# papers

title: PanDA: Towards Panoramic Depth Anything with Unlabeled Panoramas and Mobius Spatial Augmentation
authors: Zidong Cao, Jinjing Zhu, Weiming Zhang, Hao Ai, Haotian Bai, Hengshuang Zhao, Lin Wang
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 982-992
affiliations: ["AI Thrust, HKUST(GZ)", "University of Birmingham", "HKU", "NTU"]
cities: ["Guangzhou", "Birmingham", "Hong Kong", "Singapore"]
countries: ["China", "UK", "China", "Singapore"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Cao_PanDA_Towards_Panoramic_Depth_Anything_with_Unlabeled_Panoramas_and_Mobius_CVPR_2025_paper.html

abstract:
Recently, Depth Anything Models (DAMs) - a type of depth foundation models - have demonstrated impressive zero-shot capabilities across diverse perspective images. Despite its success, it remains an open question regarding DAMs' performance on panorama images that enjoy a large field-of-view (180x360) but suffer from spherical distortions. To address this gap, we conduct an empirical analysis to evaluate the performance of DAMs on panoramic images and identify their limitations. For this, we undertake comprehensive experiments to assess the performance of DAMs from three key factors: panoramic representations, 360 camera positions for capturing scenarios, and spherical spatial transformations. This way, we reveal some key findings, e.g., DAMs are sensitive to spatial transformations. We then propose a semi-supervised learning (SSL) framework to learn a panoramic DAM, dubbed PanDA. Under the umbrella of SSL, PanDA first learns a teacher model by fine-tuning DAM through joint training on synthetic indoor and outdoor panoramic datasets. Then, a student model is trained using large-scale unlabeled data, leveraging pseudo-labels generated by the teacher model. To enhance PanDA's generalization capability, Mobius transformation-based spatial augmentation (MTSA) is proposed to impose consistency regularization between the predicted depth maps from the original and spatially transformed ones. This subtly improves the student model's robustness to various spatial transformations, even under severe distortions. Extensive experiments demonstrate that PanDA exhibits remarkable zero-shot capability across diverse scenes, and outperforms the data-specific panoramic depth estimation methods on two popular real-world benchmarks.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Cao_PanDA_Towards_Panoramic_Depth_Anything_with_Unlabeled_Panoramas_and_Mobius_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Cao_PanDA_Towards_Panoramic_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Cao_2025_CVPR,
    author    = {Cao, Zidong and Zhu, Jinjing and Zhang, Weiming and Ai, Hao and Bai, Haotian and Zhao, Hengshuang and Wang, Lin},
    title     = {PanDA: Towards Panoramic Depth Anything with Unlabeled Panoramas and Mobius Spatial Augmentation},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {982-992}
}

raw_affiliation:
[{"affiliation": "AI Thrust, HKUST(GZ)", "city": "Guangzhou", "country": "China"}, {"affiliation": "University of Birmingham", "city": "Birmingham", "country": "UK"}, {"affiliation": "HKU", "city": "Hong Kong", "country": "China"}, {"affiliation": "NTU", "city": "Singapore", "country": "Singapore"}]


title: Towards High-fidelity 3D Talking Avatar with Personalized Dynamic Texture
authors: Xuanchen Li, Jianyu Wang, Yuhao Cheng, Yikun Zeng, Xingyu Ren, Wenhan Zhu, Weiming Zhao, Yichao Yan
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 204-214
cities: ["Shanghai", "Shanghai"]
countries: ["China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Li_Towards_High-fidelity_3D_Talking_Avatar_with_Personalized_Dynamic_Texture_CVPR_2025_paper.html

abstract:
Significant progress has been made for speech-driven 3D face animation, but most works focus on learning the motion of mesh/geometry, ignoring the impact of dynamic texture. In this work, we reveal that dynamic texture plays a key role in rendering high-fidelity talking avatars, and introduce a high-resolution 4D dataset TexTalk4D, consisting of 100 minutes of audio-synced scan-level meshes with detailed 8K dynamic textures from 100 subjects. Based on the dataset, we explore the inherent correlation between motion and texture, and propose a diffusion-based framework TexTalker to simultaneously generate facial motions and dynamic textures from speech. Furthermore, we propose a novel pivot-based style injection strategy to capture the complicity of different texture and motion styles, which allows disentangled control. TexTalker, as the first method to generate audio-synced facial motion with dynamic texture, not only outperforms the prior arts in synthesising facial motions, but also produces realistic textures that are consistent with the underlying facial movements. Project page: https://xuanchenli.github.io/TexTalk/.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Li_Towards_High-fidelity_3D_Talking_Avatar_with_Personalized_Dynamic_Texture_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Li_Towards_High-fidelity_3D_CVPR_2025_supplemental.zip

bibtex:
@InProceedings{Li_2025_CVPR,
    author    = {Li, Xuanchen and Wang, Jianyu and Cheng, Yuhao and Zeng, Yikun and Ren, Xingyu and Zhu, Wenhan and Zhao, Weiming and Yan, Yichao},
    title     = {Towards High-fidelity 3D Talking Avatar with Personalized Dynamic Texture},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {204-214}
}

raw_affiliation:
[{"affiliation": "MoE Key Lab of Artificial Intelligence, AI Institute, Shanghai Jiao Tong University", "city": "Shanghai", "country": "China"}, {"affiliation": "Xueshen AI 3Student Innovation Center, Shanghai Jiao Tong University", "city": "Shanghai", "country": "China"}]

affiliations:
["MoE Key Lab of Artificial Intelligence, AI Institute, Shanghai Jiao Tong University", "Xueshen AI 3Student Innovation Center, Shanghai Jiao Tong University"]


title: Scene Splatter: Momentum 3D Scene Generation from Single Image with Video Diffusion Model
authors: Shengjun Zhang, Jinzhao Li, Xin Fei, Hao Liu, Yueqi Duan
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 6089-6098
affiliations: ["Tsinghua University", "WeChat Vision, Tecent Inc."]
cities: ["Beijing", "Shenzhen"]
countries: ["China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Zhang_Scene_Splatter_Momentum_3D_Scene_Generation_from_Single_Image_with_CVPR_2025_paper.html

abstract:
In this paper, we propose Scene Splatter, a momentum-based paradigm for video diffusion to generate generic scenes from single image. Existing methods, which employ video generation models to synthesize novel views, suffer from limited video length and scene inconsistency, leading to artifacts and distortions during further reconstruction. To address this issue, we construct noisy samples from original features as momentum to enhance video details and maintain scene consistency. However, for latent features with the perception field that spans both known and unknown regions, such latent-level momentum restricts the generative ability of video diffusion in unknown regions. Therefore, we further introduce the aforementioned consistent video as a pixel-level momentum to a directly generated video without momentum for better recovery of unseen regions. Our cascaded momentum enables video diffusion models to generate both high-fidelity and consistent novel views. We further finetune the global Gaussian representations with enhanced frames and render new frames for momentum update in the next step. In this manner, we can iteratively recover a 3D scene, avoiding the limitation of video length. Extensive experiments demonstrate the generalization capability and superior performance of our method in high-fidelity and consistent scene generation.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Zhang_Scene_Splatter_Momentum_3D_Scene_Generation_from_Single_Image_with_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Zhang_Scene_Splatter_Momentum_CVPR_2025_supplemental.zip

bibtex:
@InProceedings{Zhang_2025_CVPR,
    author    = {Zhang, Shengjun and Li, Jinzhao and Fei, Xin and Liu, Hao and Duan, Yueqi},
    title     = {Scene Splatter: Momentum 3D Scene Generation from Single Image with Video Diffusion Model},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {6089-6098}
}

raw_affiliation:
[{"affiliation": "Tsinghua University", "city": "Beijing", "country": "China"}, {"affiliation": "WeChat Vision, Tecent Inc.", "city": "Shenzhen", "country": "China"}]


authors: Runjian Chen, Wenqi Shao, Bo Zhang, Shaoshuai Shi, Li Jiang, Ping Luo
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 6792-6801
cities: ["Hong Kong", "Shanghai", "Unknown", "Shenzhen", "Shanghai"]
countries: ["China", "China", "China", "China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Chen_JiSAM_Alleviate_Labeling_Burden_and_Corner_Case_Problems_in_Autonomous_CVPR_2025_paper.html

title:
JiSAM: Alleviate Labeling Burden and Corner Case Problems in Autonomous Driving via Minimal Real-World Data

abstract:
Deep-learning-based autonomous driving (AD) perception introduces a promising picture for safe and environment-friendly transportation. However, the over-reliance on real labeled data in LiDAR perception limits the scale of on-road attempts. 3D real world data is notoriously time-and-energy-consuming to annotate and lacks corner cases like rare traffic participants. On the contrary, in simulators like CARLA, generating labeled LiDAR point clouds with corner cases is a piece of cake. However, introducing synthetic point clouds to improve real perception is non-trivial. This stems from two challenges: 1) sample efficiency of simulation datasets 2) simulation-to-real gaps. To overcome both challenges, we propose a plug-and-play method called JiSAM, shorthand for  Jittering augmentation, domain-aware backbone and memory-based Sectorized AlignMent. In extensive experiments conducted on the famous AD dataset NuScenes, we demonstrate that, with SOTA 3D object detector, JiSAM is able to utilize the simulation data and only labels on 2.5% available real data to achieve comparable performance to models trained on all real data. Additionally, JiSAM achieves more than 15 mAPs on the objects not labeled in the real training set. We will release models and codes.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Chen_JiSAM_Alleviate_Labeling_Burden_and_Corner_Case_Problems_in_Autonomous_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Chen_JiSAM_Alleviate_Labeling_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Chen_2025_CVPR,
    author    = {Chen, Runjian and Shao, Wenqi and Zhang, Bo and Shi, Shaoshuai and Jiang, Li and Luo, Ping},
    title     = {JiSAM: Alleviate Labeling Burden and Corner Case Problems in Autonomous Driving via Minimal Real-World Data},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {6792-6801}
}

raw_affiliation:
[{"affiliation": "The University of Hong Kong", "city": "Hong Kong", "country": "China"}, {"affiliation": "Shanghai AI Laboratory", "city": "Shanghai", "country": "China"}, {"affiliation": "Voyager Research, Didi Chuxing", "city": "Unknown", "country": "China"}, {"affiliation": "The Chinese University of Hong Kong (Shenzhen)", "city": "Shenzhen", "country": "China"}, {"affiliation": "HKU Shanghai Intelligent Computing Research Center", "city": "Shanghai", "country": "China"}]

affiliations:
["The University of Hong Kong", "Shanghai AI Laboratory", "Voyager Research, Didi Chuxing", "The Chinese University of Hong Kong (Shenzhen)", "HKU Shanghai Intelligent Computing Research Center"]


title: OSMamba: Omnidirectional Spectral Mamba with Dual-Domain Prior Generator for Exposure Correction
authors: Gehui Li, Bin Chen, Chen Zhao, Lei Zhang, Jian Zhang
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 7480-7490
cities: ["Beijing", "Shenzhen", "Thuwal", "Hong Kong", "Shenzhen"]
countries: ["China", "China", "Saudi Arabia", "China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Li_OSMamba_Omnidirectional_Spectral_Mamba_with_Dual-Domain_Prior_Generator_for_Exposure_CVPR_2025_paper.html

abstract:
Exposure correction is a fundamental problem in computer vision and image processing. Recently, frequency domain-based methods have achieved impressive improvement, yet they still struggle with complex real-world scenarios under extreme exposure conditions. This is due to the local convolutional receptive fields failing to model long-range dependencies in the spectrum, and the non-generative learning paradigm being inadequate for retrieving lost details from severely degraded regions. In this paper, we propose Omnidirectional Spectral Mamba (OSMamba), a novel exposure correction network that incorporates the advantages of state space models and generative diffusion models to address these limitations. Specifically, OSMamba introduces an omnidirectional spectral scanning mechanism that adapts Mamba to the frequency domain to capture comprehensive long-range dependencies in both the amplitude and phase spectra of deep image features, hence enhancing illumination correction and structure recovery. Furthermore, we develop a dual-domain prior generator that learns from well-exposed images to generate a degradation-free diffusion prior containing correct information about severely under- and over-exposed regions for better detail restoration. Extensive experiments on multiple-exposure and mixed-exposure datasets demonstrate that the proposed OSMamba achieves state-of-the-art performance both quantitatively and qualitatively.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Li_OSMamba_Omnidirectional_Spectral_Mamba_with_Dual-Domain_Prior_Generator_for_Exposure_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Li_OSMamba_Omnidirectional_Spectral_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Li_2025_CVPR,
    author    = {Li, Gehui and Chen, Bin and Zhao, Chen and Zhang, Lei and Zhang, Jian},
    title     = {OSMamba: Omnidirectional Spectral Mamba with Dual-Domain Prior Generator for Exposure Correction},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {7480-7490}
}

raw_affiliation:
[{"affiliation": "School of Electronic and Computer Engineering, Peking University", "city": "Beijing", "country": "China"}, {"affiliation": "Guangdong Provincial Key Laboratory of Ultra High Definition Immersive Media Technology, Shenzhen Graduate School, Peking University", "city": "Shenzhen", "country": "China"}, {"affiliation": "King Abdullah University of Science and Technology", "city": "Thuwal", "country": "Saudi Arabia"}, {"affiliation": "The Hong Kong Polytechnic University", "city": "Hong Kong", "country": "China"}, {"affiliation": "OPPO Research Institute", "city": "Shenzhen", "country": "China"}]

affiliations:
["School of Electronic and Computer Engineering, Peking University", "Guangdong Provincial Key Laboratory of Ultra High Definition Immersive Media Technology, Shenzhen Graduate School, Peking University", "King Abdullah University of Science and Technology", "The Hong Kong Polytechnic University", "OPPO Research Institute"]


title: Image is All You Need to Empower Large-scale Diffusion Models for In-Domain Generation
authors: Pu Cao, Feng Zhou, Lu Yang, Tianrui Huang, Qing Song
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 18358-18368
affiliations: ["Beijing University of Posts and Telecommunications"]
cities: ["Beijing"]
countries: ["China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Cao_Image_is_All_You_Need_to_Empower_Large-scale_Diffusion_Models_CVPR_2025_paper.html

abstract:
In-domain generation aims to perform a variety of tasks within a specific domain, such as unconditional generation, text-to-image, image editing, 3D generation, and more. Early research typically required training specialized generators for each unique task and domain, often relying on fully-labeled data. Motivated by the powerful generative capabilities and broad applications of diffusion models, we are driven to explore leveraging label-free data to empower these models for in-domain generation.Fine-tuning a pre-trained generative model on domain data is an intuitive but challenging way and often requires complex manual hyper-parameter adjustments since the limited diversity of the training data can easily disrupt the model's original generative capabilities.To address this challenge, we propose a guidance-decoupled prior preservation mechanism to achieve high generative quality and controllability by image-only data, inspired by preserving the pre-trained model from a denoising guidance perspective.We decouple domain-related guidance from the conditional guidance used in classifier-free guidance mechanisms to preserve open-world control guidance and unconditional guidance from the pre-trained model. We further propose an efficient domain knowledge learning technique to train an additional text-free UNet copy to predict domain guidance.Besides, we theoretically illustrate a multi-guidance in-domain generation pipeline for a variety of generative tasks, leveraging multiple guidances from distinct diffusion models and conditions. Extensive experiments demonstrate the superiority of our method in domain-specific synthesis and its compatibility with various diffusion-based control methods and applications.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Cao_Image_is_All_You_Need_to_Empower_Large-scale_Diffusion_Models_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Cao_Image_is_All_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Cao_2025_CVPR,
    author    = {Cao, Pu and Zhou, Feng and Yang, Lu and Huang, Tianrui and Song, Qing},
    title     = {Image is All You Need to Empower Large-scale Diffusion Models for In-Domain Generation},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {18358-18368}
}


authors: Ziyang Zhang, Yang Yu, Yucheng Chen, Xulei Yang, Si Yong Yeo
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 29744-29755
cities: ["Evanston", "Evanston", "Singapore", "Singapore"]
countries: ["USA", "USA", "Singapore", "Singapore"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Zhang_MedUnifier_Unifying_Vision-and-Language_Pre-training_on_Medical_Data_with_Vision_Generation_CVPR_2025_paper.html

title:
MedUnifier: Unifying Vision-and-Language Pre-training on Medical Data with Vision Generation Task using Discrete Visual Representations

abstract:
Despite significant progress in Vision-Language Pre-training (VLP), current approaches predominantly emphasize feature extraction and cross-modal comprehension, with limited attention to generating or transforming visual content. This gap hinders the model's ability to synthesize coherent and novel visual representations from textual prompts, thereby reducing the effectiveness of multi-modal learning. In this work, we propose MedUnifier, a unified VLP framework tailored for medical data. MedUnifier seamlessly integrates text-grounded image generation capabilities with multi-modal learning strategies, including image-text contrastive alignment, image-text matching and image-grounded text generation. Unlike traditional methods that reply on continuous visual representations, our approach employs visual vector quantization, which not only facilitates a more cohesive learning strategy for cross-modal understanding but also enhances multi-modal generation quality by effectively leveraging discrete representations. Our framework's effectiveness is evidenced by the experiments on established benchmarks, including uni-modal tasks, cross-modal tasks, and multi-modal tasks, where it achieves state-of-the-art performance across various tasks. MedUnifier also offers a highly adaptable tool for a wide range of language and vision tasks in healthcare, marking advancement toward the development of a generalizable AI model for medical applications.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Zhang_MedUnifier_Unifying_Vision-and-Language_Pre-training_on_Medical_Data_with_Vision_Generation_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Zhang_MedUnifier_Unifying_Vision-and-Language_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Zhang_2025_CVPR,
    author    = {Zhang, Ziyang and Yu, Yang and Chen, Yucheng and Yang, Xulei and Yeo, Si Yong},
    title     = {MedUnifier: Unifying Vision-and-Language Pre-training on Medical Data with Vision Generation Task using Discrete Visual Representations},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {29744-29755}
}

raw_affiliation:
[{"affiliation": "MedVisAI Lab", "city": "Evanston", "country": "USA"}, {"affiliation": "ECE, Northwestern University", "city": "Evanston", "country": "USA"}, {"affiliation": "Institute for Infocomm Research (I2R),A*STAR", "city": "Singapore", "country": "Singapore"}, {"affiliation": "Lee Kong Chian School of Medicine, Nanyang Technological University", "city": "Singapore", "country": "Singapore"}]

affiliations:
["MedVisAI Lab", "ECE, Northwestern University", "Institute for Infocomm Research (I2R),A*STAR", "Lee Kong Chian School of Medicine, Nanyang Technological University"]


title: RandAR: Decoder-only Autoregressive Visual Generation in Random Orders
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 45-55
affiliations: ["UIUC", "MIT", "Adobe Research"]
cities: ["Urbana", "Cambridge", "San Jose"]
countries: ["USA", "USA", "USA"]
oral: yes
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Pang_RandAR_Decoder-only_Autoregressive_Visual_Generation_in_Random_Orders_CVPR_2025_paper.html

authors:
Ziqi Pang, Tianyuan Zhang, Fujun Luan, Yunze Man, Hao Tan, Kai Zhang, William T. Freeman, Yu-Xiong Wang

abstract:
We introduce RandAR, a decoder-only visual autoregressive (AR) model capable of generatng images in arbitrary token orders. Unlike previous decoder-only AR models that rely on a predefined generation order, RandAR removes this inductive bias, unlocking new capabilities in decoder-only generation. Our essential design enabling random order is to insert a "position instruction token" before each image token to be predicted, representing the spatial location of the next image token. Trained on randomly permuted token sequences -- a more challenging task than fixed-order generation, RandAR achieves comparable performance to conventional raster-order counterpart. More importantly, decoder-only transformers trained from random orders acquire new capabilities. For the efficiency bottleneck of AR models, RandAR adopts parallel decoding with KV-Cache at inference time, enjoying 2.5x acceleration without sacrificing generation quality. Additionally, RandAR supports in-painting, outpainting and resolution extrapolation in a zero-shot manner.We hope RandAR inspires new directions for decoder-only visual generation models and broadens their applications across diverse scenarios. Our project page is at https://rand-ar.github.io/.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Pang_RandAR_Decoder-only_Autoregressive_Visual_Generation_in_Random_Orders_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Pang_RandAR_Decoder-only_Autoregressive_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Pang_2025_CVPR,
    author    = {Pang, Ziqi and Zhang, Tianyuan and Luan, Fujun and Man, Yunze and Tan, Hao and Zhang, Kai and Freeman, William T. and Wang, Yu-Xiong},
    title     = {RandAR: Decoder-only Autoregressive Visual Generation in Random Orders},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {45-55}
}

raw_affiliation:
[{"affiliation": "UIUC", "city": "Urbana", "country": "USA"}, {"affiliation": "MIT", "city": "Cambridge", "country": "USA"}, {"affiliation": "Adobe Research", "city": "San Jose", "country": "USA"}]


authors: You Shen, Zhipeng Zhang, Xinyang Li, Yansong Qu, Yu Lin, Shengchuan Zhang, Liujuan Cao
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 16346-16355
cities: ["Xiamen", "Shanghai"]
countries: ["China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Shen_Evolving_High-Quality_Rendering_and_Reconstruction_in_a_Unified_Framework_with_CVPR_2025_paper.html

title:
Evolving High-Quality Rendering and Reconstruction in a Unified Framework with Contribution-Adaptive Regularization

abstract:
Representing 3D scenes from multiview images is a core challenge in computer vision and graphics, which requires both precise rendering and accurate reconstruction. Recently, 3D Gaussian Splatting (3DGS) has garnered significant attention for its high-quality rendering and fast inference speed. Yet, due to the unstructured and irregular nature of Gaussian point clouds, ensuring accurate geometry reconstruction remains difficult. Existing methods primarily focus on geometry regularization, with common approaches including primitive-based and dual-model frameworks. However, the former suffers from inherent conflicts between rendering and reconstruction, while the latter is computationally and storage-intensive. To address these challenges, we propose CarGS, a unified model leveraging Contribution adaptive regularization to achieve simultaneous, high-quality rendering and surface reconstruction. The essence of our framework is learning adaptive contribution for Gaussian primitives by squeezing the knowledge from geometry regularization into a compact MLP. Additionally, we introduce a geometry-guided densification strategy with clues from both normals and Signed Distance Fields (SDF) to improve the capability of capturing high-frequency details. Our design improves the mutual learning of the two tasks, meanwhile its unified structure doesn't require separate models as in dual-model based approaches, guaranteeing efficiency. Extensive experiments demonstrate CarGS's ability to achieve state-of-the-art (SOTA) results in both rendering fidelity and reconstruction accuracy while maintaining real-time speed and minimal storage size.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Shen_Evolving_High-Quality_Rendering_and_Reconstruction_in_a_Unified_Framework_with_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Shen_Evolving_High-Quality_Rendering_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Shen_2025_CVPR,
    author    = {Shen, You and Zhang, Zhipeng and Li, Xinyang and Qu, Yansong and Lin, Yu and Zhang, Shengchuan and Cao, Liujuan},
    title     = {Evolving High-Quality Rendering and Reconstruction in a Unified Framework with Contribution-Adaptive Regularization},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {16346-16355}
}

raw_affiliation:
[{"affiliation": "Key Laboratory of Multimedia Trusted Perception and Efficient Computing, Ministry of Education of China, Xiamen University", "city": "Xiamen", "country": "China"}, {"affiliation": "School of Artificial Intelligence, Shanghai Jiao Tong University", "city": "Shanghai", "country": "China"}]

affiliations:
["Key Laboratory of Multimedia Trusted Perception and Efficient Computing, Ministry of Education of China, Xiamen University", "School of Artificial Intelligence, Shanghai Jiao Tong University"]


authors: Junfu Guo, Yu Xin, Gaoyi Liu, Kai Xu, Ligang Liu, Ruizhen Hu
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 27144-27153
cities: ["Hefei", "Changsha", "Shenzhen"]
countries: ["China", "China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Guo_ArticulatedGS_Self-supervised_Digital_Twin_Modeling_of_Articulated_Objects_using_3D_CVPR_2025_paper.html

title:
ArticulatedGS: Self-supervised Digital Twin Modeling of Articulated Objects using 3D Gaussian Splatting

abstract:
We tackle the challenge of concurrent reconstruction at the part level with the RGB appearance and estimation of motion parameters for building digital twins of articulated objects using the 3D Gaussian Splatting (3D-GS) method. With two distinct sets of multi-view imagery, each depicting an object in separate static articulation configurations, we reconstruct the articulated object in 3D Gaussian representations with both appearance and geometry information at the same time. Our approach decoupled multiple highly interdependent parameters through a multi-step optimization process, thereby achieving a stable optimization procedure and high-quality outcomes. We introduce ArticulatedGS, a self-supervised, comprehensive framework that autonomously learns to model shapes and appearances at the part level and synchronizes the optimization of motion parameters, all without reliance on 3D supervision, motion cues, or semantic labels. Our experimental results demonstrate that, among comparable methodologies, our approach has achieved optimal outcomes in terms of part segmentation accuracy, motion estimation accuracy, and visual quality.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Guo_ArticulatedGS_Self-supervised_Digital_Twin_Modeling_of_Articulated_Objects_using_3D_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Guo_ArticulatedGS_Self-supervised_Digital_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Guo_2025_CVPR,
    author    = {Guo, Junfu and Xin, Yu and Liu, Gaoyi and Xu, Kai and Liu, Ligang and Hu, Ruizhen},
    title     = {ArticulatedGS: Self-supervised Digital Twin Modeling of Articulated Objects using 3D Gaussian Splatting},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {27144-27153}
}

