX Robotics", "city": "Shenzhen", "country": "China"}, {"affiliation": "GDIIST", "city": "Guangzhou", "country": "China"}, {"affiliation": "HKU Shanghai Intelligent Computing Research Center", "city": "Shanghai", "country": "China"}]

affiliations:
["The University of Hong Kong", "Institute of Arti\ufb01cial Intelligence (TeleAI), China Telecom", "Shenzhen University", "AgileX Robotics", "GDIIST", "HKU Shanghai Intelligent Computing Research Center"]


title: Generative Hard Example Augmentation for Semantic Point Cloud Segmentation
authors: Qi Zhang, Jibin Peng, Zhao Huang, Wei Feng, Di Lin
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 22205-22214
affiliations: ["College of Intelligence and Computing, Tianjin University, China"]
cities: ["Tianjin"]
countries: ["China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Zhang_Generative_Hard_Example_Augmentation_for_Semantic_Point_Cloud_Segmentation_CVPR_2025_paper.html

abstract:
The recent progress in semantic point cloud segmentation is attributed to deep networks, which require a large amount of point cloud data for training. However, how to collect substantial point-wise annotations of the point clouds at affordable cost for the end-to-end network training still needs to be solved. In this paper, we propose Generative Hard Example Augmentation (GHEA) to achieve novel examples of point clouds, which enrich the data for training the segmentation network. Firstly, GHEA employs the generative network to embed the discrepancy between the point clouds into the latent space. From the latent space, we sample multiple discrepancies for reshaping a point cloud to various examples, contributing to the richness of the training data. Secondly, GHEA mixes the reshaped point clouds by respecting their segmentation errors. This mixup allows the reshaped point clouds, which are difficult to segment, to join as the challenging example for network training. We evaluate the effectiveness of GHEA, which helps the popular segmentation networks to improve the performances.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Zhang_Generative_Hard_Example_Augmentation_for_Semantic_Point_Cloud_Segmentation_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Zhang_Generative_Hard_Example_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Zhang_2025_CVPR,
    author    = {Zhang, Qi and Peng, Jibin and Huang, Zhao and Feng, Wei and Lin, Di},
    title     = {Generative Hard Example Augmentation for Semantic Point Cloud Segmentation},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {22205-22214}
}

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


title: Toward Generalized Image Quality Assessment: Relaxing the Perfect Reference Quality Assumption
authors: Du Chen, Tianhe Wu, Kede Ma, Lei Zhang
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 12742-12752
affiliations: ["The Hong Kong Polytechnic University", "City University of Hong Kong", "OPPO Research Institute"]
cities: ["Hong Kong", "Hong Kong", "Hong Kong"]
countries: ["Hong Kong", "Hong Kong", "Hong Kong"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Chen_Toward_Generalized_Image_Quality_Assessment_Relaxing_the_Perfect_Reference_Quality_CVPR_2025_paper.html

abstract:
Full-reference image quality assessment (FR-IQA) generally assumes that reference images are of perfect quality. However, this assumption is flawed due to the sensor and optical limitations of modern imaging systems. Moreover, recent generative enhancement methods are capable of producing images of higher quality than their original. All of these challenge the effectiveness and applicability of current FR-IQA models. To relax the assumption of perfect reference image quality, we build a large-scale IQA database, namely DiffIQA, containing approximately 180,000 images generated by a diffusion-based image enhancer with adjustable hyper-parameters. Each image is annotated by human subjects as either worse, similar, or better quality compared to its reference. Building on this, we present a generalized FR-IQA model, namely Adaptive FIdelity-Naturalness Evaluator (A-FINE), to accurately assess and adaptively combine the fidelity and naturalness of a test image. A-FINE aligns well with standard FR-IQA when the reference image is much more natural than the test image. We demonstrate by extensive experiments that A-FINE surpasses standard FR-IQA models on well-established IQA datasets and our newly created DiffIQA. To further validate A-FINE, we additionally construct a super-resolution IQA benchmark (SRIQA-Bench), encompassing test images derived from ten state-of-the-art SR methods with reliable human quality annotations. Tests on SRIQA-Bench re-affirm the advantages of A-FINE. The code and dataset are available at https://tianhewu.github.io/A-FINE-page.github.io/.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Chen_Toward_Generalized_Image_Quality_Assessment_Relaxing_the_Perfect_Reference_Quality_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Chen_Toward_Generalized_Image_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Chen_2025_CVPR,
    author    = {Chen, Du and Wu, Tianhe and Ma, Kede and Zhang, Lei},
    title     = {Toward Generalized Image Quality Assessment: Relaxing the Perfect Reference Quality Assumption},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {12742-12752}
}

raw_affiliation:
[{"affiliation": "The Hong Kong Polytechnic University", "city": "Hong Kong", "country": "Hong Kong"}, {"affiliation": "City University of Hong Kong", "city": "Hong Kong", "country": "Hong Kong"}, {"affiliation": "OPPO Research Institute", "city": "Hong Kong", "country": "Hong Kong"}]


title: Explaining Domain Shifts in Language: Concept Erasing for Interpretable Image Classification
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 9517-9526
cities: ["Xi'an", "Xi'an", "Hong Kong"]
countries: ["China", "China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Zeng_Explaining_Domain_Shifts_in_Language_Concept_Erasing_for_Interpretable_Image_CVPR_2025_paper.html

authors:
Zequn Zeng, Yudi Su, Jianqiao Sun, Tiansheng Wen, Hao Zhang, Zhengjue Wang, Bo Chen, Hongwei Liu, Jiawei Ma

abstract:
Concept-based models can map black-box representations to human-understandable concepts, which makes the decision-making process more transparent and then allows users to understand the reason behind predictions. However, domain-specific concepts often impact the final predictions, which subsequently undermine the model generalization capabilities, and prevent the model from being used in high-stake applications. In this paper, we propose a novel Language-guided Concept-Erasing (LanCE) framework. In particular, we empirically demonstrate that pre-trained vision-language models (VLMs) can approximate distinct visual domain shifts via domain descriptors while prompting large Language Models (LLMs) can easily simulate a wide range of descriptors of unseen visual domains. Then, we introduce a novel plug-in domain descriptor orthogonality (DDO) regularizer to mitigate the impact of these domain-specific concepts on the final predictions. Notably, the DDO regularizer is agnostic to the design of conceptbased models and we integrate it into several prevailing models. Through evaluation of domain generalization on four standard benchmarks and three newly introduced benchmarks, we demonstrate that DDO can significantly improve the out-of-distribution (OOD) generalization over the previous state-of-the-art concept-based models. Our code is available at https://github.com/joeyz0z/LanCE.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Zeng_Explaining_Domain_Shifts_in_Language_Concept_Erasing_for_Interpretable_Image_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Zeng_Explaining_Domain_Shifts_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Zeng_2025_CVPR,
    author    = {Zeng, Zequn and Su, Yudi and Sun, Jianqiao and Wen, Tiansheng and Zhang, Hao and Wang, Zhengjue and Chen, Bo and Liu, Hongwei and Ma, Jiawei},
    title     = {Explaining Domain Shifts in Language: Concept Erasing for Interpretable Image Classification},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {9517-9526}
}

raw_affiliation:
[{"affiliation": "National Key Laboratory of Radar Signal Processing, Xidian University", "city": "Xi'an", "country": "China"}, {"affiliation": "State Key Laboratory of Integrated Service Networks, Xidian University", "city": "Xi'an", "country": "China"}, {"affiliation": "City University of Hong Kong", "city": "Hong Kong", "country": "China"}]

affiliations:
["National Key Laboratory of Radar Signal Processing, Xidian University", "State Key Laboratory of Integrated Service Networks, Xidian University", "City University of Hong Kong"]


authors: Ning Ni, Libao Zhang
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 12690-12699
affiliations: ["School of Arti\ufb01cial Intelligence, Beijing Normal University"]
cities: ["Beijing"]
countries: ["China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Ni_Hazy_Low-Quality_Satellite_Video_Restoration_Via_Learning_Optimal_Joint_Degradation_CVPR_2025_paper.html

title:
Hazy Low-Quality Satellite Video Restoration Via Learning Optimal Joint Degradation Patterns and Continuous-Scale Super-Resolution Reconstruction

abstract:
Currently, the demand for higher video quality has grown significantly. However, satellite video has low resolution, complex motion, and weak textures. Haze interference further exacerbates the loss of motion information and texture details, hindering effective spatiotemporal feature fusion and fine-grained feature mining. This presents significant challenges for subsequent super-resolution (SR) reconstruction, especially at continuous scales. To address these problems, this paper models the double-degradation process of hazy low-quality satellite videos and proposes a novel network to learn the optimal joint degradation pattern (ODPNet) for continuous-scale SR of hazy satellite videos. First, we design a prior-based feature soft dehazing module to eliminate haze interference at the feature level. Second, we develop a spatiotemporal self-attention (SSA) to capture long-range feature dependencies, thereby achieving effective spatiotemporal feature fusion. Third, we devise a tri-branch cross-aggregation block (TCB) to enhance feature representations of weak textures in satellite videos by effectively aggregating contextual information. Finally, we propose a cross-scale feature Top-k selection Transformer (CFTST), which aims to adaptively select and aggregate cross-scale latent codes to learn feature representations of satellite videos at arbitrary resolutions, thus enabling continuous-scale SR. Experiments show that ODPNet outperforms existing methods and achieves a better balance between model parameters and performance.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Ni_Hazy_Low-Quality_Satellite_Video_Restoration_Via_Learning_Optimal_Joint_Degradation_CVPR_2025_paper.pdf

bibtex:
@InProceedings{Ni_2025_CVPR,
    author    = {Ni, Ning and Zhang, Libao},
    title     = {Hazy Low-Quality Satellite Video Restoration Via Learning Optimal Joint Degradation Patterns and Continuous-Scale Super-Resolution Reconstruction},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {12690-12699}
}

raw_affiliation:
[{"affiliation": "School of Arti\ufb01cial Intelligence, Beijing Normal University", "city": "Beijing", "country": "China"}]


title: Textured Gaussians for Enhanced 3D Scene Appearance Modeling
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 8964-8974
affiliations: ["Stanford University", "Meta", "University of Maryland College Park"]
cities: ["Stanford", "Menlo Park", "College Park"]
countries: ["USA", "USA", "USA"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Chao_Textured_Gaussians_for_Enhanced_3D_Scene_Appearance_Modeling_CVPR_2025_paper.html

authors:
Brian Chao, Hung-Yu Tseng, Lorenzo Porzi, Chen Gao, Tuotuo Li, Qinbo Li, Ayush Saraf, Jia-Bin Huang, Johannes Kopf, Gordon Wetzstein, Changil Kim

abstract:
3D Gaussian Splatting (3DGS) has recently emerged as a state-of-the-art 3D reconstruction and rendering technique due to its high-quality results and fast training and rendering time. However, pixels covered by the same Gaussian are always shaded in the same color up to a Gaussian falloff scaling factor. Furthermore, the finest geometric detail any individual Gaussian can represent is a simple ellipsoid. These properties of 3DGS greatly limit the expressivity of individual Gaussian primitives. To address these issues, we draw inspiration from texture and alpha mapping in traditional graphics and integrate it with 3DGS. Specifically, we propose a new generalized Gaussian appearance representation that augments each Gaussian with alpha (A), RGB, or RGBA texture maps to model spatially varying color and opacity across the extent of each Gaussian. As such, each Gaussian can represent a richer set of texture patterns and geometric structures, instead of just a single color and ellipsoid as in naive Gaussian Splatting. Surprisingly, we found that the expressivity of Gaussians can be greatly improved by using alpha-only texture maps, and further augmenting Gaussians with RGB texture maps achieves the highest expressivity. We validate our method on a wide variety of standard benchmark datasets and our own custom captures at both the object and scene levels. We demonstrate image quality improvements over existing methods while using a similar or lower number of Gaussians.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Chao_Textured_Gaussians_for_Enhanced_3D_Scene_Appearance_Modeling_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Chao_Textured_Gaussians_for_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Chao_2025_CVPR,
    author    = {Chao, Brian and Tseng, Hung-Yu and Porzi, Lorenzo and Gao, Chen and Li, Tuotuo and Li, Qinbo and Saraf, Ayush and Huang, Jia-Bin and Kopf, Johannes and Wetzstein, Gordon and Kim, Changil},
    title     = {Textured Gaussians for Enhanced 3D Scene Appearance Modeling},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {8964-8974}
}

raw_affiliation:
[{"affiliation": "Stanford University", "city": "Stanford", "country": "USA"}, {"affiliation": "Meta", "city": "Menlo Park", "country": "USA"}, {"affiliation": "University of Maryland College Park", "city": "College Park", "country": "USA"}]


title: NeighborRetr: Balancing Hub Centrality in Cross-Modal Retrieval
authors: Zengrong Lin, Zheng Wang, Tianwen Qian, Pan Mu, Sixian Chan, Cong Bai
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 9263-9273
cities: ["Hangzhou", "Shanghai"]
countries: ["China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Lin_NeighborRetr_Balancing_Hub_Centrality_in_Cross-Modal_Retrieval_CVPR_2025_paper.html

abstract:
Cross-modal retrieval aims to bridge the semantic gap between different modalities, such as visual and textual data, enabling accurate retrieval across them. Despite significant advancements with models like CLIP that align cross-modal representations, a persistent challenge remains: the hubness problem, where a small subset of samples (hubs) dominate as nearest neighbors, leading to biased representations and degraded retrieval accuracy. Existing methods often mitigate hubness through post-hoc normalization techniques, relying on prior data distributions that may not be practical in real-world scenarios. In this paper, we directly mitigate hubness during training and introduce NeighborRetr, a novel method that effectively balances the learning of hubs and adaptively adjusts the relations of various kinds of neighbors. Our approach not only mitigates the hubness problem but also enhances retrieval performance, achieving state-of-the-art results on multiple cross-modal retrieval benchmarks. Furthermore, NeighborRetr demonstrates robust generalization to new domains with substantial distribution shifts, highlighting its effectiveness in real-world applications.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Lin_NeighborRetr_Balancing_Hub_Centrality_in_Cross-Modal_Retrieval_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Lin_NeighborRetr_Balancing_Hub_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Lin_2025_CVPR,
    author    = {Lin, Zengrong and Wang, Zheng and Qian, Tianwen and Mu, Pan and Chan, Sixian and Bai, Cong},
    title     = {NeighborRetr: Balancing Hub Centrality in Cross-Modal Retrieval},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {9263-9273}
}

raw_affiliation:
[{"affiliation": "College of Computer Science, Zhejiang University of Technology", "city": "Hangzhou", "country": "China"}, {"affiliation": "College of Computer Science and Technology, East China Normal University", "city": "Shanghai", "country": "China"}]

affiliations:
["College of Computer Science, Zhejiang University of Technology", "College of Computer Science and Technology, East China Normal University"]


title: ETAP: Event-based Tracking of Any Point
authors: Friedhelm Hamann, Daniel Gehrig, Filbert Febryanto, Kostas Daniilidis, Guillermo Gallego
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 27186-27196
cities: ["Berlin", "Philadelphia", "Berlin", "Bonn", "Athens", "Berlin"]
countries: ["Germany", "USA", "Germany", "Germany", "Greece", "Germany"]
highlight: yes
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Hamann_ETAP_Event-based_Tracking_of_Any_Point_CVPR_2025_paper.html

abstract:
Tracking any point (TAP) recently shifted the motion estimation paradigm from focusing on individual salient points with local templates to tracking arbitrary points with global image contexts. However, while research has mostly focused on driving the accuracy of models in nominal settings, addressing scenarios with difficult lighting conditions and high-speed motions remains out of reach due to the limitations of the sensor. This work addresses this challenge with the first event camera-based TAP method. It leverages the high temporal resolution and high dynamic range of event cameras for robust high-speed tracking, and the global contexts in TAP methods to handle asynchronous and sparse event measurements. We further extend the TAP framework to handle event feature variations induced by motion -- thereby addressing an open challenge in purely event-based tracking -- with a novel feature alignment-loss which ensures the learning of motion-robust features. Our method is trained with data from a new data generation pipeline and systematically ablated across all design decisions. Our method shows strong cross-dataset generalization and performs 136% better on the average Jaccard metric than the baselines. Moreover, on an established feature tracking benchmark, it achieves a 20% improvement over the previous best event-only method and even surpasses the previous best events-and-frames method by 4.1%. Our code is available at https:// github.com/ tub-rip/ ETAP.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Hamann_ETAP_Event-based_Tracking_of_Any_Point_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Hamann_ETAP_Event-based_Tracking_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Hamann_2025_CVPR,
    author    = {Hamann, Friedhelm and Gehrig, Daniel and Febryanto, Filbert and Daniilidis, Kostas and Gallego, Guillermo},
    title     = {ETAP: Event-based Tracking of Any Point},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {27186-27196}
}

raw_affiliation:
[{"affiliation": "Technische Universit\u00a8at Berlin", "city": "Berlin", "country": "Germany"}, {"affiliation": "University of Pennsylvania", "city": "Philadelphia", "country": "USA"}, {"affiliation": "Einstein Center for Digital Future", "city": "Berlin", "country": "Germany"}, {"affiliation": "Robotics Institute Germany", "city": "Bonn", "country": "Germany"}, {"affiliation": "Archimedes, Athena RC", "city": "Athens", "country": "Greece"}, {"affiliation": "Science of Intelligence Excellence Cluster", "city": "Berlin", "country": "Germany"}]

affiliations:
["Technische Universit\u00a8at Berlin", "University of Pennsylvania", "Einstein Center for Digital Future", "Robotics Institute Germany", "Archimedes, Athena RC", "Science of Intelligence Excellence Cluster"]


title: Beyond Sight: Towards Cognitive Alignment in LVLM via Enriched Visual Knowledge
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 24950-24959
affiliations: ["Peking University", "University of Science and Technology of China", "Baichuan Inc."]
cities: ["Beijing", "Hefei", "Beijing"]
countries: ["China", "China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Zhao_Beyond_Sight_Towards_Cognitive_Alignment_in_LVLM_via_Enriched_Visual_CVPR_2025_paper.html

authors:
Yaqi Zhao, Yuanyang Yin, Lin Li, Mingan Lin, Victor Shea-Jay Huang, Siwei Chen, Weipeng Chen, Baoqun Yin, Zenan Zhou, Wentao Zhang

abstract:
Does seeing always mean knowing? Large Vision-Language Models (LVLMs) integrate separately pre-trained vision and language components, often using CLIP-ViT as vision backbone.  However, these models frequently encounter a core issue of "cognitive misalignment" between the vision encoder (VE) and the large language model (LLM). Specifically, the VE's representation of visual information may not fully align with LLM's cognitive framework, leading to a mismatch where visual features exceed the language model's interpretive range. To address this, we investigate how variations in VE representations influence LVLM comprehension, especially when the LLM faces VE-Unknown data--images whose ambiguous visual representations challenge the VE's interpretive precision. Accordingly, we construct a multi-granularity landmark dataset and systematically examine the impact of VE-Known and VE-Unknown data on interpretive abilities. Our results show that VE-Unknown data limits LVLM's capacity for accurate understanding, while VE-Known data, rich in distinctive features, helps reduce cognitive misalignment. Building on these insights, we propose Entity-Enhanced Cognitive Alignment (EECA), a method that employs multi-granularity supervision to generate visually enriched, well-aligned tokens that not only integrate within the embedding space but also align with the LLM's cognitive framework. This alignment markedly enhances LVLM performance in landmark recognition. Our findings underscore the challenges posed by VE-Unknown data and highlight the essential role of cognitive alignment in advancing multimodal systems.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Zhao_Beyond_Sight_Towards_Cognitive_Alignment_in_LVLM_via_Enriched_Visual_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Zhao_Beyond_Sight_Towards_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Zhao_2025_CVPR,
    author    = {Zhao, Yaqi and Yin, Yuanyang and Li, Lin and Lin, Mingan and Huang, Victor Shea-Jay and Chen, Siwei and Chen, Weipeng and Yin, Baoqun and Zhou, Zenan and Zhang, Wentao},
    title     = {Beyond Sight: Towards Cognitive Alignment in LVLM via Enriched Visual Knowledge},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {24950-24959}
}

raw_affiliation:
[{"affiliation": "Peking University", "city": "Beijing", "country": "China"}, {"affiliation": "University of Science and Technology of China", "city": "Hefei", "country": "China"}, {"affiliation": "Baichuan Inc.", "city": "Beijing", "country": "China"}]


title: Global-Local Tree Search in VLMs for 3D Indoor Scene Generation
authors: Wei Deng, Mengshi Qi, Huadong Ma
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 8975-8984
affiliations: ["Beijing University of Posts and Telecommunications"]
cities: ["Beijing"]
countries: ["China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Deng_Global-Local_Tree_Search_in_VLMs_for_3D_Indoor_Scene_Generation_CVPR_2025_paper.html

abstract:
Large Vision-Language Models (VLMs), such as GPT-4, have achieved remarkable success across various fields. However, there are few studies on 3D indoor scene generation with VLMs. This paper considers this task as a planning problem subject to spatial and layout common sense constraints. To solve the problem with a VLM, we propose a new global-local tree search algorithm. Globally, the method places each object sequentially and explores multiple placements during each placement process, where the problem space is represented as a tree. To reduce the depth of the tree, we decompose the scene structure hierarchically, i.e. room level, region level, floor object level, and supported object level. The algorithm independently generates the floor objects in different regions and supported objects placed on different floor objects. Locally, we also decompose the sub-task, the placement of each object, into multiple steps. The algorithm searches the tree of problem space. To leverage the VLM model to produce positions of objects, we discretize the top-down view space as a dense grid and fill each cell with diverse emojis to make to cells distinct. We prompt the VLM with the emoji grid and the VLM produces a reasonable location for the object by describing the position with the name of emojis. The quantitative and qualitative experimental results illustrate our approach generates more plausible 3D scenes than state-of-the-art approaches. Our source code is available at https://github.com/dw-dengwei/TreeSearchGen .

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Deng_Global-Local_Tree_Search_in_VLMs_for_3D_Indoor_Scene_Generation_CVPR_2025_paper.pdf

bibtex:
@InProceedings{Deng_2025_CVPR,
    author    = {Deng, Wei and Qi, Mengshi and Ma, Huadong},
    title     = {Global-Local Tree Search in VLMs for 3D Indoor Scene Generation},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {8975-8984}
}

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


title: Volumetric Surfaces: Representing Fuzzy Geometries with Layered Meshes
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 21370-21380
affiliations: ["University of T\u00a8ubingen", "Meta Reality Labs"]
cities: ["T\u00fcbingen", "Unknown"]
countries: ["Germany", "Unknown"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Esposito_Volumetric_Surfaces_Representing_Fuzzy_Geometries_with_Layered_Meshes_CVPR_2025_paper.html

authors:
Stefano Esposito, Anpei Chen, Christian Reiser, Samuel Rota Bulò, Lorenzo Porzi, Katja Schwarz, Christian Richardt, Michael Zollhöfer, Peter Kontschieder, Andreas Geiger

abstract:
High-quality view synthesis relies on volume rendering, splatting, or surface rendering. While surface rendering is typically the fastest, it struggles to accurately model fuzzy geometry like hair. In turn, alpha-blending techniques excel at representing fuzzy materials but require an unbounded number of samples per ray (P1). Further overheads are induced by empty space skipping in volume rendering (P2) and sorting input primitives in splatting (P3). We present a novel representation for real-time view synthesis where the (P1) number of sampling locations is small and bounded, (P2) sampling locations are efficiently found via rasterization, and (P3) rendering is sorting-free. We achieve this by representing objects as semi-transparent multi-layer meshes rendered in a fixed order. First, we model surface layers as signed distance function (SDF) shells with optimal spacing learned during training. Then, we bake them as meshes and fit UV textures. Unlike single-surface methods, our multi-layer representation effectively models fuzzy objects. In contrast to volume and splatting-based methods, our approach enables real-time rendering on low-power laptops and smartphones.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Esposito_Volumetric_Surfaces_Representing_Fuzzy_Geometries_with_Layered_Meshes_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Esposito_Volumetric_Surfaces_Representing_CVPR_2025_supplemental.zip

bibtex:
@InProceedings{Esposito_2025_CVPR,
    author    = {Esposito, Stefano and Chen, Anpei and Reiser, Christian and Bul\`o, Samuel Rota and Porzi, Lorenzo and Schwarz, Katja and Richardt, Christian and Zollh\"ofer, Michael and Kontschieder, Peter and Geiger, Andreas},
    title     = {Volumetric Surfaces: Representing Fuzzy Geometries with Layered Meshes},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {21370-21380}
}

raw_affiliation:
[{"affiliation": "University of T\u00a8ubingen", "city": "T\u00fcbingen", "country": "Germany"}, {"affiliation": "Meta Reality Labs", "city": "Unknown", "country": "Unknown"}]


title: Overcoming Shortcut Problem in VLM for Robust Out-of-Distribution Detection
authors: Zhuo Xu, Xiang Xiang, Yifan Liang
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 15402-15412
cities: ["Wuhan", "Shenzhen"]
countries: ["China", "China"]
highlight: yes
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Xu_Overcoming_Shortcut_Problem_in_VLM_for_Robust_Out-of-Distribution_Detection_CVPR_2025_paper.html

abstract:
Vision-language models (VLMs), such as CLIP, have shown remarkable capabilities in downstream tasks. However, the coupling of semantic information between the foreground and the background in images leads to significant shortcut issues that adversely affect out-of-distribution (OOD) detection abilities. When confronted with a background OOD sample, VLMs are prone to misidentifying it as in-distribution (ID) data. In this paper, we analyze the OOD problem from the perspective of shortcuts in VLMs and propose OSPCoOp which includes background decoupling and mask-guided region regularization. We first decouple images into ID-relevant and ID-irrelevant regions and utilize the latter to generate a large number of augmented OOD background samples as pseudo-OOD supervision. We then use the masks from background decoupling to adjust the model's attention, minimizing its focus on ID-irrelevant regions. To assess the model's robustness against background interference, we introduce a new OOD evaluation dataset, ImageNet-Bg, which solely consists of background images with all ID-relevant regions removed. Our method demonstrates exceptional performance in few-shot scenarios, achieving strong results even in one-shot setting, and outperforms existing methods. The code and proposed ImageNet-Bg are available at https://github.com/HAIV-Lab/OSPCoOp_Imagenet-bg.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Xu_Overcoming_Shortcut_Problem_in_VLM_for_Robust_Out-of-Distribution_Detection_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Xu_Overcoming_Shortcut_Problem_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Xu_2025_CVPR,
    author    = {Xu, Zhuo and Xiang, Xiang and Liang, Yifan},
    title     = {Overcoming Shortcut Problem in VLM for Robust Out-of-Distribution Detection},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {15402-15412}
}

raw_affiliation:
[{"affiliation": "National Key Lab of Multi-Spectral Information Intelligent Processing Technology, School of Artificial Intelligence and Automation, Huazhong University of Science and Technology", "city": "Wuhan", "country": "China"}, {"affiliation": "Peng Cheng National Laboratory", "city": "Shenzhen", "country": "China"}]

affiliations:
["National Key Lab of Multi-Spectral Information Intelligent Processing Technology, School of Artificial Intelligence and Automation, Huazhong University of Science and Technology", "Peng Cheng National Laboratory"]


title: GFlowVLM: Enhancing Multi-step Reasoning in Vision-Language Models with Generative Flow Networks
authors: Haoqiang Kang, Enna Sachdeva, Piyush Gupta, Sangjae Bae, Kwonjoon Lee
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 3815-3825
affiliations: ["Honda Research Institute USA", "University of California San Diego"]
cities: ["Mountain View", "San Diego"]
countries: ["USA", "USA"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Kang_GFlowVLM_Enhancing_Multi-step_Reasoning_in_Vision-Language_Models_with_Generative_Flow_CVPR_2025_paper.html

abstract:
Vision-Language Models (VLMs) have recently shown promising advancements in sequential decision-making tasks through task-specific fine-tuning. However, common fine-tuning methods, such as Supervised Fine-Tuning (SFT) and Reinforcement Learning (RL) techniques like Proximal Policy Optimization (PPO), present notable limitations: SFT assumes Independent and Identically Distributed (IID) data, while PPO focuses on maximizing cumulative rewards. These limitations often restrict solution diversity and hinder generalization in multi-step reasoning tasks. To address these challenges, we introduce a novel framework, GFlowVLM, a framework that fine-tune VLMs using Generative Flow Networks (GFlowNets) to promote generation of diverse solutions for complex reasoning tasks. GFlowVLM models the environment as a non-Markovian decision process, allowing it to capture long-term dependencies essential for real-world applications. It takes observations and task descriptions as inputs to prompt chain-of-thought (CoT) reasoning which subsequently guides action selection. We use task based rewards to fine-tune VLM with GFlowNets. This approach enables VLMs to outperform prior fine-tuning methods, including SFT and RL. Empirical results demonstrate the effectiveness of GFlowVLM on complex tasks such as card games (NumberLine, BlackJack) and embodied planning tasks (ALFWorld), showing enhanced training efficiency, solution diversity, and stronger generalization capabilities across both in-distribution and out-of-distribution scenarios. Project page is available at \href https://mk322.github.io/gflowvlm/  https://mk322.github.io/gflowvlm/ .