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

affiliations:
["University of Science and Technology of China", "National University of Defense Technology", "Shenzhen University"]


title: NoiseCtrl: A Sampling-Algorithm-Agnostic Conditional Generation Method for Diffusion Models
authors: Longquan Dai, He Wang, Jinhui Tang
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 18093-18102
affiliations: ["Nanjing University of Science and Technology"]
cities: ["Nanjing"]
countries: ["China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Dai_NoiseCtrl_A_Sampling-Algorithm-Agnostic_Conditional_Generation_Method_for_Diffusion_Models_CVPR_2025_paper.html

abstract:
In training-free conditional generative tasks, diffusion models utilize differentiable loss functions to steer the generative reverse process, necessitating modifications to sampling algorithms like DDPM and DDIM. However, such adjustments likely reduce flexibility and reliability. In this paper, we propose NoiseCtrl, a sampling-algorithm-agnostic technique for controlled image generation. Essentially, diffusion models generate denoised results  z_ t-1   by adding a predicted mean \mu_  t   with random noise epsilon_  t  . NoiseCtrl specifically adjusts the random noise while leaving the underlying sampling algorithms unchanged. At each  step  t , NoiseCtrl converts the unconditional Gaussian noise into conditional noise epsilon'_  t   by substituting the isotropic Gaussian distribution with the von Mises-Fisher distribution. This substitution introduces a directional focus while preserving the randomness required for conditional image generation. Thanks to this non-intrusive design, NoiseCtrl is straightforward to integrate and has been extensively validated through experiments, demonstrating its adaptability for different  diffusion algorithms and superior performance across various conditional generation tasks.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Dai_NoiseCtrl_A_Sampling-Algorithm-Agnostic_Conditional_Generation_Method_for_Diffusion_Models_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Dai_NoiseCtrl_A_Sampling-Algorithm-Agnostic_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Dai_2025_CVPR,
    author    = {Dai, Longquan and Wang, He and Tang, Jinhui},
    title     = {NoiseCtrl: A Sampling-Algorithm-Agnostic Conditional Generation Method for Diffusion Models},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {18093-18102}
}

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


title: Leveraging 3D Geometric Priors in 2D Rotation Symmetry Detection
authors: Ahyun Seo, Minsu Cho
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 22109-22118
affiliations: ["Pohang University of Science and T echnology (POSTECH)"]
cities: ["Pohang"]
countries: ["South Korea"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Seo_Leveraging_3D_Geometric_Priors_in_2D_Rotation_Symmetry_Detection_CVPR_2025_paper.html

abstract:
Symmetry plays a vital role in understanding structural patterns, aiding object recognition and scene interpretation. This paper focuses on rotation symmetry, where objects remain unchanged when rotated around a central axis, requiring detection of rotation centers and supporting vertices. Traditional methods relied on hand-crafted feature matching, while recent segmentation models based on convolutional neural networks (CNNs) detect rotation centers but struggle with 3D geometric consistency due to viewpoint distortions. To overcome this, we propose a model that directly predicts rotation centers and vertices in 3D space and projects the results back to 2D while preserving structural integrity. By incorporating a vertex reconstruction stage enforcing 3D geometric priors--such as equal side lengths and interior angles--our model enhances robustness and accuracy. Experiments on the DENDI dataset show superior performance in rotation axis detection and validate the impact of 3D priors through ablation studies.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Seo_Leveraging_3D_Geometric_Priors_in_2D_Rotation_Symmetry_Detection_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Seo_Leveraging_3D_Geometric_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Seo_2025_CVPR,
    author    = {Seo, Ahyun and Cho, Minsu},
    title     = {Leveraging 3D Geometric Priors in 2D Rotation Symmetry Detection},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {22109-22118}
}

raw_affiliation:
[{"affiliation": "Pohang University of Science and T echnology (POSTECH)", "city": "Pohang", "country": "South Korea"}]


authors: Tianyi Liu, Haochuan Jiang, Kaizhu Huang
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 15663-15671
affiliations: ["Xi\u2019an Jiaotong-Liverpool University", "University of Liverpool", "Duke Kunshan University"]
cities: ["Suzhou", "Liverpool", "Kunshan"]
countries: ["China", "UK", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Liu_KMD_Koopman_Multi-modality_Decomposition_for_Generalized_Brain_Tumor_Segmentation_under_CVPR_2025_paper.html

title:
KMD: Koopman Multi-modality Decomposition for Generalized Brain Tumor Segmentation under Incomplete Modalities

abstract:
Magnetic resonance imaging (MRI), with modalities including T1, T2, T1ce, and Flair, providing complementary information critical for sub-region analysis, is widely used for brain tumor diagnosis. However, clinical practice often suffers from varying degrees of incompleteness of necessary modalities due to reasons such as susceptibility to artifacts. It significantly impairs segmentation model performance. Given the limited available modalities at hand, existing approaches attempt to project them into a shared latent space. However, they ignore decomposing the modality-shared and modality-specific information and failed to construct the relationship among different modalities. Such deficiency limits the effectiveness of the segmentation performance, particularly at the time when the amount of data in each modality is different. In this paper,  we propose the plug-and-play Koopman Multi-modality Decomposition (KMD) module, leveraging the Koopman Invariant Subspace to disentangle modality-common and modality-specific information. It is capable of constructing modality relationships that minimize bias toward modalities across various modality-incomplete scenarios. More importantly, it can be integrated into several existing backbones feasibility. Through theoretical deductions and extensive empirical experiences on the BraTS2018 and BraTS2020 datasets, we have sufficiently demonstrated the effectiveness of the proposed KMD to promote generalization performance.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Liu_KMD_Koopman_Multi-modality_Decomposition_for_Generalized_Brain_Tumor_Segmentation_under_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Liu_KMD_Koopman_Multi-modality_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Liu_2025_CVPR,
    author    = {Liu, Tianyi and Jiang, Haochuan and Huang, Kaizhu},
    title     = {KMD: Koopman Multi-modality Decomposition for Generalized Brain Tumor Segmentation under Incomplete Modalities},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {15663-15671}
}

raw_affiliation:
[{"affiliation": "Xi\u2019an Jiaotong-Liverpool University", "city": "Suzhou", "country": "China"}, {"affiliation": "University of Liverpool", "city": "Liverpool", "country": "UK"}, {"affiliation": "Duke Kunshan University", "city": "Kunshan", "country": "China"}]


title: Vid2Sim: Realistic and Interactive Simulation from Video for Urban Navigation
authors: Ziyang Xie, Zhizheng Liu, Zhenghao Peng, Wayne Wu, Bolei Zhou
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 1581-1591
affiliations: ["University of Illinois Urbana-Champaign", "University of California, Los Angeles"]
cities: ["Urbana-Champaign", "Los Angeles"]
countries: ["USA", "USA"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Xie_Vid2Sim_Realistic_and_Interactive_Simulation_from_Video_for_Urban_Navigation_CVPR_2025_paper.html

abstract:
Sim-to-real gap has long posed a significant challenge for robot learning in simulation, preventing the deployment of learned models in the real world. Previous work has primarily focused on domain randomization and system identification to mitigate this gap. However, these methods are often limited by the realism of the simulation and graphics engines. In this work, we propose Vid2Sim, a novel framework that effectively bridges the sim2real gap through a scalable and cost-efficient real2sim pipeline for neural 3D scene reconstruction and simulation. Given a monocular video as input, Vid2Sim can generate photo-realistic and physically interactable 3D simulation environments to enable the reinforcement learning of visual navigation agents in complex urban environments. Extensive experiments demonstrate that Vid2Sim significantly improves the performance of urban navigation in the digital twins and real world by 31.2% and 68.3% in success rate compared with agents trained with prior simulation methods. Code and data will be made publicly available.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Xie_Vid2Sim_Realistic_and_Interactive_Simulation_from_Video_for_Urban_Navigation_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Xie_Vid2Sim_Realistic_and_CVPR_2025_supplemental.zip

bibtex:
@InProceedings{Xie_2025_CVPR,
    author    = {Xie, Ziyang and Liu, Zhizheng and Peng, Zhenghao and Wu, Wayne and Zhou, Bolei},
    title     = {Vid2Sim: Realistic and Interactive Simulation from Video for Urban Navigation},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {1581-1591}
}

raw_affiliation:
[{"affiliation": "University of Illinois Urbana-Champaign", "city": "Urbana-Champaign", "country": "USA"}, {"affiliation": "University of California, Los Angeles", "city": "Los Angeles", "country": "USA"}]


title: DORNet: A Degradation Oriented and Regularized Network for Blind Depth Super-Resolution
authors: Zhengxue Wang, Zhiqiang Yan, Jinshan Pan, Guangwei Gao, Kai Zhang, Jian Yang
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 15813-15822
cities: ["Nanjing", "Nanjing", "Nanjing"]
countries: ["China", "China", "China"]
oral: yes
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Wang_DORNet_A_Degradation_Oriented_and_Regularized_Network_for_Blind_Depth_CVPR_2025_paper.html

abstract:
Recent RGB-guided depth super-resolution methods have achieved impressive performance under the assumption of fixed and known degradation (e.g., bicubic downsampling). However, in real-world scenarios, captured depth data often suffer from unconventional and unknown degradation due to sensor limitations and complex imaging environments (e.g., low reflective surfaces, varying illumination). Consequently, the performance of these methods significantly declines when real-world degradation deviate from their assumptions. In this paper, we propose the Degradation Oriented and Regularized Network (DORNet), a novel framework designed to adaptively address unknown degradation in real-world scenes through implicit degradation representations. Our approach begins with the development of a self-supervised degradation learning strategy, which models the degradation representations of low-resolution depth data using routing selection-based degradation regularization. To facilitate effective RGB-D fusion, we further introduce a degradation-oriented feature transformation module that selectively propagates RGB content into the depth data based on the learned degradation priors. Extensive experimental results on both real and synthetic datasets demonstrate the superiority of our DORNet in handling unknown degradations, outperforming existing methods.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Wang_DORNet_A_Degradation_Oriented_and_Regularized_Network_for_Blind_Depth_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Wang_DORNet_A_Degradation_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Wang_2025_CVPR,
    author    = {Wang, Zhengxue and Yan, Zhiqiang and Pan, Jinshan and Gao, Guangwei and Zhang, Kai and Yang, Jian},
    title     = {DORNet: A Degradation Oriented and Regularized Network for Blind Depth Super-Resolution},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {15813-15822}
}

raw_affiliation:
[{"affiliation": "PCA Lab\u2021, Nanjing University of Science and Technology", "city": "Nanjing", "country": "China"}, {"affiliation": "Nanjing University of Posts and Telecommunications", "city": "Nanjing", "country": "China"}, {"affiliation": "Nanjing University", "city": "Nanjing", "country": "China"}]

affiliations:
["PCA Lab\u2021, Nanjing University of Science and Technology", "Nanjing University of Posts and Telecommunications", "Nanjing University"]


title: Fractal Calibration for Long-tailed Object Detection
authors: Konstantinos Panagiotis Alexandridis, Ismail Elezi, Jiankang Deng, Anh Nguyen, Shan Luo
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 15139-15150
cities: ["Shenzhen", "London", "Liverpool", "London"]
countries: ["China", "UK", "UK", "UK"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Alexandridis_Fractal_Calibration_for_Long-tailed_Object_Detection_CVPR_2025_paper.html

abstract:
Real-world datasets follow an imbalanced distribution, which poses significant challenges in rare-category object detection. Recent studies tackle this problem by developing re-weighting and re-sampling methods, that utilise the class frequencies of the dataset. However, these techniques focus solely on the frequency statistics and ignore the distribution of the classes in image space, missing important information. In contrast to them, we propose Fractal CALibration (FRACAL): a novel post-calibration method for long-tailed object detection. FRACAL devises a logit adjustment method that utilises the fractal dimension to estimate how uniformly classes are distributed in image space. During inference, it uses the fractal dimension to inversely downweight the probabilities of uniformly spaced class predictions achieving balance in two axes: between frequent and rare categories, and between uniformly spaced and sparsely spaced classes. FRACAL is a post-processing method and it does not require any training, also it can be combined with many off-the-shelf models such as one-stage sigmoid detectors and two-stage instance segmentation models. FRACAL boosts the rare class performance by up to 8.6% and surpasses all previous methods on LVIS dataset, while showing good generalisation to other datasets such as COCO, V3Det and OpenImages. We provide the code in the Appendix.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Alexandridis_Fractal_Calibration_for_Long-tailed_Object_Detection_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Alexandridis_Fractal_Calibration_for_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Alexandridis_2025_CVPR,
    author    = {Alexandridis, Konstantinos Panagiotis and Elezi, Ismail and Deng, Jiankang and Nguyen, Anh and Luo, Shan},
    title     = {Fractal Calibration for Long-tailed Object Detection},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {15139-15150}
}

raw_affiliation:
[{"affiliation": "Huawei Noah\u2019s Ark Lab", "city": "Shenzhen", "country": "China"}, {"affiliation": "Imperial College London", "city": "London", "country": "UK"}, {"affiliation": "University of Liverpool", "city": "Liverpool", "country": "UK"}, {"affiliation": "King\u2019s College London", "city": "London", "country": "UK"}]

affiliations:
["Huawei Noah\u2019s Ark Lab", "Imperial College London", "University of Liverpool", "King\u2019s College London"]


authors: Qingzheng Xu, Ru Cao, Xin Shen, Heming Du, Sen Wang, Xin Yu
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 12289-12300
affiliations: ["The University of Queensland", "City University of Macau", "Follow Me AI Pty LTD"]
cities: ["Brisbane", "Macau", "Brisbane"]
countries: ["Australia", "China", "Australia"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Xu_M3GYM_A_Large-Scale_Multimodal_Multi-view_Multi-person_Pose_Dataset_for_Fitness_CVPR_2025_paper.html

title:
M3GYM: A Large-Scale Multimodal Multi-view Multi-person Pose Dataset for Fitness Activity Understanding in Real-world Settings

abstract:
Human pose estimation is a critical task in computer vision for applications in sports analysis, healthcare monitoring, and human-computer interaction. However, existing human pose datasets are collected either from custom-configured laboratories with complex devices or they only include data on single individuals, and both types typically capture daily activities. In this paper, we introduce the M3GYM dataset, a large-scale multimodal, multi-view, and multi-person pose dataset collected from a real gym to address the limitations of existing datasets.Specifically, we collect videos for 82 sessions from the gym, each session lasting between 40 to 60 minutes. These videos are gathered by 8 cameras, including over 50 subjects and 47 million frames. These sessions include 51 Normal fitness exercise sessions as well as 17 Pilates and 14 Yoga sessions. The exercises cover a wide range of poses and typical fitness activities, particularly in Yoga and Pilates, featuring poses with stretches, bends, and twists, e.g., humble warrior, fire hydrants and knee hover side twists. Each session involves multiple subjects, leading to significant self-occlusion and mutual occlusion in single views.Moreover, the gym has two symmetric floor mirrors, a feature not seen in previous datasets, and seven lighting conditions. We provide frame-level multimodal annotations, including 2D&3D keypoints, subject IDs, and meshes. Additionally, M3GYM uniquely offers labels for over 500 actions along with corresponding assessments from sports experts.We benchmark a variety of state-of-the-art methods for several tasks, i.e., 2D human pose estimation, single-view and multi-view 3D human pose estimation, and human mesh recovery. To simulate real-world applications, we also conduct cross-domain experiments across Normal, Yoga, and Pilates sessions. The results show that M3GYM significantly improves model generalization in complex real-world settings.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Xu_M3GYM_A_Large-Scale_Multimodal_Multi-view_Multi-person_Pose_Dataset_for_Fitness_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Xu_M3GYM_A_Large-Scale_CVPR_2025_supplemental.zip

bibtex:
@InProceedings{Xu_2025_CVPR,
    author    = {Xu, Qingzheng and Cao, Ru and Shen, Xin and Du, Heming and Wang, Sen and Yu, Xin},
    title     = {M3GYM: A Large-Scale Multimodal Multi-view Multi-person Pose Dataset for Fitness Activity Understanding in Real-world Settings},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {12289-12300}
}

raw_affiliation:
[{"affiliation": "The University of Queensland", "city": "Brisbane", "country": "Australia"}, {"affiliation": "City University of Macau", "city": "Macau", "country": "China"}, {"affiliation": "Follow Me AI Pty LTD", "city": "Brisbane", "country": "Australia"}]


title: Noise Calibration and Spatial-Frequency Interactive Network for STEM Image Enhancement
authors: Hesong Li, Ziqi Wu, Ruiwen Shao, Tao Zhang, Ying Fu
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 21287-21296
affiliations: ["Beijing Institute of Technology", "Hangzhou Dianzi University"]
cities: ["Beijing", "Hangzhou"]
countries: ["China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Li_Noise_Calibration_and_Spatial-Frequency_Interactive_Network_for_STEM_Image_Enhancement_CVPR_2025_paper.html

abstract:
Scanning Transmission Electron Microscopy (STEM) enables the observation of atomic arrangements at sub-angstrom resolution, allowing for atomically resolved analysis of the physical and chemical properties of materials. However, due to the effects of noise, electron beam damage, sample thickness, etc, obtaining satisfactory atomic-level images is often challenging. Enhancing STEM images can reveal clearer structural details of materials. Nonetheless, existing STEM image enhancement methods usually overlook unique features in the frequency domain, and existing datasets lack realism and generality. To resolve these issues, in this paper, we develop noise calibration, data synthesis, and enhancement methods for STEM images. We first present a STEM noise calibration method, which is used to synthesize more realistic STEM images. The parameters of background noise, scan noise, and pointwise noise are obtained by statistical analysis and fitting of real STEM images containing atoms. Then we use these parameters to develop a more general dataset that considers both regular and random atomic arrangements and includes both HAADF and BF mode images. Finally, we design a spatial-frequency interactive network for STEM image enhancement, which can explore the information in the frequency domain formed by the periodicity of atomic arrangement. Experimental results show that our data is closer to real STEM images and achieves better enhancement performances together with our network. Code will be available at https://github.com/HeasonLee/SFIN.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Li_Noise_Calibration_and_Spatial-Frequency_Interactive_Network_for_STEM_Image_Enhancement_CVPR_2025_paper.pdf

bibtex:
@InProceedings{Li_2025_CVPR,
    author    = {Li, Hesong and Wu, Ziqi and Shao, Ruiwen and Zhang, Tao and Fu, Ying},
    title     = {Noise Calibration and Spatial-Frequency Interactive Network for STEM Image Enhancement},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {21287-21296}
}

raw_affiliation:
[{"affiliation": "Beijing Institute of Technology", "city": "Beijing", "country": "China"}, {"affiliation": "Hangzhou Dianzi University", "city": "Hangzhou", "country": "China"}]


title: Type-R: Automatically Retouching Typos for Text-to-Image Generation
authors: Wataru Shimoda, Naoto Inoue, Daichi Haraguchi, Hayato Mitani, Seiichi Uchida, Kota Yamaguchi
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 2745-2754
affiliations: ["CyberAgent", "Kyushu University"]
cities: ["Unknown", "Fukuoka"]
countries: ["Unknown", "Japan"]
highlight: yes
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Shimoda_Type-R_Automatically_Retouching_Typos_for_Text-to-Image_Generation_CVPR_2025_paper.html

abstract:
While recent text-to-image models can generate photorealistic images from text prompts that reflect detailed instructions, they still face significant challenges in accurately rendering words in the image.In this paper, we propose to retouch erroneous text renderings in the post-processing pipeline.Our approach, called Type-R, identifies typographical errors in the generated image, erases the erroneous text, regenerates text boxes for missing words, and finally corrects typos in the rendered words.Through extensive experiments, we show that Type-R, in combination with the latest text-to-image models such as Stable Diffusion or Flux, achieves the highest text rendering accuracy while maintaining image quality and also outperforms text-focused generation baselines in terms of balancing text accuracy and image quality.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Shimoda_Type-R_Automatically_Retouching_Typos_for_Text-to-Image_Generation_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Shimoda_Type-R_Automatically_Retouching_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Shimoda_2025_CVPR,
    author    = {Shimoda, Wataru and Inoue, Naoto and Haraguchi, Daichi and Mitani, Hayato and Uchida, Seiichi and Yamaguchi, Kota},
    title     = {Type-R: Automatically Retouching Typos for Text-to-Image Generation},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {2745-2754}
}

raw_affiliation:
[{"affiliation": "CyberAgent", "city": "Unknown", "country": "Unknown"}, {"affiliation": "Kyushu University", "city": "Fukuoka", "country": "Japan"}]


title: Video-3D LLM: Learning Position-Aware Video Representation for 3D Scene Understanding
authors: Duo Zheng, Shijia Huang, Liwei Wang
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 8995-9006
raw_affiliation: [{"affiliation": "The Chinese University of Hong Kong", "city": "Hong Kong", "country": "China"}]
affiliations: ["The Chinese University of Hong Kong"]
cities: ["Hong Kong"]
countries: ["China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Zheng_Video-3D_LLM_Learning_Position-Aware_Video_Representation_for_3D_Scene_Understanding_CVPR_2025_paper.html

abstract:
The rapid advancement of Multimodal Large Language Models (MLLMs) has significantly impacted various multimodal tasks. However, these models face challenges in tasks that require spatial understanding within 3D environments. Efforts to enhance MLLMs, such as incorporating point cloud features, have been made, yet a considerable gap remains between the models' learned representations and the inherent complexity of 3D scenes. This discrepancy largely stems from the training of MLLMs on predominantly 2D data, which restricts their effectiveness in comprehending 3D spaces. To address this issue, in this paper, we propose a novel generalist model, i.e., Video-3D LLM, for 3D scene understanding. By treating 3D scenes as dynamic videos and incorporating 3D position encoding into these representations, our Video-3D LLM aligns video representations with real-world spatial contexts more accurately. In addition, we have implemented a maximum coverage sampling technique to optimize the trade-off between computational cost and performance. Extensive experiments demonstrate that our model achieves state-of-the-art performance on several 3D scene understanding benchmarks, including ScanRefer, Multi3DRefer, Scan2Cap, ScanQA, and SQA3D.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Zheng_Video-3D_LLM_Learning_Position-Aware_Video_Representation_for_3D_Scene_Understanding_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Zheng_Video-3D_LLM_Learning_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Zheng_2025_CVPR,
    author    = {Zheng, Duo and Huang, Shijia and Wang, Liwei},
    title     = {Video-3D LLM: Learning Position-Aware Video Representation for 3D Scene Understanding},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {8995-9006}
}


title: FedSPA: Generalizable Federated Graph Learning under Homophily Heterogeneity
authors: Zihan Tan, Guancheng Wan, Wenke Huang, He Li, Guibin Zhang, Carl Yang, Mang Ye
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 15464-15475
cities: ["Wuhan", "Wuhan", "Singapore", "Atlanta"]
countries: ["China", "China", "Singapore", "USA"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Tan_FedSPA_Generalizable_Federated_Graph_Learning_under_Homophily_Heterogeneity_CVPR_2025_paper.html

abstract:
Federated Graph Learning (FGL) has emerged as a solution to address real-world privacy concerns and data silos in graph learning, which relies on Graph Neural Networks (GNNs). Nevertheless, the homophily level discrepancies within the local graph data of clients, termed homophily heterogeneity, significantly degrade the generalizability of a global GNN. Existing research ignores this issue and suffers from unpromising collaboration. In this paper, we propose FedSPA, an effective framework that addresses homophily heterogeneity from the perspectives of homophily conflict and homophily bias. In the first place, the homophily conflict arises when training on inconsistent homophily levels across clients. Correspondingly, we propose Subgraph Feature Propagation Decoupling (SFPD), thereby achieving collaboration on unified homophily levels across clients. To further address homophily bias, we design Homophily Bias-Driven Aggregation (HBDA) which emphasizes clients with lower biases. It enables the adaptive adjustment of each client contribution to the global GNN based on its homophily bias. The superiority of FedSPA is validated through extensive experiments. The code is available at https://github.com/OakleyTan/FedSPA.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Tan_FedSPA_Generalizable_Federated_Graph_Learning_under_Homophily_Heterogeneity_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Tan_FedSPA_Generalizable_Federated_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Tan_2025_CVPR,
    author    = {Tan, Zihan and Wan, Guancheng and Huang, Wenke and Li, He and Zhang, Guibin and Yang, Carl and Ye, Mang},
    title     = {FedSPA: Generalizable Federated Graph Learning under Homophily Heterogeneity},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {15464-15475}
}

raw_affiliation:
[{"affiliation": "National Engineering Research Center for Multimedia Software, Institute of Artificial Intelligence, Hubei Key Laboratory of Multimedia and Network Communication Engineering, School of Computer Science, Wuhan University", "city": "Wuhan", "country": "China"}, {"affiliation": "Taikang Center for Life and Medical Sciences, Wuhan University", "city": "Wuhan", "country": "China"}, {"affiliation": "National University of Singapore", "city": "Singapore", "country": "Singapore"}, {"affiliation": "Department of Computer Science, Emory University", "city": "Atlanta", "country": "USA"}]

affiliations:
["National Engineering Research Center for Multimedia Software, Institute of Artificial Intelligence, Hubei Key Laboratory of Multimedia and Network Communication Engineering, School of Computer Science, Wuhan University", "Taikang Center for Life and Medical Sciences, Wuhan University", "National University of Singapore", "Department of Computer Science, Emory University"]


title: Homogeneous Dynamics Space for Heterogeneous Humans
authors: Xinpeng Liu, Junxuan Liang, Chenshuo Zhang, Zixuan Cai, Cewu Lu, Yong-Lu Li
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 27782-27793
affiliations: ["Shanghai Jiao Tong University", "Shanghai Innovation Institute", "Soochow University"]
cities: ["Shanghai", "Shanghai", "Suzhou"]
countries: ["China", "China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Liu_Homogeneous_Dynamics_Space_for_Heterogeneous_Humans_CVPR_2025_paper.html

abstract:
Analyses of human motion kinematics have achieved tremendous advances. However, the production mechanism, known as human dynamics, is still undercovered. In this paper, we aim to push data-driven human dynamics understanding forward. We identify a major obstacle to this as the heterogeneity of existing human motion understanding efforts. Specifically, heterogeneity exists in not only the diverse kinematics representations and hierarchical dynamics representations but also in the data from different domains, namely biomechanics and reinforcement learning. With an in-depth analysis of the existing heterogeneity, we propose to emphasize the beneath homogeneity: all of them represent the homogeneous fact of human motion, though from different perspectives. Given this, we propose Homogeneous Dynamics Space (HDyS) as a fundamental space for human dynamics by aggregating heterogeneous data and training a homogeneous latent space with inspiration from the inverse-forward dynamics procedure. Leveraging the heterogeneous representations and datasets, HDyS achieves decent mapping between human kinematics and dynamics. We demonstrate the feasibility of HDyS with extensive experiments and applications.  The project page is https://foruck.github.io/HDyS.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Liu_Homogeneous_Dynamics_Space_for_Heterogeneous_Humans_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Liu_Homogeneous_Dynamics_Space_CVPR_2025_supplemental.zip

bibtex:
@InProceedings{Liu_2025_CVPR,
    author    = {Liu, Xinpeng and Liang, Junxuan and Zhang, Chenshuo and Cai, Zixuan and Lu, Cewu and Li, Yong-Lu},
    title     = {Homogeneous Dynamics Space for Heterogeneous Humans},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {27782-27793}
}

raw_affiliation:
[{"affiliation": "Shanghai Jiao Tong University", "city": "Shanghai", "country": "China"}, {"affiliation": "Shanghai Innovation Institute", "city": "Shanghai", "country": "China"}, {"affiliation": "Soochow University", "city": "Suzhou", "country": "China"}]


title: TailedCore: Few-Shot Sampling for Unsupervised Long-Tail Noisy Anomaly Detection
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 25539-25548
cities: ["Boston", "Seoul", "Seoul", "Cambridge"]
countries: ["USA", "South Korea", "South Korea", "USA"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Jung_TailedCore_Few-Shot_Sampling_for_Unsupervised_Long-Tail_Noisy_Anomaly_Detection_CVPR_2025_paper.html

authors:
Yoon Gyo Jung, Jaewoo Park, Jaeho Yoon, Kuan-Chuan Peng, Wonchul Kim, Andrew Beng Jin Teoh, Octavia Camps

abstract:
We aim to solve unsupervised anomaly detection in a practical challenging environment where the normal dataset is both contaminated with defective regions and its product class distribution is tailed but unknown. We observe that existing models suffer from tail-versus-noise trade-off where if a model is robust against pixel noise, then its performance deteriorates on tail class samples, and vice versa. To mitigate the issue, we handle the tail class and noise samples independently. To this end, we propose TailSampler, a novel class size predictor that estimates the class cardinality of samples based on a symmetric assumption on the class-wise distribution of embedding similarities. TailSampler can be utilized to sample the tail class samples exclusively, allowing to handle them separately. Based on these facets, we build a memory-based anomaly detection model TailedCore, whose memory both well captures tail class information and is noise-robust. We extensively validate the effectiveness of TailedCore on the unsupervised long-tail noisy anomaly detection setting, and show that TailedCore outperforms the state-of-the-art in most settings. Code is available in TailedCore