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Kang_GFlowVLM_Enhancing_Multi-step_Reasoning_in_Vision-Language_Models_with_Generative_Flow_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Kang_GFlowVLM_Enhancing_Multi-step_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Kang_2025_CVPR,
    author    = {Kang, Haoqiang and Sachdeva, Enna and Gupta, Piyush and Bae, Sangjae and Lee, Kwonjoon},
    title     = {GFlowVLM: Enhancing Multi-step Reasoning in Vision-Language Models with Generative Flow Networks},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {3815-3825}
}

raw_affiliation:
[{"affiliation": "Honda Research Institute USA", "city": "Mountain View", "country": "USA"}, {"affiliation": "University of California San Diego", "city": "San Diego", "country": "USA"}]


title: STEPS: Sequential Probability Tensor Estimation for Text-to-Image Hard Prompt Search
authors: Yuning Qiu, Andong Wang, Chao Li, Haonan Huang, Guoxu Zhou, Qibin Zhao
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 28640-28650
affiliations: ["RIKEN Center for Advanced Intelligence Project", "Guangdong University of Technology"]
cities: ["Tokyo", "Guangzhou"]
countries: ["Japan", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Qiu_STEPS_Sequential_Probability_Tensor_Estimation_for_Text-to-Image_Hard_Prompt_Search_CVPR_2025_paper.html

abstract:
Recent text-to-image (T2I) diffusion models have demonstrated remarkable capabilities in visual synthesis, yet their performance heavily relies on the quality of input prompts. However, optimizing discrete prompts remains challenging because the discrete nature of tokens prevents the direct application of the gradient descent method and the vast search space of possible token combinations. As a result, existing approaches either suffer from quantization errors when employing continuous optimization techniques or become trapped in local optima due to coordinate-wise greedy search. In this paper, we propose STEPS, a novel Sequential probability Tensor Estimation approach for hard Prompt Search. Our method reformulates discrete prompt optimization as a sequential probability tensor estimation problem, leveraging the inherent low-rank characteristics to address the curse of dimensionality. To further improve the computational efficiency, we develop a memory-bounded sampling approach that shrinks the prompt space without the iteration step dependency while preserving sequential optimization dynamics. Extensive experiments on various public datasets demonstrate that our method consistently outperforms existing approaches in T2I generation, cross-model prompt transferability, and harmful prompt optimization, validating the effectiveness of the proposed framework.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Qiu_STEPS_Sequential_Probability_Tensor_Estimation_for_Text-to-Image_Hard_Prompt_Search_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Qiu_STEPS_Sequential_Probability_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Qiu_2025_CVPR,
    author    = {Qiu, Yuning and Wang, Andong and Li, Chao and Huang, Haonan and Zhou, Guoxu and Zhao, Qibin},
    title     = {STEPS: Sequential Probability Tensor Estimation for Text-to-Image Hard Prompt Search},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {28640-28650}
}

raw_affiliation:
[{"affiliation": "RIKEN Center for Advanced Intelligence Project", "city": "Tokyo", "country": "Japan"}, {"affiliation": "Guangdong University of Technology", "city": "Guangzhou", "country": "China"}]


title: RoboSpatial: Teaching Spatial Understanding to 2D and 3D Vision-Language Models for Robotics
authors: Chan Hee Song, Valts Blukis, Jonathan Tremblay, Stephen Tyree, Yu Su, Stan Birchfield
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 15768-15780
affiliations: ["The Ohio State University", "NVIDIA"]
cities: ["Columbus", "Santa Clara"]
countries: ["USA", "USA"]
oral: yes
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Song_RoboSpatial_Teaching_Spatial_Understanding_to_2D_and_3D_Vision-Language_Models_CVPR_2025_paper.html

abstract:
Spatial understanding is a crucial capability that enables robots to perceive their surroundings, reason about their environment, and interact with it meaningfully. In modern robotics, these capabilities are increasingly provided by vision-language models. However, these models face significant challenges in spatial reasoning tasks, as their training data are based on general-purpose image datasets that often lack sophisticated spatial understanding. For example, datasets frequently do not capture reference frame comprehension, yet effective spatial reasoning requires understanding whether to reason from ego-, world-, or object-centric perspectives. To address this issue, we introduce RoboSpatial, a large-scale dataset for spatial understanding in robotics.  It consists of real indoor and tabletop scenes, captured as 3D scans and egocentric images, and annotated with rich spatial information relevant to robotics. The dataset includes 1M images, 5k 3D scans, and 3M annotated spatial relationships, and the pairing of 2D egocentric images with 3D scans makes it both 2D- and 3D- ready. Our experiments show that models trained with RoboSpatial outperform baselines on downstream tasks such as spatial affordance prediction, spatial relationship prediction, and robotics manipulation.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Song_RoboSpatial_Teaching_Spatial_Understanding_to_2D_and_3D_Vision-Language_Models_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Song_RoboSpatial_Teaching_Spatial_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Song_2025_CVPR,
    author    = {Song, Chan Hee and Blukis, Valts and Tremblay, Jonathan and Tyree, Stephen and Su, Yu and Birchfield, Stan},
    title     = {RoboSpatial: Teaching Spatial Understanding to 2D and 3D Vision-Language Models for Robotics},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {15768-15780}
}

raw_affiliation:
[{"affiliation": "The Ohio State University", "city": "Columbus", "country": "USA"}, {"affiliation": "NVIDIA", "city": "Santa Clara", "country": "USA"}]


title: VIRES: Video Instance Repainting via Sketch and Text Guided Generation
authors: Shuchen Weng, Haojie Zheng, Peixuan Zhang, Yuchen Hong, Han Jiang, Si Li, Boxin Shi
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 28416-28425
cities: ["Beijing", "Beijing", "Beijing", "Beijing", "Beijing"]
countries: ["China", "China", "China", "China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Weng_VIRES_Video_Instance_Repainting_via_Sketch_and_Text_Guided_Generation_CVPR_2025_paper.html

abstract:
We introduce VIRES, a video instance repainting method with sketch and text guidance, enabling video instance repainting, replacement, generation, and removal. Existing approaches struggle with temporal consistency and accurate alignment with the provided sketch sequence. VIRES leverages the generative priors of text-to-video models to maintain temporal consistency and produce visually pleasing results. We propose the Sequential ControlNet with the standardized self-scaling, which effectively extracts structure layouts and adaptively captures high-contrast sketch details. We further augment the diffusion transformer backbone with the sketch attention to interpret and inject fine-grained sketch semantics. A sketch-aware encoder ensures that repainted results are aligned with the provided sketch sequence. Additionally, we contribute the VireSet, a dataset with detailed annotations tailored for training and evaluating video instance editing methods. Experimental results demonstrate the effectiveness of VIRES, which outperforms state-of-the-art methods in visual quality, temporal consistency, condition alignment, and human ratings. The code, dataset and pretrained models are available at: https://hjzheng.net/projects/VIRES.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Weng_VIRES_Video_Instance_Repainting_via_Sketch_and_Text_Guided_Generation_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Weng_VIRES_Video_Instance_CVPR_2025_supplemental.zip

bibtex:
@InProceedings{Weng_2025_CVPR,
    author    = {Weng, Shuchen and Zheng, Haojie and Zhang, Peixuan and Hong, Yuchen and Jiang, Han and Li, Si and Shi, Boxin},
    title     = {VIRES: Video Instance Repainting via Sketch and Text Guided Generation},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {28416-28425}
}

raw_affiliation:
[{"affiliation": "State Key Laboratory for Multimedia Information Processing, School of Computer Science, Peking University", "city": "Beijing", "country": "China"}, {"affiliation": "National Engineering Research Center of Visual Technology, School of Computer Science, Peking University", "city": "Beijing", "country": "China"}, {"affiliation": "OpenBayes Information Technology Co., Ltd.", "city": "Beijing", "country": "China"}, {"affiliation": "School of Software and Microelectronics, Peking University", "city": "Beijing", "country": "China"}, {"affiliation": "School of Artificial Intelligence, Beijing University of Posts and Telecommunications", "city": "Beijing", "country": "China"}]

affiliations:
["State Key Laboratory for Multimedia Information Processing, School of Computer Science, Peking University", "National Engineering Research Center of Visual Technology, School of Computer Science, Peking University", "OpenBayes Information Technology Co., Ltd.", "School of Software and Microelectronics, Peking University", "School of Artificial Intelligence, Beijing University of Posts and Telecommunications"]


authors: Yunze Liu, Li Yi
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 9676-9685
cities: ["Beijing", "Shanghai", "Shanghai"]
countries: ["China", "China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Liu_MAP_Unleashing_Hybrid_Mamba-Transformer_Vision_Backbones_Potential_with_Masked_Autoregressive_CVPR_2025_paper.html

title:
MAP: Unleashing Hybrid Mamba-Transformer Vision Backbone's Potential with Masked Autoregressive Pretraining

abstract:
Hybrid Mamba-Transformer networks have recently garnered broad attention. These networks can leverage the scalability of Transformers while capitalizing on Mamba's strengths in long-context modeling and computational efficiency. However, the challenge of effectively pretraining such hybrid networks remains an open question. Existing methods, such as Masked Autoencoders (MAE) or autoregressive (AR) pretraining, primarily focus on single-type network architectures. In contrast, pretraining strategies for hybrid architectures must be effective for both Mamba and Transformer components. Based on this, we propose Masked Autoregressive Pretraining (MAP) to pretrain a hybrid Mamba-Transformer vision backbone network. This strategy combines the strengths of both MAE and Autoregressive pretraining, improving the performance of Mamba and Transformer modules within a unified paradigm. Experimental results show that the hybrid Mamba-Transformer vision backbone network pretrained with MAP significantly outperforms other pretraining strategies, achieving state-of-the-art performance. We validate the method's effectiveness on both 2D and 3D datasets and provide detailed ablation studies to support the design choices for each component.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Liu_MAP_Unleashing_Hybrid_Mamba-Transformer_Vision_Backbones_Potential_with_Masked_Autoregressive_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Liu_MAP_Unleashing_Hybrid_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Liu_2025_CVPR,
    author    = {Liu, Yunze and Yi, Li},
    title     = {MAP: Unleashing Hybrid Mamba-Transformer Vision Backbone's Potential with Masked Autoregressive Pretraining},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {9676-9685}
}

raw_affiliation:
[{"affiliation": "IIIS, Tsinghua University", "city": "Beijing", "country": "China"}, {"affiliation": "Shanghai Artificial Intelligence Laboratory", "city": "Shanghai", "country": "China"}, {"affiliation": "Shanghai Qi Zhi Institute", "city": "Shanghai", "country": "China"}]

affiliations:
["IIIS, Tsinghua University", "Shanghai Artificial Intelligence Laboratory", "Shanghai Qi Zhi Institute"]


title: Segment Any-Quality Images with Generative Latent Space Enhancement
authors: Guangqian Guo, Yong Guo, Xuehui Yu, Wenbo Li, Yaoxing Wang, Shan Gao
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 2366-2376
affiliations: ["Northwestern Polytechnical University", "Huawei", "Tencent", "Huawei Noah\u2019s Ark Lab"]
cities: ["Xi'an", "Shenzhen", "Shenzhen", "Shenzhen"]
countries: ["China", "China", "China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Guo_Segment_Any-Quality_Images_with_Generative_Latent_Space_Enhancement_CVPR_2025_paper.html

abstract:
Despite their success, Segment Anything Models (SAMs) experience significant performance drops on severely degraded, low-quality images, limiting their effectiveness in real-world scenarios. To address this, we propose GleSAM, which utilizes Generative Latent space Enhancement to boost robustness on low-quality images, thus enabling generalization across various image qualities.  Specifically, we adapt the concept of latent diffusion to SAM-based segmentation frameworks and perform the generative diffusion process in the latent space of SAM to reconstruct high-quality representation, thereby improving segmentation. Additionally, we introduce two techniques to improve compatibility between the pre-trained diffusion model and the segmentation framework. Our method can be applied to pre-trained SAM and SAM2 with only minimal additional learnable parameters, allowing for efficient optimization. We also construct the LQSeg dataset with a greater diversity of degradation types and levels for training and evaluating the model. Extensive experiments demonstrate that GleSAM significantly improves segmentation robustness on complex degradations while maintaining generalization to clear images. Furthermore, GleSAM also performs well on unseen degradations, underscoring the versatility of our approach and dataset. Codes and datasets will be available soon.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Guo_Segment_Any-Quality_Images_with_Generative_Latent_Space_Enhancement_CVPR_2025_paper.pdf

bibtex:
@InProceedings{Guo_2025_CVPR,
    author    = {Guo, Guangqian and Guo, Yong and Yu, Xuehui and Li, Wenbo and Wang, Yaoxing and Gao, Shan},
    title     = {Segment Any-Quality Images with Generative Latent Space Enhancement},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {2366-2376}
}

raw_affiliation:
[{"affiliation": "Northwestern Polytechnical University", "city": "Xi'an", "country": "China"}, {"affiliation": "Huawei", "city": "Shenzhen", "country": "China"}, {"affiliation": "Tencent", "city": "Shenzhen", "country": "China"}, {"affiliation": "Huawei Noah\u2019s Ark Lab", "city": "Shenzhen", "country": "China"}]


title: BG-Triangle: Bezier Gaussian Triangle for 3D Vectorization and Rendering
authors: Minye Wu, Haizhao Dai, Kaixin Yao, Tinne Tuytelaars, Jingyi Yu
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 16197-16207
affiliations: ["KU Leuven", "ShanghaiTech University", "Cellverse Co, Ltd."]
cities: ["Leuven", "Shanghai", "Shanghai"]
countries: ["Belgium", "China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Wu_BG-Triangle_Bezier_Gaussian_Triangle_for_3D_Vectorization_and_Rendering_CVPR_2025_paper.html

abstract:
Differentiable rendering enables efficient optimization by allowing gradients to be computed through the rendering process, facilitating 3D reconstruction, inverse rendering and neural scene representation learning. To ensure differentiability, existing solutions approximate or re-formulate traditional rendering operations using smooth, probabilistic proxies such as volumes or Gaussian primitives. Consequently, they struggle to preserve sharp edges due to the lack of explicit boundary definitions. We present a novel hybrid representation, Bezier Gaussian Triangle (BG-Triangle), that combines Bezier triangle-based vector graphics primitives with Gaussian-based probabilistic models, to maintain accurate shape modeling while conducting resolution-independent differentiable rendering. We present a robust and effective discontinuity-aware rendering technique to reduce uncertainties at object boundaries. We also employ an adaptive densification and pruning scheme for efficient training while reliably handling level-of-detail (LoD) variations. Experiments show that BG-Triangle achieves comparable rendering quality as 3DGS but with superior boundary preservation. More importantly, BG-Triangle uses a much smaller number of primitives than its alternatives, showcasing the benefits of vectorized graphics primitives and the potential to bridge the gap between classic and emerging representations.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Wu_BG-Triangle_Bezier_Gaussian_Triangle_for_3D_Vectorization_and_Rendering_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Wu_BG-Triangle_Bezier_Gaussian_CVPR_2025_supplemental.zip

bibtex:
@InProceedings{Wu_2025_CVPR,
    author    = {Wu, Minye and Dai, Haizhao and Yao, Kaixin and Tuytelaars, Tinne and Yu, Jingyi},
    title     = {BG-Triangle: Bezier Gaussian Triangle for 3D Vectorization and Rendering},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {16197-16207}
}

raw_affiliation:
[{"affiliation": "KU Leuven", "city": "Leuven", "country": "Belgium"}, {"affiliation": "ShanghaiTech University", "city": "Shanghai", "country": "China"}, {"affiliation": "Cellverse Co, Ltd.", "city": "Shanghai", "country": "China"}]


title: MIMO: Controllable Character Video Synthesis with Spatial Decomposed Modeling
authors: Yifang Men, Yuan Yao, Miaomiao Cui, Liefeng Bo
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 21181-21191
raw_affiliation: [{"affiliation": "Tongyi Lab, Alibaba Group", "city": "Hangzhou", "country": "China"}]
affiliations: ["Tongyi Lab, Alibaba Group"]
cities: ["Hangzhou"]
countries: ["China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Men_MIMO_Controllable_Character_Video_Synthesis_with_Spatial_Decomposed_Modeling_CVPR_2025_paper.html

abstract:
Character video synthesis aims to produce realistic videos of animatable characters within lifelike scenes. As a fundamental problem in the computer vision and graphics community, 3D works typically require multi-view captures for per-case training, which severely limits their applicability of modeling arbitrary characters in a short time. Recent 2D methods break this limitation via pre-trained diffusion models, but they struggle for flexible controls, pose generality and scene interaction. To this end, we propose MIMO, a novel framework which can not only synthesize realistic character videos with controllable attributes (i.e., character, motion and scene) provided by simple user inputs, but also simultaneously achieve advanced scalability to arbitrary characters, generality to novel 3D motions, and applicability to interactive real-world scenes in a unified framework. The core idea is to encode the 2D video to compact spatial codes, considering the inherent 3D nature of video occurrence. Concretely, we lift the 2D frame pixels into 3D using monocular depth estimators, and decompose the video clip into three spatial components (i.e., main human, underlying scene, and floating occlusion) in hierarchical layers based on the 3D depth. These components are further encoded to canonical identity code, structured motion code and full scene code, which are utilized as control signals of the synthesis process. The design of spatial decomposed modeling enables flexible user control, complex motion expression, as well as 3D-aware synthesis for scene interactions. Experimental results show that the proposed method outperforms prior works by a large margin in character animation synthesis and is effective in providing a high degree of controllability (i.e., arbitrary characters, novel 3D motions, interactive scenes), thus enabling brand-new editing tasks (e.g., video character replacement).

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Men_MIMO_Controllable_Character_Video_Synthesis_with_Spatial_Decomposed_Modeling_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Men_MIMO_Controllable_Character_CVPR_2025_supplemental.zip

bibtex:
@InProceedings{Men_2025_CVPR,
    author    = {Men, Yifang and Yao, Yuan and Cui, Miaomiao and Bo, Liefeng},
    title     = {MIMO: Controllable Character Video Synthesis with Spatial Decomposed Modeling},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {21181-21191}
}


title: TKG-DM: Training-free Chroma Key Content Generation Diffusion Model
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 13031-13040
affiliations: ["Faculty of Science and Engineering, Hosei University", "RPTU Kaiserslautern-Landau & DFKI GmbH"]
cities: ["Tokyo", "Kaiserslautern"]
countries: ["Japan", "Germany"]
highlight: yes
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Morita_TKG-DM_Training-free_Chroma_Key_Content_Generation_Diffusion_Model_CVPR_2025_paper.html

authors:
Ryugo Morita, Stanislav Frolov, Brian Bernhard Moser, Takahiro Shirakawa, Ko Watanabe, Andreas Dengel, Jinjia Zhou

abstract:
Diffusion models have enabled the generation of high-quality images with a strong focus on realism and textual fidelity. Yet, large-scale text-to-image models, such as Stable Diffusion, struggle to generate images where foreground objects are placed over a chroma key background, limiting their ability to separate foreground and background elements without fine-tuning. To address this limitation, we present a novel Training-Free Chroma Key Content Generation Diffusion Model (TKG-DM), which optimizes the initial random noise to produce images with foreground objects on a specifiable color background. Our proposed method is the first to explore the manipulation of the color aspects in initial noise for controlled background generation, enabling precise separation of foreground and background without fine-tuning. Extensive experiments demonstrate that our training-free method outperforms existing methods in both qualitative and quantitative evaluations, matching or surpassing fine-tuned models. Finally, we successfully extend it to other tasks (e.g., consistency models and text-to-video), highlighting its transformative potential across various generative applications where independent control of foreground and background is crucial.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Morita_TKG-DM_Training-free_Chroma_Key_Content_Generation_Diffusion_Model_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Morita_TKG-DM_Training-free_Chroma_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Morita_2025_CVPR,
    author    = {Morita, Ryugo and Frolov, Stanislav and Moser, Brian Bernhard and Shirakawa, Takahiro and Watanabe, Ko and Dengel, Andreas and Zhou, Jinjia},
    title     = {TKG-DM: Training-free Chroma Key Content Generation Diffusion Model},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {13031-13040}
}

raw_affiliation:
[{"affiliation": "Faculty of Science and Engineering, Hosei University", "city": "Tokyo", "country": "Japan"}, {"affiliation": "RPTU Kaiserslautern-Landau & DFKI GmbH", "city": "Kaiserslautern", "country": "Germany"}]


title: Lift3D Policy: Lifting 2D Foundation Models for Robust 3D Robotic Manipulation
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 17347-17358
cities: ["Beijing", "Beijing", "Hong Kong"]
countries: ["China", "China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Jia_Lift3D_Policy_Lifting_2D_Foundation_Models_for_Robust_3D_Robotic_CVPR_2025_paper.html

authors:
Yueru Jia, Jiaming Liu, Sixiang Chen, Chenyang Gu, Zhilve Wang, Longzan Luo, Xiaoqi Li, Pengwei Wang, Zhongyuan Wang, Renrui Zhang, Shanghang Zhang

abstract:
3D geometric information is essential for manipulation tasks, as robots need to perceive the 3D environment, reason about spatial relationships, and interact with intricate spatial configurations. Recent research has increasingly focused on the explicit extraction of 3D features, while still facing challenges such as the lack of large-scale robotic 3D data and the potential loss of spatial geometry. To address these limitations, we propose the Lift3D framework, which progressively enhances 2D foundation models with implicit and explicit 3D robotic representations to construct a robust 3D manipulation policy. Specifically, we first design a task-aware masked autoencoder that masks task-relevant affordance patches and reconstructs depth information, enhancing the 2D foundation model's implicit 3D robotic representation. After self-supervised fine-tuning, we introduce a 2D model-lifting strategy that establishes a positional mapping between the input 3D points and the positional embeddings of the 2D model. Based on the mapping, Lift3D utilizes the 2D foundation model to directly encode point cloud data, leveraging large-scale pretrained knowledge to construct explicit 3D robotic representations while minimizing spatial information loss. In experiments, Lift3D consistently outperforms previous state-of-the-art methods across several simulation benchmarks and real-world scenarios.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Jia_Lift3D_Policy_Lifting_2D_Foundation_Models_for_Robust_3D_Robotic_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Jia_Lift3D_Policy_Lifting_CVPR_2025_supplemental.zip

bibtex:
@InProceedings{Jia_2025_CVPR,
    author    = {Jia, Yueru and Liu, Jiaming and Chen, Sixiang and Gu, Chenyang and Wang, Zhilve and Luo, Longzan and Li, Xiaoqi and Wang, Pengwei and Wang, Zhongyuan and Zhang, Renrui and Zhang, Shanghang},
    title     = {Lift3D Policy: Lifting 2D Foundation Models for Robust 3D Robotic Manipulation},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {17347-17358}
}

raw_affiliation:
[{"affiliation": "State Key Laboratory of Multimedia Information Processing, School of Computer Science, Peking University", "city": "Beijing", "country": "China"}, {"affiliation": "Beijing Academy of Artificial Intelligence (BAAI)", "city": "Beijing", "country": "China"}, {"affiliation": "CUHK", "city": "Hong Kong", "country": "China"}]

affiliations:
["State Key Laboratory of Multimedia Information Processing, School of Computer Science, Peking University", "Beijing Academy of Artificial Intelligence (BAAI)", "CUHK"]


title: Multi-View Pose-Agnostic Change Localization with Zero Labels
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 11600-11610
affiliations: ["Queensland University of Technology", "ARIAM*", "ACFR, University of Sydney", "Abyss Solutions"]
cities: ["Brisbane", "Brisbane", "Sydney", "Sydney"]
countries: ["Australia", "Australia", "Australia", "Australia"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Galappaththige_Multi-View_Pose-Agnostic_Change_Localization_with_Zero_Labels_CVPR_2025_paper.html

authors:
Chamuditha Jayanga Galappaththige, Jason Lai, Lloyd Windrim, Donald Dansereau, Niko Sunderhauf, Dimity Miller

abstract:
Autonomous agents often require accurate methods for detecting and localizing changes in their environment, particularly when observations are captured from unconstrained and inconsistent viewpoints. We propose a novel label-free, pose-agnostic change detection method that integrates information from multiple viewpoints to construct a change-aware 3D Gaussian Splatting (3DGS) representation of the scene. With as few as 5 images of the post-change scene, our approach can learn an additional change channel in a 3DGS and produce change masks that outperform single-view techniques. Our change-aware 3D scene representation additionally enables the generation of accurate change masks for unseen viewpoints. Experimental results demonstrate state-of-the-art performance in complex multi-object scenes, achieving a 1.7x and 1.5x improvement in Mean Intersection Over Union and F1 score respectively over other baselines. We also contribute a new real-world dataset to benchmark change detection in diverse challenging scenes in the presence of lighting variations.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Galappaththige_Multi-View_Pose-Agnostic_Change_Localization_with_Zero_Labels_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Galappaththige_Multi-View_Pose-Agnostic_Change_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Galappaththige_2025_CVPR,
    author    = {Galappaththige, Chamuditha Jayanga and Lai, Jason and Windrim, Lloyd and Dansereau, Donald and Sunderhauf, Niko and Miller, Dimity},
    title     = {Multi-View Pose-Agnostic Change Localization with Zero Labels},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {11600-11610}
}

raw_affiliation:
[{"affiliation": "Queensland University of Technology", "city": "Brisbane", "country": "Australia"}, {"affiliation": "ARIAM*", "city": "Brisbane", "country": "Australia"}, {"affiliation": "ACFR, University of Sydney", "city": "Sydney", "country": "Australia"}, {"affiliation": "Abyss Solutions", "city": "Sydney", "country": "Australia"}]


authors: Zhiwei Huang, Hailin Yu, Yichun Shentu, Jin Yuan, Guofeng Zhang
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 27059-27069
affiliations: ["State Key Lab of CAD & CG, Zhejiang University", "SenseTime Research"]
cities: ["Hangzhou", "Hong Kong"]
countries: ["China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Huang_From_Sparse_to_Dense_Camera_Relocalization_with_Scene-Specific_Detector_from_CVPR_2025_paper.html

title:
From Sparse to Dense: Camera Relocalization with Scene-Specific Detector from Feature Gaussian Splatting

abstract:
This paper presents a novel camera relocalization method, STDLoc, which leverages Feature Gaussian as scene representation. STDLoc is a full relocalization pipeline that can achieve accurate relocalization without relying on any pose prior.  Unlike previous coarse-to-fine localization methods that require image retrieval first and then feature matching, we propose a novel sparse-to-dense localization paradigm. Based on this scene representation, we introduce a novel matching-oriented Gaussian sampling strategy and a scene-specific detector to achieve efficient and robust initial pose estimation. Furthermore, based on the initial localization results, we align the query feature map to the Gaussian feature field by dense feature matching to enable accurate localization. The experiments on indoor and outdoor datasets show that STDLoc outperforms current state-of-the-art localization methods in terms of localization accuracy and recall. Our code is available on the project website: https://zju3dv.github.io/STDLoc.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Huang_From_Sparse_to_Dense_Camera_Relocalization_with_Scene-Specific_Detector_from_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Huang_From_Sparse_to_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Huang_2025_CVPR,
    author    = {Huang, Zhiwei and Yu, Hailin and Shentu, Yichun and Yuan, Jin and Zhang, Guofeng},
    title     = {From Sparse to Dense: Camera Relocalization with Scene-Specific Detector from Feature Gaussian Splatting},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {27059-27069}
}

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


authors: Zhiyuan Chen, Keyi Li, Yifan Jia, Le Ye, Yufei Ma
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 18011-18020
raw_affiliation: [{"affiliation": "Peking University", "city": "Beijing", "country": "China"}]
affiliations: ["Peking University"]
cities: ["Beijing"]
countries: ["China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Chen_Accelerating_Diffusion_Transformer_via_Increment-Calibrated_Caching_with_Channel-Aware_Singular_Value_CVPR_2025_paper.html

title:
Accelerating Diffusion Transformer via Increment-Calibrated Caching with Channel-Aware Singular Value Decomposition

abstract:
Diffusion transformer (DiT) models have achieved remarkable success in image generation, thanks for their exceptional generative capabilities and scalability. Nonetheless, the iterative nature of diffusion models (DMs) results in high computation complexity, posing challenges for deployment. Although existing cache-based acceleration methods try to utilize the inherent temporal similarity to skip redundant computations of DiT, the lack of correction may induce potential quality degradation. In this paper, we propose increment-calibrated caching, a training-free method for DiT acceleration, where the calibration parameters are generated from the pre-trained model itself with low-rank approximation. To deal with the possible correction failure arising from outlier activations, we introduce channel-aware Singular Value Decomposition (SVD), which further strengthens the calibration effect. Experimental results show that our method always achieve better performance than existing naive caching methods with a similar computation resource budget. When compared with 35-step DDIM, our method eliminates more than 45% computation and improves IS by 12 at the cost of less than 0.06 FID increase.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Chen_Accelerating_Diffusion_Transformer_via_Increment-Calibrated_Caching_with_Channel-Aware_Singular_Value_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Chen_Accelerating_Diffusion_Transformer_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Chen_2025_CVPR,
    author    = {Chen, Zhiyuan and Li, Keyi and Jia, Yifan and Ye, Le and Ma, Yufei},
    title     = {Accelerating Diffusion Transformer via Increment-Calibrated Caching with Channel-Aware Singular Value Decomposition},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {18011-18020}
}


title: CityWalker: Learning Embodied Urban Navigation from Web-Scale Videos
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 6875-6885
raw_affiliation: [{"affiliation": "New York University", "city": "New York", "country": "USA"}]
affiliations: ["New York University"]
cities: ["New York"]
countries: ["USA"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Liu_CityWalker_Learning_Embodied_Urban_Navigation_from_Web-Scale_Videos_CVPR_2025_paper.html

authors:
Xinhao Liu, Jintong Li, Yicheng Jiang, Niranjan Sujay, Zhicheng Yang, Juexiao Zhang, John Abanes, Jing Zhang, Chen Feng

abstract:
Navigating dynamic urban environments presents significant challenges for embodied agents, requiring advanced spatial reasoning and adherence to common-sense norms. Despite progress, existing visual navigation methods struggle in map-free or off-street settings, limiting the deployment of autonomous agents like last-mile delivery robots. To overcome these obstacles, we propose a scalable, data-driven approach for human-like urban navigation by training agents on thousands of hours of in-the-wild city walking and driving videos sourced from the web. We introduce a simple and scalable data processing pipeline that extracts action supervision from these videos, enabling large-scale imitation learning without costly annotations. Our model learns sophisticated navigation policies to handle diverse challenges and critical scenarios. Experimental results show that training on large-scale, diverse datasets significantly enhances navigation performance, surpassing current methods. This work shows the potential of using abundant online video data to develop robust navigation policies for embodied agents in dynamic urban settings.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Liu_CityWalker_Learning_Embodied_Urban_Navigation_from_Web-Scale_Videos_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Liu_CityWalker_Learning_Embodied_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Liu_2025_CVPR,
    author    = {Liu, Xinhao and Li, Jintong and Jiang, Yicheng and Sujay, Niranjan and Yang, Zhicheng and Zhang, Juexiao and Abanes, John and Zhang, Jing and Feng, Chen},
    title     = {CityWalker: Learning Embodied Urban Navigation from Web-Scale Videos},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {6875-6885}
}


title: A Simple yet Effective Layout Token in Large Language Models for Document Understanding
authors: Zhaoqing Zhu, Chuwei Luo, Zirui Shao, Feiyu Gao, Hangdi Xing, Qi Zheng, Ji Zhang
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 14472-14482
affiliations: ["Alibaba Group", "Zhejiang University"]
cities: ["Hangzhou", "Hangzhou"]
countries: ["China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Zhu_A_Simple_yet_Effective_Layout_Token_in_Large_Language_Models_CVPR_2025_paper.html

abstract:
Recent methods that integrate spatial layouts with text for document understanding in large language models (LLMs) have shown promising results. A commonly used method is to represent layout information as text tokens and interleave them with text content as inputs to the LLMs. However, such a method still demonstrates limitations, as it requires additional position IDs for tokens that are used to represent layout information. Due to the constraint on max position IDs, assigning them to layout information reduces those available for text content, reducing the capacity for the model to learn from the text during training, while also introducing a large number of potentially untrained position IDs during long-context inference, which can hinder performance on document understanding tasks. To address these issues, we propose LayTokenLLM, a simple yet effective method for document understanding. LayTokenLLM represents layout information as a single token per text segment and uses a specialized positional encoding scheme. It shares position IDs between text and layout tokens, eliminating the need for additional position IDs. This design maintains the model's capacity to learn from text while mitigating long-context issues during inference. Furthermore, a novel pre-training objective called Next Interleaved Text and Layout Token Prediction (NTLP) is devised to enhance cross-modality learning between text and layout tokens. Extensive experiments show that LayTokenLLM outperforms existing layout-integrated LLMs and MLLMs of similar scales on multi-page document understanding tasks, as well as most single-page tasks.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Zhu_A_Simple_yet_Effective_Layout_Token_in_Large_Language_Models_CVPR_2025_paper.pdf

bibtex:
@InProceedings{Zhu_2025_CVPR,
    author    = {Zhu, Zhaoqing and Luo, Chuwei and Shao, Zirui and Gao, Feiyu and Xing, Hangdi and Zheng, Qi and Zhang, Ji},
    title     = {A Simple yet Effective Layout Token in Large Language Models for Document Understanding},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {14472-14482}
}

raw_affiliation:
[{"affiliation": "Alibaba Group", "city": "Hangzhou", "country": "China"}, {"affiliation": "Zhejiang University", "city": "Hangzhou", "country": "China"}]


title: Reconstruction vs. Generation: Taming Optimization Dilemma in Latent Diffusion Models
authors: Jingfeng Yao, Bin Yang, Xinggang Wang
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 15703-15712
affiliations: ["Huazhong University of Science and Technology", "Independent Researcher"]
cities: ["Wuhan", "Unknown"]
countries: ["China", "Unknown"]
oral: yes
award_candidate: yes
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Yao_Reconstruction_vs._Generation_Taming_Optimization_Dilemma_in_Latent_Diffusion_Models_CVPR_2025_paper.html

abstract:
Latent diffusion models with Transformer architectures excel at generating high-fidelity images. However, recent studies reveal an optimization dilemma in this two-stage design: while increasing the per-token feature dimension in visual tokenizers improves reconstruction quality, it requires substantially larger diffusion models and more training iterations to achieve comparable generation performance. Consequently, existing systems often settle for sub-optimal solutions, either producing visual artifacts due to information loss within tokenizers or failing to converge fully due to expensive computation costs. We argue that this dilemma stems from the inherent difficulty in learning unconstrained high-dimensional latent spaces. To address this, we propose aligning the latent space with pre-trained vision foundation models when training the visual tokenizers. Our proposed VA-VAE (Vision foundation model Aligned Variational AutoEncoder) significantly expands the reconstruction-generation frontier of latent diffusion models, enabling faster convergence of Diffusion Transformers (DiT) in high-dimensional latent spaces. To exploit the full potential of VA-VAE, we build an enhanced DiT baseline with improved training strategies and architecture designs, termed LightningDiT. The integrated system achieves state-of-the-art (SOTA) performance on ImageNet 256 generation with an FID score of 1.35 while demonstrating remarkable training efficiency by reaching an FID score of 2.11 in just 64 epochs -- representing an over 21 times convergence speedup compared to the original DiT. Models and codes are available at https://github.com/hustvl/LightningDiT.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Yao_Reconstruction_vs._Generation_Taming_Optimization_Dilemma_in_Latent_Diffusion_Models_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Yao_Reconstruction_vs._Generation_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Yao_2025_CVPR,
    author    = {Yao, Jingfeng and Yang, Bin and Wang, Xinggang},
    title     = {Reconstruction vs. Generation: Taming Optimization Dilemma in Latent Diffusion Models},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {15703-15712}
}

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


title: StableAnimator: High-Quality Identity-Preserving Human Image Animation
authors: Shuyuan Tu, Zhen Xing, Xintong Han, Zhi-Qi Cheng, Qi Dai, Chong Luo, Zuxuan Wu
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 21096-21106
cities: ["Shanghai", "Shanghai", "Beijing", "Guangzhou", "Pittsburgh"]
countries: ["China", "China", "China", "China", "USA"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Tu_StableAnimator_High-Quality_Identity-Preserving_Human_Image_Animation_CVPR_2025_paper.html

abstract:
Current diffusion models for human image animation struggle to ensure identity (ID) consistency. This paper presents StableAnimator, the first end-to-end ID-preserving video diffusion framework, which synthesizes high-quality videos without any post-processing, conditioned on a reference image and a sequence of poses. Building upon a video diffusion model, StableAnimator contains carefully designed modules for both training and inference striving for identity consistency. In particular, StableAnimator begins by computing image and face embeddings with off-the-shelf extractors, respectively and face embeddings are further refined by interacting with image embeddings using a global content-aware Face Encoder. Then, StableAnimator introduces a novel distribution-aware ID Adapter that prevents interference caused by temporal layers while preserving ID via alignment. During inference, we propose a novel Hamilton-Jacobi-Bellman (HJB) equation-based optimization to further enhance the face quality. We demonstrate that solving the HJB equation can be integrated into the diffusion denoising process, and the resulting solution constrains the denoising path and thus benefits ID preservation. Experiments on multiple benchmarks show the effectiveness of StableAnimator both qualitatively and quantitatively.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Tu_StableAnimator_High-Quality_Identity-Preserving_Human_Image_Animation_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Tu_StableAnimator_High-Quality_Identity-Preserving_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Tu_2025_CVPR,
    author    = {Tu, Shuyuan and Xing, Zhen and Han, Xintong and Cheng, Zhi-Qi and Dai, Qi and Luo, Chong and Wu, Zuxuan},
    title     = {StableAnimator: High-Quality Identity-Preserving Human Image Animation},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {21096-21106}
}

raw_affiliation:
[{"affiliation": "Shanghai Key Lab of Intell. Info. Processing, School of CS, Fudan University", "city": "Shanghai", "country": "China"}, {"affiliation": "Shanghai Collaborative Innovation Center of Intelligent Visual Computing", "city": "Shanghai", "country": "China"}, {"affiliation": "Microsoft Research Asia", "city": "Beijing", "country": "China"}, {"affiliation": "Huya Inc.", "city": "Guangzhou", "country": "China"}, {"affiliation": "Carnegie Mellon University", "city": "Pittsburgh", "country": "USA"}]

affiliations:
["Shanghai Key Lab of Intell. Info. Processing, School of CS, Fudan University", "Shanghai Collaborative Innovation Center of Intelligent Visual Computing", "Microsoft Research Asia", "Huya Inc.", "Carnegie Mellon University"]


title: Learning Visual Composition through Improved Semantic Guidance
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 3740-3750
raw_affiliation: [{"affiliation": "Google DeepMind", "city": "London", "country": "UK"}]
affiliations: ["Google DeepMind"]
cities: ["London"]
countries: ["UK"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Stone_Learning_Visual_Composition_through_Improved_Semantic_Guidance_CVPR_2025_paper.html

authors:
Austin Stone, Hagen Soltau, Robert Geirhos, Xi Yi, Ye Xia, Bingyi Cao, Kaifeng Chen, Abhijit Ogale, Jonathon Shlens

abstract:
Visual imagery does not consist of solitary objects, but in-stead reflects the composition of a multitude of fluid con-cepts. While there have been great advances in visual repre-sentation learning, such advances have focused on buildingbetter representations for a small number of discrete objectsbereft of an understanding of how these objects are inter-acting.  One can observe this limitation in representationslearned through captions or contrastive learning - wherethe learned model treats an image essentially as a bag ofwords.  Several works have attempted to address this lim-itation  through  the  development  of  bespoke  architectures.In this work, we focus on simple and scalable approaches.In particular, we demonstrate that by improving weakly la-beled data, i.e. captions, we can vastly improve the perfor-mance of standard contrastive learning approaches. Previ-ous CLIP models achieved near chance rate on challengingtasks probing compositional learning.   However,  our sim-ple approach boosts performance of CLIP substantially andachieves  state  of  the  art  results  on  compositional  bench-marks  such  as  ARO  and  SugarCrepe.    Furthermore,  weshowcase our results on a relatively new captioning bench-mark derived from DOCCI. We demonstrate through a se-ries of ablations that a standard CLIP model trained withenhanced data may demonstrate impressive performance onimage retrieval tasks.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Stone_Learning_Visual_Composition_through_Improved_Semantic_Guidance_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Stone_Learning_Visual_Composition_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Stone_2025_CVPR,
    author    = {Stone, Austin and Soltau, Hagen and Geirhos, Robert and Yi, Xi and Xia, Ye and Cao, Bingyi and Chen, Kaifeng and Ogale, Abhijit and Shlens, Jonathon},
    title     = {Learning Visual Composition through Improved Semantic Guidance},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {3740-3750}
}


title: OODD: Test-time Out-of-Distribution Detection with Dynamic Dictionary
authors: Yifeng Yang, Lin Zhu, Zewen Sun, Hengyu Liu, Qinying Gu, Nanyang Ye
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 30630-30639
cities: ["Shanghai", "Tianjin", "Hong Kong", "Shanghai"]
countries: ["China", "China", "China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Yang_OODD_Test-time_Out-of-Distribution_Detection_with_Dynamic_Dictionary_CVPR_2025_paper.html

abstract:
Out-of-distribution (OOD) detection remains challenging for deep learning models, particularly when test-time OOD samples differ significantly from training outliers. We propose OODD, a novel test-time OOD detection method that dynamically maintains and updates an OOD dictionary without fine-tuning. Our approach leverages a priority queue-based dictionary that accumulates representative OOD features during testing, combined with an informative inlier sampling strategy for in-distribution (ID) samples. To ensure stable performance during early testing, we propose a dual OOD stabilization mechanism that leverages strategically generated outliers derived from ID data. To our best knowledge, extensive experiments on the OpenOOD benchmark demonstrate that OODD significantly outperforms existing methods, achieving a 26.0% improvement in FPR95 on CIFAR-100 Far OOD detection compared to the state-of-the-art approach. Furthermore, we present an optimized variant of the KNN-based OOD detection framework that achieves a 3x speedup while maintaining detection performance.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Yang_OODD_Test-time_Out-of-Distribution_Detection_with_Dynamic_Dictionary_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Yang_OODD_Test-time_Out-of-Distribution_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Yang_2025_CVPR,
    author    = {Yang, Yifeng and Zhu, Lin and Sun, Zewen and Liu, Hengyu and Gu, Qinying and Ye, Nanyang},
    title     = {OODD: Test-time Out-of-Distribution Detection with Dynamic Dictionary},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {30630-30639}
}

raw_affiliation:
[{"affiliation": "Shanghai Jiao Tong University", "city": "Shanghai", "country": "China"}, {"affiliation": "Tianjin University", "city": "Tianjin", "country": "China"}, {"affiliation": "The Chinese University of Hong Kong", "city": "Hong Kong", "country": "China"}, {"affiliation": "Shanghai Artificial Intelligence Laboratory", "city": "Shanghai", "country": "China"}]

affiliations:
["Shanghai Jiao Tong University", "Tianjin University", "The Chinese University of Hong Kong", "Shanghai Artificial Intelligence Laboratory"]


title: MEAT: Multiview Diffusion Model for Human Generation on Megapixels with Mesh Attention
authors: Yuhan Wang, Fangzhou Hong, Shuai Yang, Liming Jiang, Wayne Wu, Chen Change Loy
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 11297-11306
affiliations: ["S-Lab, Nanyang Technological University", "WICT, Peking University", "UCLA"]
cities: ["Singapore", "Beijing", "Los Angeles"]
countries: ["Singapore", "China", "USA"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Wang_MEAT_Multiview_Diffusion_Model_for_Human_Generation_on_Megapixels_with_CVPR_2025_paper.html

abstract:
Multiview diffusion models have shown considerable success in image-to-3D generation for general objects. However, when applied to human data, existing methods have yet to deliver promising results, largely due to the challenges of scaling multiview attention to higher resolutions. In this paper, we explore human multiview diffusion models at the megapixel level and introduce a solution called "mesh attention" to enable training at 1024x1024 resolution. Using a clothed human mesh as a central coarse geometric representation, the proposed mesh attention leverages rasterization and projection to establish direct cross-view coordinate correspondences. This approach significantly reduces the complexity of multiview attention while maintaining cross-view consistency. Building on this foundation, we devise a mesh attention block and combine it with keypoint conditioning to create our human-specific multiview diffusion model, MEAT. In addition, we present valuable insights into applying multiview human motion videos for diffusion training, addressing the longstanding issue of data scarcity. Extensive experiments show that MEAT effectively generates dense, consistent multiview human images at the megapixel level, outperforming existing multiview diffusion methods. Code is available at https://johann.wang/MEAT/.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Wang_MEAT_Multiview_Diffusion_Model_for_Human_Generation_on_Megapixels_with_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Wang_MEAT_Multiview_Diffusion_CVPR_2025_supplemental.zip

bibtex:
@InProceedings{Wang_2025_CVPR,
    author    = {Wang, Yuhan and Hong, Fangzhou and Yang, Shuai and Jiang, Liming and Wu, Wayne and Loy, Chen Change},
    title     = {MEAT: Multiview Diffusion Model for Human Generation on Megapixels with Mesh Attention},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {11297-11306}
}

raw_affiliation:
[{"affiliation": "S-Lab, Nanyang Technological University", "city": "Singapore", "country": "Singapore"}, {"affiliation": "WICT, Peking University", "city": "Beijing", "country": "China"}, {"affiliation": "UCLA", "city": "Los Angeles", "country": "USA"}]


title: Free Lunch Enhancements for Multi-modal Crowd Counting
authors: Haoliang Meng, Xiaopeng Hong, Zhengqin Lai, Miao Shang
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 14013-14023
affiliations: ["Harbin Institute of Technology", "Pengcheng Laboratory"]
cities: ["Harbin", "Shenzhen"]
countries: ["China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Meng_Free_Lunch_Enhancements_for_Multi-modal_Crowd_Counting_CVPR_2025_paper.html

abstract:
This paper addresses multi-modal crowd counting with a novel `free lunch' training enhancement strategy that requires no additional data, parameters, or increased inference complexity. First, we introduce a cross-modal alignment technique as a plug-in post-processing step for the pre-trained backbone network, enhancing the model's ability to capture shared information across modalities. Second, we incorporate a regional density supervision mechanism during the fine-tuning stage, which differentiates features in regions with varying crowd densities. Extensive experiments on three multi-modal crowd counting datasets validate our approach, making it the first to achieve an MAE below 10 on RGBT-CC. The code is available at https://github.com/HenryCilence/Free-Lunch-Multimodal-Counting.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Meng_Free_Lunch_Enhancements_for_Multi-modal_Crowd_Counting_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Meng_Free_Lunch_Enhancements_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Meng_2025_CVPR,
    author    = {Meng, Haoliang and Hong, Xiaopeng and Lai, Zhengqin and Shang, Miao},
    title     = {Free Lunch Enhancements for Multi-modal Crowd Counting},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {14013-14023}
}

raw_affiliation:
[{"affiliation": "Harbin Institute of Technology", "city": "Harbin", "country": "China"}, {"affiliation": "Pengcheng Laboratory", "city": "Shenzhen", "country": "China"}]


title: BIMBA: Selective-Scan Compression for Long-Range Video Question Answering
authors: Md Mohaiminul Islam, Tushar Nagarajan, Huiyu Wang, Gedas Bertasius, Lorenzo Torresani
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 29096-29107
affiliations: ["UNC Chapel Hill", "Meta AI"]
cities: ["Chapel Hill", "Menlo Park"]
countries: ["USA", "USA"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Islam_BIMBA_Selective-Scan_Compression_for_Long-Range_Video_Question_Answering_CVPR_2025_paper.html

abstract:
Video Question Answering (VQA) in long videos poses the key challenge of extracting relevant information and modeling long-range dependencies from many redundant frames. The self-attention mechanism provides a general solution for sequence modeling, but it has a prohibitive cost when applied to a massive number of spatiotemporal tokens in long videos. Most prior methods rely on compression strategies to lower the computational cost, such as reducing the input length via sparse frame sampling or compressing the output sequence passed to the large language model (LLM) via space-time pooling. However, these naive approaches over-represent redundant information and often miss salient events or fast-occurring space-time patterns. In this work, we introduce BIMBA, an efficient state-space model to handle long-form videos. Our model leverages the selective scan algorithm to learn to effectively select critical information from high-dimensional video and transform it into a reduced token sequence for efficient LLM processing. Extensive experiments demonstrate that BIMBA achieves state-of-the-art accuracy on multiple long-form VQA benchmarks, including PerceptionTest, NExT-QA, EgoSchema, VNBench, LongVideoBench, Video-MME, and MLVU.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Islam_BIMBA_Selective-Scan_Compression_for_Long-Range_Video_Question_Answering_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Islam_BIMBA_Selective-Scan_Compression_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Islam_2025_CVPR,
    author    = {Islam, Md Mohaiminul and Nagarajan, Tushar and Wang, Huiyu and Bertasius, Gedas and Torresani, Lorenzo},
    title     = {BIMBA: Selective-Scan Compression for Long-Range Video Question Answering},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {29096-29107}
}

raw_affiliation:
[{"affiliation": "UNC Chapel Hill", "city": "Chapel Hill", "country": "USA"}, {"affiliation": "Meta AI", "city": "Menlo Park", "country": "USA"}]


title: EVolSplat: Efficient Volume-based Gaussian Splatting for Urban View Synthesis
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 11286-11296
cities: ["Hangzhou", "Shenzhen", "T\u00fcbingen", "T\u00fcbingen"]
countries: ["China", "China", "Germany", "Germany"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Miao_EVolSplat_Efficient_Volume-based_Gaussian_Splatting_for_Urban_View_Synthesis_CVPR_2025_paper.html

authors:
Sheng Miao, Jiaxin Huang, Dongfeng Bai, Xu Yan, Hongyu Zhou, Yue Wang, Bingbing Liu, Andreas Geiger, Yiyi Liao

abstract:
Novel view synthesis of urban scenes is essential for autonomous driving-related applications. Existing NeRF and 3DGS-based methods show promising results in achieving photorealistic renderings but require slow, per-scene optimization. We introduce EVolSplat, an efficient 3D Gaussian Splatting model for urban scenes that works in a feed-forward manner. Unlike existing feed-forward, pixel-aligned 3DGS methods, which often suffer from issues like multi-view inconsistencies and duplicated content, our approach predicts 3D Gaussians across multiple frames within a unified volume using a 3D convolutional network. This is achieved by initializing 3D Gaussians with noisy depth predictions, and then refining their geometric properties in 3D space and predicting color based on 2D textures. Our model also handles distant views and the sky with a flexible hemisphere background model. This enables us to perform fast, feed-forward reconstruction while achieving real-time rendering. Experimental evaluations on the KITTI-360 and Waymo datasets show that our method achieves state-of-the-art quality compared to existing feed-forward 3DGS- and NeRF-based methods.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Miao_EVolSplat_Efficient_Volume-based_Gaussian_Splatting_for_Urban_View_Synthesis_CVPR_2025_paper.pdf