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Jung_TailedCore_Few-Shot_Sampling_for_Unsupervised_Long-Tail_Noisy_Anomaly_Detection_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Jung_TailedCore_Few-Shot_Sampling_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Jung_2025_CVPR,
    author    = {Jung, Yoon Gyo and Park, Jaewoo and Yoon, Jaeho and Peng, Kuan-Chuan and Kim, Wonchul and Teoh, Andrew Beng Jin and Camps, Octavia},
    title     = {TailedCore: Few-Shot Sampling for Unsupervised Long-Tail Noisy Anomaly Detection},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {25539-25548}
}

raw_affiliation:
[{"affiliation": "Northeastern University", "city": "Boston", "country": "USA"}, {"affiliation": "AiV Co.", "city": "Seoul", "country": "South Korea"}, {"affiliation": "Yonsei University", "city": "Seoul", "country": "South Korea"}, {"affiliation": "Mitsubishi Electric Research Laboratories", "city": "Cambridge", "country": "USA"}]

affiliations:
["Northeastern University", "AiV Co.", "Yonsei University", "Mitsubishi Electric Research Laboratories"]


title: GazeGene: Large-scale Synthetic Gaze Dataset with 3D Eyeball Annotations
authors: Yiwei Bao, Zhiming Wang, Feng Lu
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 18749-18759
affiliations: ["State Key Laboratory of VR Technology and Systems, School of CSE, Beihang University"]
cities: ["Beijing"]
countries: ["China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Bao_GazeGene_Large-scale_Synthetic_Gaze_Dataset_with_3D_Eyeball_Annotations_CVPR_2025_paper.html

abstract:
Thanks to the introduction of large-scale datasets, deep-learning has become the mainstream approach for appearance-based gaze estimation problems. However, current large-scale datasets contain annotation errors and provide only a single vector for gaze annotation, lacking key information such as 3D eyeball structures. Limitations in annotation accuracy and variety have constrained the progress in research and development of deep-learning methods for appearance-based gaze-related tasks. In this paper, we present GazeGene, a new large-scale synthetic gaze dataset with photo-realistic samples. More importantly, GazeGene not only provides accurate gaze annotations, but also offers 3D annotations of vital eye structures such as the pupil, iris, eyeball, optical and visual axes for the first time. Experiments show that GazeGene achieves comparable quality and generalization ability with real-world datasets, even outperforms most existing datasets on high-resolution images. Furthermore, its 3D eyeball annotations expand the application of deep-learning methods on various gaze-related tasks, offering new insights into this field. The dataset is available at: https://phiai.buaa.edu.cn/GazeGene/.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Bao_GazeGene_Large-scale_Synthetic_Gaze_Dataset_with_3D_Eyeball_Annotations_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Bao_GazeGene_Large-scale_Synthetic_CVPR_2025_supplemental.zip

bibtex:
@InProceedings{Bao_2025_CVPR,
    author    = {Bao, Yiwei and Wang, Zhiming and Lu, Feng},
    title     = {GazeGene: Large-scale Synthetic Gaze Dataset with 3D Eyeball Annotations},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {18749-18759}
}

raw_affiliation:
[{"affiliation": "State Key Laboratory of VR Technology and Systems, School of CSE, Beihang University", "city": "Beijing", "country": "China"}]


title: VideoHandles: Editing 3D Object Compositions in Videos Using Video Generative Priors
authors: Juil Koo, Paul Guerrero, Chun-Hao P. Huang, Duygu Ceylan, Minhyuk Sung
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 17692-17701
affiliations: ["KAIST", "Adobe Research"]
cities: ["Daejeon", "San Jose"]
countries: ["South Korea", "USA"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Koo_VideoHandles_Editing_3D_Object_Compositions_in_Videos_Using_Video_Generative_CVPR_2025_paper.html

abstract:
Generative methods for image and video editing use generative models as priors to perform edits despite incomplete information, such as changing the composition of 3D objects shown in a single image. Recent methods have shown promising composition editing results in the image setting, but in the video setting, editing methods have focused on editing object appearance, object motion, or camera motion, and as a result, methods to edit object composition in videos are still missing. We propose VideoHandles as a method for editing 3D object compositions in videos of static scenes with camera motion. Our approach allows editing the 3D position of a 3D object across all frames of a video in a temporally consistent manner. This is achieved by lifting intermediate features of a generative model to a 3D reconstruction that is shared between all frames, editing the reconstruction, and projecting the features on the edited reconstruction back to each frame. To the best of our knowledge, this is the first generative approach to edit object compositions in videos. Our approach is simple and training-free, while outperforming state-of-the-art image editing baselines.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Koo_VideoHandles_Editing_3D_Object_Compositions_in_Videos_Using_Video_Generative_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Koo_VideoHandles_Editing_3D_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Koo_2025_CVPR,
    author    = {Koo, Juil and Guerrero, Paul and Huang, Chun-Hao P. and Ceylan, Duygu and Sung, Minhyuk},
    title     = {VideoHandles: Editing 3D Object Compositions in Videos Using Video Generative Priors},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {17692-17701}
}

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


proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 28071-28080
cities: ["Shanghai", "Shanghai", "Hong Kong", "Beijing", "Nanjing"]
countries: ["China", "China", "China", "China", "China"]
highlight: yes
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Tu_Satellite_Observations_Guided_Diffusion_Model_for_Accurate_Meteorological_States_at_CVPR_2025_paper.html

title:
Satellite Observations Guided Diffusion Model for Accurate Meteorological States at Arbitrary Resolution

authors:
Siwei Tu, Ben Fei, Weidong Yang, Fenghua Ling, Hao Chen, Zili Liu, Kun Chen, Hang Fan, Wanli Ouyang, Lei Bai

abstract:
Accurate acquisition of surface meteorological conditions at arbitrary locations holds significant importance for weather forecasting and climate simulation. Meteorological states derived from satellite observations are often provided in the form of low-resolution grid fields. If spatial interpolation is applied directly to obtain meteorological states for specific locations, there will often be significant discrepancies compared to actual observations. Existing downscaling methods for acquiring meteorological state information at higher resolutions commonly overlook the correlation with satellite observations. To bridge the gap, we propose Satellite-observations Guided Diffusion Model (SGD), a conditional diffusion model pre-trained on ERA5 reanalysis data with satellite observations (GridSat) as conditions, which is employed for sampling downscaled meteorological states through a zero-shot guided sampling strategy and patch-based methods. During the training process, we propose to fuse the information from GridSat satellite observations into ERA5 maps via the attention mechanism, enabling SGD to generate atmospheric states that align more accurately with actual conditions. In the sampling, we employed optimizable convolutional kernels to simulate the upscale process, thereby generating high-resolution ERA5 maps using low-resolution ERA5 maps as well as observations from weather stations as guidance. Moreover, our devised patch-based method promotes SGD to generate meteorological states at arbitrary resolutions. Experiments demonstrate SGD fulfills accurate meteorological states downscaling to 6.25km.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Tu_Satellite_Observations_Guided_Diffusion_Model_for_Accurate_Meteorological_States_at_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Tu_Satellite_Observations_Guided_CVPR_2025_supplemental.zip

bibtex:
@InProceedings{Tu_2025_CVPR,
    author    = {Tu, Siwei and Fei, Ben and Yang, Weidong and Ling, Fenghua and Chen, Hao and Liu, Zili and Chen, Kun and Fan, Hang and Ouyang, Wanli and Bai, Lei},
    title     = {Satellite Observations Guided Diffusion Model for Accurate Meteorological States at Arbitrary Resolution},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {28071-28080}
}

raw_affiliation:
[{"affiliation": "Fudan University", "city": "Shanghai", "country": "China"}, {"affiliation": "Shanghai Arti\ufb01cial Intelligence Laboratory", "city": "Shanghai", "country": "China"}, {"affiliation": "The Chinese University of Hong Kong", "city": "Hong Kong", "country": "China"}, {"affiliation": "Beihang University", "city": "Beijing", "country": "China"}, {"affiliation": "Nanjing University of Information Science and Technology", "city": "Nanjing", "country": "China"}]

affiliations:
["Fudan University", "Shanghai Arti\ufb01cial Intelligence Laboratory", "The Chinese University of Hong Kong", "Beihang University", "Nanjing University of Information Science and Technology"]


title: Reconstructing People, Places, and Cameras
authors: Lea Müller, Hongsuk Choi, Anthony Zhang, Brent Yi, Jitendra Malik, Angjoo Kanazawa
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 21948-21958
raw_affiliation: [{"affiliation": "UC Berkeley", "city": "Berkeley", "country": "USA"}]
affiliations: ["UC Berkeley"]
cities: ["Berkeley"]
countries: ["USA"]
highlight: yes
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Muller_Reconstructing_People_Places_and_Cameras_CVPR_2025_paper.html

abstract:
We present "Humans and Structure from Motion" (HSfM), a method for jointly reconstructing multiple human meshes, scene point clouds, and camera parameters in a metric world coordinate system from a sparse set of uncalibrated multi-view images featuring people. Our approach combines data-driven scene reconstruction with the traditional Structure-from-Motion (SfM) framework to achieve more accurate scene reconstruction and camera estimation while simultaneously recovering human meshes. In contrast to existing scene reconstruction and SfM methods that lack metric scale information, our method estimates approximate metric scale by leveraging the human statistical model. Furthermore, our method reconstructs multiple human meshes within the same world coordinate system with the scene point cloud, effectively capturing spatial relationships among individuals and their positions in the environment. We initialize the reconstruction of humans, scenes, and cameras using robust foundational models and jointly optimize these elements. This joint optimization synergistically improves the accuracy of each component. We compare our method with existing methods on two challenging benchmarks, EgoHumans and EgoExo4D, demonstrating significant improvements in human localization accuracy within the world coordinate frame (reducing error from 3.59m to 1.04m in EgoHumans and from 3.01m to 0.50m in EgoExo4D). Notably, our results show that incorporating human data into the SfM pipeline improves camera pose estimation (e.g., increasing RRA@15 by 20.3% on EgoHumans). Additionally, qualitative results show that our approach improves scene reconstruction quality. Our code is available at muelea.github.io/hsfm.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Muller_Reconstructing_People_Places_and_Cameras_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Muller_Reconstructing_People_Places_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Muller_2025_CVPR,
    author    = {M\"uller, Lea and Choi, Hongsuk and Zhang, Anthony and Yi, Brent and Malik, Jitendra and Kanazawa, Angjoo},
    title     = {Reconstructing People, Places, and Cameras},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {21948-21958}
}


authors: Yunhong Lu, Qichao Wang, Hengyuan Cao, Xierui Wang, Xiaoyin Xu, Min Zhang
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 28629-28639
affiliations: ["Zhejiang University", "Shanghai Institute for Advanced Study-Zhejiang University"]
cities: ["Hangzhou", "Shanghai"]
countries: ["China", "China"]
highlight: yes
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Lu_InPO_Inversion_Preference_Optimization_with_Reparametrized_DDIM_for_Efficient_Diffusion_CVPR_2025_paper.html

title:
InPO: Inversion Preference Optimization with Reparametrized DDIM for Efficient Diffusion Model Alignment

abstract:
Without using explicit reward, direct preference optimization (DPO) employs paired human preference data to fine-tune generative models, a method that has garnered considerable attention in large language models (LLMs). However, exploration of aligning text-to-image (T2I) diffusion models with human preferences remains limited. In comparison to supervised fine-tuning, existing methods that align diffusion model suffer from low training efficiency and subpar generation quality due to the long Markov chain process and the intractability of the reverse process. To address these limitations, we introduce DDIM-InPO, an efficient method for direct preference alignment of diffusion models. Our approach conceptualizes diffusion model as a single-step generative model, allowing us to fine-tune the outputs of specific latent variables selectively. In order to accomplish this objective, we first assign implicit rewards to any latent variable directly via a reparameterization technique. Then we construct an Inversion technique to estimate appropriate latent variables for preference optimization. This modification process enables the diffusion model to only fine-tune the outputs of latent variables that have a strong correlation with the preference dataset. Experimental results indicate that our DDIM-InPO achieves state-of-the-art performance with just 400 steps of fine-tuning, surpassing all preference aligning baselines for T2I diffusion models in human preference evaluation tasks.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Lu_InPO_Inversion_Preference_Optimization_with_Reparametrized_DDIM_for_Efficient_Diffusion_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Lu_InPO_Inversion_Preference_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Lu_2025_CVPR,
    author    = {Lu, Yunhong and Wang, Qichao and Cao, Hengyuan and Wang, Xierui and Xu, Xiaoyin and Zhang, Min},
    title     = {InPO: Inversion Preference Optimization with Reparametrized DDIM for Efficient Diffusion Model Alignment},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {28629-28639}
}

raw_affiliation:
[{"affiliation": "Zhejiang University", "city": "Hangzhou", "country": "China"}, {"affiliation": "Shanghai Institute for Advanced Study-Zhejiang University", "city": "Shanghai", "country": "China"}]


title: GaussTR: Foundation Model-Aligned Gaussian Transformer for Self-Supervised 3D Spatial Understanding
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 11960-11970
affiliations: ["Huazhong University of Science & Technology", "Horizon Robotics"]
cities: ["Wuhan", "Beijing"]
countries: ["China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Jiang_GaussTR_Foundation_Model-Aligned_Gaussian_Transformer_for_Self-Supervised_3D_Spatial_Understanding_CVPR_2025_paper.html

authors:
Haoyi Jiang, Liu Liu, Tianheng Cheng, Xinjie Wang, Tianwei Lin, Zhizhong Su, Wenyu Liu, Xinggang Wang

abstract:
3D Semantic Occupancy Prediction is fundamental for spatial understanding, yet existing approaches face challenges in scalability and generalization due to their reliance on extensive labeled data and computationally intensive voxel-wise representations. In this paper, we introduce GaussTR, a novel Gaussian-based Transformer framework that unifies sparse 3D modeling with foundation model alignment through Gaussian representations to advance 3D spatial understanding. GaussTR predicts sparse sets of Gaussians in a feed-forward manner to represent 3D scenes. By splatting the Gaussians into 2D views and aligning the rendered features with foundation models, GaussTR facilitates self-supervised 3D representation learning and enables open-vocabulary semantic occupancy prediction without requiring explicit annotations. Empirical experiments on the Occ3D-nuScenes dataset demonstrate GaussTR's state-of-the-art zero-shot performance of 12.27 mIoU, along with a 40% reduction in training time. These results highlight the efficacy of GaussTR for scalable and holistic 3D spatial understanding, with promising implications in autonomous driving and embodied agents. The code is available at https://github.com/hustvl/GaussTR.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Jiang_GaussTR_Foundation_Model-Aligned_Gaussian_Transformer_for_Self-Supervised_3D_Spatial_Understanding_CVPR_2025_paper.pdf

bibtex:
@InProceedings{Jiang_2025_CVPR,
    author    = {Jiang, Haoyi and Liu, Liu and Cheng, Tianheng and Wang, Xinjie and Lin, Tianwei and Su, Zhizhong and Liu, Wenyu and Wang, Xinggang},
    title     = {GaussTR: Foundation Model-Aligned Gaussian Transformer for Self-Supervised 3D Spatial Understanding},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {11960-11970}
}

raw_affiliation:
[{"affiliation": "Huazhong University of Science & Technology", "city": "Wuhan", "country": "China"}, {"affiliation": "Horizon Robotics", "city": "Beijing", "country": "China"}]


title: Single Domain Generalization for Few-Shot Counting via Universal Representation Matching
authors: Xianing Chen, Si Huo, Borui Jiang, Hailin Hu, Xinghao Chen
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 4639-4649
raw_affiliation: [{"affiliation": "Huawei Noah\u2019s Ark Lab.", "city": "Shenzhen", "country": "China"}]
affiliations: ["Huawei Noah\u2019s Ark Lab."]
cities: ["Shenzhen"]
countries: ["China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Chen_Single_Domain_Generalization_for_Few-Shot_Counting_via_Universal_Representation_Matching_CVPR_2025_paper.html

abstract:
Few-shot counting estimates the number of target objects in an image using only a few annotated exemplars. However, domain shift severely hinders existing methods to generalize to unseen scenarios.  This falls into the realm of single domain generalization that remains unexplored in few-shot counting. To solve this problem, we begin by analyzing the main limitations of current methods, which typically follow a standard pipeline that extract the object prototypes from exemplars and then match them with image feature to construct the correlation map. We argue that existing methods overlook the significance of learning highly generalized prototypes. Building on this insight, we propose the first domain generalization few-shot counter, Universal Representation Matching, termed URM. Our primary contribution is the discovery that incorporating universal vision-language representations distilled from a large scale pretrained vision-language model into the correlation construction process substantially improves robustness to domain shifts without compromising in domain performance. As a result, URM achieves state-of-the-art performance on both in domain and the newly introduced domain generalization setting.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Chen_Single_Domain_Generalization_for_Few-Shot_Counting_via_Universal_Representation_Matching_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Chen_Single_Domain_Generalization_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Chen_2025_CVPR,
    author    = {Chen, Xianing and Huo, Si and Jiang, Borui and Hu, Hailin and Chen, Xinghao},
    title     = {Single Domain Generalization for Few-Shot Counting via Universal Representation Matching},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {4639-4649}
}


title: Discovering Hidden Visual Concepts Beyond Linguistic Input in Infant Learning
authors: Xueyi Ke, Satoshi Tsutsui, Yayun Zhang, Bihan Wen
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 4343-4352
affiliations: ["Nanyang Technological University", "The Max Planck Institute for Psycholinguistics"]
cities: ["Singapore", "Nijmegen"]
countries: ["Singapore", "Netherlands"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Ke_Discovering_Hidden_Visual_Concepts_Beyond_Linguistic_Input_in_Infant_Learning_CVPR_2025_paper.html

abstract:
Infants develop complex visual understanding rapidly, even preceding of the acquisition of linguistic skills. As computer vision seeks to replicate the human vision system, understanding infant visual development may offer valuable insights. In this paper, we present an interdisciplinary study exploring this question: can a computational model that imitates the infant learning process develop broader visual concepts that extend beyond the vocabulary it has heard, similar to how infants naturally learn? To investigate this, we analyze a recently published model in Science by Vong et al., which is trained on longitudinal, egocentric images of a single child paired with transcribed parental speech. We perform neuron labeling to identify visual concept neurons hidden in the model's internal representations. We then demonstrate that these neurons can recognize objects beyond the model's original vocabulary. Furthermore, we compare the differences in representation between infant models and those in modern computer vision models, such as CLIP and ImageNet pre-trained model. Ultimately, our work bridges cognitive science and computer vision by analyzing the internal representations of a computational model trained on an infant visual and linguistic inputs. Our code is available at https://github.com/Kexueyi/discover_infant_vis.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Ke_Discovering_Hidden_Visual_Concepts_Beyond_Linguistic_Input_in_Infant_Learning_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Ke_Discovering_Hidden_Visual_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Ke_2025_CVPR,
    author    = {Ke, Xueyi and Tsutsui, Satoshi and Zhang, Yayun and Wen, Bihan},
    title     = {Discovering Hidden Visual Concepts Beyond Linguistic Input in Infant Learning},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {4343-4352}
}

raw_affiliation:
[{"affiliation": "Nanyang Technological University", "city": "Singapore", "country": "Singapore"}, {"affiliation": "The Max Planck Institute for Psycholinguistics", "city": "Nijmegen", "country": "Netherlands"}]


title: Do We Always Need the Simplicity Bias? Looking for Optimal Inductive Biases in the Wild
authors: Damien Teney, Liangze Jiang, Florin Gogianu, Ehsan Abbasnejad
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 79-90
affiliations: ["Idiap Research Institute", "EPFL", "Bitdefender", "University of Adelaide"]
cities: ["Martigny", "Lausanne", "Bucharest", "Adelaide"]
countries: ["Switzerland", "Switzerland", "Romania", "Australia"]
oral: yes
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Teney_Do_We_Always_Need_the_Simplicity_Bias_Looking_for_Optimal_CVPR_2025_paper.html

abstract:
Common choices of architecture give neural networks a preference for fitting data with simple functions. This simplicity bias is known as key to their success. This paper explores the limits of this assumption. Building on recent work that showed that activation functions are the origin of the simplicity bias (Teney, 2024), we introduce a method to meta-learn activation functions to modulate this bias.**Findings.** We discover multiple tasks where the assumption of simplicity is inadequate, and standard ReLU architectures are therefore suboptimal. In these cases, we find activation functions that perform better by inducing a prior of higher complexity. Interestingly, these cases correspond to domains where neural networks have historically struggled: tabular data, regression tasks, cases of shortcut learning, and algorithmic grokking tasks. In comparison, the simplicity bias proves adequate on image tasks, where learned activations are nearly identical to ReLUs and GeLUs.**Implications.** (1) Contrary to common belief, the simplicity bias is not universally useful. There exist real tasks where it is suboptimal. (2) The suitability of ReLU models for image classification is not accidental. (3) The success of ML ultimately depends on the adequacy between data and architectures, and there may be benefits for architectures tailored to specific distributions of tasks.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Teney_Do_We_Always_Need_the_Simplicity_Bias_Looking_for_Optimal_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Teney_Do_We_Always_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Teney_2025_CVPR,
    author    = {Teney, Damien and Jiang, Liangze and Gogianu, Florin and Abbasnejad, Ehsan},
    title     = {Do We Always Need the Simplicity Bias? Looking for Optimal Inductive Biases in the Wild},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {79-90}
}

raw_affiliation:
[{"affiliation": "Idiap Research Institute", "city": "Martigny", "country": "Switzerland"}, {"affiliation": "EPFL", "city": "Lausanne", "country": "Switzerland"}, {"affiliation": "Bitdefender", "city": "Bucharest", "country": "Romania"}, {"affiliation": "University of Adelaide", "city": "Adelaide", "country": "Australia"}]


title: A General Adaptive Dual-level Weighting Mechanism for Remote Sensing Pansharpening
authors: Jie Huang, Haorui Chen, Jiaxuan Ren, Siran Peng, Liangjian Deng
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 7447-7456
cities: ["Chengdu", "Beijing", "Beijing"]
countries: ["China", "China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Huang_A_General_Adaptive_Dual-level_Weighting_Mechanism_for_Remote_Sensing_Pansharpening_CVPR_2025_paper.html

abstract:
Currently, deep learning-based methods for remote sensing pansharpening have advanced rapidly. However, many existing methods struggle to fully leverage feature heterogeneity and redundancy, thereby limiting their effectiveness. To address these challenges across two key dimensions, we introduce a general adaptive dual-level weighting mechanism (ADWM), designed to enhance a wide range of existing deep-learning methods. First, Intra-Feature Weighting (IFW) evaluates correlations among channels within each feature and selectively weighs to reduce redundancy and enhance unique information. Second, Cross-Feature Weighting (CFW) adjusts contributions across layers based on inter-layer correlations, refining the final output by preserving key distinctions across feature depths. This dual-level weighting is efficiently implemented through our proposed Correlation-Aware Covariance Weighting (CACW), which generates weights by utilizing the correlations captured within the covariance matrix. Extensive experiments demonstrate the superior performance of ADWM compared to recent state-of-the-art (SOTA) methods. Furthermore, we validate the effectiveness of our approach through generality experiments, ablation studies, comparison experiments, and detailed visual analysis.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Huang_A_General_Adaptive_Dual-level_Weighting_Mechanism_for_Remote_Sensing_Pansharpening_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Huang_A_General_Adaptive_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Huang_2025_CVPR,
    author    = {Huang, Jie and Chen, Haorui and Ren, Jiaxuan and Peng, Siran and Deng, Liangjian},
    title     = {A General Adaptive Dual-level Weighting Mechanism for Remote Sensing Pansharpening},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {7447-7456}
}