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

bibtex:
@InProceedings{Miao_2025_CVPR,
    author    = {Miao, Sheng and Huang, Jiaxin and Bai, Dongfeng and Yan, Xu and Zhou, Hongyu and Wang, Yue and Liu, Bingbing and Geiger, Andreas and Liao, Yiyi},
    title     = {EVolSplat: Efficient Volume-based Gaussian Splatting for Urban View Synthesis},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {11286-11296}
}

raw_affiliation:
[{"affiliation": "Zhejiang University", "city": "Hangzhou", "country": "China"}, {"affiliation": "Huawei Noah\u2019s Ark Lab", "city": "Shenzhen", "country": "China"}, {"affiliation": "University of T\u00a8ubingen", "city": "T\u00fcbingen", "country": "Germany"}, {"affiliation": "T\u00a8ubingen AI Center", "city": "T\u00fcbingen", "country": "Germany"}]

affiliations:
["Zhejiang University", "Huawei Noah\u2019s Ark Lab", "University of T\u00a8ubingen", "T\u00a8ubingen AI Center"]


title: Diff2Flow: Training Flow Matching Models via Diffusion Model Alignment
authors: Johannes Schusterbauer, Ming Gui, Frank Fundel, Björn Ommer
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 28347-28357
raw_affiliation: [{"affiliation": "CompVis @ LMU Munich, MCML", "city": "Munich", "country": "Germany"}]
affiliations: ["CompVis @ LMU Munich, MCML"]
cities: ["Munich"]
countries: ["Germany"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Schusterbauer_Diff2Flow_Training_Flow_Matching_Models_via_Diffusion_Model_Alignment_CVPR_2025_paper.html

abstract:
Diffusion models have revolutionized generative tasks through high-fidelity outputs, yet flow matching (FM) offers faster inference and empirical performance gains. However, current foundation FM models are computationally prohibitive for finetuning, while diffusion models like Stable Diffusion benefit from efficient architectures and ecosystem support. This work addresses the critical challenge of efficiently transferring knowledge from pre-trained diffusion models to flow matching. We propose Diff2Flow, a novel framework that systematically bridges diffusion and FM paradigms by rescaling timesteps, aligning interpolants, and deriving FM-compatible velocity fields from diffusion predictions. This alignment enables direct and efficient FM finetuning of diffusion priors with no extra computation overhead. Our experiments demonstrate that Diff2Flow outperforms naive FM and diffusion finetuning particularly under parameter-efficient constraints, while achieving superior or competitive performance across diverse downstream tasks compared to state-of-the-art methods.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Schusterbauer_Diff2Flow_Training_Flow_Matching_Models_via_Diffusion_Model_Alignment_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Schusterbauer_Diff2Flow_Training_Flow_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Schusterbauer_2025_CVPR,
    author    = {Schusterbauer, Johannes and Gui, Ming and Fundel, Frank and Ommer, Bj\"orn},
    title     = {Diff2Flow: Training Flow Matching Models via Diffusion Model Alignment},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {28347-28357}
}


proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 7739-7751
affiliations: ["DeepSeek-AI", "Peking University", "The University of Hong Kong", "Tsinghua University"]
cities: ["Beijing", "Beijing", "Hong Kong", "Beijing"]
countries: ["China", "China", "China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Ma_JanusFlow_Harmonizing_Autoregression_and_Rectified_Flow_for_Unified_Multimodal_Understanding_CVPR_2025_paper.html

title:
JanusFlow: Harmonizing Autoregression and Rectified Flow for Unified Multimodal Understanding and Generation

authors:
Yiyang Ma, Xingchao Liu, Xiaokang Chen, Wen Liu, Chengyue Wu, Zhiyu Wu, Zizheng Pan, Zhenda Xie, Haowei Zhang, Xingkai Yu, Liang Zhao, Yisong Wang, Jiaying Liu, Chong Ruan

abstract:
We present JanusFlow, a powerful framework that unifies image understanding and generation in a single model.JanusFlow introduces a minimalist architecture that integrates autoregressive language models with rectified flow, a state-of-the-art method in generative modeling.Our key finding demonstrates that rectified flow can be straightforwardly trained within the large language model framework, eliminating the need for complex architectural modifications.To further improve the performance of our unified model, we adopt two key strategies: (i) decoupling the understanding and generation encoders, and (ii) aligning their representations during unified training.Extensive experiments show that JaunsFlow achieves comparable or superior performance to specialized models in their respective domains, while significantly outperforming existing unified approaches.This work represents a step toward more efficient and versatile vision-language models.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Ma_JanusFlow_Harmonizing_Autoregression_and_Rectified_Flow_for_Unified_Multimodal_Understanding_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Ma_JanusFlow_Harmonizing_Autoregression_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Ma_2025_CVPR,
    author    = {Ma, Yiyang and Liu, Xingchao and Chen, Xiaokang and Liu, Wen and Wu, Chengyue and Wu, Zhiyu and Pan, Zizheng and Xie, Zhenda and Zhang, Haowei and Yu, Xingkai and Zhao, Liang and Wang, Yisong and Liu, Jiaying and Ruan, Chong},
    title     = {JanusFlow: Harmonizing Autoregression and Rectified Flow for Unified Multimodal Understanding and Generation},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {7739-7751}
}

raw_affiliation:
[{"affiliation": "DeepSeek-AI", "city": "Beijing", "country": "China"}, {"affiliation": "Peking University", "city": "Beijing", "country": "China"}, {"affiliation": "The University of Hong Kong", "city": "Hong Kong", "country": "China"}, {"affiliation": "Tsinghua University", "city": "Beijing", "country": "China"}]


title: Visual Prompting for One-shot Controllable Video Editing without Inversion
authors: Zhengbo Zhang, Yuxi Zhou, Duo Peng, Joo-Hwee Lim, Zhigang Tu, De Wen Soh, Lin Geng Foo
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 7784-7794
cities: ["Singapore", "Wuhan", "Singapore"]
countries: ["Singapore", "China", "Singapore"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Zhang_Visual_Prompting_for_One-shot_Controllable_Video_Editing_without_Inversion_CVPR_2025_paper.html

abstract:
One-shot controllable video editing (OCVE) is an important yet challenging task, aiming to propagate user edits that are made---using any image editing tool---on the first frame of a video to all subsequent frames, while ensuring content consistency between edited frames and source frames. To achieve this, prior methods employ DDIM inversion to transform source frames into latent noise, which is then fed into a pre-trained diffusion model, conditioned on the user-edited first frame, to generate the edited video. However, the DDIM inversion process accumulates errors, which hinder the latent noise from accurately reconstructing the source frames, ultimately compromising content consistency in the generated edited frames. To overcome it, our method eliminates the need for DDIM inversion by performing OCVE through a novel perspective based on visual prompting. Furthermore, inspired by consistency models that can perform multi-step consistency sampling to generate a sequence of content-consistent images, we propose a content consistency sampling (CCS) to ensure content consistency between the generated edited frames and the source frames. Moreover, we introduce a temporal-content consistency sampling (TCS)  based on Stein Variational Gradient Descent to ensure temporal consistency across the edited frames. Extensive experiments validate the effectiveness of our approach.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Zhang_Visual_Prompting_for_One-shot_Controllable_Video_Editing_without_Inversion_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Zhang_Visual_Prompting_for_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Zhang_2025_CVPR,
    author    = {Zhang, Zhengbo and Zhou, Yuxi and Peng, Duo and Lim, Joo-Hwee and Tu, Zhigang and Soh, De Wen and Foo, Lin Geng},
    title     = {Visual Prompting for One-shot Controllable Video Editing without Inversion},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {7784-7794}
}

raw_affiliation:
[{"affiliation": "Singapore University of Technology and Design", "city": "Singapore", "country": "Singapore"}, {"affiliation": "Wuhan University", "city": "Wuhan", "country": "China"}, {"affiliation": "Institute for Infocomm Research, Agency for Science, Technology and Research, Singapore", "city": "Singapore", "country": "Singapore"}]

affiliations:
["Singapore University of Technology and Design", "Wuhan University", "Institute for Infocomm Research, Agency for Science, Technology and Research, Singapore"]


title: PIDSR: Complementary Polarized Image Demosaicing and Super-Resolution
authors: Shuangfan Zhou, Chu Zhou, Youwei Lyu, Heng Guo, Zhanyu Ma, Boxin Shi, Imari Sato
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 16081-16090
cities: ["Beijing", "Tokyo", "Beijing", "Beijing"]
countries: ["China", "Japan", "China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Zhou_PIDSR_Complementary_Polarized_Image_Demosaicing_and_Super-Resolution_CVPR_2025_paper.html

abstract:
Polarization cameras can capture multiple polarized images with different polarizer angles in a single shot, bringing convenience to polarization-based downstream tasks. However, their direct outputs are color-polarization filter array (CPFA) raw images, requiring demosaicing to reconstruct full-resolution, full-color polarized images; unfortunately, this necessary step introduces artifacts that make polarization-related parameters such as the degree of polarization (DoP) and angle of polarization (AoP) prone to error. Besides, limited by the hardware design, the resolution of a polarization camera is often much lower than that of a conventional RGB camera. Existing polarized image demosaicing (PID) methods are limited in that they cannot enhance resolution, while polarized image super-resolution (PISR) methods, though designed to obtain high-resolution (HR) polarized images from the demosaicing results, tend to retain or even amplify errors in the DoP and AoP introduced by demosaicing artifacts. In this paper, we propose PIDSR, a joint framework that performs complementary Polarized Image Demosaicing and Super-Resolution, showing the ability to robustly obtain high-quality HR polarized images with more accurate DoP and AoP from a CPFA raw image in a direct manner. Experiments show our PIDSR not only achieves state-of-the-art performance on both synthetic and real data, but also facilitates downstream tasks.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Zhou_PIDSR_Complementary_Polarized_Image_Demosaicing_and_Super-Resolution_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Zhou_PIDSR_Complementary_Polarized_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Zhou_2025_CVPR,
    author    = {Zhou, Shuangfan and Zhou, Chu and Lyu, Youwei and Guo, Heng and Ma, Zhanyu and Shi, Boxin and Sato, Imari},
    title     = {PIDSR: Complementary Polarized Image Demosaicing and Super-Resolution},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {16081-16090}
}

raw_affiliation:
[{"affiliation": "School of Artificial Intelligence, Beijing University of Posts and Telecommunications", "city": "Beijing", "country": "China"}, {"affiliation": "National Institute of Informatics", "city": "Tokyo", "country": "Japan"}, {"affiliation": "State Key Laboratory for Multimedia Information Processing, School of Computer Science, Peking University", "city": "Beijing", "country": "China"}, {"affiliation": "National Engineering Research Center of Visual Technology, School of Computer Science, Peking University", "city": "Beijing", "country": "China"}]

affiliations:
["School of Artificial Intelligence, Beijing University of Posts and Telecommunications", "National Institute of Informatics", "State Key Laboratory for Multimedia Information Processing, School of Computer Science, Peking University", "National Engineering Research Center of Visual Technology, School of Computer Science, Peking University"]


title: MegaSynth: Scaling Up 3D Scene Reconstruction with Synthesized Data
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 16441-16452
cities: ["Austin", "Unknown", "Stony Brook", "Corvallis", "Ithaca"]
countries: ["USA", "USA", "USA", "USA", "USA"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Jiang_MegaSynth_Scaling_Up_3D_Scene_Reconstruction_with_Synthesized_Data_CVPR_2025_paper.html

authors:
Hanwen Jiang, Zexiang Xu, Desai Xie, Ziwen Chen, Haian Jin, Fujun Luan, Zhixin Shu, Kai Zhang, Sai Bi, Xin Sun, Jiuxiang Gu, Qixing Huang, Georgios Pavlakos, Hao Tan

abstract:
We propose scaling up 3D scene reconstruction by training with synthesized data. At the core of our work is MegaSynth, a procedurally generated 3D dataset comprising 700K scenes - over 50 times larger than the prior real dataset DL3DV - dramatically scaling the training data. To enable scalable data generation, our key idea is eliminating semantic information, removing the need to model complex semantic priors such as object affordances and scene composition. Instead, we model scenes with basic spatial structures and geometry primitives, offering scalability. Besides, we control data complexity to facilitate training while loosely aligning it with real-world data distribution to benefit real-world generalization. We explore training LRMs with both MegaSynth and available real data. Experiment results show that joint training or pre-training with MegaSynth improves reconstruction quality by 1.2 to 1.8 dB PSNR across diverse image domains. Moreover, models trained solely on MegaSynth perform comparably to those trained on real data, underscoring the low-level nature of 3D reconstruction. Additionally, we provide an in-depth analysis of MegaSynth's properties for enhancing model capability, training stability, and generalization.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Jiang_MegaSynth_Scaling_Up_3D_Scene_Reconstruction_with_Synthesized_Data_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Jiang_MegaSynth_Scaling_Up_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Jiang_2025_CVPR,
    author    = {Jiang, Hanwen and Xu, Zexiang and Xie, Desai and Chen, Ziwen and Jin, Haian and Luan, Fujun and Shu, Zhixin and Zhang, Kai and Bi, Sai and Sun, Xin and Gu, Jiuxiang and Huang, Qixing and Pavlakos, Georgios and Tan, Hao},
    title     = {MegaSynth: Scaling Up 3D Scene Reconstruction with Synthesized Data},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {16441-16452}
}

raw_affiliation:
[{"affiliation": "The University of Texas at Austin", "city": "Austin", "country": "USA"}, {"affiliation": "Adobe Research", "city": "Unknown", "country": "USA"}, {"affiliation": "Stony Brook University", "city": "Stony Brook", "country": "USA"}, {"affiliation": "Oregon State University", "city": "Corvallis", "country": "USA"}, {"affiliation": "Cornell University", "city": "Ithaca", "country": "USA"}]

affiliations:
["The University of Texas at Austin", "Adobe Research", "Stony Brook University", "Oregon State University", "Cornell University"]


title: Prof. Robot: Differentiable Robot Rendering Without Static and Self-Collisions
authors: Quanyuan Ruan, Jiabao Lei, Wenhao Yuan, Yanglin Zhang, Dekun Lu, Guiliang Liu, Kui Jia
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 22562-22572
cities: ["Guangzhou", "Shenzhen"]
countries: ["China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Ruan_Prof._Robot_Differentiable_Robot_Rendering_Without_Static_and_Self-Collisions_CVPR_2025_paper.html

abstract:
Differentiable rendering has gained significant attention in the field of robotics, with differentiable robot rendering emerging as an effective paradigm for learning robotic actions from image-space supervision. However, the lack of physical world perception in this approach may lead to potential collisions during action optimization. In this work, we introduce a novel improvement on previous efforts by incorporating physical awareness of collisions through the learning of a neural robotic collision classifier. This enables the optimization of actions that avoid collisions with static, non-interactable environments as well as the robot itself. To facilitate effective gradient optimization with the classifier, we identify the underlying issue and propose leveraging Eikonal regularization to ensure consistent gradients for optimization. Our solution can be seamlessly integrated into existing differentiable robot rendering frameworks, utilizing gradients for optimization and providing a foundation for future applications of differentiable rendering in robotics with improved reliability of interactions with the physical world. Both qualitative and quantitative experiments demonstrate the necessity and effectiveness of our method compared to previous solutions.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Ruan_Prof._Robot_Differentiable_Robot_Rendering_Without_Static_and_Self-Collisions_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Ruan_Prof._Robot_Differentiable_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Ruan_2025_CVPR,
    author    = {Ruan, Quanyuan and Lei, Jiabao and Yuan, Wenhao and Zhang, Yanglin and Lu, Dekun and Liu, Guiliang and Jia, Kui},
    title     = {Prof. Robot: Differentiable Robot Rendering Without Static and Self-Collisions},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {22562-22572}
}

raw_affiliation:
[{"affiliation": "South China University of Technology", "city": "Guangzhou", "country": "China"}, {"affiliation": "School of Data Science, The Chinese University of Hong Kong, Shenzhen", "city": "Shenzhen", "country": "China"}]

affiliations:
["South China University of Technology", "School of Data Science, The Chinese University of Hong Kong, Shenzhen"]


title: AVQACL: A Novel Benchmark for Audio-Visual Question Answering Continual Learning
authors: Kaixuan Wu, Xinde Li, Xinling Li, Chuanfei Hu, Guoliang Wu
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 3252-3261
cities: ["Nanjing", "Nanjing", "Shenzhen", "Nanjing"]
countries: ["China", "China", "China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Wu_AVQACL_A_Novel_Benchmark_for_Audio-Visual_Question_Answering_Continual_Learning_CVPR_2025_paper.html

abstract:
In this paper, a novel benchmark for audio-visual question answering continual learning (AVQACL) is introduced, aiming to study fine-grained scene understanding and spatial-temporal reasoning in videos under a continual learning setting. To facilitate this multimodal continual leaning task, we create two audio-visual question answering continual learning datasets, named Split-AVQA and Split-MUSIC-AVQA based on the AVQA and MUSIC-AVQA datasets, respectively. The experimental results suggest that the model exhibits limited cognitive and reasoning abilities and experiences catastrophic forgetting when processing three modalities simultaneously in a continuous data stream. To address above challenges, we propose a novel continual learning method that incorporates question-guided cross-modal information fusion (QCIF) to focus on question-relevant details for improved feature representation and task-specific knowledge distillation with spatial-temporal feature constraints (TKD-STFC) to preserve the spatial-temporal reasoning knowledge acquired from previous dynamic scenarios. Furthermore, a question semantic consistency constraint (QSCC) is employed to ensure that the model maintains a consistent understanding of question semantics across tasks throughout the continual learning process. Extensive experimental results on Split-AVQA and Split-MUSIC-AVQA datasets illustrate that our method achieves state-of-the-art audio-visual question answering continual learning performance. The code is available at https://github.com/kx-wu/CVPR2025_AVQACL.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Wu_AVQACL_A_Novel_Benchmark_for_Audio-Visual_Question_Answering_Continual_Learning_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Wu_AVQACL_A_Novel_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Wu_2025_CVPR,
    author    = {Wu, Kaixuan and Li, Xinde and Li, Xinling and Hu, Chuanfei and Wu, Guoliang},
    title     = {AVQACL: A Novel Benchmark for Audio-Visual Question Answering Continual Learning},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {3252-3261}
}

raw_affiliation:
[{"affiliation": "School of Automation, Southeast University", "city": "Nanjing", "country": "China"}, {"affiliation": "Nanjing Center for Applied Mathematics", "city": "Nanjing", "country": "China"}, {"affiliation": "Southeast University Shenzhen Research Institute", "city": "Shenzhen", "country": "China"}, {"affiliation": "Key Laboratory of Measurement and Control of Complex Systems of Engineering, Ministry of Education", "city": "Nanjing", "country": "China"}]

affiliations:
["School of Automation, Southeast University", "Nanjing Center for Applied Mathematics", "Southeast University Shenzhen Research Institute", "Key Laboratory of Measurement and Control of Complex Systems of Engineering, Ministry of Education"]


title: Flash-Split: 2D Reflection Removal with Flash Cues and Latent Diffusion Separation
authors: Tianfu Wang, Mingyang Xie, Haoming Cai, Sachin Shah, Christopher A. Metzler
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 5688-5698
raw_affiliation: [{"affiliation": "University of Maryland", "city": "College Park", "country": "USA"}]
affiliations: ["University of Maryland"]
cities: ["College Park"]
countries: ["USA"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Wang_Flash-Split_2D_Reflection_Removal_with_Flash_Cues_and_Latent_Diffusion_CVPR_2025_paper.html

abstract:
Transparent surfaces, such as glass, create complex reflections that obscure images and challenge downstream computer vision applications. We introduce Flash-Split, a robust framework for separating transmitted and reflected light using a single (potentially misaligned) pair of flash/no-flash images. Our core idea is to perform flash-informed reflection separation iteratively in a low-dimensional latent space. Specifically, Flash-Split consists of two stages. Stage 1 separates the reflection latent and transmission latent via a dual-branch diffusion model that is conditioned on an encoded flash/no-flash latent pair. This stage effectively mitigates the flash/no-flash misalignment issue. Stage 2 restores high-resolution, faithful details to the separated latents via a cross-latent decoding process that is conditioned on the original images before separation. We validate Flash-Split on challenging real-world scenes and demonstrate it significantly outperforms existing methods. Our project webpage is at https://flash-split.github.io/.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Wang_Flash-Split_2D_Reflection_Removal_with_Flash_Cues_and_Latent_Diffusion_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Wang_Flash-Split_2D_Reflection_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Wang_2025_CVPR,
    author    = {Wang, Tianfu and Xie, Mingyang and Cai, Haoming and Shah, Sachin and Metzler, Christopher A.},
    title     = {Flash-Split: 2D Reflection Removal with Flash Cues and Latent Diffusion Separation},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {5688-5698}
}


title: Attention IoU: Examining Biases in CelebA using Attention Maps
authors: Aaron Serianni, Tyler Zhu, Olga Russakovsky, Vikram V. Ramaswamy
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 4386-4397
raw_affiliation: [{"affiliation": "Princeton University", "city": "Princeton", "country": "USA"}]
affiliations: ["Princeton University"]
cities: ["Princeton"]
countries: ["USA"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Serianni_Attention_IoU_Examining_Biases_in_CelebA_using_Attention_Maps_CVPR_2025_paper.html

abstract:
Computer vision models have been shown to exhibit and amplify biases across a wide array of datasets and tasks. Existing methods for quantifying bias in classification models primarily focus on dataset distribution and model performance on subgroups, overlooking the internal workings of a model. We introduce the Attention-IoU (Attention Intersection over Union) metric and related scores, which use attention maps to reveal biases within a model's internal representations and identify image features potentially causing the biases. First, we validate Attention-IoU on the synthetic Waterbirds dataset, showing that the metric accurately measures model bias. We then analyze the CelebA dataset, finding that Attention-IoU uncovers correlations beyond accuracy disparities. Through an investigation of individual attributes through the protected attribute of Male, we examine the distinct ways biases are represented in CelebA. Lastly, by subsampling the training set to change attribute correlations, we demonstrate that Attention-IoU reveals potential confounding variables not present in dataset labels. Our code is available at https://github.com/aaronserianni/attention-iou.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Serianni_Attention_IoU_Examining_Biases_in_CelebA_using_Attention_Maps_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Serianni_Attention_IoU_Examining_CVPR_2025_supplemental.zip

bibtex:
@InProceedings{Serianni_2025_CVPR,
    author    = {Serianni, Aaron and Zhu, Tyler and Russakovsky, Olga and Ramaswamy, Vikram V.},
    title     = {Attention IoU: Examining Biases in CelebA using Attention Maps},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {4386-4397}
}


title: HEIE: MLLM-Based Hierarchical Explainable AIGC Image Implausibility Evaluator
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 3856-3866
cities: ["Beijing", "Beijing", "Shenzhen", "Beijing", "Hangzhou"]
countries: ["China", "China", "China", "China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Yang_HEIE_MLLM-Based_Hierarchical_Explainable_AIGC_Image_Implausibility_Evaluator_CVPR_2025_paper.html

authors:
Fan Yang, Ru Zhen, Jianing Wang, Yanhao Zhang, Haoxiang Chen, Haonan Lu, Sicheng Zhao, Guiguang Ding

abstract:
AIGC images are prevalent across various fields, yet they frequently suffer from quality issues like artifacts and unnatural textures. Specialized models aim to predict defect region heatmaps but face two primary challenges: (1) lack of explainability, failing to provide reasons and analyses for subtle defects, and (2) inability to leverage common sense and logical reasoning, leading to poor generalization. Multimodal large language models (MLLMs) promise better comprehension and reasoning but face their own challenges: (1) difficulty in fine-grained defect localization due to the limitations in capturing tiny details, and (2) constraints in providing pixel-wise outputs necessary for precise heatmap generation. To address these challenges, we propose HEIE: a novel MLLM-Based Hierarchical Explainable Image Implausibility Evaluator. We introduce the CoT-Driven Explainable Trinity Evaluator, which integrates heatmaps, scores, and explanation outputs, using CoT to decompose complex tasks into subtasks of increasing difficulty and enhance interpretability. Our Adaptive Hierarchical Implausibility Mapper synergizes low-level image features with high-level mapper tokens from LLMs, enabling precise local-to-global hierarchical heatmap predictions through an uncertainty-based adaptive token approach. Moreover, we propose a new dataset: Expl-AIGI-Eval, designed to facilitate interpretable implausibility evaluation of AIGC images. Our method demonstrates state-of-the-art performance through extensive experiments. Our project is at https://yfthu.github.io/HEIE/.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Yang_HEIE_MLLM-Based_Hierarchical_Explainable_AIGC_Image_Implausibility_Evaluator_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Yang_HEIE_MLLM-Based_Hierarchical_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Yang_2025_CVPR,
    author    = {Yang, Fan and Zhen, Ru and Wang, Jianing and Zhang, Yanhao and Chen, Haoxiang and Lu, Haonan and Zhao, Sicheng and Ding, Guiguang},
    title     = {HEIE: MLLM-Based Hierarchical Explainable AIGC Image Implausibility Evaluator},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {3856-3866}
}

raw_affiliation:
[{"affiliation": "Tsinghua University", "city": "Beijing", "country": "China"}, {"affiliation": "BNRist", "city": "Beijing", "country": "China"}, {"affiliation": "OPPO AI Center", "city": "Shenzhen", "country": "China"}, {"affiliation": "Peking University", "city": "Beijing", "country": "China"}, {"affiliation": "Hangzhou Zhuoxi Institute of Brain and Intelligence", "city": "Hangzhou", "country": "China"}]

affiliations:
["Tsinghua University", "BNRist", "OPPO AI Center", "Peking University", "Hangzhou Zhuoxi Institute of Brain and Intelligence"]


title: Segment Any Motion in Videos
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 3406-3416
affiliations: ["UC Berkeley", "Peking University"]
cities: ["Berkeley", "Beijing"]
countries: ["USA", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Huang_Segment_Any_Motion_in_Videos_CVPR_2025_paper.html

authors:
Nan Huang, Wenzhao Zheng, Chenfeng Xu, Kurt Keutzer, Shanghang Zhang, Angjoo Kanazawa, Qianqian Wang

abstract:
Moving object segmentation is a crucial task for achieving a high-level understanding of visual scenes and has numerous downstream applications. Humans can effortlessly segment moving objects in videos. Previous work has largely relied on optical flow to provide motion cues; however, this approach often results in imperfect predictions due to challenges such as partial motion, complex deformations, motion blur and background distractions. We propose a novel approach for moving object segmentation that combines long-range trajectory motion cues with DINO-based semantic features and leverages SAM2 for pixel-level mask densification through an iterative prompting strategy. Our model employs Spatio-Temporal Trajectory Attention and Motion-Semantic Decoupled Embedding to prioritize motion while integrating semantic support. Extensive testing on diverse datasets demonstrates state-of-the-art performance, excelling in challenging scenarios and fine-grained segmentation of multiple objects. Our code is available at https://motion-seg.github.io/.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Huang_Segment_Any_Motion_in_Videos_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Huang_Segment_Any_Motion_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Huang_2025_CVPR,
    author    = {Huang, Nan and Zheng, Wenzhao and Xu, Chenfeng and Keutzer, Kurt and Zhang, Shanghang and Kanazawa, Angjoo and Wang, Qianqian},
    title     = {Segment Any Motion in Videos},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {3406-3416}
}

raw_affiliation:
[{"affiliation": "UC Berkeley", "city": "Berkeley", "country": "USA"}, {"affiliation": "Peking University", "city": "Beijing", "country": "China"}]


title: HandOS: 3D Hand Reconstruction in One Stage
authors: Xingyu Chen, Zhuheng Song, Xiaoke Jiang, Yaoqing Hu, Junzhi Yu, Lei Zhang
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 17304-17314
cities: ["Beijing", "Shenzhen", "Beijing"]
countries: ["China", "China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Chen_HandOS_3D_Hand_Reconstruction_in_One_Stage_CVPR_2025_paper.html

abstract:
Existing approaches of hand reconstruction predominantly adhere to a multi-stage framework, encompassing detection, left-right classification, and pose estimation. This paradigm induces redundant computation and cumulative errors. In this work, we propose HandOS, an end-to-end framework for 3D hand reconstruction. Our central motivation lies in leveraging a frozen detector as the foundation while incorporating auxiliary modules for 2D and 3D keypoint estimation. In this manner, we integrate the pose estimation capacity into the detection framework, while at the same time obviating the necessity of using the left-right category as a prerequisite.  Specifically, we propose an interactive 2D-3D decoder, where 2D joint semantics is derived from detection cues while 3D representation is lifted from those of 2D joints. Furthermore, hierarchical attention is designed to enable the concurrent modeling of 2D joints, 3D vertices, and camera translation. Consequently, we achieve an end-to-end integration of hand detection, 2D pose estimation, and 3D mesh reconstruction within a one-stage framework, so that the above multi-stage drawbacks are overcome. Meanwhile, the HandOS reaches state-of-the-art performances on public benchmarks, e.g., 5.0 PA-MPJPE on FreiHand and 64.6% PCK@0.05 on HInt-Ego4D.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Chen_HandOS_3D_Hand_Reconstruction_in_One_Stage_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Chen_HandOS_3D_Hand_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Chen_2025_CVPR,
    author    = {Chen, Xingyu and Song, Zhuheng and Jiang, Xiaoke and Hu, Yaoqing and Yu, Junzhi and Zhang, Lei},
    title     = {HandOS: 3D Hand Reconstruction in One Stage},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {17304-17314}
}

raw_affiliation:
[{"affiliation": "Department of Advanced Manufacturing and Robotics, College of Engineering, Peking University", "city": "Beijing", "country": "China"}, {"affiliation": "International Digital Economy Academy (IDEA Research)", "city": "Shenzhen", "country": "China"}, {"affiliation": "University of Chinese Academy of Sciences", "city": "Beijing", "country": "China"}]

affiliations:
["Department of Advanced Manufacturing and Robotics, College of Engineering, Peking University", "International Digital Economy Academy (IDEA Research)", "University of Chinese Academy of Sciences"]


authors: Wenbo Chen, Zhen Xu, Ruotao Xu, Si Wu, Hau-San Wong
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 3931-3941
cities: ["Guangzhou", "Guangzhou", "Hong Kong"]
countries: ["China", "China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Chen_Task-aware_Cross-modal_Feature_Refinement_Transformer_with_Large_Language_Models_for_CVPR_2025_paper.html

title:
Task-aware Cross-modal Feature Refinement Transformer with Large Language Models for Visual Grounding

abstract:
The goal of visual grounding is to establish connections between target objects and textual descriptions. Large Language Models (LLMs) have demonstrated strong comprehension abilities across a variety of visual tasks. To establish precise associations between the text and the corresponding visual region, we propose a Task-aware Cross-modal feature Refinement Transformer with LLMs for visual grounding, and our model is referred to as TCRT. To enable the LLM trained solely on text to understand images, we introduce an LLM adaptation module that extracts text-related visual features to bridge the domain discrepancy between the textual and visual modalities.  We feed the text and visual features into the LLM to obtain task-aware priors. To enable the priors to guide feature fusion process, we further incorporate a cross-modal feature fusion module, which allows task-aware embeddings to refine visual features and facilitate information interaction between the Referring Expression Comprehension (REC) and Referring Expression Segmentation (RES) tasks. We have performed extensive experiments to verify the effectiveness of the main components and demonstrate the superior performance of the proposed TCRT over state-of-the-art end-to-end visual grounding methods on RefCOCO, RefCOCOg, RefCOCO+ and ReferItGame.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Chen_Task-aware_Cross-modal_Feature_Refinement_Transformer_with_Large_Language_Models_for_CVPR_2025_paper.pdf