raw_affiliation:
[{"affiliation": "University of Electronic Science and Technology of China", "city": "Chengdu", "country": "China"}, {"affiliation": "Institute of Automation, Chinese Academy of Sciences", "city": "Beijing", "country": "China"}, {"affiliation": "School of Arti\ufb01cial Intelligence, University of Chinese Academy of Sciences", "city": "Beijing", "country": "China"}]

affiliations:
["University of Electronic Science and Technology of China", "Institute of Automation, Chinese Academy of Sciences", "School of Arti\ufb01cial Intelligence, University of Chinese Academy of Sciences"]


title: RASP: Revisiting 3D Anamorphic Art for Shadow-Guided Packing of Irregular Objects
authors: Soumyaratna Debnath, Ashish Tiwari, Kaustubh Sadekar, Shanmuganathan Raman
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 5849-5858
affiliations: ["Indian Institute of Technology Gandhinagar", "Portland State University"]
cities: ["Gandhinagar", "Portland"]
countries: ["India", "USA"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Debnath_RASP_Revisiting_3D_Anamorphic_Art_for_Shadow-Guided_Packing_of_Irregular_CVPR_2025_paper.html

abstract:
Recent advancements in learning-based methods have opened new avenues for exploring and interpreting art forms, such as shadow art, origami, and sketch art, through computational models. One notable visual art form is 3D Anamorphic Art in which an ensemble of arbitrarily shaped 3D objects creates a realistic and meaningful expression when observed from a particular viewpoint and loses its coherence over the other viewpoints. In this work, we build on insights from 3D Anamorphic Art to perform 3D object arrangement. We introduce RASP, a differentiable-rendering-based framework to arrange arbitrarily shaped 3D objects within a bounded volume via shadow (or silhouette)-guided optimization with an aim of minimal inter-object spacing and near-maximal occupancy. Furthermore, we propose a novel SDF-based formulation to handle inter-object intersection and container extrusion. We demonstrate that RASP can be extended to part assembly alongside object packing considering 3D objects to be "parts" of another 3D object. Finally, we present artistic illustrations of multi-view anamorphic art, achieving meaningful expressions from multiple viewpoints within a single ensemble.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Debnath_RASP_Revisiting_3D_Anamorphic_Art_for_Shadow-Guided_Packing_of_Irregular_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Debnath_RASP_Revisiting_3D_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Debnath_2025_CVPR,
    author    = {Debnath, Soumyaratna and Tiwari, Ashish and Sadekar, Kaustubh and Raman, Shanmuganathan},
    title     = {RASP: Revisiting 3D Anamorphic Art for Shadow-Guided Packing of Irregular Objects},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {5849-5858}
}

raw_affiliation:
[{"affiliation": "Indian Institute of Technology Gandhinagar", "city": "Gandhinagar", "country": "India"}, {"affiliation": "Portland State University", "city": "Portland", "country": "USA"}]


title: Identifying and Mitigating Spurious Correlation in Multi-Task Learning
authors: Junyi Chai, Shenyu Lu, Xiaoqian Wang
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 25698-25707
affiliations: ["Elmore Family School of Electrical and Computer Engineering, Purdue University"]
cities: ["West Lafayette"]
countries: ["USA"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Chai_Identifying_and_Mitigating_Spurious_Correlation_in_Multi-Task_Learning_CVPR_2025_paper.html

abstract:
Multi-task learning (MTL) is a paradigm that aims to improve the generalization of models by simultaneously learning multiple related tasks, leveraging shared representations and task-specific information to enhance performance on individual tasks. However, existing work has shown that MTL can potentially hinder generalization, with one key factor being spurious correlations between tasks. Owing to the knowledge-sharing property, the per-task predictors are more likely to develop reliance on spurious features. Most existing approaches address this issue through distributional robustness, aiming to maintain consistent performance across different distributions under unknown covariate shifts. However, this formulation lacks theoretical guarantees and can be sensitive to the construction of covariate shifts. In this work, we propose a novel perspective, where we seek to identify spurious correlations between tasks. Drawing inspirations from conventional formulations on spurious correlation, for each task, we propose to distinguish its spurious tasks using the difference in correlation coefficients between the empirical distribution and class-wise resampled distributions, thereby capturing the correlations between task labels w.r.t. each class. We prove theoretically the feasibility of the resampling strategy in characterizing spurious correlations between tasks. Furthermore, we propose a simple fine-tuning strategy, debiased adversarial training, where the per-task predictors are adversarially trained to disregard information associated with their spurious tasks. Experimental results on six benchmark datasets show that our method effectively mitigates spurious correlations and outperforms state-of-the-art methods in improving generalization.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Chai_Identifying_and_Mitigating_Spurious_Correlation_in_Multi-Task_Learning_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Chai_Identifying_and_Mitigating_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Chai_2025_CVPR,
    author    = {Chai, Junyi and Lu, Shenyu and Wang, Xiaoqian},
    title     = {Identifying and Mitigating Spurious Correlation in Multi-Task Learning},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {25698-25707}
}

raw_affiliation:
[{"affiliation": "Elmore Family School of Electrical and Computer Engineering, Purdue University", "city": "West Lafayette", "country": "USA"}]


title: Continuous, Subject-Specific Attribute Control in T2I Models by Identifying Semantic Directions
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 13231-13241
affiliations: ["CompVis @ LMU Munich, MCML", "TU Munich", "ENS Paris-Saclay"]
cities: ["Munich", "Munich", "Paris"]
countries: ["Germany", "Germany", "France"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Baumann_Continuous_Subject-Specific_Attribute_Control_in_T2I_Models_by_Identifying_Semantic_CVPR_2025_paper.html

authors:
Stefan Andreas Baumann, Felix Krause, Michael Neumayr, Nick Stracke, Melvin Sevi, Vincent Tao Hu, Björn Ommer

abstract:
Recent advances in text-to-image (T2I) diffusion models have significantly improved the quality of generated images. However, providing efficient control over individual subjects, particularly the attributes characterizing them, remains a key challenge. While existing methods have introduced mechanisms to modulate attribute expression, they typically provide either detailed, object-specific localization of such a modification or full-scale fine-grained, nuanced control of attributes. No current approach offers both simultaneously, resulting in a gap when trying to achieve precise continuous and subject-specific attribute modulation in image generation. In this work, we demonstrate that token-level directions exist within commonly used CLIP text embeddings that enable fine-grained, subject-specific control of high-level attributes in T2I models. We introduce two methods to identify these directions: a simple, optimization-free technique and a learning-based approach that utilizes the T2I model to characterize semantic concepts more specifically. Our methods allow the augmentation of the prompt text input, enabling fine-grained control over multiple attributes of individual subjects simultaneously, without requiring any modifications to the diffusion model itself. This approach offers a unified solution that fills the gap between global and localized control, providing competitive flexibility and precision in text-guided image generation.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Baumann_Continuous_Subject-Specific_Attribute_Control_in_T2I_Models_by_Identifying_Semantic_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Baumann_Continuous_Subject-Specific_Attribute_CVPR_2025_supplemental.zip

bibtex:
@InProceedings{Baumann_2025_CVPR,
    author    = {Baumann, Stefan Andreas and Krause, Felix and Neumayr, Michael and Stracke, Nick and Sevi, Melvin and Hu, Vincent Tao and Ommer, Bj\"orn},
    title     = {Continuous, Subject-Specific Attribute Control in T2I Models by Identifying Semantic Directions},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {13231-13241}
}

raw_affiliation:
[{"affiliation": "CompVis @ LMU Munich, MCML", "city": "Munich", "country": "Germany"}, {"affiliation": "TU Munich", "city": "Munich", "country": "Germany"}, {"affiliation": "ENS Paris-Saclay", "city": "Paris", "country": "France"}]


authors: Jeong Ryong Lee, Yejee Shin, Geonhui Son, Dosik Hwang
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 4050-4059
affiliations: ["Yonsei University", "Korea Institute of Science and Technology"]
cities: ["Seoul", "Seoul"]
countries: ["South Korea", "South Korea"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Lee_Diffusion_Bridge_Leveraging_Diffusion_Model_to_Reduce_the_Modality_Gap_CVPR_2025_paper.html

title:
Diffusion Bridge: Leveraging Diffusion Model to Reduce the Modality Gap Between Text and Vision for Zero-Shot Image Captioning

abstract:
The modality gap between vision and text embeddings in CLIP presents a significant challenge for zero-shot image captioning, limiting effective cross-modal representation. Traditional approaches, such as noise injection and memory-based similarity matching, attempt to address this gap, yet these methods either rely on indirect alignment or relatively naive solutions with heavy computation. Diffusion Bridge introduces a novel approach to directly reduce this modality gap by leveraging Denoising Diffusion Probabilistic Models (DDPM), trained exclusively on text embeddings to model their distribution. Our approach is motivated by the observation that, while paired vision and text embeddings are relatively close, a modality gap still exists due to stable regions created by the contrastive loss. This gap can be interpreted as noise in cross-modal mappings, which we approximate as Gaussian noise. To bridge this gap, we employ a reverse diffusion process, where image embeddings are strategically introduced at an intermediate step in the reverse process, allowing them to be refined progressively toward the text embedding distribution. This process transforms vision embeddings into text-like representations closely aligned with paired text embeddings, effectively minimizing discrepancies between modalities. Experimental results demonstrate that these text-like vision embeddings significantly enhance alignment with their paired text embeddings, leading to improved zero-shot captioning performance on MSCOCO and Flickr30K. Diffusion Bridge achieves competitive results without reliance on memory banks or entity-driven methods, offering a novel pathway for cross-modal alignment and opening new possibilities for the application of diffusion models in multi-modal tasks. The source code is available at: https://github.com/mongeoroo/diffusion-bridge

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Lee_Diffusion_Bridge_Leveraging_Diffusion_Model_to_Reduce_the_Modality_Gap_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Lee_Diffusion_Bridge_Leveraging_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Lee_2025_CVPR,
    author    = {Lee, Jeong Ryong and Shin, Yejee and Son, Geonhui and Hwang, Dosik},
    title     = {Diffusion Bridge: Leveraging Diffusion Model to Reduce the Modality Gap Between Text and Vision for Zero-Shot Image Captioning},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {4050-4059}
}

raw_affiliation:
[{"affiliation": "Yonsei University", "city": "Seoul", "country": "South Korea"}, {"affiliation": "Korea Institute of Science and Technology", "city": "Seoul", "country": "South Korea"}]


title: MODfinity: Unsupervised Domain Adaptation with Multimodal Information Flow Intertwining
authors: Shanglin Liu, Jianming Lv, Jingdan Kang, Huaidong Zhang, Zequan Liang, Shengfeng He
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 5092-5101
cities: ["Guangzhou", "Davis", "Singapore"]
countries: ["China", "USA", "Singapore"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Liu_MODfinity_Unsupervised_Domain_Adaptation_with_Multimodal_Information_Flow_Intertwining_CVPR_2025_paper.html

abstract:
Multimodal unsupervised domain adaptation leverages unlabeled data in the target domain to enhance multimodal systems continuously. While current state-of-the-art methods encourage interaction between sub-models of different modalities through pseudo-labeling and feature-level exchange, varying sample quality across modalities can lead to the propagation of inaccurate information, resulting in error accumulation. To address this, we propose Modal-Affinity Multimodal Domain Adaptation (MODfinity), a method that dynamically manages multimodal information flow through fine-grained control over teacher model selection, guiding information intertwining at both feature and label levels. By treating labels as an independent modality, MODfinity enables balanced performance assessment across modalities, employing a novel modal-affinity measurement to evaluate information quality. Additionally, we introduce a modal-affinity distillation technique to control sample-level information exchange, ensuring reliable multimodal interaction based on affinity evaluations within the feature space. Extensive experiments on three multimodal datasets demonstrate that our framework consistently outperforms state-of-the-art methods, particularly in high-noise environments.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Liu_MODfinity_Unsupervised_Domain_Adaptation_with_Multimodal_Information_Flow_Intertwining_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Liu_MODfinity_Unsupervised_Domain_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Liu_2025_CVPR,
    author    = {Liu, Shanglin and Lv, Jianming and Kang, Jingdan and Zhang, Huaidong and Liang, Zequan and He, Shengfeng},
    title     = {MODfinity: Unsupervised Domain Adaptation with Multimodal Information Flow Intertwining},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {5092-5101}
}

raw_affiliation:
[{"affiliation": "South China University of Technology", "city": "Guangzhou", "country": "China"}, {"affiliation": "University of California, Davis", "city": "Davis", "country": "USA"}, {"affiliation": "Singapore Management University", "city": "Singapore", "country": "Singapore"}]

affiliations:
["South China University of Technology", "University of California, Davis", "Singapore Management University"]


title: Towards Universal Soccer Video Understanding
authors: Jiayuan Rao, Haoning Wu, Hao Jiang, Ya Zhang, Yanfeng Wang, Weidi Xie
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 8384-8394
cities: ["Shanghai", "Shanghai", "Hangzhou"]
countries: ["China", "China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Rao_Towards_Universal_Soccer_Video_Understanding_CVPR_2025_paper.html

abstract:
As a globally celebrated sport, soccer has attracted widespread interest from fans over the world. This paper aims to develop a comprehensive multi-modal framework for soccer video understanding.Specifically, we make the following contributions in this paper:(i) we introduce **SoccerReplay-1988**, the largest multi-modal soccer dataset to date, featuring videos and detailed annotations from 1,988 complete matches, with an automated annotation pipeline;(ii) we present the first visual-language foundation model in the soccer domain, **MatchVision**, which leverages spatiotemporal information across soccer videos and excels in various downstream tasks;(iii) we conduct extensive experiments and ablation studies on action classification, commentary generation, and multi-view foul recognition,and demonstrate state-of-the-art performance on all of them, substantially outperforming existing models, which has demonstrated the superiority of our proposed data and model. We believe that this work will offer a standard paradigm for sports understanding research. The code and model will be publicly available for reproduction.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Rao_Towards_Universal_Soccer_Video_Understanding_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Rao_Towards_Universal_Soccer_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Rao_2025_CVPR,
    author    = {Rao, Jiayuan and Wu, Haoning and Jiang, Hao and Zhang, Ya and Wang, Yanfeng and Xie, Weidi},
    title     = {Towards Universal Soccer Video Understanding},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {8384-8394}
}

raw_affiliation:
[{"affiliation": "School of Artificial Intelligence, Shanghai Jiao Tong University", "city": "Shanghai", "country": "China"}, {"affiliation": "CMIC, Shanghai Jiao Tong University", "city": "Shanghai", "country": "China"}, {"affiliation": "Alibaba Group", "city": "Hangzhou", "country": "China"}]

affiliations:
["School of Artificial Intelligence, Shanghai Jiao Tong University", "CMIC, Shanghai Jiao Tong University", "Alibaba Group"]


title: SimLingo: Vision-Only Closed-Loop Autonomous Driving with Language-Action Alignment
authors: Katrin Renz, Long Chen, Elahe Arani, Oleg Sinavski
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 11993-12003
affiliations: ["Wayve", "University of T\u00a8ubingen", "T\u00a8ubingen AI Center"]
cities: ["London", "T\u00fcbingen", "T\u00fcbingen"]
countries: ["UK", "Germany", "Germany"]
highlight: yes
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Renz_SimLingo_Vision-Only_Closed-Loop_Autonomous_Driving_with_Language-Action_Alignment_CVPR_2025_paper.html

abstract:
Integrating large language models (LLMs) into autonomous driving has attracted significant attention with the hope of improving generalization and explainability. However, existing methods often focus on either driving or vision-language understanding but achieving both high driving performance and extensive language understanding remains challenging. In addition, the dominant approach to tackle vision-language understanding is using visual question answering. However, for autonomous driving, this is only useful if it is aligned with the action space. Otherwise, the model's answers could be inconsistent with its behavior. Therefore, we propose a model that can handle three different tasks: (1) closed-loop driving, (2) vision-language understanding, and (3) language-action alignment. Our model SimLingo is based on a vision language model (VLM) and works using only camera, excluding expensive sensors like LiDAR. SimLingo obtains state-of-the-art performance on the widely used CARLA simulator on the Bench2Drive benchmark and is the winning entry at the CARLA challenge 2024. Additionally, we achieve strong results in a wide variety of language-related tasks while maintaining high driving performance.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Renz_SimLingo_Vision-Only_Closed-Loop_Autonomous_Driving_with_Language-Action_Alignment_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Renz_SimLingo_Vision-Only_Closed-Loop_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Renz_2025_CVPR,
    author    = {Renz, Katrin and Chen, Long and Arani, Elahe and Sinavski, Oleg},
    title     = {SimLingo: Vision-Only Closed-Loop Autonomous Driving with Language-Action Alignment},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {11993-12003}
}

raw_affiliation:
[{"affiliation": "Wayve", "city": "London", "country": "UK"}, {"affiliation": "University of T\u00a8ubingen", "city": "T\u00fcbingen", "country": "Germany"}, {"affiliation": "T\u00a8ubingen AI Center", "city": "T\u00fcbingen", "country": "Germany"}]


title: Improved Video VAE for Latent Video Diffusion Model
authors: Pingyu Wu, Kai Zhu, Yu Liu, Liming Zhao, Wei Zhai, Yang Cao, Zheng-Jun Zha
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 18124-18133
cities: ["Hefei", "Unknown"]
countries: ["China", "Unknown"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Wu_Improved_Video_VAE_for_Latent_Video_Diffusion_Model_CVPR_2025_paper.html

abstract:
Variational Autoencoder (VAE) aims to compress pixel data into low-dimensional latent space, playing an important role in OpenAI's Sora and other latent video diffusion generation models. While most existing video VAEs inflate a pre-trained image VAE into the 3D causal structure for temporal-spatial compression, this paper presents two astonishing findings: (1) The initialization from a well-trained image VAE with the same latent dimensions is not an optimal scheme. (2) The adoption of causal reasoning leads to unequal information interactions and unbalanced performance between frames. To alleviate these problems, we propose a keyframe-based temporal compression (KTC) architecture and a group causal convolution (GCConv) module to further improve video VAE (IV-VAE). Specifically, the KTC architecture divides the latent space into two branches, in which one half completely inherits the compression prior of keyframes from a lower-dimension image VAE while the other half involves temporal compression through 3D group causal convolution, reducing temporal-spatial conflicts and accelerating the convergence speed of video VAE. The GCConv in the above 3D half uses standard convolution within each frame group to ensure inter-frame equivalence, and employs causal logical padding between groups to maintain flexibility in processing variable frame video. Extensive experiments on five benchmarks demonstrate the SOTA video reconstruction and generation abilities of our IV-VAE.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Wu_Improved_Video_VAE_for_Latent_Video_Diffusion_Model_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Wu_Improved_Video_VAE_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Wu_2025_CVPR,
    author    = {Wu, Pingyu and Zhu, Kai and Liu, Yu and Zhao, Liming and Zhai, Wei and Cao, Yang and Zha, Zheng-Jun},
    title     = {Improved Video VAE for Latent Video Diffusion Model},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {18124-18133}
}

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

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


title: Immune: Improving Safety Against Jailbreaks in Multi-modal LLMs via Inference-Time Alignment
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 25038-25049
cities: ["College Park", "Mumbai", "Princeton", "Boulder", "Orlando"]
countries: ["USA", "India", "USA", "USA", "USA"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Ghosal_Immune_Improving_Safety_Against_Jailbreaks_in_Multi-modal_LLMs_via_Inference-Time_CVPR_2025_paper.html

authors:
Soumya Suvra Ghosal, Souradip Chakraborty, Vaibhav Singh, Tianrui Guan, Mengdi Wang, Ahmad Beirami, Furong Huang, Alvaro Velasquez, Dinesh Manocha, Amrit Singh Bedi

abstract:
With the widespread deployment of Multimodal Large Language Models (MLLMs) for visual-reasoning tasks, improving their safety has become crucial. Recent research indicates that despite training-time safety alignment, these models remain vulnerable to jailbreak attacks--carefully crafted image-prompt pairs that compel the model to generate harmful content. In this work, we first highlight a critical safety gap, demonstrating that alignment achieved solely through safety training may be insufficient against jailbreak attacks. To address this vulnerability, we propose Immune, an inference-time defense framework that leverages a safe reward model during decoding to defend against jailbreak attacks. Additionally, we provide a mathematical characterization of Immune, offering insights on why it improves safety against jailbreak. Extensive evaluations on diverse jailbreak benchmarks using recent MLLMs reveal that Immune effectively enhances model safety while preserving the model's original capabilities. For instance, against text-based jailbreak attacks on LLaVA-1.6, Immune reduces the attack success rate by 57.82% and 16.78% compared to the base MLLM and state-of-the-art defense strategy, respectively.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Ghosal_Immune_Improving_Safety_Against_Jailbreaks_in_Multi-modal_LLMs_via_Inference-Time_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Ghosal_Immune_Improving_Safety_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Ghosal_2025_CVPR,
    author    = {Ghosal, Soumya Suvra and Chakraborty, Souradip and Singh, Vaibhav and Guan, Tianrui and Wang, Mengdi and Beirami, Ahmad and Huang, Furong and Velasquez, Alvaro and Manocha, Dinesh and Bedi, Amrit Singh},
    title     = {Immune: Improving Safety Against Jailbreaks in Multi-modal LLMs via Inference-Time Alignment},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {25038-25049}
}

raw_affiliation:
[{"affiliation": "University of Maryland", "city": "College Park", "country": "USA"}, {"affiliation": "Indian Institute of Technology Bombay", "city": "Mumbai", "country": "India"}, {"affiliation": "Princeton University", "city": "Princeton", "country": "USA"}, {"affiliation": "University of Colorado Boulder", "city": "Boulder", "country": "USA"}, {"affiliation": "University of Central Florida", "city": "Orlando", "country": "USA"}]

affiliations:
["University of Maryland", "Indian Institute of Technology Bombay", "Princeton University", "University of Colorado Boulder", "University of Central Florida"]


title: Efficient Video Super-Resolution for Real-time Rendering with Decoupled G-buffer Guidance
authors: Mingjun Zheng, Long Sun, Jiangxin Dong, Jinshan Pan
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 11328-11337
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/Zheng_Efficient_Video_Super-Resolution_for_Real-time_Rendering_with_Decoupled_G-buffer_Guidance_CVPR_2025_paper.html

abstract:
Latency is a key driver for real-time rendering applications, making super-resolution techniques increasingly popular to accelerate rendering processes. In contrast to existing methods that directly concatenate low-resolution frames and G-buffers as input without discrimination, we develop an asymmetric UNet-based super-resolution network with decoupled G-buffer guidance, dubbed RDG, to facilitate the spatial and temporal feature exploration for minimizing performance overheads and latency.We first propose a dynamic feature modulator (DFM) to selectively encode the spatial information to capture precise structural information.We then incorporate auxiliary G-buffer information to guide the decoder to generate detail-rich, temporally stable results.Specifically, we adopt a high-frequency feature booster (HFB) to adaptively transfer the high-frequency information from the normal and bidirectional reflectance distribution function (BRDF) components of the G-buffer, enhancing the details of the generated results.To further enhance the temporal stability, we design a cross-frame temporal refiner (CTR) with depth and motion vector constraints to aggregate the previous and current frames.Extensive experimental results reveal that our proposed method is capable of generating high-quality and temporally stable results in real-time rendering.The proposed RDG-s produces 1080P rendering results on a RTX 3090 GPU with a speed of 126 FPS.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Zheng_Efficient_Video_Super-Resolution_for_Real-time_Rendering_with_Decoupled_G-buffer_Guidance_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Zheng_Efficient_Video_Super-Resolution_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Zheng_2025_CVPR,
    author    = {Zheng, Mingjun and Sun, Long and Dong, Jiangxin and Pan, Jinshan},
    title     = {Efficient Video Super-Resolution for Real-time Rendering with Decoupled G-buffer Guidance},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {11328-11337}
}

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


title: CustomKD: Customizing Large Vision Foundation for Edge Model Improvement via Knowledge Distillation
authors: Jungsoo Lee, Debasmit Das, Munawar Hayat, Sungha Choi, Kyuwoong Hwang, Fatih Porikli
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 25176-25186
raw_affiliation: [{"affiliation": "Qualcomm AI Research\u2021", "city": "San Diego", "country": "USA"}]
affiliations: ["Qualcomm AI Research\u2021"]
cities: ["San Diego"]
countries: ["USA"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Lee_CustomKD_Customizing_Large_Vision_Foundation_for_Edge_Model_Improvement_via_CVPR_2025_paper.html

abstract:
We propose a novel knowledge distillation approach, CustomKD, that effectively leverages large vision foundation models (LVFMs) to enhance the performance of edge models (e.g., MobileNetV3). Despite recent advancements in LVFMs, such as DINOv2 and CLIP, their potential in knowledge distillation for enhancing edge models remains underexplored. While knowledge distillation is a promising approach for improving the performance of edge models, the discrepancy in model capacities and heterogeneous architectures between LVFMs and edge models poses a significant challenge. Our observation indicates that although utilizing larger backbones (e.g., ViT-S to ViT-L) in teacher models improves their downstream task performances, the knowledge distillation from the large teacher models fails to bring as much performance gain for student models as for teacher models due to the large model discrepancy. Our simple yet effective CustomKD customizes the well-generalized features inherent in LVFMs to a given student model in order to reduce model discrepancies. Specifically, beyond providing well-generalized original knowledge from teachers, CustomKD aligns the features of teachers to those of students, making it easy for students to understand and overcome the large model discrepancy overall. CustomKD significantly improves the performances of edge models in scenarios with unlabeled data such as unsupervised domain adaptation (e.g., OfficeHome and DomainNet) and semi-supervised learning (e.g., CIFAR-100 with 400 labeled samples and ImageNet with 1% labeled samples), achieving the new state-of-the-art performances.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Lee_CustomKD_Customizing_Large_Vision_Foundation_for_Edge_Model_Improvement_via_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Lee_CustomKD_Customizing_Large_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Lee_2025_CVPR,
    author    = {Lee, Jungsoo and Das, Debasmit and Hayat, Munawar and Choi, Sungha and Hwang, Kyuwoong and Porikli, Fatih},
    title     = {CustomKD: Customizing Large Vision Foundation for Edge Model Improvement via Knowledge Distillation},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {25176-25186}
}


title: Enhancing Privacy-Utility Trade-offs to Mitigate Memorization in Diffusion Models
authors: Chen Chen, Daochang Liu, Mubarak Shah, Chang Xu
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 8182-8191
cities: ["Sydney", "Perth", "Orlando"]
countries: ["Australia", "Australia", "USA"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Chen_Enhancing_Privacy-Utility_Trade-offs_to_Mitigate_Memorization_in_Diffusion_Models_CVPR_2025_paper.html

abstract:
Text-to-image diffusion models have demonstrated remarkable capabilities in creating images highly aligned with user prompts, yet their proclivity for memorizing training set images has sparked concerns about the originality of the generated images and privacy issues, potentially leading to legal complications for both model owners and users, particularly when the memorized images contain proprietary content. Although methods to mitigate these issues have been suggested, enhancing privacy often results in a significant decrease in the utility of the outputs, as indicated by text-alignment scores. To bridge the research gap, we introduce a novel method, PRSS, which refines the classifier-free guidance approach in diffusion models by integrating prompt re-anchoring (PR) to improve privacy and incorporating semantic prompt search (SS) to enhance utility. Extensive experiments across various privacy levels demonstrate that our approach consistently improves the privacy-utility trade-off, establishing a new state-of-the-art.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Chen_Enhancing_Privacy-Utility_Trade-offs_to_Mitigate_Memorization_in_Diffusion_Models_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Chen_Enhancing_Privacy-Utility_Trade-offs_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Chen_2025_CVPR,
    author    = {Chen, Chen and Liu, Daochang and Shah, Mubarak and Xu, Chang},
    title     = {Enhancing Privacy-Utility Trade-offs to Mitigate Memorization in Diffusion Models},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {8182-8191}
}

raw_affiliation:
[{"affiliation": "School of Computer Science, Faculty of Engineering, The University of Sydney, Australia", "city": "Sydney", "country": "Australia"}, {"affiliation": "School of Physics, Mathematics and Computing, The University of Western Australia, Australia", "city": "Perth", "country": "Australia"}, {"affiliation": "Center for Research in Computer Vision, University of Central Florida, USA", "city": "Orlando", "country": "USA"}]

affiliations:
["School of Computer Science, Faculty of Engineering, The University of Sydney, Australia", "School of Physics, Mathematics and Computing, The University of Western Australia, Australia", "Center for Research in Computer Vision, University of Central Florida, USA"]


title: Learned Image Compression with Dictionary-based Entropy Model
authors: Jingbo Lu, Leheng Zhang, Xingyu Zhou, Mu Li, Wen Li, Shuhang Gu
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 12850-12859
cities: ["Chengdu", "Shenzhen"]
countries: ["China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Lu_Learned_Image_Compression_with_Dictionary-based_Entropy_Model_CVPR_2025_paper.html

abstract:
Learned image compression methods have attracted great research interest and exhibited superior rate-distortion performance to the best classical image compression standards of the present.The entropy model plays a key role in learned image compression, which estimates the probability distribution of the latent representation for further entropy coding.Most existing methods employed hyper-prior and auto-regressive architectures to form their entropy models.However, they only aimed to explore the internal dependencies of latent representation while neglecting the importance of extracting prior from training data.In this work, we propose a novel entropy model named Dictionary-based Cross Attention Entropy model, which introduces a learnable dictionary to summarize the typical structures occurring in the training dataset to enhance the entropy model.Extensive experimental results have demonstrated that the proposed model strikes a better balance between performance and latency, achieving state-of-the-art results on various benchmark datasets.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Lu_Learned_Image_Compression_with_Dictionary-based_Entropy_Model_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Lu_Learned_Image_Compression_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Lu_2025_CVPR,
    author    = {Lu, Jingbo and Zhang, Leheng and Zhou, Xingyu and Li, Mu and Li, Wen and Gu, Shuhang},
    title     = {Learned Image Compression with Dictionary-based Entropy Model},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {12850-12859}
}

raw_affiliation:
[{"affiliation": "University of Electronic Science and Technology of China", "city": "Chengdu", "country": "China"}, {"affiliation": "Harbin Institute of Technology, Shenzhen", "city": "Shenzhen", "country": "China"}]

affiliations:
["University of Electronic Science and Technology of China", "Harbin Institute of Technology, Shenzhen"]


title: PMNI: Pose-free Multi-view Normal Integration for Reflective and Textureless Surface Reconstruction
authors: Mingzhi Pei, Xu Cao, Xiangyi Wang, Heng Guo, Zhanyu Ma
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 26834-26843
affiliations: ["Beijing University of Posts and Telecommunications", "Independent Researcher"]
cities: ["Beijing", "Unknown"]
countries: ["China", "Unknown"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Pei_PMNI_Pose-free_Multi-view_Normal_Integration_for_Reflective_and_Textureless_Surface_CVPR_2025_paper.html

abstract:
Reflective and textureless surfaces remain a challenge in multi-view 3D reconstruction. Both camera pose calibration and shape reconstruction often fail due to insufficient or unreliable cross-view visual features. To address these issues, we present PMNI (Pose-free Multi-view Normal Integration), a neural surface reconstruction method that incorporates rich geometric information by leveraging surface normal maps instead of RGB images. By enforcing geometric constraints from surface normals and multi-view shape consistency within a neural signed distance function (SDF) optimization framework, PMNI simultaneously recovers accurate camera poses and high-fidelity surface geometry.  Experimental results on synthetic and real-world datasets show that our method achieves state-of-the-art performance in the reconstruction of reflective surfaces, even without reliable initial camera poses.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Pei_PMNI_Pose-free_Multi-view_Normal_Integration_for_Reflective_and_Textureless_Surface_CVPR_2025_paper.pdf

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

bibtex:
@InProceedings{Pei_2025_CVPR,
    author    = {Pei, Mingzhi and Cao, Xu and Wang, Xiangyi and Guo, Heng and Ma, Zhanyu},
    title     = {PMNI: Pose-free Multi-view Normal Integration for Reflective and Textureless Surface Reconstruction},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {26834-26843}
}

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


title: NVComposer: Boosting Generative Novel View Synthesis with Multiple Sparse and Unposed Images
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 777-787
affiliations: ["The Chinese University of Hong Kong", "ARC Lab, Tencent PCG", "Peking University"]
cities: ["Hong Kong", "Shenzhen", "Beijing"]
countries: ["China", "China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Li_NVComposer_Boosting_Generative_Novel_View_Synthesis_with_Multiple_Sparse_and_CVPR_2025_paper.html

authors:
Lingen Li, Zhaoyang Zhang, Yaowei Li, Jiale Xu, Wenbo Hu, Xiaoyu Li, Weihao Cheng, Jinwei Gu, Tianfan Xue, Ying Shan

abstract:
Recent advancements in generative models have significantly improved novel view synthesis (NVS) from multi-view data. However, existing methods depend on external multi-view alignment processes, such as explicit pose estimation or pre-reconstruction, which limits their flexibility and accessibility, especially when alignment is unstable due to insufficient overlap or occlusions between views. In this paper, we propose NVComposer, a novel approach that eliminates the need for explicit external alignment. NVComposer enables the generative model to implicitly infer spatial and geometric relationships between multiple conditional views by introducing two key components: 1) an image-pose dual-stream diffusion model that simultaneously generates target novel views and condition camera poses, and 2) a geometry-aware feature alignment module that distills geometric priors from dense stereo models during training. Extensive experiments demonstrate that NVComposer achieves state-of-the-art performance in generative multi-view NVS tasks, removing the reliance on external alignment and thus improving model accessibility. Our approach shows substantial improvements in synthesis quality as the number of unposed input views increases, highlighting its potential for more flexible and accessible generative NVS systems.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Li_NVComposer_Boosting_Generative_Novel_View_Synthesis_with_Multiple_Sparse_and_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Li_NVComposer_Boosting_Generative_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Li_2025_CVPR,
    author    = {Li, Lingen and Zhang, Zhaoyang and Li, Yaowei and Xu, Jiale and Hu, Wenbo and Li, Xiaoyu and Cheng, Weihao and Gu, Jinwei and Xue, Tianfan and Shan, Ying},
    title     = {NVComposer: Boosting Generative Novel View Synthesis with Multiple Sparse and Unposed Images},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {777-787}
}

raw_affiliation:
[{"affiliation": "The Chinese University of Hong Kong", "city": "Hong Kong", "country": "China"}, {"affiliation": "ARC Lab, Tencent PCG", "city": "Shenzhen", "country": "China"}, {"affiliation": "Peking University", "city": "Beijing", "country": "China"}]


title: LeanGaussian: Breaking Pixel or Point Cloud Correspondence in Modeling 3D Gaussians
authors: Jiamin Wu, Kenkun Liu, Han Gao, Xiaoke Jiang, Yuan Yao, Lei Zhang
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 26641-26651
cities: ["Shenzhen", "Hong Kong", "Shenzhen"]
countries: ["China", "China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Wu_LeanGaussian_Breaking_Pixel_or_Point_Cloud_Correspondence_in_Modeling_3D_CVPR_2025_paper.html

abstract:
Rencently, Gaussian splatting has demonstrated significant success in novel view synthesis. Current methods often regress Gaussians with pixel or point cloud correspondence, linking each Gaussian with a pixel or a 3D point. This leads to the redundancy of Gaussians being used to overfit the correspondence rather than the objects represented by the 3D Gaussians themselves, consequently wasting resources and lacking accurate geometries or textures.In this paper, we introduce LeanGaussian, a novel approach that treats each query in deformable Transformer as one 3D Gaussian ellipsoid, breaking the pixel or point cloud correspondence constraints. We leverage deformable decoder to iteratively refine the Gaussians layer-by-layer with the image features as keys and values.Notably, the center of each 3D Gaussian is defined as 3D reference points, which are then projected onto the image for deformable attention in 2D space.On both the ShapeNet SRN dataset (category level) and the Google Scanned Objects dataset (open-category level, trained with the Objaverse dataset), our approach, outperforms prior methods by approximately 6.1%, achieving a PSNR of 25.44 and 22.36, respectively. Additionally, our method achieves a 3D reconstruction speed of 7.2 FPS and rendering speed 500 FPS.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Wu_LeanGaussian_Breaking_Pixel_or_Point_Cloud_Correspondence_in_Modeling_3D_CVPR_2025_paper.pdf

bibtex:
@InProceedings{Wu_2025_CVPR,
    author    = {Wu, Jiamin and Liu, Kenkun and Gao, Han and Jiang, Xiaoke and Yao, Yuan and Zhang, Lei},
    title     = {LeanGaussian: Breaking Pixel or Point Cloud Correspondence in Modeling 3D Gaussians},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {26641-26651}
}

raw_affiliation:
[{"affiliation": "International Digital Economy Academy (IDEA)", "city": "Shenzhen", "country": "China"}, {"affiliation": "Hong Kong University of Science and Technology", "city": "Hong Kong", "country": "China"}, {"affiliation": "The Chinese University of Hong Kong, Shenzhen", "city": "Shenzhen", "country": "China"}]

affiliations:
["International Digital Economy Academy (IDEA)", "Hong Kong University of Science and Technology", "The Chinese University of Hong Kong, Shenzhen"]


title: Modeling Multiple Normal Action Representations for Error Detection in Procedural Tasks
authors: Wei-Jin Huang, Yuan-Ming Li, Zhi-Wei Xia, Yu-Ming Tang, Kun-Yu Lin, Jian-Fang Hu, Wei-Shi Zheng
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 27794-27804
cities: ["Guangzhou", "Shenzhen", "Beijing", "Guangzhou"]
countries: ["China", "China", "China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Huang_Modeling_Multiple_Normal_Action_Representations_for_Error_Detection_in_Procedural_CVPR_2025_paper.html

abstract:
Error detection in procedural activities is essential for consistent and correct outcomes in AR-assisted and robotic systems. Existing methods often focus on temporal ordering errors or rely on static prototypes to represent normal actions. However, these approaches typically overlook the common scenario where multiple, distinct actions are valid following a given sequence of executed actions. This leads to two issues: (1) the model cannot effectively detect errors using static prototypes when the inference environment or action execution distribution differs from training; and (2) the model may also use the wrong prototypes to detect errors if the ongoing action label is not the same as the predicted one. To address this problem, we propose an Adaptive Multiple Normal Action Representation (AMNAR) framework. AMNAR predicts all valid next actions and reconstructs their corresponding normal action representations, which are compared against the ongoing action to detect errors. Extensive experiments demonstrate that AMNAR achieves state-of-the-art performance, highlighting the effectiveness of AMNAR and the importance of modeling multiple valid next actions in error detection. The code is available at https://github.com/iSEE-Laboratory/AMNAR.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Huang_Modeling_Multiple_Normal_Action_Representations_for_Error_Detection_in_Procedural_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Huang_Modeling_Multiple_Normal_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Huang_2025_CVPR,
    author    = {Huang, Wei-Jin and Li, Yuan-Ming and Xia, Zhi-Wei and Tang, Yu-Ming and Lin, Kun-Yu and Hu, Jian-Fang and Zheng, Wei-Shi},
    title     = {Modeling Multiple Normal Action Representations for Error Detection in Procedural Tasks},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {27794-27804}
}

raw_affiliation:
[{"affiliation": "School of Computer Science and Engineering, Sun Yat-sen University, China", "city": "Guangzhou", "country": "China"}, {"affiliation": "Peng Cheng Laboratory, China", "city": "Shenzhen", "country": "China"}, {"affiliation": "Key Laboratory of Machine Intelligence and Advanced Computing, Ministry of Education, China", "city": "Beijing", "country": "China"}, {"affiliation": "Guangdong Province Key Laboratory of Information Security Technology, China", "city": "Guangzhou", "country": "China"}]

affiliations:
["School of Computer Science and Engineering, Sun Yat-sen University, China", "Peng Cheng Laboratory, China", "Key Laboratory of Machine Intelligence and Advanced Computing, Ministry of Education, China", "Guangdong Province Key Laboratory of Information Security Technology, China"]


title: Efficient Personalization of Quantized Diffusion Model without Backpropagation
authors: Hoigi Seo, Wongi Jeong, Kyungryeol Lee, Se Young Chun
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 7717-7727
cities: ["Seoul", "Seoul"]
countries: ["Republic of Korea", "Republic of Korea"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Seo_Efficient_Personalization_of_Quantized_Diffusion_Model_without_Backpropagation_CVPR_2025_paper.html

abstract:
Diffusion models have shown remarkable performance in image synthesis, but they demand extensive computational and memory resources for training, fine-tuning and inference. Although advanced quantization techniques have successfully minimized memory usage for inference, training and fine-tuning these quantized models still require large memory possibly due to dequantization for accurate computation of gradients and/or backpropagation for gradient-based algorithms. However, memory-efficient fine-tuning is particularly desirable for applications such as personalization that often must be run on edge devices like mobile phones with private data. In this work, we address this challenge by quantizing a diffusion model with personalization via Textual Inversion and by leveraging a zeroth-order optimization on personalization tokens without dequantization so that it does not require gradient and activation storage for backpropagation that consumes considerable memory. Since a gradient estimation using zeroth-order optimization is quite noisy for a single or a few images in personalization, we propose to denoise the estimated gradient by projecting it onto a subspace that is constructed with the past history of the tokens, dubbed Subspace Gradient. In addition, we investigated the influence of text embedding in image generation, leading to our proposed time steps sampling, dubbed Partial Uniform Timestep Sampling for sampling with effective diffusion timesteps. Our method achieves comparable performance to prior methods in image and text alignment scores for personalizing Stable Diffusion with only forward passes while reducing training memory demand up to 8.2x. Project page: https://ignoww.github.io/ZOODiP_project/

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Seo_Efficient_Personalization_of_Quantized_Diffusion_Model_without_Backpropagation_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Seo_Efficient_Personalization_of_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Seo_2025_CVPR,
    author    = {Seo, Hoigi and Jeong, Wongi and Lee, Kyungryeol and Chun, Se Young},
    title     = {Efficient Personalization of Quantized Diffusion Model without Backpropagation},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {7717-7727}
}

raw_affiliation:
[{"affiliation": "Dept. of Electrical and Computer Engineering, Seoul National University", "city": "Seoul", "country": "Republic of Korea"}, {"affiliation": "INMC & IPAI, Seoul National University", "city": "Seoul", "country": "Republic of Korea"}]

affiliations:
["Dept. of Electrical and Computer Engineering, Seoul National University", "INMC & IPAI, Seoul National University"]


authors: Yifan Zhou, Zeqi Xiao, Shuai Yang, Xingang Pan
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 34-44
cities: ["Singapore", "Beijing"]
countries: ["Singapore", "China"]
oral: yes
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Zhou_Alias-Free_Latent_Diffusion_Models_Improving_Fractional_Shift_Equivariance_of_Diffusion_CVPR_2025_paper.html

title:
Alias-Free Latent Diffusion Models: Improving Fractional Shift Equivariance of Diffusion Latent Space

abstract:
Latent Diffusion Models (LDMs) are known to have an unstable generation process, where even small perturbations or shifts in the input noise can lead to significantly different outputs. This hinders their applicability in applications requiring consistent results. In this work, we redesign LDMs to enhance consistency by making them shift-equivariant. While introducing anti-aliasing operations can partially improve shift-equivariance, significant aliasing and inconsistency persist due to the unique challenges in LDMs, including 1) aliasing amplification during VAE training and multiple U-Net inferences, and 2) self-attention modules that inherently lack shift-equivariance. To address these issues, we redesign the attention modules to be shift-equivariant and propose an equivariance loss that effectively suppresses the frequency bandwidth of the features in the continuous domain. The resulting alias-free LDM (AF-LDM) achieves strong shift-equivariance and is also robust to irregular warping. Extensive experiments demonstrate that AF-LDM produces significantly more consistent results than vanilla LDM across various applications, including video editing and image-to-image translation.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Zhou_Alias-Free_Latent_Diffusion_Models_Improving_Fractional_Shift_Equivariance_of_Diffusion_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Zhou_Alias-Free_Latent_Diffusion_CVPR_2025_supplemental.zip

bibtex:
@InProceedings{Zhou_2025_CVPR,
    author    = {Zhou, Yifan and Xiao, Zeqi and Yang, Shuai and Pan, Xingang},
    title     = {Alias-Free Latent Diffusion Models: Improving Fractional Shift Equivariance of Diffusion Latent Space},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {34-44}
}

raw_affiliation:
[{"affiliation": "S-Lab, Nanyang Technological University", "city": "Singapore", "country": "Singapore"}, {"affiliation": "Wangxuan Institute of Computer Technology, Peking University", "city": "Beijing", "country": "China"}]

affiliations:
["S-Lab, Nanyang Technological University", "Wangxuan Institute of Computer Technology, Peking University"]


authors: Shilhora Akshay, Niveditha Lakshmi Narasimhan, Jacob George, Vineeth N Balasubramanian
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 25528-25538
affiliations: ["Indian Institute of Technology", "KLA Corporation"]
cities: ["Hyderabad", "Not Available"]
countries: ["India", "Not Available"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Akshay_A_Unified_Latent_Schrodinger_Bridge_Diffusion_Model_for_Unsupervised_Anomaly_CVPR_2025_paper.html

title:
A Unified Latent Schrodinger Bridge Diffusion Model for Unsupervised Anomaly Detection and Localization

abstract:
Anomaly detection and localization remain pivotal challenges in computer vision, with applications ranging from industrial inspection to medical diagnostics. While current supervised methods offer high precision, they are often impractical due to the scarcity of annotated data and the infrequent occurrence of anomalies. Recent advancements in unsupervised approaches, particularly reconstruction-based methods, have addressed these issues by training models exclusively on normal data, enabling them to identify anomalies during inference. However, these methods frequently rely on auxiliary networks or specialized adaptations, which can limit their robustness and practicality. This work introduces the Latent Anomaly Schrodinger Bridge (LASB), a unified unsupervised anomaly detection model that operates entirely in the latent space without requiring additional networks or custom modifications. LASB transforms anomaly images into normal images by preserving structural integrity across varying anomaly classes, lighting, and pose conditions, making it highly robust and versatile. Unlike previous methods, LASB does not focus solely on reconstructing anomaly features, but emphasizes anomaly transformation, achieving smooth anomaly-to-normal image conversions. Our method achieves state-of-the-art performance on both the MVTec-AD and VisA datasets, excelling in detection and localization tasks.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Akshay_A_Unified_Latent_Schrodinger_Bridge_Diffusion_Model_for_Unsupervised_Anomaly_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Akshay_A_Unified_Latent_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Akshay_2025_CVPR,
    author    = {Akshay, Shilhora and Narasimhan, Niveditha Lakshmi and George, Jacob and Balasubramanian, Vineeth N},
    title     = {A Unified Latent Schrodinger Bridge Diffusion Model for Unsupervised Anomaly Detection and Localization},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {25528-25538}
}

raw_affiliation:
[{"affiliation": "Indian Institute of Technology", "city": "Hyderabad", "country": "India"}, {"affiliation": "KLA Corporation", "city": "Not Available", "country": "Not Available"}]


title: KVQ: Boosting Video Quality Assessment via Saliency-guided Local Perception
authors: Yunpeng Qu, Kun Yuan, Qizhi Xie, Ming Sun, Chao Zhou, Jian Wang
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 2150-2160
affiliations: ["Tsinghua University", "Kuaishou Technology", "BNRist, Tsinghua University"]
cities: ["Beijing", "Beijing", "Beijing"]
countries: ["China", "China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Qu_KVQ_Boosting_Video_Quality_Assessment_via_Saliency-guided_Local_Perception_CVPR_2025_paper.html

abstract:
Video Quality Assessment (VQA), which intends to predict the perceptual quality of videos, has attracted increasing attention. Due to factors like motion blur or specific distortions, the quality of different regions in a video varies. Recognizing the region-wise local quality within a video is beneficial for assessing global quality and can guide us in adopting fine-grained enhancement or transcoding strategies. Due to the heavy cost of annotating region-wise quality, the lack of ground truth constraints from relevant datasets further complicates the utilization of local perception. Inspired by the Human Visual System (HVS) that links global quality to the local texture of different regions and their visual saliency, we propose a Kaleidoscope Video Quality Assessment (KVQ) framework, which aims to effectively assess both saliency and local texture, thereby facilitating the assessment of global quality. Our framework extracts visual saliency and allocates attention using Fusion-Window Attention (FWA) while incorporating a Local Perception Constraint (LPC) to mitigate the reliance of regional texture perception on neighboring areas. KVQ obtains significant improvements across multiple scenarios on five VQA benchmarks compared to SOTA methods. Furthermore, to assess local perception, we establish a new Local Perception Visual Quality (LPVQ) dataset with region-wise annotations. Experimental results demonstrate the capability of KVQ in perceiving local distortions. KVQ models and the LPVQ dataset will be available at https://github.com/qyp2000/KVQ.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Qu_KVQ_Boosting_Video_Quality_Assessment_via_Saliency-guided_Local_Perception_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Qu_KVQ_Boosting_Video_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Qu_2025_CVPR,
    author    = {Qu, Yunpeng and Yuan, Kun and Xie, Qizhi and Sun, Ming and Zhou, Chao and Wang, Jian},
    title     = {KVQ: Boosting Video Quality Assessment via Saliency-guided Local Perception},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {2150-2160}
}

raw_affiliation:
[{"affiliation": "Tsinghua University", "city": "Beijing", "country": "China"}, {"affiliation": "Kuaishou Technology", "city": "Beijing", "country": "China"}, {"affiliation": "BNRist, Tsinghua University", "city": "Beijing", "country": "China"}]


title: MambaVision: A Hybrid Mamba-Transformer Vision Backbone
authors: Ali Hatamizadeh, Jan Kautz
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 25261-25270
raw_affiliation: [{"affiliation": "NVIDIA", "city": "Santa Clara", "country": "USA"}]
affiliations: ["NVIDIA"]
cities: ["Santa Clara"]
countries: ["USA"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Hatamizadeh_MambaVision_A_Hybrid_Mamba-Transformer_Vision_Backbone_CVPR_2025_paper.html

abstract:
We propose a novel hybrid Mamba-Transformer backbone, MambaVision, specifically tailored for vision applications. Our core contribution includes redesigning the Mamba formulation to enhance its capability for efficient modeling of visual features. Through a comprehensive ablation study, we demonstrate the feasibility of integrating Vision Transformers (ViT) with Mamba. Our results show that equipping the Mamba architecture with self-attention blocks in the final layers greatly improves its capacity to capture long-range spatial dependencies. Based on these findings, we introduce a family of MambaVision models with a hierarchical architecture to meet various design criteria. For classification on the ImageNet-1K dataset, MambaVision variants achieve state-of-the-art (SOTA) performance in terms of both Top-1 accuracy and throughput. In downstream tasks such as object detection, instance segmentation, and semantic segmentation on MS COCO and ADE20K datasets, MambaVision outperforms comparably sized backbones while demonstrating favorable performance. Code: https://github.com/NVlabs/MambaVision