bibtex:
@InProceedings{Chen_2025_CVPR,
    author    = {Chen, Wenbo and Xu, Zhen and Xu, Ruotao and Wu, Si and Wong, Hau-San},
    title     = {Task-aware Cross-modal Feature Refinement Transformer with Large Language Models for Visual Grounding},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {3931-3941}
}

raw_affiliation:
[{"affiliation": "School of Computer Science and Engineering, South China University of Technology", "city": "Guangzhou", "country": "China"}, {"affiliation": "Institute for Super Robotics (Huangpu)", "city": "Guangzhou", "country": "China"}, {"affiliation": "Department of Computer Science, City University of Hong Kong", "city": "Hong Kong", "country": "China"}]

affiliations:
["School of Computer Science and Engineering, South China University of Technology", "Institute for Super Robotics (Huangpu)", "Department of Computer Science, City University of Hong Kong"]


title: DropGaussian: Structural Regularization for Sparse-view Gaussian Splatting
authors: Hyunwoo Park, Gun Ryu, Wonjun Kim
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 21600-21609
raw_affiliation: [{"affiliation": "Konkuk University", "city": "Seoul", "country": "South Korea"}]
affiliations: ["Konkuk University"]
cities: ["Seoul"]
countries: ["South Korea"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Park_DropGaussian_Structural_Regularization_for_Sparse-view_Gaussian_Splatting_CVPR_2025_paper.html

abstract:
Recently, 3D Gaussian splatting (3DGS) has gained considerable attentions in the field of novel view synthesis due to its fast performance while yielding the excellent image quality. However, 3DGS in sparse-view settings (e.g., three-view inputs) often faces with the problem of overfitting to training views, which significantly drops the visual quality of novel view images. Many existing approaches have tackled this issue by using strong priors, such as 2D generative contextual information and external depth signals. In contrast, this paper introduces a prior-free method, so-called DropGaussian, with simple changes in 3D Gaussian splatting. Specifically, we randomly remove Gaussians during the training process in a similar way of dropout, which allows non-excluded Gaussians to have larger gradients while improving their visibility. This makes the remaining Gaussians to contribute more to the optimization process for rendering with sparse input views. Such simple operation effectively alleviates the overfitting problem and enhances the quality of novel view synthesis. By simply applying DropGaussian to the original 3DGS framework, we can achieve the competitive performance with existing prior-based 3DGS methods in sparse-view settings of benchmark datasets without any additional complexity.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Park_DropGaussian_Structural_Regularization_for_Sparse-view_Gaussian_Splatting_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Park_DropGaussian_Structural_Regularization_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Park_2025_CVPR,
    author    = {Park, Hyunwoo and Ryu, Gun and Kim, Wonjun},
    title     = {DropGaussian: Structural Regularization for Sparse-view Gaussian Splatting},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {21600-21609}
}


title: All-Day Multi-Camera Multi-Target Tracking
authors: Huijie Fan, Yu Qiao, Yihao Zhen, Tinghui Zhao, Baojie Fan, Qiang Wang
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 16892-16901
cities: ["Shenyang", "Shenyang", "Beijing", "Nanjing"]
countries: ["China", "China", "China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Fan_All-Day_Multi-Camera_Multi-Target_Tracking_CVPR_2025_paper.html

abstract:
The capability of tracking objects in low-light environments like nighttime is crucial for numerous real-world applications such as crowd behavior analysis and traffic scene understanding. However, previous Multi-Camera Multi-Target(MCMT) tracking methods are primarily focused on tracking during daytime with favorable lighting, shying away from low-light environments. The main difficulty of tracking under low light condition is lack of detailed visible appearance features. To address this issue, we incorporate the infrared modality into MCMT tracking framework to provide more useful information. We constructed the first Multi-modality(RGBT) Multi-camera Multi-target tracking dataset named M3Track, which contains sequences captured in low-light environments, laying a solid foundation for all-day multi-camera tracking. Based on the proposed dataset, we propose All-Day Multi-Camera Multi-Target tracking network, termed as ADMCMT. Specifically, we propose an All-Day Mamba Fusion(ADMF) model to adaptively fuse information from different modalities. Within ADMF, the Lighting Guidance Model(IGM) extracts lighting relevant information to guide the fusion process. Furthermore, the Nearby Target Collection(NTC) strategy is designed to enhance tracking accuracy by leveraging information derived from surrounding objects of target. Experiments conducted on M3Track demonstrate that ADMCMT exhibits strong generalization across different lighting conditions. The code will be released soon at https://github.com/QTRACKY/ADMCMT.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Fan_All-Day_Multi-Camera_Multi-Target_Tracking_CVPR_2025_paper.pdf

bibtex:
@InProceedings{Fan_2025_CVPR,
    author    = {Fan, Huijie and Qiao, Yu and Zhen, Yihao and Zhao, Tinghui and Fan, Baojie and Wang, Qiang},
    title     = {All-Day Multi-Camera Multi-Target Tracking},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {16892-16901}
}

raw_affiliation:
[{"affiliation": "State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences", "city": "Shenyang", "country": "China"}, {"affiliation": "Key Laboratory of Manufacturing Industrial Integrated Automation, Shenyang University", "city": "Shenyang", "country": "China"}, {"affiliation": "University of Chinese Academy of Sciences", "city": "Beijing", "country": "China"}, {"affiliation": "Nanjing University of Posts and Telecommunications", "city": "Nanjing", "country": "China"}]

affiliations:
["State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences", "Key Laboratory of Manufacturing Industrial Integrated Automation, Shenyang University", "University of Chinese Academy of Sciences", "Nanjing University of Posts and Telecommunications"]


title: Blurred LiDAR for Sharper 3D: Robust Handheld 3D Scanning with Diffuse LiDAR and RGB
authors: Nikhil Behari, Aaron Young, Siddharth Somasundaram, Tzofi Klinghoffer, Akshat Dave, Ramesh Raskar
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 26954-26964
raw_affiliation: [{"affiliation": "Massachusetts Institute of Technology", "city": "Cambridge", "country": "USA"}]
affiliations: ["Massachusetts Institute of Technology"]
cities: ["Cambridge"]
countries: ["USA"]
highlight: yes
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Behari_Blurred_LiDAR_for_Sharper_3D_Robust_Handheld_3D_Scanning_with_CVPR_2025_paper.html

abstract:
3D surface reconstruction is essential across applications of virtual reality, robotics, and mobile scanning. However, RGB-based reconstruction often fails in low-texture, low-light, and low-albedo scenes. Handheld LiDARs, now common on mobile devices, aim to address these challenges by capturing depth information from time-of-flight measurements of a coarse grid of projected dots. Yet, these sparse LiDARs struggle with scene coverage on limited input views, leaving large gaps in depth information. In this work, we propose using an alternative class of "blurred" LiDAR that emits a diffuse flash, greatly improving scene coverage but introducing spatial ambiguity from mixed time-of-flight measurements across a wide field of view. To handle these ambiguities, we propose leveraging the complementary strengths of diffuse LiDAR with RGB. We introduce a Gaussian surfel-based rendering framework with a scene-adaptive loss function that dynamically balances RGB and diffuse LiDAR signals. We demonstrate that, surprisingly, diffuse LiDAR can outperform traditional sparse LiDAR, enabling robust 3D scanning with accurate color and geometry estimation in challenging environments.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Behari_Blurred_LiDAR_for_Sharper_3D_Robust_Handheld_3D_Scanning_with_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Behari_Blurred_LiDAR_for_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Behari_2025_CVPR,
    author    = {Behari, Nikhil and Young, Aaron and Somasundaram, Siddharth and Klinghoffer, Tzofi and Dave, Akshat and Raskar, Ramesh},
    title     = {Blurred LiDAR for Sharper 3D: Robust Handheld 3D Scanning with Diffuse LiDAR and RGB},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {26954-26964}
}


authors: Jianrong Zhang, Hehe Fan, Yi Yang
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 17592-17602
affiliations: ["ReLER, AAII, University of Technology Sydney", "CCAI, Zhejiang University"]
cities: ["Sydney", "Hangzhou"]
countries: ["Australia", "China"]
highlight: yes
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Zhang_EnergyMoGen_Compositional_Human_Motion_Generation_with_Energy-Based_Diffusion_Model_in_CVPR_2025_paper.html

title:
EnergyMoGen: Compositional Human Motion Generation with Energy-Based Diffusion Model in Latent Space

abstract:
Diffusion models, particularly latent diffusion models, have demonstrated remarkable success in text-driven human motion generation. However, it remains challenging for latent diffusion models to effectively compose multiple semantic concepts into a single, coherent motion sequence. To address this issue, we propose EnergyMoGen, which includes two spectrums of Energy-Based Models: (1) We interpret the diffusion model as a latent-aware energy-based model that generates motions by composing a set of diffusion models in latent space; (2) We introduce a semantic-aware energy model based on cross-attention, which enables semantic composition and adaptive gradient descent for text embeddings. To overcome the challenges of semantic inconsistency and motion distortion across these two spectrums, we introduce Synergistic Energy Fusion. This design allows the motion latent diffusion model to synthesize high-quality, complex motions by combining multiple energy terms corresponding to textual descriptions. Experiments show that our approach outperforms existing state-of-the-art models on various motion generation tasks, including text-to-motion generation, compositional motion generation, and multi-concept motion generation. Additionally, we demonstrate that our method can be used to extend motion datasets and improve the text-to-motion task.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Zhang_EnergyMoGen_Compositional_Human_Motion_Generation_with_Energy-Based_Diffusion_Model_in_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Zhang_EnergyMoGen_Compositional_Human_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Zhang_2025_CVPR,
    author    = {Zhang, Jianrong and Fan, Hehe and Yang, Yi},
    title     = {EnergyMoGen: Compositional Human Motion Generation with Energy-Based Diffusion Model in Latent Space},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {17592-17602}
}

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


title: PatchDEMUX: A Certifiably Robust Framework for Multi-label Classifiers Against Adversarial Patches
authors: Dennis Jacob, Chong Xiang, Prateek Mittal
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 9944-9953
affiliations: ["UC Berkeley", "Princeton University", "Princeton University"]
cities: ["Berkeley", "Princeton", "Princeton"]
countries: ["USA", "USA", "USA"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Jacob_PatchDEMUX_A_Certifiably_Robust_Framework_for_Multi-label_Classifiers_Against_Adversarial_CVPR_2025_paper.html

abstract:
Deep learning techniques have enabled vast improvements in computer vision technologies. Nevertheless, these models are vulnerable to adversarial patch attacks which  catastrophically impair performance. The physically realizable nature of these attacks calls for certifiable defenses, which feature provable guarantees on robustness. While certifiable defenses have been successfully applied to single-label classification, limited work has been done for multi-label classification. In this work, we present PatchDEMUX, a certifiably robust framework for multi-label classifiers against adversarial patches. Our approach is a generalizable method which can extend any existing certifiable defense for single-label classification; this is done by considering the multi-label classification task as a series of isolated binary classification problems to provably guarantee robustness. Furthermore, in the scenario where an attacker is limited to a single patch we propose an additional certification procedure that can provide tighter robustness bounds. Using the current state-of-the-art (SOTA) single-label certifiable defense PatchCleanser as a backbone, we find that PatchDEMUX can achieve non-trivial robustness on the MS-COCO and PASCAL VOC datasets while maintaining high clean performance

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Jacob_PatchDEMUX_A_Certifiably_Robust_Framework_for_Multi-label_Classifiers_Against_Adversarial_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Jacob_PatchDEMUX_A_Certifiably_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Jacob_2025_CVPR,
    author    = {Jacob, Dennis and Xiang, Chong and Mittal, Prateek},
    title     = {PatchDEMUX: A Certifiably Robust Framework for Multi-label Classifiers Against Adversarial Patches},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {9944-9953}
}

raw_affiliation:
[{"affiliation": "UC Berkeley", "city": "Berkeley", "country": "USA"}, {"affiliation": "Princeton University", "city": "Princeton", "country": "USA"}, {"affiliation": "Princeton University", "city": "Princeton", "country": "USA"}]


title: StarVector: Generating Scalable Vector Graphics Code from Images and Text
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 16175-16186
cities: ["unknown", "Montreal", "unknown", "Montreal", "Vancouver", "unknown"]
countries: ["unknown", "Canada", "Canada", "Canada", "Canada", "unknown"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Rodriguez_StarVector_Generating_Scalable_Vector_Graphics_Code_from_Images_and_Text_CVPR_2025_paper.html

authors:
Juan A. Rodriguez, Abhay Puri, Shubham Agarwal, Issam H. Laradji, Pau Rodriguez, Sai Rajeswar, David Vazquez, Christopher Pal, Marco Pedersoli

abstract:
Scalable Vector Graphics (SVGs) are vital for modern image rendering due to their scalability and versatility. Previous SVG generation methods have focused on curve-based vectorization, lacking semantic understanding, often producing artifacts, and struggling with SVG primitives beyond path curves. To address these issues, we introduce StarVector, a multimodal large language model for SVG generation. It performs image vectorization by understanding image semantics and using SVG primitives for compact, precise outputs. Unlike traditional methods, StarVector works directly in the SVG code space, leveraging visual understanding to apply accurate SVG primitives. To train StarVector, we create SVG-Stack, a diverse dataset of 2M samples that enables generalization across vectorization tasks and precise use of primitives like ellipses, polygons, and text. We address challenges in SVG evaluation, showing that pixel-based metrics like MSE fail to capture the unique qualities of vector graphics. We introduce SVG-Bench, a benchmark across 10 datasets, and three tasks: image vectorization, text-driven SVG generation, and diagram generation. Using this contribution, StarVector achieves state-of-the-art performance, producing more compact and semantically rich SVGs.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Rodriguez_StarVector_Generating_Scalable_Vector_Graphics_Code_from_Images_and_Text_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Rodriguez_StarVector_Generating_Scalable_CVPR_2025_supplemental.zip

bibtex:
@InProceedings{Rodriguez_2025_CVPR,
    author    = {Rodriguez, Juan A. and Puri, Abhay and Agarwal, Shubham and Laradji, Issam H. and Rodriguez, Pau and Rajeswar, Sai and Vazquez, David and Pal, Christopher and Pedersoli, Marco},
    title     = {StarVector: Generating Scalable Vector Graphics Code from Images and Text},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {16175-16186}
}

raw_affiliation:
[{"affiliation": "ServiceNow Research", "city": "unknown", "country": "unknown"}, {"affiliation": "Mila - Quebec AI Institute", "city": "Montreal", "country": "Canada"}, {"affiliation": "Canada CIFAR AI Chair", "city": "unknown", "country": "Canada"}, {"affiliation": "\u00c9TS", "city": "Montreal", "country": "Canada"}, {"affiliation": "UBC", "city": "Vancouver", "country": "Canada"}, {"affiliation": "Apple", "city": "unknown", "country": "unknown"}]

affiliations:
["ServiceNow Research", "Mila - Quebec AI Institute", "Canada CIFAR AI Chair", "\u00c9TS", "UBC", "Apple"]


title: Novel View Synthesis with Pixel-Space Diffusion Models
authors: Noam Elata, Bahjat Kawar, Yaron Ostrovsky-Berman, Miriam Farber, Ron Sokolovsky
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 26756-26766
affiliations: ["Technion", "Apple", "Apple", "Apple", "Apple"]
cities: ["Haifa", "Cupertino", "Cupertino", "Cupertino", "Cupertino"]
countries: ["Israel", "USA", "USA", "USA", "USA"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Elata_Novel_View_Synthesis_with_Pixel-Space_Diffusion_Models_CVPR_2025_paper.html

abstract:
Synthesizing a novel view from a single input image is a challenging task. Traditionally, this task was approached by estimating scene depth, warping, and inpainting, with machine learning models enabling parts of the pipeline. More recently, generative models are being increasingly employed in novel view synthesis (NVS), often encompassing the entire end-to-end system.In this work, we adapt a modern diffusion model architecture for end-to-end NVS in the pixel space, substantially outperforming previous state-of-the-art (SOTA) techniques. We explore different ways to encode geometric information into the network.Our experiments show that while these methods may enhance performance, their impact is minor compared to utilizing improved generative models. Moreover, we introduce a novel NVS training scheme that utilizes single-view datasets, capitalizing on their relative abundance compared to their multi-view counterparts. This leads to improved generalization capabilities to scenes with out-of-domain content.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Elata_Novel_View_Synthesis_with_Pixel-Space_Diffusion_Models_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Elata_Novel_View_Synthesis_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Elata_2025_CVPR,
    author    = {Elata, Noam and Kawar, Bahjat and Ostrovsky-Berman, Yaron and Farber, Miriam and Sokolovsky, Ron},
    title     = {Novel View Synthesis with Pixel-Space Diffusion Models},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {26756-26766}
}

raw_affiliation:
[{"affiliation": "Technion", "city": "Haifa", "country": "Israel"}, {"affiliation": "Apple", "city": "Cupertino", "country": "USA"}, {"affiliation": "Apple", "city": "Cupertino", "country": "USA"}, {"affiliation": "Apple", "city": "Cupertino", "country": "USA"}, {"affiliation": "Apple", "city": "Cupertino", "country": "USA"}]


authors: Xiao Wang, Yu Jin, Wentao Wu, Wei Zhang, Lin Zhu, Bo Jiang, Yonghong Tian
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 29321-29330
cities: ["Hefei", "Hefei", "Shenzhen", "Beijing", "Beijing", "Shenzhen"]
countries: ["China", "China", "China", "China", "China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Wang_Object_Detection_using_Event_Camera_A_MoE_Heat_Conduction_based_CVPR_2025_paper.html

title:
Object Detection using Event Camera: A MoE Heat Conduction based Detector and A New Benchmark Dataset

abstract:
Object detection in event streams has emerged as a cutting-edge research area, demonstrating superior performance in low-light conditions, scenarios with motion blur, and rapid movements. Current detectors leverage spiking neural networks, Transformers, or convolutional neural networks as their core architectures, each with its own set of limitations including restricted performance, high computational overhead, or limited local receptive fields. This paper introduces a novel MoE (Mixture of Experts) heat conduction-based object detection algorithm that strikingly balances accuracy and computational efficiency. Initially, we employ a stem network for event data embedding, followed by processing through our innovative MoE-HCO blocks. Each block integrates various expert modules to mimic heat conduction within event streams. Subsequently, an IoU-based query selection module is utilized for efficient token extraction, which is then channeled into a detection head for the final object detection process. Furthermore, we are pleased to introduce EvDET200K, a novel benchmark dataset for event-based object detection. Captured with a high-definition Prophesee EVK4-HD event camera, this dataset encompasses 10 distinct categories, 200,000 bounding boxes, and 10,054 samples, each spanning 2 to 5 seconds. We also provide comprehensive results from over 15 state-of-the-art detectors, offering a solid foundation for future research and comparison.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Wang_Object_Detection_using_Event_Camera_A_MoE_Heat_Conduction_based_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Wang_Object_Detection_using_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Wang_2025_CVPR,
    author    = {Wang, Xiao and Jin, Yu and Wu, Wentao and Zhang, Wei and Zhu, Lin and Jiang, Bo and Tian, Yonghong},
    title     = {Object Detection using Event Camera: A MoE Heat Conduction based Detector and A New Benchmark Dataset},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {29321-29330}
}

raw_affiliation:
[{"affiliation": "School of Computer Science and Technology, Anhui University", "city": "Hefei", "country": "China"}, {"affiliation": "School of Artificial Intelligence, Anhui University", "city": "Hefei", "country": "China"}, {"affiliation": "Peng Cheng Laboratory", "city": "Shenzhen", "country": "China"}, {"affiliation": "Beijing Institute of Technology", "city": "Beijing", "country": "China"}, {"affiliation": "National Key Laboratory for Multimedia Information Processing, School of Computer Science, Peking University", "city": "Beijing", "country": "China"}, {"affiliation": "School of Electronic and Computer Engineering, Shenzhen Graduate School, Peking University", "city": "Shenzhen", "country": "China"}]

affiliations:
["School of Computer Science and Technology, Anhui University", "School of Artificial Intelligence, Anhui University", "Peng Cheng Laboratory", "Beijing Institute of Technology", "National Key Laboratory for Multimedia Information Processing, School of Computer Science, Peking University", "School of Electronic and Computer Engineering, Shenzhen Graduate School, Peking University"]


title: EgoTextVQA: Towards Egocentric Scene-Text Aware Video Question Answering
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 3363-3373
cities: ["Hefei", "Singapore", "Hefei"]
countries: ["China", "Singapore", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Zhou_EgoTextVQA_Towards_Egocentric_Scene-Text_Aware_Video_Question_Answering_CVPR_2025_paper.html

authors:
Sheng Zhou, Junbin Xiao, Qingyun Li, Yicong Li, Xun Yang, Dan Guo, Meng Wang, Tat-Seng Chua, Angela Yao

abstract:
We introduce EgoTextVQA, a novel and rigorously constructed benchmark for egocentric QA assistance involving scene text. EgoTextVQA contains 1.5K ego-view videos and 7K scene-text aware questions that reflect real user needs in outdoor driving and indoor house-keeping activities. The questions are designed to elicit identification and reasoning on scene text in an egocentric and dynamic environment. With EgoTextVQA, we comprehensively evaluate 10 prominent multimodal large language models. Currently, all models struggle, and the best results (Gemini 1.5 Pro) are around 33% accuracy, highlighting the severe deficiency of these techniques in egocentric QA assistance. Our further investigations suggest that precise temporal grounding and multi-frame reasoning, along with high resolution and auxiliary scene-text inputs, are key for better performance. With thorough analyses and heuristic suggestions, we hope EgoTextVQA can serve as a solid testbed for research in egocentric scene-text QA assistance.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Zhou_EgoTextVQA_Towards_Egocentric_Scene-Text_Aware_Video_Question_Answering_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Zhou_EgoTextVQA_Towards_Egocentric_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Zhou_2025_CVPR,
    author    = {Zhou, Sheng and Xiao, Junbin and Li, Qingyun and Li, Yicong and Yang, Xun and Guo, Dan and Wang, Meng and Chua, Tat-Seng and Yao, Angela},
    title     = {EgoTextVQA: Towards Egocentric Scene-Text Aware Video Question Answering},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {3363-3373}
}

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

affiliations:
["Hefei University of Technology", "National University of Singapore", "University of Science and Technology of China"]


title: Token Cropr: Faster ViTs for Quite a Few Tasks
authors: Benjamin Bergner, Christoph Lippert, Aravindh Mahendran
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 9740-9750
cities: ["Potsdam", "New York", "Seattle", "London"]
countries: ["Germany", "USA", "USA", "UK"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Bergner_Token_Cropr_Faster_ViTs_for_Quite_a_Few_Tasks_CVPR_2025_paper.html

abstract:
The adoption of Vision Transformers (ViTs) in resource-constrained applications necessitates improvements in inference throughput. To this end several token pruning and merging approaches have been proposed that improve efficiency by successively reducing the number of tokens. However, it remains an open problem to design a token reduction method that is fast, maintains high performance, and is applicable to various vision tasks. In this work, we present a token pruner that uses auxiliary prediction heads that learn to select tokens end-to-end based on task relevance. These auxiliary heads can be removed after training, leading to throughput close to that of a random pruner. We evaluate our method on image classification, semantic segmentation, object detection, and instance segmentation, and show speedups of 1.5 to 4x with small drops in performance. As a best case, on the ADE20k semantic segmentation benchmark, we observe a 2x speedup relative to the no-pruning baseline, with a negligible performance penalty of 0.1 median mIoU across 5 seeds.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Bergner_Token_Cropr_Faster_ViTs_for_Quite_a_Few_Tasks_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Bergner_Token_Cropr_Faster_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Bergner_2025_CVPR,
    author    = {Bergner, Benjamin and Lippert, Christoph and Mahendran, Aravindh},
    title     = {Token Cropr: Faster ViTs for Quite a Few Tasks},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {9740-9750}
}

raw_affiliation:
[{"affiliation": "Hasso Plattner Institute for Digital Engineering, University of Potsdam", "city": "Potsdam", "country": "Germany"}, {"affiliation": "Hasso Plattner Institute for Digital Health at the Icahn School of Medicine at Mount Sinai", "city": "New York", "country": "USA"}, {"affiliation": "Amazon", "city": "Seattle", "country": "USA"}, {"affiliation": "Google DeepMind", "city": "London", "country": "UK"}]

affiliations:
["Hasso Plattner Institute for Digital Engineering, University of Potsdam", "Hasso Plattner Institute for Digital Health at the Icahn School of Medicine at Mount Sinai", "Amazon", "Google DeepMind"]


title: STCOcc: Sparse Spatial-Temporal Cascade Renovation for 3D Occupancy and Scene Flow Prediction
authors: Zhimin Liao, Ping Wei, Shuaijia Chen, Haoxuan Wang, Ziyang Ren
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 1516-1526
affiliations: ["Institute of Artificial Intelligence and Robotics, Xi\u2019an Jiaotong University"]
cities: ["Xi'an"]
countries: ["China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Liao_STCOcc_Sparse_Spatial-Temporal_Cascade_Renovation_for_3D_Occupancy_and_Scene_CVPR_2025_paper.html

abstract:
3D occupancy and scene flow offer a detailed and dynamic representation of 3D scene. Recognizing the sparsity and complexity of 3D space, previous vision-centric methods have employed implicit learning-based approaches to model spatial and temporal information. However, these approaches struggle to capture local details and diminish the model's spatial discriminative ability. To address these challenges, we propose a novel explicit state-based modeling method designed to leverage the occupied state to renovate the 3D features. Specifically, we propose a sparse occlusion-aware attention mechanism, integrated with a cascade refinement strategy, which accurately renovates 3D features with the guidance of occupied state information. Additionally, we introduce a novel method for modeling long-term dynamic interactions, which reduces computational costs and preserves spatial information. Compared to the previous state-of-the-art methods, our efficient explicit renovation strategy not only delivers superior performance in terms of RayIoU and mAVE for occupancy and scene flow prediction but also markedly reduces GPU memory usage during training, bringing it down to 8.7GB. Our code is available on https://github.com/lzzzzzm/STCOcc