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Hatamizadeh_MambaVision_A_Hybrid_Mamba-Transformer_Vision_Backbone_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Hatamizadeh_MambaVision_A_Hybrid_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Hatamizadeh_2025_CVPR,
    author    = {Hatamizadeh, Ali and Kautz, Jan},
    title     = {MambaVision: A Hybrid Mamba-Transformer Vision Backbone},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {25261-25270}
}


title: Learning Flow Fields in Attention for Controllable Person Image Generation
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 2491-2501
affiliations: ["Meta AI", "King\u2019s College London", "Tongji University"]
cities: ["Menlo Park", "London", "Shanghai"]
countries: ["USA", "UK", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Zhou_Learning_Flow_Fields_in_Attention_for_Controllable_Person_Image_Generation_CVPR_2025_paper.html

authors:
Zijian Zhou, Shikun Liu, Xiao Han, Haozhe Liu, Kam Woh Ng, Tian Xie, Yuren Cong, Hang Li, Mengmeng Xu, Juan-Manuel Perez-Rua, Aditya Patel, Tao Xiang, Miaojing Shi, Sen He

abstract:
Controllable person image generation aims to generate a person image conditioned on reference images, allowing precise control over the person's appearance or pose.However, prior methods often distort fine-grained textural details from the reference image, despite achieving high overall image quality.We attribute these distortions to inadequate attention to corresponding regions in the reference image.To address this, we thereby propose learning flow fields in attention (Leffa), which explicitly guides the target query to attend to the correct reference key in the attention layer during training.Specifically, it is realized via a regularization loss on top of the attention map within a diffusion-based baseline.Our extensive experiments show that Leffa achieves state-of-the-art performance in controlling appearance (virtual try-on) and pose (pose transfer), significantly reducing fine-grained detail distortion while maintaining high image quality.Additionally, we show that our loss is model-agnostic and can be used to improve the performance of other diffusion models.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Zhou_Learning_Flow_Fields_in_Attention_for_Controllable_Person_Image_Generation_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Zhou_Learning_Flow_Fields_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Zhou_2025_CVPR,
    author    = {Zhou, Zijian and Liu, Shikun and Han, Xiao and Liu, Haozhe and Ng, Kam Woh and Xie, Tian and Cong, Yuren and Li, Hang and Xu, Mengmeng and Perez-Rua, Juan-Manuel and Patel, Aditya and Xiang, Tao and Shi, Miaojing and He, Sen},
    title     = {Learning Flow Fields in Attention for Controllable Person Image Generation},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {2491-2501}
}

raw_affiliation:
[{"affiliation": "Meta AI", "city": "Menlo Park", "country": "USA"}, {"affiliation": "King\u2019s College London", "city": "London", "country": "UK"}, {"affiliation": "Tongji University", "city": "Shanghai", "country": "China"}]


title: Multi-Label Prototype Visual Spatial Search for Weakly Supervised Semantic Segmentation
authors: Songsong Duan, Xi Yang, Nannan Wang
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 30241-30250
cities: ["Xi'an"]
countries: ["China"]
highlight: yes
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Duan_Multi-Label_Prototype_Visual_Spatial_Search_for_Weakly_Supervised_Semantic_Segmentation_CVPR_2025_paper.html

abstract:
Existing Weakly Supervised Semantic Segmentation (WSSS) relies on the CNN-based Class Activation Map (CAM) and Transformer-based self-attention map to generate class-specific masks for semantic segmentation. However, CAM and self-attention maps usually cause incomplete segmentation due to classification bias issue. To address this issue, we propose a Multi-Label Prototype Visual Spatial Search (MuP-VSS) method with a spatial query mechanism, which learns a set of learnable class token vectors as queries to search the similarity visual tokens from image patch tokens. Specifically, MuP-VSS consists of two key components: multi-label prototype representation and multi-label prototype optimization. The former designs a global embedding to learn the global tokens from the images, and then proposes a Prototype Embedding Module (PEM) to interact with patch tokens to understand the local semantic information. The latter utilizes the exclusivity and consistency principles of the multi-label prototypes to design three prototype losses to optimize them, which contain cross-class prototype (CCP) contrastive loss, cross-image prototype (CIP) contrastive loss, and patch-to-prototype (P2P) consistency loss. CCP loss models exclusivity of multi-label prototypes learned from a single image to enhance the discriminative properties of each class better. CCP loss learns the consistency of the same class-specific prototypes extracted from multiple images to enhance the semantic consistency. P2P loss is proposed to control the semantic response of the prototype to the image patches. Experimental results on Pascal VOC 2012 and MS COCO show that MuP-VSS significantly outperforms recent methods and achieves state-of-the-art performance.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Duan_Multi-Label_Prototype_Visual_Spatial_Search_for_Weakly_Supervised_Semantic_Segmentation_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Duan_Multi-Label_Prototype_Visual_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Duan_2025_CVPR,
    author    = {Duan, Songsong and Yang, Xi and Wang, Nannan},
    title     = {Multi-Label Prototype Visual Spatial Search for Weakly Supervised Semantic Segmentation},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {30241-30250}
}

raw_affiliation:
[{"affiliation": "State Key Laboratory of Integrated Services Networks, School of Telecommunications Engineering, Xidian University", "city": "Xi'an", "country": "China"}]

affiliations:
["State Key Laboratory of Integrated Services Networks, School of Telecommunications Engineering, Xidian University"]


authors: Lexington Whalen, Zhenbang Du, Haoran You, Chaojian Li, Sixu Li, Yingyan Lin
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 7675-7684
raw_affiliation: [{"affiliation": "Georgia Institute of Technology", "city": "Atlanta", "country": "USA"}]
affiliations: ["Georgia Institute of Technology"]
cities: ["Atlanta"]
countries: ["USA"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Whalen_Early-Bird_Diffusion_Investigating_and_Leveraging_Timestep-Aware_Early-Bird_Tickets_in_Diffusion_CVPR_2025_paper.html

title:
Early-Bird Diffusion: Investigating and Leveraging Timestep-Aware Early-Bird Tickets in Diffusion Models for Efficient Training

abstract:
Training diffusion models (DMs) requires substantial computational resources due to multiple forward and backward passes across numerous timesteps, motivating research into efficient training techniques. In this paper, we propose EB-Diff-Train, a new efficient DM training approach that is orthogonal to other methods of accelerating DM training, by investigating and leveraging Early-Bird (EB) tickets--sparse subnetworks that manifest early in the training process and maintain high generation quality. We first investigate the existence of traditional EB tickets in DMs, enabling competitive generation quality without fully training a dense model. Then, we delve into the concept of diffusion-dedicated EB tickets, drawing on insights from varying importance of different timestep regions. These tickets adapt their sparsity levels according to the importance of corresponding timestep regions, allowing for aggressive sparsity during non-critical regions while conserving computational resources for crucial timestep regions. Building on this, we develop an efficient DM training technique that derives timestep-aware EB tickets, trains them in parallel, and combines them during inference for image generation.  Extensive experiments validate the existence of both traditional and timestep-aware EB tickets, as well as the effectiveness of our proposed EB-Diff-Train method. This approach can significantly reduce training time both spatially and temporally--achieving 2.9x 5.8x speedups over training unpruned dense models, and up to 10.3x faster training compared to standard train-prune-finetune pipelines--without compromising generative quality. Our code is available at https://github.com/GATECH-EIC/Early-Bird-Diffusion.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Whalen_Early-Bird_Diffusion_Investigating_and_Leveraging_Timestep-Aware_Early-Bird_Tickets_in_Diffusion_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Whalen_Early-Bird_Diffusion_Investigating_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Whalen_2025_CVPR,
    author    = {Whalen, Lexington and Du, Zhenbang and You, Haoran and Li, Chaojian and Li, Sixu and Lin, Yingyan},
    title     = {Early-Bird Diffusion: Investigating and Leveraging Timestep-Aware Early-Bird Tickets in Diffusion Models for Efficient Training},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {7675-7684}
}


title: FireEdit: Fine-grained Instruction-based Image Editing via Region-aware Vision Language Model
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 13093-13103
affiliations: ["Shenzhen Campus of Sun Yat-sen University", "Hunyuan, Tencent", "Tsinghua University", "HKUST"]
cities: ["Shenzhen", "Shenzhen", "Beijing", "Hong Kong"]
countries: ["China", "China", "China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Zhou_FireEdit_Fine-grained_Instruction-based_Image_Editing_via_Region-aware_Vision_Language_Model_CVPR_2025_paper.html

authors:
Jun Zhou, Jiahao Li, Zunnan Xu, Hanhui Li, Yiji Cheng, Fa-Ting Hong, Qin Lin, Qinglin Lu, Xiaodan Liang

abstract:
Currently, instruction-based image editing methods have made significant progress by leveraging the powerful cross-modal understanding capabilities of visual language models (VLMs). However, they still face challenges in three key areas: 1) complex scenarios; 2) semantic consistency; and 3) fine-grained editing. To address these issues, we propose FireEdit, an innovative Fine-grained Instruction-based image editing framework that exploits a REgion-aware VLM. FireEdit is designed to accurately comprehend user instructions and ensure effective control over the editing process. Specifically, we enhance the fine-grained visual perception capabilities of the VLM by introducing additional region tokens. Relying solely on the output of the LLM to guide the diffusion model may lead to suboptimal editing results. Therefore, we propose a Time-Aware Target Injection module and a Hybrid Visual Cross Attention module. The former dynamically adjusts the guidance strength at various denoising stages by integrating timestep embeddings with the text embeddings. The latter enhances visual details for image editing, thereby preserving semantic consistency between the edited result and the source image. By combining the VLM enhanced with fine-grained region tokens and the time-dependent diffusion model, FireEdit demonstrates significant advantages in comprehending editing instructions and maintaining high semantic consistency. Extensive experiments indicate that our approach surpasses the state-of-the-art instruction-based image editing methods.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Zhou_FireEdit_Fine-grained_Instruction-based_Image_Editing_via_Region-aware_Vision_Language_Model_CVPR_2025_paper.pdf

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

bibtex:
@InProceedings{Zhou_2025_CVPR,
    author    = {Zhou, Jun and Li, Jiahao and Xu, Zunnan and Li, Hanhui and Cheng, Yiji and Hong, Fa-Ting and Lin, Qin and Lu, Qinglin and Liang, Xiaodan},
    title     = {FireEdit: Fine-grained Instruction-based Image Editing via Region-aware Vision Language Model},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {13093-13103}
}

raw_affiliation:
[{"affiliation": "Shenzhen Campus of Sun Yat-sen University", "city": "Shenzhen", "country": "China"}, {"affiliation": "Hunyuan, Tencent", "city": "Shenzhen", "country": "China"}, {"affiliation": "Tsinghua University", "city": "Beijing", "country": "China"}, {"affiliation": "HKUST", "city": "Hong Kong", "country": "China"}]


title: Doppelgangers++: Improved Visual Disambiguation with Geometric 3D Features
authors: Yuanbo Xiangli, Ruojin Cai, Hanyu Chen, Jeffrey Byrne, Noah Snavely
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 27166-27175
affiliations: ["Cornell University", "Visym Labs"]
cities: ["Ithaca", "Unknown"]
countries: ["USA", "Unknown"]
highlight: yes
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Xiangli_Doppelgangers_Improved_Visual_Disambiguation_with_Geometric_3D_Features_CVPR_2025_paper.html

abstract:
Accurate 3D reconstruction is frequently hindered by visual aliasing, where visually similar but distinct surfaces (aka, doppelgangers), are incorrectly matched. These spurious matches distort the structure-from-motion (SfM) process, leading to misplaced model elements and reduced accuracy. Prior efforts addressed this with CNN classifiers trained on curated datasets, but these approaches struggle to generalize across diverse real-world scenes and can require extensive parameter tuning. In this work, we present Doppelgangers++, a method to enhance doppelganger detection and improve 3D reconstruction accuracy. Our contributions include a diversified training dataset that incorporates geo-tagged images from everyday scenes to expand robustness beyond landmark-based datasets. We further propose a Transformer-based classifier that leverages 3D-aware features from the MASt3R model, achieving superior precision and recall across both in-domain and out-of-domain tests. Doppelgangers++ integrates seamlessly into standard SfM and MASt3R-SfM pipelines, offering efficiency and adaptability across varied scenes. To evaluate SfM accuracy, we introduce an automated, geotag-based method for validating reconstructed models, eliminating the need for manual inspection. Through extensive experiments, we demonstrate that Doppelgangers++ significantly enhances pairwise visual disambiguation and improves 3D reconstruction quality in complex and diverse scenarios.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Xiangli_Doppelgangers_Improved_Visual_Disambiguation_with_Geometric_3D_Features_CVPR_2025_paper.pdf

bibtex:
@InProceedings{Xiangli_2025_CVPR,
    author    = {Xiangli, Yuanbo and Cai, Ruojin and Chen, Hanyu and Byrne, Jeffrey and Snavely, Noah},
    title     = {Doppelgangers++: Improved Visual Disambiguation with Geometric 3D Features},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {27166-27175}
}

raw_affiliation:
[{"affiliation": "Cornell University", "city": "Ithaca", "country": "USA"}, {"affiliation": "Visym Labs", "city": "Unknown", "country": "Unknown"}]


title: Learnable Infinite Taylor Gaussian for Dynamic View Rendering
authors: Bingbing Hu, Yanyan Li, Rui Xie, Bo Xu, Haoye Dong, Junfeng Yao, Gim Hee Lee
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 26844-26854
cities: ["Xiamen", "Singapore", "Xiamen"]
countries: ["China", "Singapore", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Hu_Learnable_Infinite_Taylor_Gaussian_for_Dynamic_View_Rendering_CVPR_2025_paper.html

abstract:
Capturing the temporal evolution of Gaussian properties such as position, rotation, and scale is a challenging task due to the vast number of time-varying parameters and the limited photometric data available, which generally results in convergence issues, making it difficult to find an optimal solution. While feeding all inputs into an end-to-end neural network can effectively model complex temporal dynamics, this approach lacks explicit supervision and struggles to generate high-quality transformation fields. On the other hand, using time-conditioned polynomial functions to model Gaussian trajectories and orientations provides a more explicit and interpretable solution, but requires significant handcrafted effort and lacks generalizability across diverse scenes. To overcome these limitations, this paper introduces a novel approach based on a learnable infinite Taylor Formula to model the temporal evolution of Gaussians. This method offers both the flexibility of an implicit network-based approach and the interpretability of explicit polynomial functions, allowing for more robust and generalizable modeling of Gaussian dynamics across various dynamic scenes.Extensive experiments on dynamic novel view rendering tasks are conducted on public datasets, demonstrating that the proposed method achieves state-of-the-art performance in this domain.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Hu_Learnable_Infinite_Taylor_Gaussian_for_Dynamic_View_Rendering_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Hu_Learnable_Infinite_Taylor_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Hu_2025_CVPR,
    author    = {Hu, Bingbing and Li, Yanyan and Xie, Rui and Xu, Bo and Dong, Haoye and Yao, Junfeng and Lee, Gim Hee},
    title     = {Learnable Infinite Taylor Gaussian for Dynamic View Rendering},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {26844-26854}
}

raw_affiliation:
[{"affiliation": "School of Film, School of Informatics, Xiamen University", "city": "Xiamen", "country": "China"}, {"affiliation": "National University of Singapore", "city": "Singapore", "country": "Singapore"}, {"affiliation": "Key Laboratory of Digital Protection and Intelligent Processing of Intangible Cultural Heritage of Fujian and Taiwan Ministry of Culture and Tourism", "city": "Xiamen", "country": "China"}]

affiliations:
["School of Film, School of Informatics, Xiamen University", "National University of Singapore", "Key Laboratory of Digital Protection and Intelligent Processing of Intangible Cultural Heritage of Fujian and Taiwan Ministry of Culture and Tourism"]


title: DL2G: Degradation-guided Local-to-Global Restoration for Eyeglass Reflection Removal
authors: Zhilv Yi, Xiao Lu, Hong Ding, Jingbo Hu, Zhi Jiang, Chunxia Xiao
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 16061-16070
cities: ["Wuhan", "Changsha", "Nanning"]
countries: ["China", "China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Yi_DL2G_Degradation-guided_Local-to-Global_Restoration_for_Eyeglass_Reflection_Removal_CVPR_2025_paper.html

abstract:
Eyeglass reflection removal can restore the texture information in the reflection destructed eye area, which is meaningful for various tasks on the facial images. It is still challenging to correctly eliminate reflections, reasonably restore the lost contents, and guarantee that the final result has a consistent color and illumination with the input image. In this paper, we introduce a Degradation-guided Local-to-Global (DL2G) restoration framework to address this problem. We first propose a multiplicative reflection degradation model, which is used to alleviate reflection degradation to obtain a preliminary result. Then, in the local details restoration stage, we propose a local structure-aware diffusion model to learn the true distribution of texture details in the eye area. This helps in recovering lost contents in the regions of heavy degradation where the background is invisible. Finally, in the global consistency refinement stage, we utilize the input image as a reference image to generate the final result that is consistent with the input image in color and illumination. Extensive experiments demonstrate that our method can improve the effect of reflection removal and generate results with more reasonable semantics, exquisite details, and harmonious illumination.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Yi_DL2G_Degradation-guided_Local-to-Global_Restoration_for_Eyeglass_Reflection_Removal_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Yi_DL2G_Degradation-guided_Local-to-Global_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Yi_2025_CVPR,
    author    = {Yi, Zhilv and Lu, Xiao and Ding, Hong and Hu, Jingbo and Jiang, Zhi and Xiao, Chunxia},
    title     = {DL2G: Degradation-guided Local-to-Global Restoration for Eyeglass Reflection Removal},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {16061-16070}
}

raw_affiliation:
[{"affiliation": "School of Computer Science, Wuhan University", "city": "Wuhan", "country": "China"}, {"affiliation": "College of Engeering and Design, Hunan Normal University", "city": "Changsha", "country": "China"}, {"affiliation": "Guangxi University of Finance and Economics", "city": "Nanning", "country": "China"}]

affiliations:
["School of Computer Science, Wuhan University", "College of Engeering and Design, Hunan Normal University", "Guangxi University of Finance and Economics"]


title: DIV-FF: Dynamic Image-Video Feature Fields For Environment Understanding in Egocentric Videos
authors: Lorenzo Mur-Labadia, Josechu Guerrero, Ruben Martinez-Cantin
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 3470-3480
raw_affiliation: [{"affiliation": "I3A - Universidad de Zaragoza", "city": "Zaragoza", "country": "Spain"}]
affiliations: ["I3A - Universidad de Zaragoza"]
cities: ["Zaragoza"]
countries: ["Spain"]
highlight: yes
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Mur-Labadia_DIV-FF_Dynamic_Image-Video_Feature_Fields_For_Environment_Understanding_in_Egocentric_CVPR_2025_paper.html

abstract:
Environment understanding in egocentric videos is an important step for applications like robotics, augmented reality and assistive technologies. These videos are characterized by dynamic interactions and a strong dependence on the wearer's engagement with the environment. Traditional approaches often focus on isolated clips or fail to integrate rich semantic and geometric information, limiting scene comprehension. We introduce Dynamic Image-Video Feature Fields (DIV-FF), a framework that decomposes the egocentric scene into persistent, dynamic, and actor-based components while integrating both image and video-language features. Our model enables detailed segmentation, captures affordances, understands the surroundings and maintains consistent understanding over time. DIV-FF outperforms state-of-the-art methods, particularly in dynamically evolving scenarios, demonstrating its potential to advance long-term, spatio-temporal scene understanding.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Mur-Labadia_DIV-FF_Dynamic_Image-Video_Feature_Fields_For_Environment_Understanding_in_Egocentric_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Mur-Labadia_DIV-FF_Dynamic_Image-Video_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Mur-Labadia_2025_CVPR,
    author    = {Mur-Labadia, Lorenzo and Guerrero, Josechu and Martinez-Cantin, Ruben},
    title     = {DIV-FF: Dynamic Image-Video Feature Fields For Environment Understanding in Egocentric Videos},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {3470-3480}
}


title: SaMam: Style-aware State Space Model for Arbitrary Image Style Transfer
authors: Hongda Liu, Longguang Wang, Ye Zhang, Ziru Yu, Yulan Guo
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 28468-28478
cities: ["Shenzhen", "Beijing"]
countries: ["China", "China"]
highlight: yes
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Liu_SaMam_Style-aware_State_Space_Model_for_Arbitrary_Image_Style_Transfer_CVPR_2025_paper.html

abstract:
Global effective receptive field plays a crucial role for image style transfer (ST) to obtain high-quality stylized results. However, existing ST backbones (e.g., CNNs and Transformers) suffer huge computational complexity to achieve global receptive fields. Recently, the State Space Model (SSM), especially the improved variant Mamba, has shown great potential for long-range dependency modeling with linear complexity, which offers a approach to resolve the above dilemma. In this paper, we develop a Mamba-based style transfer framework, termed SaMam. Specifically, a mamba encoder is designed to efficiently extract content and style information. In addition, a style-aware mamba decoder is developed to flexibly adapt to various styles. Moreover, to address the problems of local pixel forgetting, channel redundancy and spatial discontinuity of existing SSMs, we introduce both local enhancement and zigzag scan. Qualitative and quantitative results demonstrate that our SaMam outperforms state-of-the-art methods in terms of both accuracy and efficiency.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Liu_SaMam_Style-aware_State_Space_Model_for_Arbitrary_Image_Style_Transfer_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Liu_SaMam_Style-aware_State_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Liu_2025_CVPR,
    author    = {Liu, Hongda and Wang, Longguang and Zhang, Ye and Yu, Ziru and Guo, Yulan},
    title     = {SaMam: Style-aware State Space Model for Arbitrary Image Style Transfer},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {28468-28478}
}

raw_affiliation:
[{"affiliation": "The Shenzhen Campus of Sun Y at-Sen University, Sun Y at-Sen University", "city": "Shenzhen", "country": "China"}, {"affiliation": "Aviation University of Air Force", "city": "Beijing", "country": "China"}]

affiliations:
["The Shenzhen Campus of Sun Y at-Sen University, Sun Y at-Sen University", "Aviation University of Air Force"]


authors: Mengqiu Xu, Kaixin Chen, Heng Guo, Yixiang Huang, Ming Wu, Zhenwei Shi, Chuang Zhang, Jun Guo
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 12637-12646
cities: ["Beijing", "Beijing", "Beijing"]
countries: ["China", "China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Xu_MFogHub_Bridging_Multi-Regional_and_Multi-Satellite_Data_for_Global_Marine_Fog_CVPR_2025_paper.html

title:
MFogHub: Bridging Multi-Regional and Multi-Satellite Data for Global Marine Fog Detection and Forecasting

abstract:
Deep learning approaches for marine fog detection and forecasting have outperformed traditional methods, demonstrating significant scientific and practical importance. However, the limited availability of open-source datasets remains a major challenge. Existing datasets, often focused on a single region or satellite, restrict the ability to evaluate model performance across diverse conditions and hinder the exploration of intrinsic marine fog characteristics. To address these limitations, we introduce MFogHub, the first multi-regional and multi-satellite dataset to integrate annotated marine fog observations from 15 coastal fog-prone regions and six geostationary satellites, comprising over 68,000 high-resolution samples. By encompassing diverse regions and satellite perspectives, MFogHub facilitates rigorous evaluation of both detection and forecasting methods under varying conditions. Extensive experiments with 16 baseline models demonstrate that MFogHub can reveal generalization fluctuations due to regional and satellite discrepancy, while also serving as a valuable resource for the development of targeted and scalable fog prediction techniques. Through MFogHub, we aim to advance both the practical monitoring and scientific understanding of marine fog dynamics on a global scale. The dataset and code are available in the supplementary materials.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Xu_MFogHub_Bridging_Multi-Regional_and_Multi-Satellite_Data_for_Global_Marine_Fog_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Xu_MFogHub_Bridging_Multi-Regional_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Xu_2025_CVPR,
    author    = {Xu, Mengqiu and Chen, Kaixin and Guo, Heng and Huang, Yixiang and Wu, Ming and Shi, Zhenwei and Zhang, Chuang and Guo, Jun},
    title     = {MFogHub: Bridging Multi-Regional and Multi-Satellite Data for Global Marine Fog Detection and Forecasting},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {12637-12646}
}

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

affiliations:
["Beijing University of Posts and Telecommunications", "Beihang University", "Beijing Wuzi University"]


authors: Naveen George, Karthik Nandan Dasaraju, Rutheesh Reddy Chittepu, Konda Reddy Mopuri
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 13393-13402
cities: ["Hyderabad", "Ropar"]
countries: ["India", "India"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/George_The_Illusion_of_Unlearning_The_Unstable_Nature_of_Machine_Unlearning_CVPR_2025_paper.html

title:
The Illusion of Unlearning: The Unstable Nature of Machine Unlearning in Text-to-Image Diffusion Models

abstract:
Text-to-image models such as Stable Diffusion, DALL*E, and Midjourney have gained immense popularity lately. However, they are trained on vast amounts of data that may include private, explicit, or copyrighted material used without permission, raising serious legal and ethical concerns. In light of the recent regulations aimed at protecting individual data privacy, there has been a surge in Machine Unlearning methods designed to remove specific concepts from these models. However, we identify a critical flaw in these unlearning techniques: unlearned concepts will revive when the models are fine-tuned, even with general or unrelated prompts. In this paper, for the first time, through an extensive study, we demonstrate the unstable nature of existing unlearning methods in text-to-image diffusion models. We introduce a framework that includes a couple of measures for analyzing the stability of existing unlearning methods. Further, the paper offers preliminary insights into the plausible explanation for the instability of the mapping-based unlearning methods that can guide future research toward more robust unlearning techniques.  Codes for implementing the proposed framework are provided.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/George_The_Illusion_of_Unlearning_The_Unstable_Nature_of_Machine_Unlearning_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/George_The_Illusion_of_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{George_2025_CVPR,
    author    = {George, Naveen and Dasaraju, Karthik Nandan and Chittepu, Rutheesh Reddy and Mopuri, Konda Reddy},
    title     = {The Illusion of Unlearning: The Unstable Nature of Machine Unlearning in Text-to-Image Diffusion Models},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {13393-13402}
}

raw_affiliation:
[{"affiliation": "Data-driven Intelligence and Learning Lab, Indian Institute of Technology, Hyderabad", "city": "Hyderabad", "country": "India"}, {"affiliation": "Indian Institute of Technology, Ropar", "city": "Ropar", "country": "India"}]

affiliations:
["Data-driven Intelligence and Learning Lab, Indian Institute of Technology, Hyderabad", "Indian Institute of Technology, Ropar"]


title: Making Old Film Great Again: Degradation-aware State Space Model for Old Film Restoration
authors: Yudong Mao, Hao Luo, Zhiwei Zhong, Peilin Chen, Zhijiang Zhang, Shiqi Wang
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 28039-28049
affiliations: ["Computer Science, City University of Hong Kong", "Inspur Intelligent Terminal Co., Ltd"]
cities: ["Hong Kong", "unknown"]
countries: ["Hong Kong", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Mao_Making_Old_Film_Great_Again_Degradation-aware_State_Space_Model_for_CVPR_2025_paper.html

abstract:
Unlike modern native digital videos, the restoration of old films requires addressing specific degradations inherent to analog sources. However, existing specialized methods still fall short compared to general video restoration techniques. In this work, we propose a new baseline to re-examine the challenges in old film restoration. First, we develop an improved Mamba-based framework, dubbed MambaOFR, which can dynamically adjust the degradation removal patterns by generating degradation-aware prompts to tackle the complex and composite degradations present in old films.  Second, we introduce a flow-guided mask deformable alignment module to mitigate the propagation of structured defect features in the temporal domain.  Third, we introduce the first benchmark dataset that includes both synthetic and real-world old film clips. Extensive experiments show that the proposed method achieves state-of-the-art performance, outperforming existing advanced approaches in old film restoration. The implementation and model is available at https://github.com/MaoAYD/MambaOFR.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Mao_Making_Old_Film_Great_Again_Degradation-aware_State_Space_Model_for_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Mao_Making_Old_Film_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Mao_2025_CVPR,
    author    = {Mao, Yudong and Luo, Hao and Zhong, Zhiwei and Chen, Peilin and Zhang, Zhijiang and Wang, Shiqi},
    title     = {Making Old Film Great Again: Degradation-aware State Space Model for Old Film Restoration},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {28039-28049}
}

raw_affiliation:
[{"affiliation": "Computer Science, City University of Hong Kong", "city": "Hong Kong", "country": "Hong Kong"}, {"affiliation": "Inspur Intelligent Terminal Co., Ltd", "city": "unknown", "country": "China"}]


authors: Junning Qiu, Minglei Lu, Fei Wang, Yu Guo, Yonggen Ling
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 16839-16849
cities: ["Xi'an", "Xi'an", "Xi'an", "Shenzhen"]
countries: ["China", "China", "China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Qiu_Leveraging_Global_Stereo_Consistency_for_Category-Level_Shape_and_6D_Pose_CVPR_2025_paper.html

title:
Leveraging Global Stereo Consistency for Category-Level Shape and 6D Pose Estimation from Stereo Images

abstract:
Stereo-based category-level shape and 6D pose estimation methods have the potential to generalize to a wider range of materials than RGBD methods, which often suffer from depth measurement errors. However, without explicit depth from two views,  parameters to be estimated can become inherently entangled, negatively impacting performance. To address this, we propose a method that leverages global stereo consistency to constrain optimization directions and mitigate parameter entanglement. We first estimate an intra-category occupancy field to represent a unified shape across views, ensuring consistency and preventing shape ambiguity. Through a divide-and-conquer approach within global shape fitting, we fit this shape to stereo images to obtain the pose, iteratively rendering normalized depth maps and exchanging information across views. This approach improves convergence toward the correct pose and scale. We validated our method on both depth-friendly and depth-challenging materials using our S-RGBD dataset and the TOD benchmark. Our method surpasses RGBD methods on challenging objects and performs comparably on depth-friendly ones. Ablation studies confirm the effectiveness of each component.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Qiu_Leveraging_Global_Stereo_Consistency_for_Category-Level_Shape_and_6D_Pose_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Qiu_Leveraging_Global_Stereo_CVPR_2025_supplemental.zip

bibtex:
@InProceedings{Qiu_2025_CVPR,
    author    = {Qiu, Junning and Lu, Minglei and Wang, Fei and Guo, Yu and Ling, Yonggen},
    title     = {Leveraging Global Stereo Consistency for Category-Level Shape and 6D Pose Estimation from Stereo Images},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {16839-16849}
}