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Liao_STCOcc_Sparse_Spatial-Temporal_Cascade_Renovation_for_3D_Occupancy_and_Scene_CVPR_2025_paper.pdf

bibtex:
@InProceedings{Liao_2025_CVPR,
    author    = {Liao, Zhimin and Wei, Ping and Chen, Shuaijia and Wang, Haoxuan and Ren, Ziyang},
    title     = {STCOcc: Sparse Spatial-Temporal Cascade Renovation for 3D Occupancy and Scene Flow Prediction},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {1516-1526}
}

raw_affiliation:
[{"affiliation": "Institute of Artificial Intelligence and Robotics, Xi\u2019an Jiaotong University", "city": "Xi'an", "country": "China"}]


title: Document Haystacks: Vision-Language Reasoning Over Piles of 1000+ Documents
authors: Jun Chen, Dannong Xu, Junjie Fei, Chun-Mei Feng, Mohamed Elhoseiny
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 24817-24826
affiliations: ["King Abdullah University of Science and Technology", "The University of Sydney", "IHPC, A*STAR"]
cities: ["Thuwal", "Sydney", "Singapore"]
countries: ["Saudi Arabia", "Australia", "Singapore"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Chen_Document_Haystacks__Vision-Language_Reasoning_Over_Piles_of_1000_Documents_CVPR_2025_paper.html

abstract:
Large multimodal models (LMMs) have achieved impressive progress in vision-language understanding, yet they face limitations in real-world applications requiring complex reasoning over a large number of images. Existing benchmarks for multi-image question-answering are limited in scope, each question is paired with only up to 30 images, which does not fully capture the demands of large-scale retrieval tasks encountered in the real-world usages. To reduce these gaps, we introduce two document haystack benchmarks, dubbed DocHaystack and InfoHaystack, designed to evaluate LMM performance on large-scale visual document retrieval and understanding. Additionally, we propose V-RAG, a novel, vision-centric retrieval-augmented generation (RAG) framework that leverages a suite of multimodal vision encoders, each optimized for specific strengths, and a dedicated question-document relevance module. V-RAG sets a new standard, with a 9% and 11% improvement in Recall@1 on the challenging DocHaystack-1000 and InfoHaystack-1000 benchmarks, respectively, compared to the previous best baseline models. Additionally, integrating V-RAG with LMMs enables them to efficiently operate across thousands of images, yielding significant improvements on our DocHaystack and InfoHaystack benchmarks. Our code and datasets will be made publicly available.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Chen_Document_Haystacks__Vision-Language_Reasoning_Over_Piles_of_1000_Documents_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Chen_Document_Haystacks__CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Chen_2025_CVPR,
    author    = {Chen, Jun and Xu, Dannong and Fei, Junjie and Feng, Chun-Mei and Elhoseiny, Mohamed},
    title     = {Document Haystacks:  Vision-Language Reasoning Over Piles of 1000+ Documents},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {24817-24826}
}

raw_affiliation:
[{"affiliation": "King Abdullah University of Science and Technology", "city": "Thuwal", "country": "Saudi Arabia"}, {"affiliation": "The University of Sydney", "city": "Sydney", "country": "Australia"}, {"affiliation": "IHPC, A*STAR", "city": "Singapore", "country": "Singapore"}]


title: Rethinking Few-Shot Adaptation of Vision-Language Models in Two Stages
authors: Matteo Farina, Massimiliano Mancini, Giovanni Iacca, Elisa Ricci
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 29989-29998
affiliations: ["University of Trento", "Fondazione Bruno Kessler"]
cities: ["Trento", "Trento"]
countries: ["Italy", "Italy"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Farina_Rethinking_Few-Shot_Adaptation_of_Vision-Language_Models_in_Two_Stages_CVPR_2025_paper.html

abstract:
An old-school recipe for training a classifier is to (i) learn a good feature extractor and (ii) optimize a linear layer atop. When only a handful of samples are available per category, as in Few-Shot Adaptation (FSA), data are insufficient to fit a large number of parameters, rendering the above impractical. This is especially true with large pre-trained Vision-Language Models (VLMs), which motivated successful research at the intersection of Parameter-Efficient Fine-tuning (PEFT) and FSA. In this work, we start by analyzing the learning dynamics of PEFT techniques when trained on few-shot data from only a subset of categories, referred to as the "base" classes. We show that such dynamics naturally splits into two distinct phases: (i) task-level feature extraction and (ii) specialization to the available concepts. To accommodate this dynamic, we then depart from prompt- or adapter-based methods and tackle FSA differently. Specifically, given a fixed computational budget, we split it to (i) learn a task-specific feature extractor via PEFT and (ii) train a linear classifier on top. We call this scheme Two-Stage Few-Shot Adaptation (2SFS). Differently from established methods, our scheme enables a novel form of selective inference at a category level, i.e., at test time, only novel categories are embedded by the adapted text encoder, while embeddings of base categories are available within the classifier. Results with fixed hyperparameters across two settings, three backbones, and eleven datasets, show that 2SFS matches or surpasses the state-of-the-art, while established methods degrade significantly across settings.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Farina_Rethinking_Few-Shot_Adaptation_of_Vision-Language_Models_in_Two_Stages_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Farina_Rethinking_Few-Shot_Adaptation_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Farina_2025_CVPR,
    author    = {Farina, Matteo and Mancini, Massimiliano and Iacca, Giovanni and Ricci, Elisa},
    title     = {Rethinking Few-Shot Adaptation of Vision-Language Models in Two Stages},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {29989-29998}
}

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


title: Resilient Sensor Fusion Under Adverse Sensor Failures via Multi-Modal Expert Fusion
authors: Konyul Park, Yecheol Kim, Daehun Kim, Jun Won Choi
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 6720-6729
affiliations: ["Seoul National University", "Hanyang University", "LG AI Research"]
cities: ["Seoul", "Seoul", "Seoul"]
countries: ["South Korea", "South Korea", "South Korea"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Park_Resilient_Sensor_Fusion_Under_Adverse_Sensor_Failures_via_Multi-Modal_Expert_CVPR_2025_paper.html

abstract:
Modern autonomous driving perception systems utilize complementary multi-modal sensors, such as LiDAR and cameras. Although sensor fusion architectures enhance performance in challenging environments, they still suffer significant performance drops under severe sensor failures, such as LiDAR beam reduction, LiDAR drop, limited field of view, camera drop, and occlusion. This limitation stems from inter-modality dependencies in current sensor fusion frameworks. In this study, we introduce an efficient and robust LiDAR-camera 3D object detector, referred to as MoME, which can achieve robust performance through a mixture of experts approach. Our MoME fully decouples modality dependencies using three parallel expert decoders, which use camera features, LiDAR features, or a combination of both to decode object queries, respectively. We propose Multi-Expert Decoding (MED) framework, where each query is decoded selectively using one of three expert decoders.  MoME utilizes an Adaptive Query Router (AQR) to select the most appropriate expert decoder for each query based on the quality of camera and LiDAR features. This ensures that each query is processed by the best-suited expert, resulting in robust performance across diverse sensor failure scenarios. We evaluated the performance of MoME on the nuScenes-R benchmark. Our MoME achieved state-of-the-art performance in extreme weather and sensor failure conditions, significantly outperforming the existing models across various sensor failure scenarios.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Park_Resilient_Sensor_Fusion_Under_Adverse_Sensor_Failures_via_Multi-Modal_Expert_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Park_Resilient_Sensor_Fusion_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Park_2025_CVPR,
    author    = {Park, Konyul and Kim, Yecheol and Kim, Daehun and Choi, Jun Won},
    title     = {Resilient Sensor Fusion Under Adverse Sensor Failures via Multi-Modal Expert Fusion},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {6720-6729}
}

raw_affiliation:
[{"affiliation": "Seoul National University", "city": "Seoul", "country": "South Korea"}, {"affiliation": "Hanyang University", "city": "Seoul", "country": "South Korea"}, {"affiliation": "LG AI Research", "city": "Seoul", "country": "South Korea"}]


title: TAGA: Self-supervised Learning for Template-free Animatable Gaussian Articulated Model
authors: Zhichao Zhai, Guikun Chen, Wenguan Wang, Dong Zheng, Jun Xiao
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 21159-21169
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/Zhai_TAGA_Self-supervised_Learning_for_Template-free_Animatable_Gaussian_Articulated_Model_CVPR_2025_paper.html

abstract:
Decoupling from customized parametric templates represents a crucial step toward the creation of fully flexible, animatable articulated models. While existing template-free methods can achieve high-fidelity reconstruction in observed views, they struggle to recover plausible canonical models, resulting in suboptimal animation quality. This limitation stems from overlooking the fundamental ambiguity in canonical reconstruction, where multiple canonical models could explain the same observed views. By revealing the entanglement between the canonical ambiguity and incorrect skinning, we present a self-supervised framework that learns both plausible skinning and accurate canonical geometry using only sparse pose data. Our method, TAGA, uses explicit 3D Gaussians as skinning carriers and characterizes the ambiguity as "Ambiguous Gaussians" with incorrect skinning weights. TAGA then corrects ambiguous Gaussians in the observation space using anomaly detection. With the corrected ones, we enforce cycle consistency constraints on both geometry and skinning to refine the corresponding Gaussians in the canonical space through a new backward method. Compared to existing state-of-the-art template-free methods, TAGA delivers superior visual fidelity for novel views and poses, while significantly improving training and rendering speeds. Experiments on challenging datasets with limited pose variations further demonstrate the robustness and generality of TAGA.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Zhai_TAGA_Self-supervised_Learning_for_Template-free_Animatable_Gaussian_Articulated_Model_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Zhai_TAGA_Self-supervised_Learning_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Zhai_2025_CVPR,
    author    = {Zhai, Zhichao and Chen, Guikun and Wang, Wenguan and Zheng, Dong and Xiao, Jun},
    title     = {TAGA: Self-supervised Learning for Template-free Animatable Gaussian Articulated Model},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {21159-21169}
}


title: MambaVO: Deep Visual Odometry Based on Sequential Matching Refinement and Training Smoothing
authors: Shuo Wang, Wanting Li, Yongcai Wang, Zhaoxin Fan, Zhe Huang, Xudong Cai, Jian Zhao, Deying Li
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 1252-1262
cities: ["Beijing", "Beijing", "Hangzhou", "Beijing", "Xi'an"]
countries: ["China", "China", "China", "China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Wang_MambaVO_Deep_Visual_Odometry_Based_on_Sequential_Matching_Refinement_and_CVPR_2025_paper.html

abstract:
Deep visual odometry has demonstrated great advancements by learning-to-optimize technology.  This approach heavily relies on the visual matching across frames. However, ambiguous matching in challenging scenarios leads to significant errors in geometric modeling and bundle adjustment optimization, which undermines the accuracy and robustness of pose estimation. To address this challenge, this paper proposes MambaVO, which conducts robust initialization, Mamba-based sequential matching refinement, and smoothed training to enhance the matching quality and improve the pose estimation in deep visual odometry. Specifically, when a new frame is received, it is matched with the closest keyframe in the maintained Point-Frame Graph (PFG) via the semi-dense based Geometric Initialization Module (GIM).  Then the initialized PFG is processed by a proposed Geometric Mamba Module (GMM), which exploits the matching features to refine the overall inter-frame pixel-to-pixel matching.  The refined PFG is finally processed by deep BA to optimize the poses and the map. To deal with the gradient variance, a Trending-Aware Penalty (TAP) is proposed to smooth training by balancing the pose loss and the matching loss to enhance convergence and stability.  A loop closure module is finally applied to enable MambaVO++.  On public benchmarks, MambaVO and MambaVO++ demonstrate SOTA accuracy performance, while ensuring real-time running performance with low GPU memory requirement. Codes will be publicly available.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Wang_MambaVO_Deep_Visual_Odometry_Based_on_Sequential_Matching_Refinement_and_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Wang_MambaVO_Deep_Visual_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Wang_2025_CVPR,
    author    = {Wang, Shuo and Li, Wanting and Wang, Yongcai and Fan, Zhaoxin and Huang, Zhe and Cai, Xudong and Zhao, Jian and Li, Deying},
    title     = {MambaVO: Deep Visual Odometry Based on Sequential Matching Refinement and Training Smoothing},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {1252-1262}
}

raw_affiliation:
[{"affiliation": "School of Information, Renmin University of China", "city": "Beijing", "country": "China"}, {"affiliation": "Beijing Advanced Innovation Center for Future Blockchain and Privacy Computing, Institute of Artificial Intelligence, Beihang University", "city": "Beijing", "country": "China"}, {"affiliation": "Hangzhou International Innovation Institute, Beihang University", "city": "Hangzhou", "country": "China"}, {"affiliation": "The Institute of AI (TeleAI), China Telecom", "city": "Beijing", "country": "China"}, {"affiliation": "School of Artificial Intelligence, Optics and Electronics (iOPEN), Northwestern Polytechnical University (NWPU)", "city": "Xi'an", "country": "China"}]

affiliations:
["School of Information, Renmin University of China", "Beijing Advanced Innovation Center for Future Blockchain and Privacy Computing, Institute of Artificial Intelligence, Beihang University", "Hangzhou International Innovation Institute, Beihang University", "The Institute of AI (TeleAI), China Telecom", "School of Artificial Intelligence, Optics and Electronics (iOPEN), Northwestern Polytechnical University (NWPU)"]


title: Explaining in Diffusion: Explaining a Classifier with Diffusion Semantics
authors: Tahira Kazimi, Ritika Allada, Pinar Yanardag
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 14799-14809
affiliations: ["Virginia Tech", "Virginia Tech", "Virginia Tech"]
cities: ["Blacksburg", "Blacksburg", "Blacksburg"]
countries: ["USA", "USA", "USA"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Kazimi_Explaining_in_Diffusion_Explaining_a_Classifier_with_Diffusion_Semantics_CVPR_2025_paper.html

abstract:
Classifiers are important components in many computer vision tasks, serving as the foundational backbone of a wide variety of models employed across diverse applications. However, understanding the decision-making process of classifiers remains a significant challenge.  We propose DiffEx, a novel method that leverages the capabilities of text-to-image diffusion models to explain classifier decisions. Unlike traditional GAN-based explainability models, which are limited to simple, single-concept analyses and typically require training a new model for each classifier, our approach can explain classifiers that focus on single concepts (such as faces or animals) as well as those that handle complex scenes involving multiple concepts. DiffEx employs vision-language models to create a hierarchical list of semantics, allowing users to identify not only the overarching semantic influences on classifiers (e.g., the 'beard' semantic in a facial classifier) but also their sub-types, such as 'goatee' or 'Balbo' beard.  Our experiments demonstrate that DiffEx is able to cover a significantly broader spectrum of semantics compared to its GAN counterparts, providing a hierarchical tool that delivers a more detailed and fine-grained understanding of classifier decisions.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Kazimi_Explaining_in_Diffusion_Explaining_a_Classifier_with_Diffusion_Semantics_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Kazimi_Explaining_in_Diffusion_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Kazimi_2025_CVPR,
    author    = {Kazimi, Tahira and Allada, Ritika and Yanardag, Pinar},
    title     = {Explaining in Diffusion: Explaining a Classifier with Diffusion Semantics},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {14799-14809}
}

raw_affiliation:
[{"affiliation": "Virginia Tech", "city": "Blacksburg", "country": "USA"}, {"affiliation": "Virginia Tech", "city": "Blacksburg", "country": "USA"}, {"affiliation": "Virginia Tech", "city": "Blacksburg", "country": "USA"}]


title: Horizon-GS: Unified 3D Gaussian Splatting for Large-Scale Aerial-to-Ground Scenes
authors: Lihan Jiang, Kerui Ren, Mulin Yu, Linning Xu, Junting Dong, Tao Lu, Feng Zhao, Dahua Lin, Bo Dai
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 26789-26799
cities: ["Hefei", "Shanghai", "Shanghai", "Hong Kong", "Providence", "Hong Kong", "Shanghai"]
countries: ["China", "China", "China", "China", "USA", "China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Jiang_Horizon-GS_Unified_3D_Gaussian_Splatting_for_Large-Scale_Aerial-to-Ground_Scenes_CVPR_2025_paper.html

abstract:
Seamless integration of both aerial and street view images remains a significant challenge in neural scene reconstruction and rendering. Existing methods predominantly focus on single domain, limiting their applications in immersive environments, which demand extensive free view exploration with large view changes both horizontally and vertically. We introduce Horizon-GS, a novel approach built upon Gaussian Splatting techniques, tackles the unified reconstruction and rendering for aerial and street views. Our method addresses the key challenges of combining these perspectives with a new training strategy, overcoming viewpoint discrepancies to generate high-fidelity scenes. We also curated a high-quality aerial-to-ground view dataset encompassing both synthetic and real-world scene to advance further research. Experiments across diverse urban scene datasets confirms the effectiveness of our method.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Jiang_Horizon-GS_Unified_3D_Gaussian_Splatting_for_Large-Scale_Aerial-to-Ground_Scenes_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Jiang_Horizon-GS_Unified_3D_CVPR_2025_supplemental.zip

bibtex:
@InProceedings{Jiang_2025_CVPR,
    author    = {Jiang, Lihan and Ren, Kerui and Yu, Mulin and Xu, Linning and Dong, Junting and Lu, Tao and Zhao, Feng and Lin, Dahua and Dai, Bo},
    title     = {Horizon-GS: Unified 3D Gaussian Splatting for Large-Scale Aerial-to-Ground Scenes},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {26789-26799}
}

raw_affiliation:
[{"affiliation": "University of Science and Technology of China", "city": "Hefei", "country": "China"}, {"affiliation": "Shanghai Jiao Tong University", "city": "Shanghai", "country": "China"}, {"affiliation": "Shanghai Artificial Intelligence Laboratory", "city": "Shanghai", "country": "China"}, {"affiliation": "The Chinese University of Hong Kong", "city": "Hong Kong", "country": "China"}, {"affiliation": "Brown University", "city": "Providence", "country": "USA"}, {"affiliation": "The University of Hong Kong", "city": "Hong Kong", "country": "China"}, {"affiliation": "CPII", "city": "Shanghai", "country": "China"}]

affiliations:
["University of Science and Technology of China", "Shanghai Jiao Tong University", "Shanghai Artificial Intelligence Laboratory", "The Chinese University of Hong Kong", "Brown University", "The University of Hong Kong", "CPII"]


title: Attention Distillation: A Unified Approach to Visual Characteristics Transfer
authors: Yang Zhou, Xu Gao, Zichong Chen, Hui Huang
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 18270-18280
affiliations: ["Visual Computing Research Center, CSSE, Shenzhen University"]
cities: ["Shenzhen"]
countries: ["China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Zhou_Attention_Distillation_A_Unified_Approach_to_Visual_Characteristics_Transfer_CVPR_2025_paper.html

abstract:
Recent advances in generative diffusion models have shown a notable inherent understanding of image style and semantics. In this paper, we leverage the self-attention features from pretrained diffusion networks to transfer the visual characteristics from a reference to generated images. Unlike previous work that uses these features as plug-and-play attributes, we propose a novel attention distillation loss calculated between the ideal and current stylization results, based on which we optimize the synthesized image via backpropagation in latent space. Next, we propose an improved Classifier Guidance that integrates attention distillation loss into the denoising sampling process, further accelerating the synthesis and enabling a broad range of image generation applications. Extensive experiments have demonstrated the extraordinary performance of our approach in transferring the examples' style, appearance, and texture to new images in synthesis.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Zhou_Attention_Distillation_A_Unified_Approach_to_Visual_Characteristics_Transfer_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Zhou_Attention_Distillation_A_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Zhou_2025_CVPR,
    author    = {Zhou, Yang and Gao, Xu and Chen, Zichong and Huang, Hui},
    title     = {Attention Distillation: A Unified Approach to Visual Characteristics Transfer},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {18270-18280}
}

raw_affiliation:
[{"affiliation": "Visual Computing Research Center, CSSE, Shenzhen University", "city": "Shenzhen", "country": "China"}]


proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 13315-13325
cities: ["Shenyang", "Hangzhou", "Beijing"]
countries: ["China", "China", "China"]
highlight: yes
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Wei_From_Words_to_Structured_Visuals_A_Benchmark_and_Framework_for_CVPR_2025_paper.html

title:
From Words to Structured Visuals: A Benchmark and Framework for Text-to-Diagram Generation and Editing

authors:
Jingxuan Wei, Cheng Tan, Qi Chen, Gaowei Wu, Siyuan Li, Zhangyang Gao, Linzhuang Sun, Bihui Yu, Ruifeng Guo

abstract:
We introduce the task of text-to-diagram generation, which focuses on creating structured visual representations directly from textual descriptions. Existing approaches in text-to-image and text-to-code generation lack the logical organization and flexibility needed to produce accurate, editable diagrams, often resulting in outputs that are either unstructured or difficult to modify. To address this gap, we introduce DiagramGenBenchmark, a comprehensive evaluation framework encompassing eight distinct diagram categories, including flowcharts, model architecture diagrams, and mind maps. Additionally, we present DiagramAgent, an innovative framework with four core modules--Plan Agent, Code Agent, Check Agent, and Diagram-to-Code Agent--designed to facilitate both the generation and refinement of complex diagrams. Our extensive experiments, which combine objective metrics with human evaluations, demonstrate that DiagramAgent significantly outperforms existing baseline models in terms of accuracy, structural coherence, and modifiability. This work not only establishes a foundational benchmark for the text-to-diagram generation task but also introduces a powerful toolset to advance research and applications in this emerging area.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Wei_From_Words_to_Structured_Visuals_A_Benchmark_and_Framework_for_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Wei_From_Words_to_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Wei_2025_CVPR,
    author    = {Wei, Jingxuan and Tan, Cheng and Chen, Qi and Wu, Gaowei and Li, Siyuan and Gao, Zhangyang and Sun, Linzhuang and Yu, Bihui and Guo, Ruifeng},
    title     = {From Words to Structured Visuals: A Benchmark and Framework for Text-to-Diagram Generation and Editing},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {13315-13325}
}

raw_affiliation:
[{"affiliation": "Shenyang institute of computing technology, Chinese academy of sciences", "city": "Shenyang", "country": "China"}, {"affiliation": "Zhejiang University", "city": "Hangzhou", "country": "China"}, {"affiliation": "University of Chinese Academy of Sciences", "city": "Beijing", "country": "China"}]

affiliations:
["Shenyang institute of computing technology, Chinese academy of sciences", "Zhejiang University", "University of Chinese Academy of Sciences"]


title: LotusFilter: Fast Diverse Nearest Neighbor Search via a Learned Cutoff Table
authors: Yusuke Matsui
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 30430-30439
raw_affiliation: [{"affiliation": "The University of Tokyo", "city": "Tokyo", "country": "Japan"}]
affiliations: ["The University of Tokyo"]
cities: ["Tokyo"]
countries: ["Japan"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Matsui_LotusFilter_Fast_Diverse_Nearest_Neighbor_Search_via_a_Learned_Cutoff_CVPR_2025_paper.html

abstract:
Approximate nearest neighbor search (ANNS) is an essential building block for applications like RAG but can sometimes yield results that are overly similar to each other. In certain scenarios, search results should be similar to the query and yet diverse. We propose LotusFilter, a post-processing module to diversify ANNS results. We precompute a cutoff table summarizing vectors that are close to each other. During the filtering, LotusFilter greedily looks up the table to delete redundant vectors from the candidates. We demonstrated that the LotusFilter operates fast (0.02 [ms/query]) in settings resembling real-world RAG applications, utilizing features such as OpenAI embeddings. Our code is publicly available at https://github.com/matsui528/lotf.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Matsui_LotusFilter_Fast_Diverse_Nearest_Neighbor_Search_via_a_Learned_Cutoff_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Matsui_LotusFilter_Fast_Diverse_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Matsui_2025_CVPR,
    author    = {Matsui, Yusuke},
    title     = {LotusFilter: Fast Diverse Nearest Neighbor Search via a Learned Cutoff Table},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {30430-30439}
}


title: DreamRelation: Bridging Customization and Relation Generation
authors: Qingyu Shi, Lu Qi, Jianzong Wu, Jinbin Bai, Jingbo Wang, Yunhai Tong, Xiangtai Li
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 15723-15732
affiliations: ["PKU", "NTU", "Insta360 Research", "Shanghai AI Laboratory", "NUS"]
cities: ["Beijing", "Singapore", "Shenzhen", "Shanghai", "Singapore"]
countries: ["China", "Singapore", "China", "China", "Singapore"]
oral: yes
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Shi_DreamRelation_Bridging_Customization_and_Relation_Generation_CVPR_2025_paper.html

abstract:
Customized image generation is essential for delivering personalized content based on user-provided prompts, enabling large-scale text-to-image diffusion models to better align with individual needs. However, existing models often neglect the relationships between customized objects in generated images. In contrast, this work addresses this gap by focusing on relation-aware customized image generation, which seeks to preserve the identities from image prompts while maintaining the predicate relations specified in text prompts. Specifically, we introduce DreamRelation, a framework that disentangles identity and relation learning using a carefully curated dataset. Our training data consists of relation-specific images, independent object images containing identity information, and text prompts to guide relation generation. Then, we propose two key modules to tackle the two main challenges--generating accurate and natural relations, especially when significant pose adjustments are required, and avoiding object confusion in cases of overlap. First, we introduce a keypoint matching loss that effectively guides the model in adjusting object poses closely tied to their relationships. Second, we incorporate local features from the image prompts to better distinguish between objects, preventing confusion in overlapping cases. Extensive results on our proposed benchmarks demonstrate the superiority of DreamRelation in generating precise relations while preserving object identities across a diverse set of objects and relations. The source code and trained models will be made available to the public.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Shi_DreamRelation_Bridging_Customization_and_Relation_Generation_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Shi_DreamRelation_Bridging_Customization_CVPR_2025_supplemental.zip

bibtex:
@InProceedings{Shi_2025_CVPR,
    author    = {Shi, Qingyu and Qi, Lu and Wu, Jianzong and Bai, Jinbin and Wang, Jingbo and Tong, Yunhai and Li, Xiangtai},
    title     = {DreamRelation: Bridging Customization and Relation Generation},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {15723-15732}
}

raw_affiliation:
[{"affiliation": "PKU", "city": "Beijing", "country": "China"}, {"affiliation": "NTU", "city": "Singapore", "country": "Singapore"}, {"affiliation": "Insta360 Research", "city": "Shenzhen", "country": "China"}, {"affiliation": "Shanghai AI Laboratory", "city": "Shanghai", "country": "China"}, {"affiliation": "NUS", "city": "Singapore", "country": "Singapore"}]


title: IndoorGS: Geometric Cues Guided Gaussian Splatting for Indoor Scene Reconstruction
authors: Cong Ruan, Yuesong Wang, Tao Guan, Bin Zhang, Lili Ju
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 844-853
cities: ["Wuhan", "Shenzhen", "Columbia"]
countries: ["China", "China", "USA"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Ruan_IndoorGS_Geometric_Cues_Guided_Gaussian_Splatting_for_Indoor_Scene_Reconstruction_CVPR_2025_paper.html

abstract:
3D Gaussian Splatting (3DGS) has shown impressive performance in scene reconstruction, offering high rendering quality and rapid rendering speed with short training time. However, it often yields unsatisfactory results when applied to indoor scenes due to its poor ability to learn geometries without enough textural information. In this paper, we propose a new 3DGS-based method "IndoorGS", that leverages the commonly found yet important geometric cues in indoor scenes to improve the reconstruction quality. Specifically, we first extract 2D lines from input images and fuse them into 3D line cues via feature-based matching, which can provide a structural understanding of the target scene. We then apply the statistical outlier removal to refine Structure-from-Motion (SfM) points, ensuring robust cues in texture-rich areas. Based on these two types of cues, we further extract reliable 3D plane cues for textureless regions. Such geometric information will be utilized not only for initialization but also in the realization of a geometric-cue-guided adaptive density control (ADC) strategy. The proposed ADC approach is grounded in the principle of divide-and-conquer and optimizes the use of each type of geometric cues to enhance overall reconstruction performance. Extensive experiments on multiple indoor datasets show that our method can deliver much more accurate geometry and higher rendering quality for indoor scenes than existing 3DGS approaches.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Ruan_IndoorGS_Geometric_Cues_Guided_Gaussian_Splatting_for_Indoor_Scene_Reconstruction_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Ruan_IndoorGS_Geometric_Cues_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Ruan_2025_CVPR,
    author    = {Ruan, Cong and Wang, Yuesong and Guan, Tao and Zhang, Bin and Ju, Lili},
    title     = {IndoorGS: Geometric Cues Guided Gaussian Splatting for Indoor Scene Reconstruction},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {844-853}
}

raw_affiliation:
[{"affiliation": "School of Computer Science & Technology, Huazhong University of Science and Technology, China", "city": "Wuhan", "country": "China"}, {"affiliation": "Shenzhen Smart City Technology Development Group Co., Ltd., China", "city": "Shenzhen", "country": "China"}, {"affiliation": "Department of Mathematics, University of South Carolina, USA", "city": "Columbia", "country": "USA"}]

affiliations:
["School of Computer Science & Technology, Huazhong University of Science and Technology, China", "Shenzhen Smart City Technology Development Group Co., Ltd., China", "Department of Mathematics, University of South Carolina, USA"]