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

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


title: AlphaPre: Amplitude-Phase Disentanglement Model for Precipitation Nowcasting
authors: Kenghong Lin, Baoquan Zhang, Demin Yu, Wenzhi Feng, Shidong Chen, Feifan Gao, Xutao Li, Yunming Ye
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 17841-17850
affiliations: ["Harbin Institute of Technology, Shenzhen"]
cities: ["Shenzhen"]
countries: ["China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Lin_AlphaPre_Amplitude-Phase_Disentanglement_Model_for_Precipitation_Nowcasting_CVPR_2025_paper.html

abstract:
Precipitation nowcasting involves using current radar observation sequences to predict future radar sequences and determine future precipitation distribution, which is crucial for disaster warning, traffic planning, and agricultural production. Despite numerous advancements, challenges persist in accurately predicting both the location and intensity of precipitation, as these factors are often interdependent, with complex atmospheric dynamics and moisture distribution causing position and intensity changes to be intricately coupled. Inspired by the fact that in the frequency domain, phase variations are shown to correspond to changes in the position of precipitation, while amplitude variations are linked to intensity changes, we propose an amplitude-phase disentanglement model called AlphaPre, which separately learn the position and intensity changes of precipitation. AlphaPre comprises three key components: a phase network, an amplitude network, and an AlphaMixer. The phase network captures positional changes by learning phase variations, while the amplitude network models intensity changes by alternating between the frequency and spatial domains. The AlphaMixer then integrates these components to produce a refined precipitation forecast. Extensive experiments on four datasets demonstrate the effectiveness and superiority of our method over state-of-the-art approaches. Our code is publicly available at https://github.com/linkenghong/AlphaPre.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Lin_AlphaPre_Amplitude-Phase_Disentanglement_Model_for_Precipitation_Nowcasting_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Lin_AlphaPre_Amplitude-Phase_Disentanglement_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Lin_2025_CVPR,
    author    = {Lin, Kenghong and Zhang, Baoquan and Yu, Demin and Feng, Wenzhi and Chen, Shidong and Gao, Feifan and Li, Xutao and Ye, Yunming},
    title     = {AlphaPre: Amplitude-Phase Disentanglement Model for Precipitation Nowcasting},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {17841-17850}
}

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


title: EfficientLLaVA: Generalizable Auto-Pruning for Large Vision-language Models
authors: Yinan Liang, Ziwei Wang, Xiuwei Xu, Jie Zhou, Jiwen Lu
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 9445-9454
affiliations: ["Tsinghua University", "Nanyang Technological University"]
cities: ["Beijing", "Singapore"]
countries: ["China", "Singapore"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Liang_EfficientLLaVA_Generalizable_Auto-Pruning_for_Large_Vision-language_Models_CVPR_2025_paper.html

abstract:
While multimodal large language models demonstrate strong performance in complex reasoning tasks, they pose significant challenges related to model complexity during deployment, especially for resource-limited devices. In this paper, we propose an automatic pruning method for large vision-language models to enhance the efficiency of multimodal reasoning. Conventional methods rely on the training data of the original model to select the proper pruning ratio for different network components. However, these methods are impractical for large vision-language models due to the unaffordable search costs caused by web-scale training corpus. In contrast, our approach only leverages a small number of samples to search for the desired pruning policy by maximizing its generalization ability on unknown training data while maintaining the model accuracy, which enables the achievement of an optimal trade-off between accuracy and efficiency for large visual language models. Specifically, we formulate the generalization gap of the pruning strategy using the structural risk minimization principle. Based on both task performance and generalization capability, we iteratively search for the optimal pruning policy within a given search space and optimize the vision projector to evolve the search space with higher upper bound of performance. We conduct extensive experiments on the ScienceQA, Vizwiz, MM-vet, and LLaVA-Bench datasets for the task of visual question answering. Using only 64 samples for pruning policy search, EfficientLLaVA achieves an accuracy of 83.05% on ScienceQA, along with a x1.8 speedup compared to the dense LLaVA-v1.5-7B model.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Liang_EfficientLLaVA_Generalizable_Auto-Pruning_for_Large_Vision-language_Models_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Liang_EfficientLLaVA_Generalizable_Auto-Pruning_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Liang_2025_CVPR,
    author    = {Liang, Yinan and Wang, Ziwei and Xu, Xiuwei and Zhou, Jie and Lu, Jiwen},
    title     = {EfficientLLaVA: Generalizable Auto-Pruning for Large Vision-language Models},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {9445-9454}
}

raw_affiliation:
[{"affiliation": "Tsinghua University", "city": "Beijing", "country": "China"}, {"affiliation": "Nanyang Technological University", "city": "Singapore", "country": "Singapore"}]


title: Detection-Friendly Nonuniformity Correction: A Union Framework for Infrared UAV Target Detection
authors: Houzhang Fang, Xiaolin Wang, Zengyang Li, Lu Wang, Qingshan Li, Yi Chang, Luxin Yan
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 11898-11907
affiliations: ["Xidian University", "Huazhong University of Science and Technology"]
cities: ["Xi'an", "Wuhan"]
countries: ["China", "China"]
highlight: yes
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Fang_Detection-Friendly_Nonuniformity_Correction_A_Union_Framework_for_Infrared_UAV_Target_CVPR_2025_paper.html

abstract:
Infrared unmanned aerial vehicle (UAV) images captured using thermal detectors are often affected by temperature-dependent low-frequency nonuniformity, which significantly reduces the contrast of the images. Detecting UAV targets under nonuniform conditions is crucial in UAV surveillance applications. Existing methods typically treat infrared nonuniformity correction (NUC) as a preprocessing step for detection, which leads to suboptimal performance. Balancing the two tasks while enhancing detection-beneficial information remains challenging. In this paper, we present a detection-friendly union framework, termed UniCD, that simultaneously addresses both infrared NUC and UAV target detection tasks in an end-to-end manner. We first model NUC as a small number of parameter estimation problem jointly driven by priors and data to generate detection-conducive images. Then, we incorporate a new auxiliary loss with target mask supervision into the backbone of the infrared UAV target detection network to strengthen target features while suppressing the background. To better balance correction and detection, we introduce a detection-guided self-supervised loss to reduce feature discrepancies between the two tasks, thereby enhancing detection robustness to varying nonuniformity levels. Additionally, we construct a new benchmark composed of 50,000 infrared images in various nonuniformity types, multi-scale UAV targets and rich backgrounds with target annotations, called IRBFD. Extensive experiments on IRBFD demonstrate that our UniCD is a robust union framework for NUC and UAV target detection while achieving real-time processing capabilities. Dataset can be available at https://github.com/IVPLaboratory/UniCD.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Fang_Detection-Friendly_Nonuniformity_Correction_A_Union_Framework_for_Infrared_UAV_Target_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Fang_Detection-Friendly_Nonuniformity_Correction_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Fang_2025_CVPR,
    author    = {Fang, Houzhang and Wang, Xiaolin and Li, Zengyang and Wang, Lu and Li, Qingshan and Chang, Yi and Yan, Luxin},
    title     = {Detection-Friendly Nonuniformity Correction: A Union Framework for Infrared UAV Target Detection},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {11898-11907}
}

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


title: Articulated Kinematics Distillation from Video Diffusion Models
authors: Xuan Li, Qianli Ma, Tsung-Yi Lin, Yongxin Chen, Chenfanfu Jiang, Ming-Yu Liu, Donglai Xiang
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 17571-17581
affiliations: ["UCLA", "NVIDIA"]
cities: ["Los Angeles", "Santa Clara"]
countries: ["USA", "USA"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Li_Articulated_Kinematics_Distillation_from_Video_Diffusion_Models_CVPR_2025_paper.html

abstract:
We present Articulated Kinematics Distillation (AKD), a framework for generating high-fidelity character animations by merging the strengths of skeleton-based animation and modern generative models. AKD uses a skeleton-based representation for rigged 3D assets, drastically reducing the Degrees of Freedom (DoFs) by focusing on joint-level control, which allows for efficient, consistent motion synthesis. Through Score Distillation Sampling (SDS) with pre-trained video diffusion models, AKD distills complex, articulated motions while maintaining structural integrity, overcoming challenges faced by 4D neural deformation fields in preserving shape consistency. This approach is naturally compatible with physics-based simulation, ensuring physically plausible interactions. Experiments show that AKD achieves superior 3D consistency and motion quality compared with existing works on text-to-4D generation.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Li_Articulated_Kinematics_Distillation_from_Video_Diffusion_Models_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Li_Articulated_Kinematics_Distillation_CVPR_2025_supplemental.zip

bibtex:
@InProceedings{Li_2025_CVPR,
    author    = {Li, Xuan and Ma, Qianli and Lin, Tsung-Yi and Chen, Yongxin and Jiang, Chenfanfu and Liu, Ming-Yu and Xiang, Donglai},
    title     = {Articulated Kinematics Distillation from Video Diffusion Models},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {17571-17581}
}

raw_affiliation:
[{"affiliation": "UCLA", "city": "Los Angeles", "country": "USA"}, {"affiliation": "NVIDIA", "city": "Santa Clara", "country": "USA"}]


title: ExpertAF: Expert Actionable Feedback from Video
authors: Kumar Ashutosh, Tushar Nagarajan, Georgios Pavlakos, Kris Kitani, Kristen Grauman
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 13582-13594
affiliations: ["University of Texas at Austin", "FAIR Meta", "Carnegie Mellon University"]
cities: ["Austin", "Unknown", "Pittsburgh"]
countries: ["USA", "Unknown", "USA"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Ashutosh_ExpertAF_Expert_Actionable_Feedback_from_Video_CVPR_2025_paper.html

abstract:
Feedback is essential for learning a new skill or improving one's current skill-level. However, current methods for skill-assessment from video only provide scores or compare demonstrations, leaving the burden of knowing what to do differently on the user. We introduce a novel method to generate actionable feedback (AF) from video of a person doing a physical activity, such as basketball or soccer. Our method takes a video demonstration and its accompanying 3D body pose and generates (1) free-form expert commentary describing what the person is doing well and what they could improve, and (2) a visual expert demonstration that incorporates the required corrections. We show how to leverage Ego-Exo4D's videos of skilled activity and expert commentary together with a strong language model to create a weakly-supervised training dataset for this task, and we devise a multimodal video-language model to infer coaching feedback. Our method is able to reason across multi-modal input combinations to output full-spectrum, actionable coaching--expert commentary, expert video retrieval, and expert pose generation--outperforming strong vision-language models on both established metrics and human preference studies.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Ashutosh_ExpertAF_Expert_Actionable_Feedback_from_Video_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Ashutosh_ExpertAF_Expert_Actionable_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Ashutosh_2025_CVPR,
    author    = {Ashutosh, Kumar and Nagarajan, Tushar and Pavlakos, Georgios and Kitani, Kris and Grauman, Kristen},
    title     = {ExpertAF: Expert Actionable Feedback from Video},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {13582-13594}
}

raw_affiliation:
[{"affiliation": "University of Texas at Austin", "city": "Austin", "country": "USA"}, {"affiliation": "FAIR Meta", "city": "Unknown", "country": "Unknown"}, {"affiliation": "Carnegie Mellon University", "city": "Pittsburgh", "country": "USA"}]


title: MP-SfM: Monocular Surface Priors for Robust Structure-from-Motion
authors: Zador Pataki, Paul-Edouard Sarlin, Johannes L. Schönberger, Marc Pollefeys
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 21891-21901
affiliations: ["ETH Zurich", "Google", "Microsoft Spatial AI Lab"]
cities: ["Zurich", "Mountain View", "Redmond"]
countries: ["Switzerland", "USA", "USA"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Pataki_MP-SfM_Monocular_Surface_Priors_for_Robust_Structure-from-Motion_CVPR_2025_paper.html

abstract:
While Structure-from-Motion (SfM) has seen much progress over the years, state-of-the-art systems are prone to failure when facing extreme viewpoint changes in low-overlap, low-parallax or high-symmetry scenarios. Because capturing images that avoid these pitfalls is challenging, this severely limits the wider use of SfM, especially by non-expert users. We overcome these limitations by augmenting the classical SfM paradigm with monocular depth and normal priors inferred by deep neural networks. Thanks to a tight integration of monocular and multi-view constraints, our approach significantly outperforms existing ones under extreme viewpoint changes, while maintaining strong performance in standard conditions. We also show that monocular priors can help reject faulty associations due to symmetries, which is a long-standing problem for SfM. This makes our approach the first capable of reliably reconstructing challenging indoor environments from few images. Through principled uncertainty propagation, it is robust to errors in the priors, can handle priors inferred by different models with little tuning, and will thus easily benefit from future progress in monocular depth and normal estimation. Our code is publicly available at https://github.com/cvg/mpsfm.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Pataki_MP-SfM_Monocular_Surface_Priors_for_Robust_Structure-from-Motion_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Pataki_MP-SfM_Monocular_Surface_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Pataki_2025_CVPR,
    author    = {Pataki, Zador and Sarlin, Paul-Edouard and Sch\"onberger, Johannes L. and Pollefeys, Marc},
    title     = {MP-SfM: Monocular Surface Priors for Robust Structure-from-Motion},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {21891-21901}
}

raw_affiliation:
[{"affiliation": "ETH Zurich", "city": "Zurich", "country": "Switzerland"}, {"affiliation": "Google", "city": "Mountain View", "country": "USA"}, {"affiliation": "Microsoft Spatial AI Lab", "city": "Redmond", "country": "USA"}]


title: OnlineAnySeg: Online Zero-Shot 3D Segmentation by Visual Foundation Model Guided 2D Mask Merging
authors: Yijie Tang, Jiazhao Zhang, Yuqing Lan, Yulan Guo, Dezun Dong, Chenyang Zhu, Kai Xu
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 3676-3685
cities: ["Changsha", "Changsha", "Beijing", "Guangzhou"]
countries: ["China", "China", "China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Tang_OnlineAnySeg_Online_Zero-Shot_3D_Segmentation_by_Visual_Foundation_Model_Guided_CVPR_2025_paper.html

abstract:
Online 3D open-vocabulary segmentation of a progressively reconstructed scene is both a critical and challenging task for embodied applications. With the success of visual foundation models (VFMs) in the image domain, leveraging 2D priors to address 3D online segmentation has become a prominent research focus. Since segmentation results provided by 2D priors often require spatial consistency to be lifted into final 3D segmentation, an efficient method for identifying spatial overlap among 2D masks is essential--yet existing methods rarely achieve this in real time, mainly limiting its use to offline approaches. To address this, we propose an efficient method that lifts 2D masks generated by VFMs into a unified 3D instance using a hashing technique. By employing voxel hashing for efficient 3D scene querying, our approach reduces the time complexity of costly spatial overlap queries from O(n^2) to O(n). Accurate spatial associations further enable 3D merging of 2D masks through simple similarity-based filtering in a zero-shot manner, making our approach more robust to incomplete and noisy data. Evaluated on the ScanNet and SceneNN benchmarks, our approach achieves state-of-the-art performance in online, open-vocabulary 3D instance segmentation with leading efficiency.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Tang_OnlineAnySeg_Online_Zero-Shot_3D_Segmentation_by_Visual_Foundation_Model_Guided_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Tang_OnlineAnySeg_Online_Zero-Shot_CVPR_2025_supplemental.zip

bibtex:
@InProceedings{Tang_2025_CVPR,
    author    = {Tang, Yijie and Zhang, Jiazhao and Lan, Yuqing and Guo, Yulan and Dong, Dezun and Zhu, Chenyang and Xu, Kai},
    title     = {OnlineAnySeg: Online Zero-Shot 3D Segmentation by Visual Foundation Model Guided 2D Mask Merging},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {3676-3685}
}

raw_affiliation:
[{"affiliation": "National University of Defense Technology", "city": "Changsha", "country": "China"}, {"affiliation": "Xiangjiang Laboratory", "city": "Changsha", "country": "China"}, {"affiliation": "CFCS, School of CS, Peking University", "city": "Beijing", "country": "China"}, {"affiliation": "Sun Yat-sen University", "city": "Guangzhou", "country": "China"}]

affiliations:
["National University of Defense Technology", "Xiangjiang Laboratory", "CFCS, School of CS, Peking University", "Sun Yat-sen University"]


title: Tora: Trajectory-oriented Diffusion Transformer for Video Generation
authors: Zhenghao Zhang, Junchao Liao, Menghao Li, ZuoZhuo Dai, Bingxue Qiu, Siyu Zhu, Long Qin, Weizhi Wang
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 2063-2073
affiliations: ["Alibaba Cloud Computing", "Fudan University"]
cities: ["Hangzhou", "Shanghai"]
countries: ["China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Zhang_Tora_Trajectory-oriented_Diffusion_Transformer_for_Video_Generation_CVPR_2025_paper.html

abstract:
Recent advancements in Diffusion Transformer (DiT) have demonstrated remarkable proficiency in producing high-quality video content. Nonetheless, the potential of transformer-based diffusion models for effectively generating videos with controllable motion remains an area of limited exploration. This paper introduces Tora, the first trajectory-oriented DiT framework that concurrently integrates textual, visual, and trajectory conditions, thereby enabling scalable video generation with effective motion guidance. Specifically, Tora consists of a Trajectory Extractor (TE), a Spatial-Temporal DiT, and a Motion-guidance Fuser (MGF). The TE encodes arbitrary trajectories into hierarchical spacetime motion patches with a 3D motion compression network. The MGF integrates the motion patches into the DiT blocks to generate consistent videos that accurately follow designated trajectories.  Our design aligns seamlessly with DiT's scalability, allowing precise control of video content's dynamics with diverse durations, aspect ratios, and resolutions. Extensive experiments demonstrate that Tora excels in achieving high motion fidelity compared to the foundational DiT model, while also accurately simulating the complex movements of the physical world. Code is made available at https://github.com/alibaba/Tora .

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Zhang_Tora_Trajectory-oriented_Diffusion_Transformer_for_Video_Generation_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Zhang_Tora_Trajectory-oriented_Diffusion_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Zhang_2025_CVPR,
    author    = {Zhang, Zhenghao and Liao, Junchao and Li, Menghao and Dai, ZuoZhuo and Qiu, Bingxue and Zhu, Siyu and Qin, Long and Wang, Weizhi},
    title     = {Tora: Trajectory-oriented Diffusion Transformer for Video Generation},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {2063-2073}
}

raw_affiliation:
[{"affiliation": "Alibaba Cloud Computing", "city": "Hangzhou", "country": "China"}, {"affiliation": "Fudan University", "city": "Shanghai", "country": "China"}]


title: Volumetrically Consistent 3D Gaussian Rasterization
authors: Chinmay Talegaonkar, Yash Belhe, Ravi Ramamoorthi, Nicholas Antipa
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 10953-10963
raw_affiliation: [{"affiliation": "University of California San Diego", "city": "San Diego", "country": "USA"}]
affiliations: ["University of California San Diego"]
cities: ["San Diego"]
countries: ["USA"]
highlight: yes
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Talegaonkar_Volumetrically_Consistent_3D_Gaussian_Rasterization_CVPR_2025_paper.html

abstract:
Recently, 3D Gaussian Splatting (3DGS) has enabled photorealistic view synthesis at high inference speeds. However, its splatting-based rendering model makes several approximations to the rendering equation, reducing physical accuracy. We show that splatting and its approximations are unnecessary, even within a rasterizer; we instead volumetrically integrate 3D Gaussians directly to compute the transmittance across them analytically. We use this analytic transmittance to derive more physically accurate alpha values than 3DGS, which can directly be used within their framework. The result is a method that more closely follows the volume rendering equation (similar to ray tracing) while enjoying the speed benefits of rasterization. Our method represents opaque surfaces with higher accuracy and fewer points than 3DGS. This enables it to outperform 3DGS for view synthesis (measured in SSIM and LPIPS). Being volumetrically consistent also enables our method to work out of the box for tomography. We match the state-of-the-art 3DGS-based tomography method with fewer points. Our code is publicly available at: https://github.com/chinmay0301ucsd/Vol3DGS