authors: Hongyu Sun, Qiuhong Ke, Ming Cheng, Yongcai Wang, Deying Li, Chenhui Gou, Jianfei Cai
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 1263-1275
cities: ["Beijing", "Melbourne"]
countries: ["China", "Australia"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Sun_Point-Cache_Test-time_Dynamic_and_Hierarchical_Cache_for_Robust_and_Generalizable_CVPR_2025_paper.html

title:
Point-Cache: Test-time Dynamic and Hierarchical Cache for Robust and Generalizable Point Cloud Analysis

abstract:
This paper proposes a general solution to enable point cloud recognition models to handle distribution shifts at test time. Unlike prior methods, which rely heavily on training data (often inaccessible during online inference) and are limited to recognizing a fixed set of point cloud classes predefined during training, we explore a more practical and challenging scenario: adapting the model solely based on online test data to recognize both previously seen classes and novel, unseen classes at test time. To this end, we develop Point-Cache, a hierarchical cache model that captures essential clues of online test samples, particularly focusing on the global structure of point clouds and their local-part details. Point-Cache, which serves as a rich 3D knowledge base, is dynamically managed to prioritize the inclusion of high-quality samples. Designed as a plug-and-play module, our method can be flexibly integrated into large multimodal 3D models to support open-vocabulary point cloud recognition. Notably, our solution operates with efficiency comparable to zero-shot inference, as it is entirely training-free. Point-Cache demonstrates substantial gains across 8 challenging benchmarks and 4 representative large 3D models, highlighting its effectiveness.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Sun_Point-Cache_Test-time_Dynamic_and_Hierarchical_Cache_for_Robust_and_Generalizable_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Sun_Point-Cache_Test-time_Dynamic_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Sun_2025_CVPR,
    author    = {Sun, Hongyu and Ke, Qiuhong and Cheng, Ming and Wang, Yongcai and Li, Deying and Gou, Chenhui and Cai, Jianfei},
    title     = {Point-Cache: Test-time Dynamic and Hierarchical Cache for Robust and Generalizable Point Cloud Analysis},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {1263-1275}
}

raw_affiliation:
[{"affiliation": "Department of Computer Science, Renmin University of China", "city": "Beijing", "country": "China"}, {"affiliation": "Department of Data Science & AI, Monash University", "city": "Melbourne", "country": "Australia"}]

affiliations:
["Department of Computer Science, Renmin University of China", "Department of Data Science & AI, Monash University"]


title: Think Small, Act Big: Primitive Prompt Learning for Lifelong Robot Manipulation
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 22573-22583
cities: ["Shanghai", "Shanghai", "Shanghai", "Hangzhou", "Xi'an", "Beijing", "Sofia"]
countries: ["China", "China", "China", "China", "China", "China", "Bulgaria"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Yao_Think_Small_Act_Big_Primitive_Prompt_Learning_for_Lifelong_Robot_CVPR_2025_paper.html

authors:
Yuanqi Yao, Siao Liu, Haoming Song, Delin Qu, Qizhi Chen, Yan Ding, Bin Zhao, Zhigang Wang, Xuelong Li, Dong Wang

abstract:
Learning a generalist robot that can effectively leverage prior knowledge for continuous skill acquisition remains significantly challenging. Despite the success of experience replay and parameter-efficient methods in maintaining knowledge across skills, naively applying these methods causes a failure to leverage the shared primitives between skills. To tackle these issues, we propose Primitive Prompt Learning (PPL), to achieve lifelong robot manipulation via reusable and extensible primitives. Within our two stage learning scheme, we first learn a set of primitive prompts to model primitives through multi-skills pre-training stage, where motion-aware prompts are learned to capture semantic and motion shared primitives across different skills. Secondly, when acquiring new skills in lifelong span, new prompts are concatenated and optimized with frozen pretrained prompts, boosting the learning via knowledge transfer from old skills to new ones. For evaluation, we construct a large-scale skill dataset and conduct extensive experiments in both simulation and real-world tasks, demonstrating PPL's superior performance over state-of-the-art methods.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Yao_Think_Small_Act_Big_Primitive_Prompt_Learning_for_Lifelong_Robot_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Yao_Think_Small_Act_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Yao_2025_CVPR,
    author    = {Yao, Yuanqi and Liu, Siao and Song, Haoming and Qu, Delin and Chen, Qizhi and Ding, Yan and Zhao, Bin and Wang, Zhigang and Li, Xuelong and Wang, Dong},
    title     = {Think Small, Act Big: Primitive Prompt Learning for Lifelong Robot Manipulation},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {22573-22583}
}

raw_affiliation:
[{"affiliation": "Shanghai AI Laboratory", "city": "Shanghai", "country": "China"}, {"affiliation": "Fudan University", "city": "Shanghai", "country": "China"}, {"affiliation": "Shanghai Jiao Tong University", "city": "Shanghai", "country": "China"}, {"affiliation": "Zhejiang University", "city": "Hangzhou", "country": "China"}, {"affiliation": "Northwestern Polytechnical University", "city": "Xi'an", "country": "China"}, {"affiliation": "TeleAI, China Telecom Corp Ltd", "city": "Beijing", "country": "China"}, {"affiliation": "INSAIT, Sofia University", "city": "Sofia", "country": "Bulgaria"}]

affiliations:
["Shanghai AI Laboratory", "Fudan University", "Shanghai Jiao Tong University", "Zhejiang University", "Northwestern Polytechnical University", "TeleAI, China Telecom Corp Ltd", "INSAIT, Sofia University"]


title: Stop Walking in Circles! Bailing Out Early in Projected Gradient Descent
authors: Philip Doldo, Derek Everett, Amol Khanna, Andre T Nguyen, Edward Raff
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 6373-6382
cities: ["McLean", "McLean", "McLean", "McLean", "McLean"]
countries: ["USA", "USA", "USA", "USA", "USA"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Doldo_Stop_Walking_in_Circles_Bailing_Out_Early_in_Projected_Gradient_CVPR_2025_paper.html

abstract:
Projected Gradient Descent (PGD) under the L_infinity ball has become one of the defacto methods used in adversarial robustness evaluation for computer vision (CV) due to its reliability and efficacy, making a strong and easy-to-implement iterative baseline. However, PGD is computationally demanding to apply, especially when thousands of iterations is current best-practice recommendations to generate an adversarial example for a single image. In this work, we introduce a simple novel method for early termination of PGD based on cycle detection by exploiting the geometry of how PGD is implemented in practice and show that it can produce large speedup factors while providing the exact same estimate of model robustness as standard PGD. This method substantially speeds up PGD without sacrificing any attack strength, enabling evaluations of robustness that were previously computationally intractable

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Doldo_Stop_Walking_in_Circles_Bailing_Out_Early_in_Projected_Gradient_CVPR_2025_paper.pdf

bibtex:
@InProceedings{Doldo_2025_CVPR,
    author    = {Doldo, Philip and Everett, Derek and Khanna, Amol and Nguyen, Andre T and Raff, Edward},
    title     = {Stop Walking in Circles! Bailing Out Early in Projected Gradient Descent},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {6373-6382}
}

raw_affiliation:
[{"affiliation": "Booz Allen Hamilton", "city": "McLean", "country": "USA"}, {"affiliation": "Booz Allen Hamilton", "city": "McLean", "country": "USA"}, {"affiliation": "Booz Allen Hamilton", "city": "McLean", "country": "USA"}, {"affiliation": "Booz Allen Hamilton", "city": "McLean", "country": "USA"}, {"affiliation": "Booz Allen Hamilton", "city": "McLean", "country": "USA"}]

affiliations:
["Booz Allen Hamilton", "Booz Allen Hamilton", "Booz Allen Hamilton", "Booz Allen Hamilton", "Booz Allen Hamilton"]


title: MoManipVLA: Transferring Vision-language-action Models for General Mobile Manipulation
authors: Zhenyu Wu, Yuheng Zhou, Xiuwei Xu, Ziwei Wang, Haibin Yan
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 1714-1723
cities: ["Beijing", "Singapore", "Beijing"]
countries: ["China", "Singapore", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Wu_MoManipVLA_Transferring_Vision-language-action_Models_for_General_Mobile_Manipulation_CVPR_2025_paper.html

abstract:
Mobile manipulation is the fundamental challenge for robotics to assist humans with diverse tasks and environments in everyday life. However, conventional mobile manipulation approaches often struggle to generalize across different tasks and environments because of the lack of large-scale training.In contrast, recent advances in vision-language-action (VLA) models have shown impressive generalization capabilities, but these foundation models are developed for fixed-base manipulation tasks.Therefore, we propose an efficient policy adaptation framework to transfer pre-trained VLA models of fix-base manipulation to mobile manipulation, so that high generalization ability across tasks and environments can be achieved in mobile manipulation policy.Specifically, we utilize pre-trained VLA models to generate waypoints of the end-effector with high generalization ability. We design motion planning objectives for the mobile base and the robot arm, which aim at maximizing the physical feasibility of the trajectory. Finally, we present an efficient bi-level objective optimization framework for trajectory generation, where the upper-level optimization predicts waypoints for base movement to enhance the manipulator policy space, and the lower-level optimization selects the optimal end-effector trajectory to complete the manipulation task. Extensive experimental results on OVMM and the real world demonstrate that our method achieves a 4.2% higher success rate than the state-of-the-art mobile manipulation, and only requires 50 training cost for real world deployment due to the strong generalization ability in the pre-trained VLA models.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Wu_MoManipVLA_Transferring_Vision-language-action_Models_for_General_Mobile_Manipulation_CVPR_2025_paper.pdf

bibtex:
@InProceedings{Wu_2025_CVPR,
    author    = {Wu, Zhenyu and Zhou, Yuheng and Xu, Xiuwei and Wang, Ziwei and Yan, Haibin},
    title     = {MoManipVLA: Transferring Vision-language-action Models for General Mobile Manipulation},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {1714-1723}
}

raw_affiliation:
[{"affiliation": "Beijing University of Posts and Telecommunications, School of Intelligent Engineering and Automation", "city": "Beijing", "country": "China"}, {"affiliation": "Nanyang Technological University, School of Electrical and Electronic Engineering", "city": "Singapore", "country": "Singapore"}, {"affiliation": "Tsinghua University, Department of Automation", "city": "Beijing", "country": "China"}]

affiliations:
["Beijing University of Posts and Telecommunications, School of Intelligent Engineering and Automation", "Nanyang Technological University, School of Electrical and Electronic Engineering", "Tsinghua University, Department of Automation"]


authors: Seokju Yun, Seunghye Chae, Dongheon Lee, Youngmin Ro
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 25602-25612
affiliations: ["Machine Intelligence Laboratory, University of Seoul, Korea"]
cities: ["Seoul"]
countries: ["Korea"]
highlight: yes
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Yun_SoMA_Singular_Value_Decomposed_Minor_Components_Adaptation_for_Domain_Generalizable_CVPR_2025_paper.html

title:
SoMA: Singular Value Decomposed Minor Components Adaptation for Domain Generalizable Representation Learning

abstract:
Domain generalization (DG) aims to adapt a model using one or multiple source domains to ensure robust performance in unseen target domains. Recently, Parameter-Efficient Fine-Tuning (PEFT) of foundation models has shown promising results in the context of DG problem. Nevertheless, existing PEFT methods still struggle to strike a balance between preserving generalizable components of the pre-trained model and learning task-specific features. To gain insights into the distribution of generalizable components, we begin by analyzing the pre-trained weights  through the lens of singular value decomposition. Building on these insights, we introduce Singular Value Decomposed Minor Components Adaptation (SoMA), an approach that selectively tunes minor singular components while keeping the residual parts frozen. SoMA effectively retains the generalization ability of the pre-trained model while efficiently acquiring task-specific skills. Moreover, we freeze domain-generalizable blocks and employ an annealing weight decay strategy, thereby achieving an optimal balance in the delicate trade-off between generalizability and discriminability. SoMA attains state-of-the-art results on multiple benchmarks that span both domain generalized semantic segmentation to domain generalized object detection. In addition, our methods introduce no additional inference overhead or regularization loss, maintain compatibility with any backbone or head, and are designed to be versatile, allowing easy integration into a wide range of tasks.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Yun_SoMA_Singular_Value_Decomposed_Minor_Components_Adaptation_for_Domain_Generalizable_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Yun_SoMA_Singular_Value_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Yun_2025_CVPR,
    author    = {Yun, Seokju and Chae, Seunghye and Lee, Dongheon and Ro, Youngmin},
    title     = {SoMA: Singular Value Decomposed Minor Components Adaptation for Domain Generalizable Representation Learning},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {25602-25612}
}

raw_affiliation:
[{"affiliation": "Machine Intelligence Laboratory, University of Seoul, Korea", "city": "Seoul", "country": "Korea"}]


title: Depth-Guided Bundle Sampling for Efficient Generalizable Neural Radiance Field Reconstruction
authors: Li Fang, Hao Zhu, Longlong Chen, Fei Hu, Long Ye, Zhan Ma
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 11217-11226
cities: ["Beijing", "Nanjing"]
countries: ["China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Fang_Depth-Guided_Bundle_Sampling_for_Efficient_Generalizable_Neural_Radiance_Field_Reconstruction_CVPR_2025_paper.html

abstract:
Recent advancements in generalizable novel view synthesis have achieved impressive quality through interpolation between nearby views. However, rendering high-resolution images remains computationally intensive due to the need for dense sampling of all rays. Recognizing that natural scenes are typically piecewise smooth and sampling all rays is often redundant, we propose a novel depth-guided bundle sampling strategy to accelerate rendering. By grouping adjacent rays into a bundle and sampling them collectively, a shared representation is generated for decoding all rays within the bundle. To further optimize efficiency, our adaptive sampling strategy dynamically allocates samples based on depth confidence, concentrating more samples in complex regions while reducing them in smoother areas. When applied to ENeRF, our method achieves up to a 1.27 dB PSNR improvement and a 47% increase in FPS on the DTU dataset. Extensive experiments on synthetic and real-world datasets demonstrate state-of-the-art rendering quality and up to 2x faster rendering compared to existing generalizable methods. Code is available at https://github.com/KLMAV-CUC/GDB-NeRF.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Fang_Depth-Guided_Bundle_Sampling_for_Efficient_Generalizable_Neural_Radiance_Field_Reconstruction_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Fang_Depth-Guided_Bundle_Sampling_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Fang_2025_CVPR,
    author    = {Fang, Li and Zhu, Hao and Chen, Longlong and Hu, Fei and Ye, Long and Ma, Zhan},
    title     = {Depth-Guided Bundle Sampling for Efficient Generalizable Neural Radiance Field Reconstruction},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {11217-11226}
}

raw_affiliation:
[{"affiliation": "Key Laboratory of Media Audio and Video (Communication University of China), Ministry of Education", "city": "Beijing", "country": "China"}, {"affiliation": "School of Electronic Science and Engineering, Nanjing University", "city": "Nanjing", "country": "China"}]

affiliations:
["Key Laboratory of Media Audio and Video (Communication University of China), Ministry of Education", "School of Electronic Science and Engineering, Nanjing University"]


title: TinyFusion: Diffusion Transformers Learned Shallow
authors: Gongfan Fang, Kunjun Li, Xinyin Ma, Xinchao Wang
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 18144-18154
raw_affiliation: [{"affiliation": "National University of Singapore", "city": "Singapore", "country": "Singapore"}]
affiliations: ["National University of Singapore"]
cities: ["Singapore"]
countries: ["Singapore"]
highlight: yes
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Fang_TinyFusion_Diffusion_Transformers_Learned_Shallow_CVPR_2025_paper.html

abstract:
Diffusion Transformers have demonstrated remarkable capabilities in image generation but often come with excessive parameterization, resulting in considerable inference overhead in real-world applications. In this work, we present TinyFusion, a depth pruning method designed to remove redundant layers from diffusion transformers via end-to-end learning. The core principle of our approach is to create a pruned model with high recoverability, allowing it to regain strong performance after fine-tuning. To accomplish this, we introduce a differentiable sampling technique to make pruning learnable, paired with a co-optimized parameter to simulate future fine-tuning. While prior works focus on minimizing loss or error after pruning, our method explicitly models and optimizes the post-fine-tuning performance of pruned models. Experimental results indicate that this learnable paradigm offers substantial benefits for layer pruning of diffusion transformers, surpassing existing importance-based and error-based methods. Additionally, TinyFusion exhibits strong generalization across diverse architectures, such as DiTs, MARs, and SiTs. Experiments with DiT-XL show that TinyFusion can craft a shallow diffusion transformer at less than 7% of the pre-training cost, achieving a 2xspeedup with an FID score of 2.86, outperforming competitors with comparable efficiency. Code is available at https://github.com/VainF/TinyFusion

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Fang_TinyFusion_Diffusion_Transformers_Learned_Shallow_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Fang_TinyFusion_Diffusion_Transformers_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Fang_2025_CVPR,
    author    = {Fang, Gongfan and Li, Kunjun and Ma, Xinyin and Wang, Xinchao},
    title     = {TinyFusion: Diffusion Transformers Learned Shallow},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {18144-18154}
}


title: Ref-GS: Directional Factorization for 2D Gaussian Splatting
authors: Youjia Zhang, Anpei Chen, Yumin Wan, Zikai Song, Junqing Yu, Yawei Luo, Wei Yang
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 26483-26492
cities: ["Wuhan", "T\t\"ubingen", "Hangzhou", "Hangzhou"]
countries: ["China", "Germany", "China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Zhang_Ref-GS_Directional_Factorization_for_2D_Gaussian_Splatting_CVPR_2025_paper.html

abstract:
In this paper, we introduce Ref-GS, a novel approach for directional light factorization in 2D Gaussian splatting, which enables photorealistic view-dependent appearance rendering and precise geometry recovery. Ref-GS builds upon the deferred rendering of Gaussian splatting and applies directional encoding to the deferred-rendered surface, effectively reducing the ambiguity between orientation and viewing angle. Next, we introduce a spherical mip-grid to capture varying levels of surface roughness, enabling roughness-aware Gaussian shading. Additionally, we propose a simple yet efficient geometry-lighting factorization that connects geometry and lighting via the vector outer product, significantly reducing renderer overhead when integrating volumetric attributes. Our method achieves superior photorealistic rendering for a range of open-world scenes while also accurately recovering geometry.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Zhang_Ref-GS_Directional_Factorization_for_2D_Gaussian_Splatting_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Zhang_Ref-GS_Directional_Factorization_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Zhang_2025_CVPR,
    author    = {Zhang, Youjia and Chen, Anpei and Wan, Yumin and Song, Zikai and Yu, Junqing and Luo, Yawei and Yang, Wei},
    title     = {Ref-GS: Directional Factorization for 2D Gaussian Splatting},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {26483-26492}
}

raw_affiliation:
[{"affiliation": "Huazhong University of Science and Technology", "city": "Wuhan", "country": "China"}, {"affiliation": "University of T\t\"ubingen, T\t\"ubingen AI Center", "city": "T\t\"ubingen", "country": "Germany"}, {"affiliation": "Westlake University", "city": "Hangzhou", "country": "China"}, {"affiliation": "Zhejiang University", "city": "Hangzhou", "country": "China"}]

affiliations:
["Huazhong University of Science and Technology", "University of T\t\"ubingen, T\t\"ubingen AI Center", "Westlake University", "Zhejiang University"]


title: SVG-IR: Spatially-Varying Gaussian Splatting for Inverse Rendering
authors: Hanxiao Sun, Yupeng Gao, Jin Xie, Jian Yang, Beibei Wang
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 16143-16152
affiliations: ["Nankai University", "Nanjing University"]
cities: ["Tianjin", "Nanjing"]
countries: ["China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Sun_SVG-IR_Spatially-Varying_Gaussian_Splatting_for_Inverse_Rendering_CVPR_2025_paper.html

abstract:
Reconstructing 3D assets from images, known as inverse rendering (IR), remains a challenging task due to its ill-posed nature. 3D Gaussian Splatting (3DGS) has demonstrated impressive capabilities for novel view synthesis (NVS) tasks. Methods apply it to relighting by separating radiance into BRDF parameters and lighting, yet produce inferior relighting quality with artifacts and unnatural indirect illumination due to the limited capability of each Gaussian, which has constant material parameters and normal, alongside the absence of physical constraints for indirect lighting. In this paper, we present a novel framework called Spatially-vayring Gaussian Inverse Rendering (SVG-IR), aimed at enhancing both NVS and relighting quality. To this end, we propose a new representation--Spatially-varying Gaussian (SVG)--that allows per-Gaussian spatially varying parameters. This enhanced representation is complemented by a SVG splatting scheme akin to vertex/fragment shading in traditional graphics pipelines. Furthermore, we integrate a physically-based indirect lighting model, enabling more realistic relighting. The proposed SVG-IR framework significantly improves rendering quality, outperforming state-of-the-art NeRF-based methods by 2.5 dB in peak signal-to-noise ratio (PSNR) and surpassing existing Gaussian-based techniques by 3.5 dB in relighting tasks, all while maintaining a real-time rendering speed. The source code is available at https://github.com/learner-shx/SVG-IR.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Sun_SVG-IR_Spatially-Varying_Gaussian_Splatting_for_Inverse_Rendering_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Sun_SVG-IR_Spatially-Varying_Gaussian_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Sun_2025_CVPR,
    author    = {Sun, Hanxiao and Gao, Yupeng and Xie, Jin and Yang, Jian and Wang, Beibei},
    title     = {SVG-IR: Spatially-Varying Gaussian Splatting for Inverse Rendering},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {16143-16152}
}

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


title: Stable-SCore: A Stable Registration-based Framework for 3D Shape Correspondence
authors: Haolin Liu, Xiaohang Zhan, Zizheng Yan, Zhongjin Luo, Yuxin Wen, Xiaoguang Han
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 917-928
affiliations: ["FNii, CUHKSZ", "Tencent", "Tencent-Hunyuan3D", "SSE, CUHKSZ"]
cities: ["Shenzhen", "Shenzhen", "Shenzhen", "Shenzhen"]
countries: ["China", "China", "China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Liu_Stable-SCore_A_Stable_Registration-based_Framework_for_3D_Shape_Correspondence_CVPR_2025_paper.html

abstract:
Establishing character shape correspondence is a critical and fundamental task in computer vision and graphics, with diverse applications including re-topology, attribute transfer, and shape interpolation. Current dominant functional map methods, while effective in controlled scenarios, struggle in real situations with more complex challenges such as non-isometric shape discrepancies. In response, we revisit registration-for-correspondence methods and tap their potential for more stable shape correspondence estimation. To overcome their common issues including unstable deformations and the necessity for careful pre-alignment or high-quality initial 3D correspondences, we introduce Stable-SCore: A Stable Registration-based Framework for 3D Shape Correspondence. We first re-purpose a foundation model for 2D character correspondence that ensures reliable and stable 2D mappings. Crucially, we propose a novel Semantic Flow Guided Registration approach that leverages 2D correspondence to guide mesh deformations. Our framework significantly surpasses existing methods in challenging scenarios, and brings possibilities for a wide array of real applications, as demonstrated in our results.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Liu_Stable-SCore_A_Stable_Registration-based_Framework_for_3D_Shape_Correspondence_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Liu_Stable-SCore_A_Stable_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Liu_2025_CVPR,
    author    = {Liu, Haolin and Zhan, Xiaohang and Yan, Zizheng and Luo, Zhongjin and Wen, Yuxin and Han, Xiaoguang},
    title     = {Stable-SCore: A Stable Registration-based Framework for 3D Shape Correspondence},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {917-928}
}

raw_affiliation:
[{"affiliation": "FNii, CUHKSZ", "city": "Shenzhen", "country": "China"}, {"affiliation": "Tencent", "city": "Shenzhen", "country": "China"}, {"affiliation": "Tencent-Hunyuan3D", "city": "Shenzhen", "country": "China"}, {"affiliation": "SSE, CUHKSZ", "city": "Shenzhen", "country": "China"}]


authors: Kai Mao, Ping Wei, Yiyang Lian, Yangyang Wang, Nanning Zheng
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 9964-9973
cities: ["Xi'an"]
countries: ["China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Mao_Beyond_Single-Modal_Boundary_Cross-Modal_Anomaly_Detection_through_Visual_Prototype_and_CVPR_2025_paper.html

title:
Beyond Single-Modal Boundary: Cross-Modal Anomaly Detection through Visual Prototype and Harmonization

abstract:
Anomaly detection is a significant task for its application and research value. While existing methods have made impressive progress within the same modality, cross-modal anomaly detection remains an open and challenging problem. In this paper, we propose a cross-modal anomaly detection model that is trained using data from a variety of existing modalities and can be generalized well to unseen modalities. The model consists of three major components: 1) the Transferable Visual Prototype directly learns normal/abnormal semantics in visual space; 2) the Prototype Harmonization strategy  adaptively utilizes the Transferable Visual Prototypes from various modalities for inference on the unknown modality; 3) the Visual Discrepancy Inference under the few-shot setting enhances performance. In the zero-shot setting, the proposed method achieves AUROC improvements of 4.1%, 6.1%, 7.6%, and 6.8% over the best competing methods in the RGB, 3D, MRI/CT, and Thermal modalities, respectively. In the few-shot setting, our model also achieves the highest AUROC/AP on ten datasets in four modalities, substantially outperforming existing methods. Codes are available at https://github.com/Kerio99/CMAD.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Mao_Beyond_Single-Modal_Boundary_Cross-Modal_Anomaly_Detection_through_Visual_Prototype_and_CVPR_2025_paper.pdf

bibtex:
@InProceedings{Mao_2025_CVPR,
    author    = {Mao, Kai and Wei, Ping and Lian, Yiyang and Wang, Yangyang and Zheng, Nanning},
    title     = {Beyond Single-Modal Boundary: Cross-Modal Anomaly Detection through Visual Prototype and Harmonization},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {9964-9973}
}

raw_affiliation:
[{"affiliation": "National Key Laboratory of Human-Machine Hybrid Augmented Intelligence\nInstitute of Artificial Intelligence and Robotics, Xi\u2019an Jiaotong University", "city": "Xi'an", "country": "China"}]

affiliations:
["National Key Laboratory of Human-Machine Hybrid Augmented Intelligence\nInstitute of Artificial Intelligence and Robotics, Xi\u2019an Jiaotong University"]


title: VDocRAG: Retrieval-Augmented Generation over Visually-Rich Documents
authors: Ryota Tanaka, Taichi Iki, Taku Hasegawa, Kyosuke Nishida, Kuniko Saito, Jun Suzuki
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 24827-24837
affiliations: ["NTT Human Informatics Laboratories, NTT Corporation", "Tohoku University"]
cities: ["Tokyo", "Sendai"]
countries: ["Japan", "Japan"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Tanaka_VDocRAG_Retrieval-Augmented_Generation_over_Visually-Rich_Documents_CVPR_2025_paper.html

abstract:
We aim to develop a retrieval-augmented generation (RAG) framework that answers questions over a corpus of visually-rich documents presented in mixed modalities (e.g., charts, tables) and diverse formats (e.g., PDF, PPTX). In this paper, we introduce a new RAG framework, VDocRAG, which can directly understand varied documents and modalities in a unified image format to prevent missing information that occurs by parsing documents to obtain text. To improve the performance, we propose novel self-supervised pre-training tasks that adapt large vision-language models for retrieval by compressing visual information into dense token representations while aligning them with textual content in documents. Furthermore, we introduce OpenDocVQA, the first unified collection of open-domain document visual question answering datasets, encompassing diverse document types and formats. OpenDocVQA provides a comprehensive resource for training and evaluating retrieval and question answering models on visually-rich documents in an open-domain setting. Experiments show that VDocRAG substantially outperforms conventional text-based RAG and has strong generalization capability, highlighting the potential of an effective RAG paradigm for real-world documents.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Tanaka_VDocRAG_Retrieval-Augmented_Generation_over_Visually-Rich_Documents_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Tanaka_VDocRAG_Retrieval-Augmented_Generation_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Tanaka_2025_CVPR,
    author    = {Tanaka, Ryota and Iki, Taichi and Hasegawa, Taku and Nishida, Kyosuke and Saito, Kuniko and Suzuki, Jun},
    title     = {VDocRAG: Retrieval-Augmented Generation over Visually-Rich Documents},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {24827-24837}
}

raw_affiliation:
[{"affiliation": "NTT Human Informatics Laboratories, NTT Corporation", "city": "Tokyo", "country": "Japan"}, {"affiliation": "Tohoku University", "city": "Sendai", "country": "Japan"}]


authors: Qianhan Feng, Wenshuo Li, Tong Lin, Xinghao Chen
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 4178-4188
cities: ["Beijing", "Shenzhen"]
countries: ["China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Feng_Align-KD_Distilling_Cross-Modal_Alignment_Knowledge_for_Mobile_Vision-Language_Large_Model_CVPR_2025_paper.html

title:
Align-KD: Distilling Cross-Modal Alignment Knowledge for Mobile Vision-Language Large Model Enhancement

abstract:
Vision-Language Models (VLMs) bring powerful understanding and reasoning capabilities to multimodal tasks. Meanwhile, the great need for capable aritificial intelligence on mobile devices also arises, such as the AI assistant software. Some efforts try to migrate VLMs to edge devices to expand their application scope. Simplifying the model structure is a common method, but as the model shrinks, the trade-off between performance and size becomes more and more difficult. Knowledge distillation (KD) can help models improve comprehensive capabilities without increasing size or data volume. However, most of the existing large model distillation techniques only consider applications on single-modal LLMs, or only use teachers to create new data environments for students. None of these methods take into account the distillation of the most important cross-modal alignment knowledge in VLMs. We propose a method called Align-KD to guide the student model to learn the cross-modal matching that occurs at the shallow layer. The teacher also helps student learn the projection of vision token into text embedding space based on the focus of text. Under the guidance of Align-KD, the 1.7B MobileVLM V2 model can learn rich knowledge from the 7B teacher model with light design of training loss, and achieve an average score improvement of 2.0 across 6 benchmarks under two training subsets respectively. Code is available at: https://github.com/fqhank/Align-KD.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Feng_Align-KD_Distilling_Cross-Modal_Alignment_Knowledge_for_Mobile_Vision-Language_Large_Model_CVPR_2025_paper.pdf

bibtex:
@InProceedings{Feng_2025_CVPR,
    author    = {Feng, Qianhan and Li, Wenshuo and Lin, Tong and Chen, Xinghao},
    title     = {Align-KD: Distilling Cross-Modal Alignment Knowledge for Mobile Vision-Language Large Model Enhancement},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {4178-4188}
}

raw_affiliation:
[{"affiliation": "State Key Laboratory of General Arti\ufb01cial Intelligence, School of Intelligence Science and T echnology , Peking University , China", "city": "Beijing", "country": "China"}, {"affiliation": "Huawei Noah\u2019s Ark Lab, China", "city": "Shenzhen", "country": "China"}]

affiliations:
["State Key Laboratory of General Arti\ufb01cial Intelligence, School of Intelligence Science and T echnology , Peking University , China", "Huawei Noah\u2019s Ark Lab, China"]


title: Pose Priors from Language Models
authors: Sanjay Subramanian, Evonne Ng, Lea Müller, Dan Klein, Shiry Ginosar, Trevor Darrell
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 7125-7135
cities: ["Berkeley", "Mountain View", "Chicago"]
countries: ["USA", "USA", "USA"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Subramanian_Pose_Priors_from_Language_Models_CVPR_2025_paper.html

abstract:
Language is often used to describe physical interaction, yet most 3D human pose estimation methods overlook this rich source of information. We bridge this gap by leveraging large multimodal models (LMMs) as priors for reconstructing contact poses, offering a scalable alternative to traditional methods that rely on human annotations or motion capture data. Our approach extracts contact-relevant descriptors from an LMM and translates them into tractable losses to constrain 3D human pose optimization. Despite its simplicity, our method produces compelling reconstructions for both two-person interactions and self-contact scenarios, accurately capturing the semantics of physical and social interactions. Our results demonstrate that LMMs can serve as powerful tools for contact prediction and pose estimation, offering an alternative to costly manual human annotations or motion capture data. Our code is publicly available at prosepose.github.io.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Subramanian_Pose_Priors_from_Language_Models_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Subramanian_Pose_Priors_from_CVPR_2025_supplemental.zip

bibtex:
@InProceedings{Subramanian_2025_CVPR,
    author    = {Subramanian, Sanjay and Ng, Evonne and M\"uller, Lea and Klein, Dan and Ginosar, Shiry and Darrell, Trevor},
    title     = {Pose Priors from Language Models},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {7125-7135}
}

raw_affiliation:
[{"affiliation": "University of California, Berkeley", "city": "Berkeley", "country": "USA"}, {"affiliation": "Google DeepMind", "city": "Mountain View", "country": "USA"}, {"affiliation": "Toyota Technological Institute at Chicago", "city": "Chicago", "country": "USA"}]

affiliations:
["University of California, Berkeley", "Google DeepMind", "Toyota Technological Institute at Chicago"]


title: Concept Lancet: Image Editing with Compositional Representation Transplant
authors: Jinqi Luo, Tianjiao Ding, Kwan Ho Ryan Chan, Hancheng Min, Chris Callison-Burch, Rene Vidal
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 28502-28512
raw_affiliation: [{"affiliation": "University of Pennsylvania", "city": "Philadelphia", "country": "USA"}]
affiliations: ["University of Pennsylvania"]
cities: ["Philadelphia"]
countries: ["USA"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Luo_Concept_Lancet_Image_Editing_with_Compositional_Representation_Transplant_CVPR_2025_paper.html

abstract:
Diffusion models are widely used for image editing tasks. Existing editing methods often design a representation manipulation procedure by curating an edit direction in the text embedding or score space. However, such a procedure faces a key challenge: overestimating the edit strength harms visual consistency while underestimating it fails the editing task. Notably, each source image may require a different editing strength, and it is costly to search for an appropriate strength via trial-and-error. To address this challenge, we propose Concept Lancet (CoLan), a zero-shot plug-and-play framework for principled representation manipulation in diffusion-based image editing. At inference time, we decompose the source input in the latent (text embedding or diffusion score) space as a sparse linear combination of the representations of the collected visual concepts and phrases. This allows us to accurately estimate the presence of concepts in each image, which informs the edit. Based on the editing task (replace/add/remove), we perform a customized concept transplant process to impose the corresponding editing direction. To sufficiently model the concept space, we curate a conceptual representation dataset, CoLan-150K, which contains diverse descriptions and scenarios of visual concepts and phrases for the latent dictionary. Experiments on multiple diffusion-based image editing baselines show that methods equipped with CoLan achieve state-of-the-art performance in editing effectiveness and consistency preservation.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Luo_Concept_Lancet_Image_Editing_with_Compositional_Representation_Transplant_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Luo_Concept_Lancet_Image_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Luo_2025_CVPR,
    author    = {Luo, Jinqi and Ding, Tianjiao and Chan, Kwan Ho Ryan and Min, Hancheng and Callison-Burch, Chris and Vidal, Rene},
    title     = {Concept Lancet: Image Editing with Compositional Representation Transplant},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {28502-28512}
}


title: Scaling Mesh Generation via Compressive Tokenization
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 11093-11103
cities: ["Guangzhou", "Shanghai", "Hong Kong", "Shenzhen"]
countries: ["China", "China", "China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Weng_Scaling_Mesh_Generation_via_Compressive_Tokenization_CVPR_2025_paper.html

authors:
Haohan Weng, Zibo Zhao, Biwen Lei, Xianghui Yang, Jian Liu, Zeqiang Lai, Zhuo Chen, Yuhong Liu, Jie Jiang, Chunchao Guo, Tong Zhang, Shenghua Gao, C.L. Philip Chen

abstract:
We propose a compressive yet effective mesh tokenization, Blocked and Patchified Tokenization (BPT), facilitating the generation of meshes exceeding 8k faces. BPT compresses mesh sequences by employing block-wise indexing and patch aggregation, reducing their length by approximately 75% compared to the vanilla sequences. This compression milestone unlocks the potential to utilize mesh data with significantly more faces, thereby enhancing detail richness and improving generation robustness. Empowered with the BPT, we have built a foundation mesh generative model training on scaled mesh data to support flexible control for point clouds and images. Our model demonstrates the capability to generate meshes with intricate details and accurate topology, achieving SoTA performance on mesh generation and reaching the level for direct product usage.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Weng_Scaling_Mesh_Generation_via_Compressive_Tokenization_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Weng_Scaling_Mesh_Generation_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Weng_2025_CVPR,
    author    = {Weng, Haohan and Zhao, Zibo and Lei, Biwen and Yang, Xianghui and Liu, Jian and Lai, Zeqiang and Chen, Zhuo and Liu, Yuhong and Jiang, Jie and Guo, Chunchao and Zhang, Tong and Gao, Shenghua and Chen, C.L. Philip},
    title     = {Scaling Mesh Generation via Compressive Tokenization},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {11093-11103}
}

raw_affiliation:
[{"affiliation": "South China University of Technology", "city": "Guangzhou", "country": "China"}, {"affiliation": "ShanghaiTech University", "city": "Shanghai", "country": "China"}, {"affiliation": "University of Hong Kong", "city": "Hong Kong", "country": "China"}, {"affiliation": "Tencent Hunyuan", "city": "Shenzhen", "country": "China"}]

affiliations:
["South China University of Technology", "ShanghaiTech University", "University of Hong Kong", "Tencent Hunyuan"]


authors: Seungtae Nam, Xiangyu Sun, Gyeongjin Kang, Younggeun Lee, Seungjun Oh, Eunbyung Park
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 26683-26693
affiliations: ["Yonsei University", "Sungkyunkwan University"]
cities: ["Seoul", "Suwon"]
countries: ["South Korea", "South Korea"]
highlight: yes
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Nam_Generative_Densification_Learning_to_Densify_Gaussians_for_High-Fidelity_Generalizable_3D_CVPR_2025_paper.html

title:
Generative Densification: Learning to Densify Gaussians for High-Fidelity Generalizable 3D Reconstruction

abstract:
Generalized feed-forward Gaussian models have made significant strides in sparse-view 3D reconstruction by leveraging prior knowledge from large multi-view datasets. However, these models often struggle to represent high-frequency details primarily due to the limited number of Gaussians. While the densification strategy from per-scene 3D Gaussian splatting can be adapted for feed-forward models, it typically requires tens of thousands of optimization steps to reconstruct fine details and can easily lead to overfitting in sparse-view scenarios. In this paper, we propose Generative Densification, an efficient and generalizable densification strategy specifically tailored for feed-forward models. Instead of iteratively splitting and cloning raw Gaussian parameters, our method up-samples feature representations produced by feed-forward models and generates their corresponding fine Gaussians in a single forward pass, leveraging learned prior knowledge for enhanced generalization. Experimental results on both object-level and scene-level reconstruction tasks demonstrate that our method outperforms state-of-the-art approaches with comparable or smaller model sizes, achieving notable improvements in representing fine details.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Nam_Generative_Densification_Learning_to_Densify_Gaussians_for_High-Fidelity_Generalizable_3D_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Nam_Generative_Densification_Learning_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Nam_2025_CVPR,
    author    = {Nam, Seungtae and Sun, Xiangyu and Kang, Gyeongjin and Lee, Younggeun and Oh, Seungjun and Park, Eunbyung},
    title     = {Generative Densification: Learning to Densify Gaussians for High-Fidelity Generalizable 3D Reconstruction},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {26683-26693}
}

raw_affiliation:
[{"affiliation": "Yonsei University", "city": "Seoul", "country": "South Korea"}, {"affiliation": "Sungkyunkwan University", "city": "Suwon", "country": "South Korea"}]


authors: Zihui Zhang, Weisheng Dai, Hongtao Wen, Bo Yang
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 1374-1384
cities: ["Shenzhen", "Hong Kong"]
countries: ["China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Zhang_LogoSP_Local-global_Grouping_of_Superpoints_for_Unsupervised_Semantic_Segmentation_of_CVPR_2025_paper.html

title:
LogoSP: Local-global Grouping of Superpoints for Unsupervised Semantic Segmentation of 3D Point Clouds

abstract:
We study the problem of unsupervised 3D semantic segmentation on raw point clouds without needing human labels in training. Existing methods usually formulate this problem into learning per-point local features followed by a simple grouping strategy, lacking the ability to discover additional and possibly richer semantic priors beyond local features. In this paper, we introduce LogoSP to learn 3D semantics from both local and global point features. The key to our approach is to discover 3D semantic information by grouping superpoints according to their global patterns in the frequency domain, thus generating highly accurate semantic pseudo-labels for training a segmentation network. Extensive experiments on two indoor and an outdoor datasets show that our LogoSP surpasses all existing unsupervised methods by large margins, achieving the state-of-the-art performance for unsupervised 3D semantic segmentation. Notably, our investigation into the learned global patterns reveals that they truly represent meaningful 3D semantics in the absence of human labels during training. Our code and data are available at https://github.com/vLARgroup/LogoSP

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Zhang_LogoSP_Local-global_Grouping_of_Superpoints_for_Unsupervised_Semantic_Segmentation_of_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Zhang_LogoSP_Local-global_Grouping_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Zhang_2025_CVPR,
    author    = {Zhang, Zihui and Dai, Weisheng and Wen, Hongtao and Yang, Bo},
    title     = {LogoSP: Local-global Grouping of Superpoints for Unsupervised Semantic Segmentation of 3D Point Clouds},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {1374-1384}
}

raw_affiliation:
[{"affiliation": "Shenzhen Research Institute, The Hong Kong Polytechnic University", "city": "Shenzhen", "country": "China"}, {"affiliation": "vLAR Group, The Hong Kong Polytechnic University", "city": "Hong Kong", "country": "China"}]

affiliations:
["Shenzhen Research Institute, The Hong Kong Polytechnic University", "vLAR Group, The Hong Kong Polytechnic University"]


title: Exploring Intrinsic Normal Prototypes within a Single Image for Universal Anomaly Detection
authors: Wei Luo, Yunkang Cao, Haiming Yao, Xiaotian Zhang, Jianan Lou, Yuqi Cheng, Weiming Shen, Wenyong Yu
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 9974-9983
affiliations: ["Tsinghua University", "Huazhong University of Science and Technology"]
cities: ["Beijing", "Wuhan"]
countries: ["China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Luo_Exploring_Intrinsic_Normal_Prototypes_within_a_Single_Image_for_Universal_CVPR_2025_paper.html

abstract:
Anomaly detection (AD) is essential for industrial inspection, yet existing methods typically rely on "comparing" test images to normal references from a training set. However, variations in appearance and positioning often complicate the alignment of these references with the test image, limiting detection accuracy. We observe that most anomalies manifest as local variations, meaning that even within anomalous images, valuable normal information remains. We argue that this information is useful and may be more aligned with the anomalies since both the anomalies and the normal information originate from the same image. Therefore, rather than relying on external normality from the training set, we propose INP-Former, a novel method that extracts Intrinsic Normal Prototypes (INPs) directly from the test image. Specifically, we introduce the INP Extractor, which linearly combines normal tokens to represent INPs. We further propose an INP Coherence Loss to ensure INPs can faithfully represent normality for the testing image. These INPs then guide the INP-Guided Decoder to reconstruct only normal tokens, with reconstruction errors serving as anomaly scores. Additionally, we propose a Soft Mining Loss to prioritize hard-to-optimize samples during training. INP-Former achieves state-of-the-art performance in single-class, multi-class, and few-shot AD tasks across MVTec-AD, VisA, and Real-IAD, positioning it as a versatile and universal solution for AD. Remarkably, INP-Former also demonstrates some zero-shot AD capability.  Code is available at: https://github.com/luow23/INP-Former.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Luo_Exploring_Intrinsic_Normal_Prototypes_within_a_Single_Image_for_Universal_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Luo_Exploring_Intrinsic_Normal_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Luo_2025_CVPR,
    author    = {Luo, Wei and Cao, Yunkang and Yao, Haiming and Zhang, Xiaotian and Lou, Jianan and Cheng, Yuqi and Shen, Weiming and Yu, Wenyong},
    title     = {Exploring Intrinsic Normal Prototypes within a Single Image for Universal Anomaly Detection},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {9974-9983}
}

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


title: Augmenting Perceptual Super-Resolution via Image Quality Predictors
authors: Fengjia Zhang, Samrudhdhi B. Rangrej, Tristan Aumentado-Armstrong, Afsaneh Fazly, Alex Levinshtein
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 2311-2322
affiliations: ["AI Center \u2013 Toronto, Samsung Electronics"]
cities: ["Toronto"]
countries: ["Canada"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Zhang_Augmenting_Perceptual_Super-Resolution_via_Image_Quality_Predictors_CVPR_2025_paper.html

abstract:
Super-resolution (SR), a classical inverse problem in computer vision, is inherently ill-posed, inducing a distribution of plausible solutions for every input. However, the desired result is not simply the expectation of this distribution, which is the blurry image obtained by minimizing pixelwise error, but rather the sample with the highest image quality. A variety of techniques, from perceptual metrics to adversarial losses, are employed to this end. In this work, we explore an alternative: utilizing powerful non-reference image quality assessment (NR-IQA) models in the SR context. We begin with a comprehensive analysis of NR-IQA metrics on human-derived SR data, identifying both the accuracy (human alignment) and complementarity of different metrics. Then, we explore two methods of applying NR-IQA models to SR learning: (i) altering data sampling, by building on an existing multi-ground-truth SR framework, and (ii) directly optimizing a differentiable quality score. Our results demonstrate a more human-centric perception-distortion tradeoff, focusing less on non-perceptual pixelwise distortion, instead improving the balance between perceptual fidelity and human-tuned NR-IQA measures.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Zhang_Augmenting_Perceptual_Super-Resolution_via_Image_Quality_Predictors_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Zhang_Augmenting_Perceptual_Super-Resolution_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Zhang_2025_CVPR,
    author    = {Zhang, Fengjia and Rangrej, Samrudhdhi B. and Aumentado-Armstrong, Tristan and Fazly, Afsaneh and Levinshtein, Alex},
    title     = {Augmenting Perceptual Super-Resolution via Image Quality Predictors},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {2311-2322}
}

raw_affiliation:
[{"affiliation": "AI Center \u2013 Toronto, Samsung Electronics", "city": "Toronto", "country": "Canada"}]


title: TurboFill: Adapting Few-step Text-to-image Model for Fast Image Inpainting
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 7613-7622
cities: ["Macau", "Shenzhen", "Unknown", "Shenzhen"]
countries: ["China", "China", "USA", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Xie_TurboFill_Adapting_Few-step_Text-to-image_Model_for_Fast_Image_Inpainting_CVPR_2025_paper.html

authors:
Liangbin Xie, Daniil Pakhomov, Zhonghao Wang, Zongze Wu, Ziyan Chen, Yuqian Zhou, Haitian Zheng, Zhifei Zhang, Zhe Lin, Jiantao Zhou, Chao Dong

abstract:
This paper introduces TurboFill, a fast image inpainting model that enhances a few-step text-to-image diffusion model with an inpainting adapter for high-quality and efficient inpainting. While standard diffusion models generate high-quality results, they incur high computational costs. We overcome this by training an inpainting adapter on a few-step distilled text-to-image model, DMD2, using a novel 3-step adversarial training scheme to ensure realistic, structurally consistent, and visually harmonious inpainted regions. To evaluate TurboFill, we propose two benchmarks: DilationBench, which tests performance across mask sizes, and HumanBench, based on human feedback for complex prompts. Experiments show that TurboFill outperforms both multi-step BrushNet and few-step inpainting methods, setting a new benchmark for high-performance inpainting tasks. The project page is available \href https://liangbinxie.github.io/projects/TurboFill/  here

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Xie_TurboFill_Adapting_Few-step_Text-to-image_Model_for_Fast_Image_Inpainting_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Xie_TurboFill_Adapting_Few-step_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Xie_2025_CVPR,
    author    = {Xie, Liangbin and Pakhomov, Daniil and Wang, Zhonghao and Wu, Zongze and Chen, Ziyan and Zhou, Yuqian and Zheng, Haitian and Zhang, Zhifei and Lin, Zhe and Zhou, Jiantao and Dong, Chao},
    title     = {TurboFill: Adapting Few-step Text-to-image Model for Fast Image Inpainting},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {7613-7622}
}

raw_affiliation:
[{"affiliation": "State Key Laboratory of Internet of Things for Smart City, University of Macau", "city": "Macau", "country": "China"}, {"affiliation": "Shenzhen University of Advanced Technology", "city": "Shenzhen", "country": "China"}, {"affiliation": "Adobe", "city": "Unknown", "country": "USA"}, {"affiliation": "Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences", "city": "Shenzhen", "country": "China"}]

affiliations:
["State Key Laboratory of Internet of Things for Smart City, University of Macau", "Shenzhen University of Advanced Technology", "Adobe", "Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences"]


title: Stochastic Human Motion Prediction with Memory of Action Transition and Action Characteristic
authors: Jianwei Tang, Hong Yang, Tengyue Chen, Jian-Fang Hu
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 1883-1893
cities: ["Guangzhou", "Guangzhou", "Guangzhou"]
countries: ["China", "China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Tang_Stochastic_Human_Motion_Prediction_with_Memory_of_Action_Transition_and_CVPR_2025_paper.html

abstract:
Action-driven stochastic human motion prediction aims to generate future motion sequences of a pre-defined target action based on given past observed sequences performing non-target actions. This task primarily presents two challenges. Firstly, generating smooth transition motions is hard due to the varying transition speeds of different actions. Secondly, the action characteristic is difficult to be learned because of the similarity of some actions. These issues cause the predicted results to be unreasonable and inconsistent. As a result, we propose two memory banks, the Soft-transition Action Bank (STAB) and Action Characteristic Bank (ACB), to tackle the problems above. The STAB stores the action transition information. It is equipped with the novel soft searching approach, which encourages the model to focus on multiple possible action categories of observed motions. The ACB records action characteristic, which produces more prior information for predicting certain actions. To fuse the features retrieved from the two banks better, we further propose the Adaptive Attention Adjustment (AAA) strategy. Extensive experiments on four motion prediction datasets demonstrate that our approach consistently outperforms the previous state-of-the-art. The demo and code are available at https://hyqlat.github.io/STABACB.github.io/.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Tang_Stochastic_Human_Motion_Prediction_with_Memory_of_Action_Transition_and_CVPR_2025_paper.pdf

bibtex:
@InProceedings{Tang_2025_CVPR,
    author    = {Tang, Jianwei and Yang, Hong and Chen, Tengyue and Hu, Jian-Fang},
    title     = {Stochastic Human Motion Prediction with Memory of Action Transition and Action Characteristic},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {1883-1893}
}

raw_affiliation:
[{"affiliation": "School of Computer Science and Engineering, Sun Yat-sen University, China", "city": "Guangzhou", "country": "China"}, {"affiliation": "Guangdong Province Key Laboratory of Information Security Technology, China", "city": "Guangzhou", "country": "China"}, {"affiliation": "Key Laboratory of Machine Intelligence and Advanced Computing, Ministry of Education, China", "city": "Guangzhou", "country": "China"}]

affiliations:
["School of Computer Science and Engineering, Sun Yat-sen University, China", "Guangdong Province Key Laboratory of Information Security Technology, China", "Key Laboratory of Machine Intelligence and Advanced Computing, Ministry of Education, China"]


title: Video-MME: The First-Ever Comprehensive Evaluation Benchmark of Multi-modal LLMs in Video Analysis
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 24108-24118
countries: ["China", "China", "Unknown", "China", "China", "China", "China", "China", "China"]
highlight: yes
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Fu_Video-MME_The_First-Ever_Comprehensive_Evaluation_Benchmark_of_Multi-modal_LLMs_in_CVPR_2025_paper.html

authors:
Chaoyou Fu, Yuhan Dai, Yongdong Luo, Lei Li, Shuhuai Ren, Renrui Zhang, Zihan Wang, Chenyu Zhou, Yunhang Shen, Mengdan Zhang, Peixian Chen, Yanwei Li, Shaohui Lin, Sirui Zhao, Ke Li, Tong Xu, Xiawu Zheng, Enhong Chen, Caifeng Shan, Ran He, Xing Sun

abstract:
In the quest for artificial general intelligence, Multi-modal Large Language Models (MLLMs) have emerged as a focal point in recent advancements. However, the predominant focus remains on developing their capabilities in static image understanding. The potential of MLLMs to process sequential visual data is still insufficiently explored, highlighting the lack of a comprehensive, high-quality assessment of their performance. In this paper, we introduce Video-MME, the first-ever full-spectrum, Multi-Modal Evaluation benchmark of MLLMs in Video analysis. Our work distinguishes from existing benchmarks through four key features: 1) Diversity in video types, spanning 6 primary visual domains with 30 subfields to ensure broad scenario generalizability; 2) Duration in temporal dimension, encompassing both short-, medium-, and long-term videos, ranging from 11 seconds to 1 hour, for robust contextual dynamics; 3) Breadth in data modalities, integrating multi-modal inputs besides video frames, including subtitles and audios, to unveil the all-round capabilities of MLLMs; 4) Quality in annotations, utilizing rigorous manual labeling by expert annotators to facilitate precise and reliable model assessment. With Video-MME, we extensively evaluate various state-of-the-art MLLMs, and reveal that Gemini 1.5 Pro is the best-performing commercial model, significantly outperforming the open-source models with an average accuracy of 75%, compared to 71.9% for GPT-4o. The results also demonstrate that Video-MME is a universal benchmark that applies to both image and video MLLMs. Further analysis indicates that subtitle and audio information could significantly enhance video understanding. Besides, a decline in MLLM performance is observed as video duration increases for all models. Our dataset along with these findings underscores the need for further improvements in handling longer sequences and multi-modal data, shedding light on future MLLM development.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Fu_Video-MME_The_First-Ever_Comprehensive_Evaluation_Benchmark_of_Multi-modal_LLMs_in_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Fu_Video-MME_The_First-Ever_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Fu_2025_CVPR,
    author    = {Fu, Chaoyou and Dai, Yuhan and Luo, Yongdong and Li, Lei and Ren, Shuhuai and Zhang, Renrui and Wang, Zihan and Zhou, Chenyu and Shen, Yunhang and Zhang, Mengdan and Chen, Peixian and Li, Yanwei and Lin, Shaohui and Zhao, Sirui and Li, Ke and Xu, Tong and Zheng, Xiawu and Chen, Enhong and Shan, Caifeng and He, Ran and Sun, Xing},
    title     = {Video-MME: The First-Ever Comprehensive Evaluation Benchmark of Multi-modal LLMs in Video Analysis},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {24108-24118}
}

raw_affiliation:
[{"affiliation": "State Key Laboratory for Novel Software Technology, Nanjing University", "city": "Nanjing", "country": "China"}, {"affiliation": "School of Intelligence Science and Technology, Nanjing University", "city": "Nanjing", "country": "China"}, {"affiliation": "State Key Laboratory of Cognitive Intelligence", "city": "Unknown", "country": "Unknown"}, {"affiliation": "XMU", "city": "Xiamen", "country": "China"}, {"affiliation": "HKU", "city": "Hong Kong", "country": "China"}, {"affiliation": "PKU", "city": "Beijing", "country": "China"}, {"affiliation": "CUHK", "city": "Hong Kong", "country": "China"}, {"affiliation": "ECNU", "city": "Shanghai", "country": "China"}, {"affiliation": "CASIA", "city": "Beijing", "country": "China"}]

affiliations:
["State Key Laboratory for Novel Software Technology, Nanjing University", "School of Intelligence Science and Technology, Nanjing University", "State Key Laboratory of Cognitive Intelligence", "XMU", "HKU", "PKU", "CUHK", "ECNU", "CASIA"]

cities:
["Nanjing", "Nanjing", "Unknown", "Xiamen", "Hong Kong", "Beijing", "Hong Kong", "Shanghai", "Beijing"]