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Talegaonkar_Volumetrically_Consistent_3D_Gaussian_Rasterization_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Talegaonkar_Volumetrically_Consistent_3D_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Talegaonkar_2025_CVPR,
    author    = {Talegaonkar, Chinmay and Belhe, Yash and Ramamoorthi, Ravi and Antipa, Nicholas},
    title     = {Volumetrically Consistent 3D Gaussian Rasterization},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {10953-10963}
}


title: Deterministic-to-Stochastic Diverse Latent Feature Mapping for Human Motion Synthesis
authors: Yu Hua, Weiming Liu, Gui Xu, Yaqing Hou, Yew-Soon Ong, Qiang Zhang
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 22724-22734
cities: ["Singapore", "Dalian", "Dalian", "Singapore"]
countries: ["Singapore", "China", "China", "Singapore"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Hua_Deterministic-to-Stochastic_Diverse_Latent_Feature_Mapping_for_Human_Motion_Synthesis_CVPR_2025_paper.html

abstract:
Human motion synthesis aims to generate plausible human motion sequences, which has raised widespread attention in computer animation. Recent score-based generative models (SGMs) have demonstrated impressive results on this task. However, their training process involves complex curvature trajectories, leading to unstable training process.In this paper, we propose a Deterministic-to-Stochastic Diverse Latent Feature Mapping (DSDFM) method for human motion synthesis.DSDFM consists of two stages. The first human motion reconstruction stage aims to learn the latent space distribution of human motions. The second diverse motion generation stage aims to build connections between the Gaussian distribution and the latent space distribution of human motions, thereby enhancing the diversity and accuracy of the generated human motions. This stage is achieved by the designed deterministic feature mapping procedure with DerODE and stochastic diverse output generation procedure with DivSDE. DSDFM is easy to train compared to previous SGMs-based methods and can enhance diversity without introducing additional training parameters.Through qualitative and quantitative experiments, DSDFM achieves state-of-the-art results surpassing the latest methods, validating its superiority in human motion synthesis.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Hua_Deterministic-to-Stochastic_Diverse_Latent_Feature_Mapping_for_Human_Motion_Synthesis_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Hua_Deterministic-to-Stochastic_Diverse_Latent_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Hua_2025_CVPR,
    author    = {Hua, Yu and Liu, Weiming and Xu, Gui and Hou, Yaqing and Ong, Yew-Soon and Zhang, Qiang},
    title     = {Deterministic-to-Stochastic Diverse Latent Feature Mapping for Human Motion Synthesis},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {22724-22734}
}

raw_affiliation:
[{"affiliation": "College of Computing and Data Science, Nanyang Technological University, Singapore", "city": "Singapore", "country": "Singapore"}, {"affiliation": "ByteDance Inc. 3Dalian University of Technology, China", "city": "Dalian", "country": "China"}, {"affiliation": "Dalian University, China", "city": "Dalian", "country": "China"}, {"affiliation": "Centre for Frontier AI Research, Institute of High Performance Computing, Agency for Science, Technology and Research, Singapore", "city": "Singapore", "country": "Singapore"}]

affiliations:
["College of Computing and Data Science, Nanyang Technological University, Singapore", "ByteDance Inc. 3Dalian University of Technology, China", "Dalian University, China", "Centre for Frontier AI Research, Institute of High Performance Computing, Agency for Science, Technology and Research, Singapore"]


title: Morpheus: Text-Driven 3D Gaussian Splat Shape and Color Stylization
authors: Jamie Wynn, Zawar Qureshi, Jakub Powierza, Jamie Watson, Mohamed Sayed
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 7825-7836
affiliations: ["Niantic", "UCL"]
cities: ["Sunnyvale", "London"]
countries: ["USA", "UK"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Wynn_Morpheus_Text-Driven_3D_Gaussian_Splat_Shape_and_Color_Stylization_CVPR_2025_paper.html

abstract:
Exploring real-world spaces using novel-view synthesis is fun, and reimagining those worlds in a different style adds another layer of excitement. Stylized worlds can also be used for downstream tasks where there is limited training data and a need to expand a model's training distribution. Most current novel-view synthesis stylization techniques lack the ability to convincingly change geometry. This is because any geometry change requires increased style strength which is often capped for stylization stability and consistency. In this work, we propose a new autoregressive 3D Gaussian Splatting stylization method. As part of this method, we contribute a new RGBD diffusion model that allows for strength control over appearance and shape stylization. To ensure consistency across stylized frames, we use a combination of novel depth-guided cross attention, feature injection, and a Warp ControlNet conditioned on composite frames for guiding the stylization of new frames. We validate our method via extensive qualitative results, quantitative experiments, and a user study.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Wynn_Morpheus_Text-Driven_3D_Gaussian_Splat_Shape_and_Color_Stylization_CVPR_2025_paper.pdf

bibtex:
@InProceedings{Wynn_2025_CVPR,
    author    = {Wynn, Jamie and Qureshi, Zawar and Powierza, Jakub and Watson, Jamie and Sayed, Mohamed},
    title     = {Morpheus: Text-Driven 3D Gaussian Splat Shape and Color Stylization},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {7825-7836}
}

raw_affiliation:
[{"affiliation": "Niantic", "city": "Sunnyvale", "country": "USA"}, {"affiliation": "UCL", "city": "London", "country": "UK"}]


title: CacheQuant: Comprehensively Accelerated Diffusion Models
authors: Xuewen Liu, Zhikai Li, Qingyi Gu
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 23269-23280
cities: ["Beijing", "Beijing"]
countries: ["China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Liu_CacheQuant_Comprehensively_Accelerated_Diffusion_Models_CVPR_2025_paper.html

abstract:
Diffusion models have gradually gained prominence in the field of image synthesis, showcasing remarkable generative capabilities. Nevertheless, the slow inference and complex networks, resulting from redundancy at both temporal and structural levels, hinder their low-latency applications in real-world scenarios. Current acceleration methods for diffusion models focus separately on temporal and structural levels. However, independent optimization at each level to further push the acceleration limits results in significant performance degradation. On the other hand, integrating optimizations at both levels can compound the acceleration effects. Unfortunately, we find that the optimizations at these two levels are not entirely orthogonal. Performing separate optimizations and then simply integrating them results in unsatisfactory performance. To tackle this issue, we propose CacheQuant, a novel training-free paradigm that comprehensively accelerates diffusion models by jointly optimizing model caching and quantization techniques. Specifically, we employ a dynamic programming approach to determine the optimal cache schedule, in which the properties of caching and quantization are carefully considered to minimize errors. Additionally, we propose decoupled error correction to further mitigate the coupled and accumulated errors step by step. Experimental results show that CacheQuant achieves a 5.18xspeedup and 4xcompression for Stable Diffusion on MS-COCO, with only a 0.02 loss in CLIP score.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Liu_CacheQuant_Comprehensively_Accelerated_Diffusion_Models_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Liu_CacheQuant_Comprehensively_Accelerated_CVPR_2025_supplemental.zip

bibtex:
@InProceedings{Liu_2025_CVPR,
    author    = {Liu, Xuewen and Li, Zhikai and Gu, Qingyi},
    title     = {CacheQuant: Comprehensively Accelerated Diffusion Models},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {23269-23280}
}

raw_affiliation:
[{"affiliation": "Institute of Automation, Chinese Academy of Sciences", "city": "Beijing", "country": "China"}, {"affiliation": "School of Arti\ufb01cial Intelligence, University of Chinese Academy of Sciences", "city": "Beijing", "country": "China"}]

affiliations:
["Institute of Automation, Chinese Academy of Sciences", "School of Arti\ufb01cial Intelligence, University of Chinese Academy of Sciences"]


authors: Marcus Nordström, Atsuto Maki, Henrik Hult
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 20820-20829
affiliations: ["KTH Royal Institute of Technology", "RaySearch Laboratories"]
cities: ["Stockholm", "Stockholm"]
countries: ["Sweden", "Sweden"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Nordstrom_The_Impact_Label_Noise_and_Choice_of_Threshold_has_on_CVPR_2025_paper.html

title:
The Impact Label Noise and Choice of Threshold has on Cross-Entropy and Soft-Dice in Image Segmentation

abstract:
In image segmentation and specifically in medical image segmentation, the soft-Dice loss is often chosen instead of the more traditional cross-entropy loss to improve performance with respect to the Dice metric.Experimental work supporting this claim exists, but how and why the two loss functions lead to different predictions is not well understood.This paper explains the observed discrepancy as a consequence of how those loss functions are effected by label noise and what threshold is used to convert the predicted soft segmentation to predicted labels.In particular, it is shown (i) how the optimal solutions to the two loss functions diverge as the noise is increased, and (ii) how the optimal solutions to soft-Dice can be recovered by thresholding the solutions to cross-entropy with an a priori unknown but efficiently computable threshold.The theoretical results are supported by numerical experiments and it is concluded that cross-entropy with the alternative threshold yields the stability and informative label probability maps associated with cross-entropy without sacrificing the performance of soft-Dice.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Nordstrom_The_Impact_Label_Noise_and_Choice_of_Threshold_has_on_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Nordstrom_The_Impact_Label_CVPR_2025_supplemental.zip

bibtex:
@InProceedings{Nordstrom_2025_CVPR,
    author    = {Nordstr\"om, Marcus and Maki, Atsuto and Hult, Henrik},
    title     = {The Impact Label Noise and Choice of Threshold has on Cross-Entropy and Soft-Dice in Image Segmentation},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {20820-20829}
}

raw_affiliation:
[{"affiliation": "KTH Royal Institute of Technology", "city": "Stockholm", "country": "Sweden"}, {"affiliation": "RaySearch Laboratories", "city": "Stockholm", "country": "Sweden"}]


title: Open-World Objectness Modeling Unifies Novel Object Detection
authors: Shan Zhang, Yao Ni, Jinhao Du, Yuan Xue, Philip Torr, Piotr Koniusz, Anton van den Hengel
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 30332-30342
cities: ["Adelaide", "Canberra", "Beijing", "Columbus", "Oxford", "Sydney"]
countries: ["Australia", "Australia", "China", "USA", "UK", "Australia"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Zhang_Open-World_Objectness_Modeling_Unifies_Novel_Object_Detection_CVPR_2025_paper.html

abstract:
The challenge in open-world object detection, similarly to few- and zero-shot learning, is to generalize beyond the class distribution of the training data. In this paper, we propose a general class-agnostic objectness measure to limit  bias toward labeled samples. One issue in open-world detection is that previously unseen objects are often misclassified as known categories or filtered as background by classifiers. To prevent this, we explicitly model the joint distribution of objectness and category labels using variational approximation. However, without sufficient labeled data, minimizing the KL divergence between the estimated posterior and a static normal prior fails to converge. Our theoretical analysis identifies the root cause of this failure and motivates adopting a Gaussian prior with variance dynamically adapted to the estimated posterior as a surrogate. To further reduce misclassification, we introduce an energy-based margin loss that encourages unknown objects to move toward high-density regions of the distribution, thus reducing the uncertainty of unknown detections. Our Open-World OBJectness modeling (OWOBJ) boosts novel object detection, especially in low-data regimes. OWOBJ is a flexible plugin that outperforms baselines in Open-World, Few-Shot, and zero-shot Open-Vocabulary Object Detection.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Zhang_Open-World_Objectness_Modeling_Unifies_Novel_Object_Detection_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Zhang_Open-World_Objectness_Modeling_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Zhang_2025_CVPR,
    author    = {Zhang, Shan and Ni, Yao and Du, Jinhao and Xue, Yuan and Torr, Philip and Koniusz, Piotr and van den Hengel, Anton},
    title     = {Open-World Objectness Modeling Unifies Novel Object Detection},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {30332-30342}
}

raw_affiliation:
[{"affiliation": "Australian Institute for Machine Learning", "city": "Adelaide", "country": "Australia"}, {"affiliation": "Australian National University", "city": "Canberra", "country": "Australia"}, {"affiliation": "Peking University", "city": "Beijing", "country": "China"}, {"affiliation": "Ohio State University", "city": "Columbus", "country": "USA"}, {"affiliation": "University of Oxford", "city": "Oxford", "country": "UK"}, {"affiliation": "Data61 CSIRO", "city": "Sydney", "country": "Australia"}]

affiliations:
["Australian Institute for Machine Learning", "Australian National University", "Peking University", "Ohio State University", "University of Oxford", "Data61 CSIRO"]


title: LLaVA-Critic: Learning to Evaluate Multimodal Models
authors: Tianyi Xiong, Xiyao Wang, Dong Guo, Qinghao Ye, Haoqi Fan, Quanquan Gu, Heng Huang, Chunyuan Li
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 13618-13628
affiliations: ["ByteDance", "University of Maryland, College Park"]
cities: ["Beijing", "College Park"]
countries: ["China", "USA"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Xiong_LLaVA-Critic_Learning_to_Evaluate_Multimodal_Models_CVPR_2025_paper.html

abstract:
We introduce LLaVA-Critic, the first open-source large multimodal model (LMM) designed as a generalist evaluator to assess performance across a wide range of multimodal tasks. LLaVA-Critic is trained using a high-quality critic instruction-following dataset that incorporates diverse evaluation criteria and scenarios. Our experiments demonstrate the model's effectiveness in two key areas: (i) LMM-as-a-Judge, where LLaVA-Critic provides reliable evaluation scores, performing on par with or surpassing GPT models on multiple evaluation benchmarks; and (ii) Preference Learning, where it generates reward signals for preference learning, enhancing model alignment capabilities. This work underscores the potential of open-source LMMs in self-critique and evaluation, setting the stage for future research into scalable, superhuman alignment feedback mechanisms for LMMs.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Xiong_LLaVA-Critic_Learning_to_Evaluate_Multimodal_Models_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Xiong_LLaVA-Critic_Learning_to_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Xiong_2025_CVPR,
    author    = {Xiong, Tianyi and Wang, Xiyao and Guo, Dong and Ye, Qinghao and Fan, Haoqi and Gu, Quanquan and Huang, Heng and Li, Chunyuan},
    title     = {LLaVA-Critic: Learning to Evaluate Multimodal Models},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {13618-13628}
}

raw_affiliation:
[{"affiliation": "ByteDance", "city": "Beijing", "country": "China"}, {"affiliation": "University of Maryland, College Park", "city": "College Park", "country": "USA"}]


title: VILA-M3: Enhancing Vision-Language Models with Medical Expert Knowledge
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 14788-14798
affiliations: ["NVIDIA", "SingHealth", "NIH"]
cities: ["Santa Clara", "Singapore", "Bethesda"]
countries: ["USA", "Singapore", "USA"]
highlight: yes
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Nath_VILA-M3_Enhancing_Vision-Language_Models_with_Medical_Expert_Knowledge_CVPR_2025_paper.html

authors:
Vishwesh Nath, Wenqi Li, Dong Yang, Andriy Myronenko, Mingxin Zheng, Yao Lu, Zhijian Liu, Hongxu Yin, Yee Man Law, Yucheng Tang, Pengfei Guo, Can Zhao, Ziyue Xu, Yufan He, Stephanie Harmon, Benjamin Simon, Greg Heinrich, Stephen Aylward, Marc Edgar, Michael Zephyr, Pavlo Molchanov, Baris Turkbey, Holger Roth, Daguang Xu

abstract:
Generalist vision language models (VLMs) have made significant strides in computer vision, but they fall short in specialized fields like healthcare, where expert knowledge is essential. Current large multimodal models like Gemini and GPT-4o are insufficient for medical tasks due to their reliance on memorized internet knowledge rather than the nuanced expertise required in healthcare. Meanwhile, existing medical VLMs (e.g. Med-Gemini) often lack expert consultation as part of their design, and many rely on outdated, static datasets that were not created with modern, large deep learning models in mind. VLMs are usually trained in three stages: vision pre-training, vision-language pre-training, and instruction fine-tuning (IFT). IFT has been typically applied using a mixture of generic and healthcare data. In contrast, we propose that for medical VLMs, a fourth stage of specialized IFT is necessary, which focuses on medical data and includes information from domain expert models. Domain expert models developed for medical use are crucial because they are specifically trained for certain clinical tasks, e.g. to detect tumors and classify abnormalities through segmentation and classification, which learn fine-grained features of medical data-features that are often too intricate for a VLM to capture effectively. This paper introduces a new framework, VILA-M3, for medical VLMs that utilizes domain knowledge via expert models. We argue that generic VLM architectures alone are not viable for real-world clinical applications and on-demand usage of domain-specialized expert model knowledge is critical for advancing AI in healthcare. Through our experiments, we show an improved state-of-the-art (SOTA) performance with an average improvement of ~9% over the prior SOTA model Med-Gemini and ~6% over models trained on the specific tasks. Our approach emphasizes the importance of domain expertise in creating precise, reliable VLMs for medical applications.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Nath_VILA-M3_Enhancing_Vision-Language_Models_with_Medical_Expert_Knowledge_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Nath_VILA-M3_Enhancing_Vision-Language_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Nath_2025_CVPR,
    author    = {Nath, Vishwesh and Li, Wenqi and Yang, Dong and Myronenko, Andriy and Zheng, Mingxin and Lu, Yao and Liu, Zhijian and Yin, Hongxu and Law, Yee Man and Tang, Yucheng and Guo, Pengfei and Zhao, Can and Xu, Ziyue and He, Yufan and Harmon, Stephanie and Simon, Benjamin and Heinrich, Greg and Aylward, Stephen and Edgar, Marc and Zephyr, Michael and Molchanov, Pavlo and Turkbey, Baris and Roth, Holger and Xu, Daguang},
    title     = {VILA-M3: Enhancing Vision-Language Models with Medical Expert Knowledge},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {14788-14798}
}

raw_affiliation:
[{"affiliation": "NVIDIA", "city": "Santa Clara", "country": "USA"}, {"affiliation": "SingHealth", "city": "Singapore", "country": "Singapore"}, {"affiliation": "NIH", "city": "Bethesda", "country": "USA"}]


title: Repurposing Pre-trained Video Diffusion Models for Event-based Video Interpolation
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 12456-12466
affiliations: ["University of Maryland", "Massachusetts Institute of Technology"]
cities: ["College Park", "Cambridge"]
countries: ["USA", "USA"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Chen_Repurposing_Pre-trained_Video_Diffusion_Models_for_Event-based_Video_Interpolation_CVPR_2025_paper.html

authors:
Jingxi Chen, Brandon Y. Feng, Haoming Cai, Tianfu Wang, Levi Burner, Dehao Yuan, Cornelia Fermuller, Christopher A. Metzler, Yiannis Aloimonos

abstract:
Video Frame Interpolation aims to recover realistic missing frames between observed frames, generating a high-frame-rate video from a low-frame-rate video. However, without additional guidance, large motion between frames makes this problem ill-posed. Event-based Video Frame Interpolation (EVFI) addresses this challenge by using sparse, high-temporal-resolution event measurements as motion guidance. This guidance allows EVFI methods to significantly outperform frame-only methods. However, to date, EVFI methods have relied upon a limited set of paired event-frame training data, severely limiting their performance and generalization capabilities. In this work,  we overcome the limited data challenge by adapting pre-trained video diffusion models trained on internet-scale datasets to EVFI. We experimentally validate our approach on real-world EVFI datasets, including a new one we introduce. Our method outperforms existing methods and generalizes across cameras far better than existing approaches.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Chen_Repurposing_Pre-trained_Video_Diffusion_Models_for_Event-based_Video_Interpolation_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Chen_Repurposing_Pre-trained_Video_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Chen_2025_CVPR,
    author    = {Chen, Jingxi and Feng, Brandon Y. and Cai, Haoming and Wang, Tianfu and Burner, Levi and Yuan, Dehao and Fermuller, Cornelia and Metzler, Christopher A. and Aloimonos, Yiannis},
    title     = {Repurposing Pre-trained Video Diffusion Models for Event-based Video Interpolation},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {12456-12466}
}

raw_affiliation:
[{"affiliation": "University of Maryland", "city": "College Park", "country": "USA"}, {"affiliation": "Massachusetts Institute of Technology", "city": "Cambridge", "country": "USA"}]


title: MotionPRO: Exploring the Role of Pressure in Human MoCap and Beyond
authors: Shenghao Ren, Yi Lu, Jiayi Huang, Jiayi Zhao, He Zhang, Tao Yu, Qiu Shen, Xun Cao
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 27760-27770
cities: ["Nanjing", "Nanjing", "Beijing"]
countries: ["China", "China", "China"]
highlight: yes
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Ren_MotionPRO_Exploring_the_Role_of_Pressure_in_Human_MoCap_and_CVPR_2025_paper.html

abstract:
Existing human Motion Capture (MoCap) methods mostly focus on the visual similarity while neglecting the physical plausibility. As a result, downstream tasks such as driving virtual human in 3D scene or humanoid robots in real world suffer from issues such as timing drift and jitter, spatial problems like sliding and penetration, and poor global trajectory accuracy. In this paper, we revisit human MoCap from the perspective of interaction between human body and physical world by exploring the role of pressure. Firstly, we construct a large-scale human Motion capture dataset with Pressure, RGB  and Optical sensors (named MotionPRO), which comprises 70 volunteers performing 400 types of motion, encompassing a total of 12.4M pose frames. Secondly, we examine both the necessity and effectiveness of the pressure signal through two challenging tasks: (1) pose and trajectory estimation based solely on pressure: We propose a network that incorporates a small kernel decoder and a long-short-term attention module, and proof that pressure could provide accurate global trajectory and plausible lower body pose.  (2) pose and trajectory estimation by fusing pressure and RGB: We impose constraints on orthographic similarity along the camera axis and whole-body contact along the vertical axis to enhance the cross-attention strategy to fuse pressure and RGB feature maps. Experiments demonstrate that fusing pressure with RGB features not only significantly improves performance in terms of objective metrics, but also plausibly drives virtual humans (SMPL) in 3D scene. Furthermore, we demonstrate that incorporating physical perception enables humanoid robots to perform more precise and stable actions, which is highly beneficial for the development of embodied artificial intelligence. Project page is available at: https://nju-cite-mocaphumanoid.github.io/MotionPRO/

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Ren_MotionPRO_Exploring_the_Role_of_Pressure_in_Human_MoCap_and_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Ren_MotionPRO_Exploring_the_CVPR_2025_supplemental.zip

bibtex:
@InProceedings{Ren_2025_CVPR,
    author    = {Ren, Shenghao and Lu, Yi and Huang, Jiayi and Zhao, Jiayi and Zhang, He and Yu, Tao and Shen, Qiu and Cao, Xun},
    title     = {MotionPRO: Exploring the Role of Pressure in Human MoCap and Beyond},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {27760-27770}
}

raw_affiliation:
[{"affiliation": "School of Electronic Science and Engineering, Nanjing University", "city": "Nanjing", "country": "China"}, {"affiliation": "Key Laboratory of Optoelectronic Devices and Systems with Extreme Performances of MOE, Nanjing University", "city": "Nanjing", "country": "China"}, {"affiliation": "BNRist, Tsinghua University", "city": "Beijing", "country": "China"}]

affiliations:
["School of Electronic Science and Engineering, Nanjing University", "Key Laboratory of Optoelectronic Devices and Systems with Extreme Performances of MOE, Nanjing University", "BNRist, Tsinghua University"]


title: DiffVsgg: Diffusion-Driven Online Video Scene Graph Generation
authors: Mu Chen, Liulei Li, Wenguan Wang, Yi Yang
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 29161-29172
affiliations: ["ReLER, CCAI, Zhejiang University", "ReLER, AAII, University of Technology Sydney"]
cities: ["Hangzhou", "Sydney"]
countries: ["China", "Australia"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Chen_DiffVsgg_Diffusion-Driven_Online_Video_Scene_Graph_Generation_CVPR_2025_paper.html

abstract:
Top-leading solutions for Video Scene Graph Generation (VSGG) typically adopt an offline pipeline.Though demonstrating promising performance, they remain unable to handle real-time video streams and consume large GPU memory.  Moreover, these approaches fall short in temporal reasoning, merely aggregating frame-level predictions over a temporal context. In response, we introduce DiffVsgg, an online VSGG solution that frames this task as an iterative scene graph update problem. Drawing inspiration from Latent Diffusion Models (LDMs) which generate images via denoising a latent feature embedding, we unify the decoding of object classification, bounding box regression, and graph generation three tasks using one shared feature embedding. Then, given an embedding containing unified features of object pairs, we conduct a step-wise Denoising on it within LDMs, so as to deliver a clean embedding which clearly indicates the relationships between objects.This embedding then serves as the input to task-specific heads for object classification, scene graph generation, etc. DiffVsgg further facilitates continuous temporal reasoning, where predictions for subsequent frames leverage results of past frames as the conditional inputs of LDMs, to guide the reverse diffusion process for current frames.Extensive experiments on three setups of Action Genome demonstrate the superiority of DiffVsgg.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Chen_DiffVsgg_Diffusion-Driven_Online_Video_Scene_Graph_Generation_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Chen_DiffVsgg_Diffusion-Driven_Online_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Chen_2025_CVPR,
    author    = {Chen, Mu and Li, Liulei and Wang, Wenguan and Yang, Yi},
    title     = {DiffVsgg: Diffusion-Driven Online Video Scene Graph Generation},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {29161-29172}
}

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


title: Large-scale Multi-view Tensor Clustering with Implicit Linear Kernels
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 20727-20736
affiliations: ["National University of Defense Technology", "North University of China"]
cities: ["Changsha", "Taiyuan"]
countries: ["China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Liu_Large-scale_Multi-view_Tensor_Clustering_with_Implicit_Linear_Kernels_CVPR_2025_paper.html

authors:
Jiyuan Liu, Xinwang Liu, Chuankun Li, Xinhang Wan, Hao Tan, Yi Zhang, Weixuan Liang, Qian Qu, Yu Feng, Renxiang Guan, Ke Liang

abstract:
Multi-view clustering is a long-standing hot topic in machine learning communities, due to its capability of integrating data information from multiple sources and modalities. By utilizing tensor Singular Value Decomposition (t-SVD) technique with the tensor rotation trick, recent advances have achieved remarkable improvements on clustering performance. However, we find this is attributed to the inadvertent use of sequential information of sorted data samples, i.e. inadvertent label use, which violates the unsupervised learning setting. On the other hand, existing large-scale approaches are mostly developed on the basis of matrix factorization or anchor techniques, thereby fail to consider the similarities among all data samples, preventing from further performance improvement. To address the above issues, we first analyze the tensor rotation trick and recommend to remove it from tensor clustering. On its basis, a novel large-scale multi-view tensor clustering method is developed by incorporating the pair-wise similarities with implicit linear kernel function. To solve the resultant optimization problem, we design an efficient algorithm of linear complexity.Moreover, extensive experiments are conducted and corresponding results well support the aforementioned finding and validate the effectiveness and efficiency of the proposed method.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Liu_Large-scale_Multi-view_Tensor_Clustering_with_Implicit_Linear_Kernels_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Liu_Large-scale_Multi-view_Tensor_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Liu_2025_CVPR,
    author    = {Liu, Jiyuan and Liu, Xinwang and Li, Chuankun and Wan, Xinhang and Tan, Hao and Zhang, Yi and Liang, Weixuan and Qu, Qian and Feng, Yu and Guan, Renxiang and Liang, Ke},
    title     = {Large-scale Multi-view Tensor Clustering with Implicit Linear Kernels},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {20727-20736}
}

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


authors: Boyong He, Yuxiang Ji, Qianwen Ye, Zhuoyue Tan, Liaoni Wu
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 9921-9932
cities: ["Xiamen", "Xiamen"]
countries: ["China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/He_Generalized_Diffusion_Detector_Mining_Robust_Features_from_Diffusion_Models_for_CVPR_2025_paper.html

title:
Generalized Diffusion Detector: Mining Robust Features from Diffusion Models for Domain-Generalized Detection

abstract:
Domain generalization (DG) for object detection aims to enhance detectors' performance in unseen scenarios. This task remains challenging due to complex variations in real-world applications. Recently, diffusion models have demonstrated remarkable capabilities in diverse scene generation, which inspires us to explore their potential for improving DG tasks. Instead of generating images, our method extracts multi-step intermediate features during the diffusion process to obtain domain-invariant features for generalized detection. Furthermore, we propose an efficient knowledge transfer framework that enables detectors to inherit the generalization capabilities of diffusion models through feature and object-level alignment, without increasing inference time. We conduct extensive experiments on six challenging DG benchmarks. The results demonstrate that our method achieves substantial improvements of 14.0% mAP over existing DG approaches across different domains and corruption types. Notably, our method even outperforms most domain adaptation methods without accessing any target domain data. Moreover, the diffusion-guided detectors show consistent improvements of 15.9% mAP on average compared to the baseline. Our work aims to present an effective approach for domain-generalized detection and provide potential insights for robust visual recognition in real-world scenarios.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/He_Generalized_Diffusion_Detector_Mining_Robust_Features_from_Diffusion_Models_for_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/He_Generalized_Diffusion_Detector_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{He_2025_CVPR,
    author    = {He, Boyong and Ji, Yuxiang and Ye, Qianwen and Tan, Zhuoyue and Wu, Liaoni},
    title     = {Generalized Diffusion Detector: Mining Robust Features from Diffusion Models for Domain-Generalized Detection},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {9921-9932}
}

raw_affiliation:
[{"affiliation": "Institute of Artifcial Intelligence, Xiamen University", "city": "Xiamen", "country": "China"}, {"affiliation": "School of Aerospace Engineering, Xiamen University", "city": "Xiamen", "country": "China"}]

affiliations:
["Institute of Artifcial Intelligence, Xiamen University", "School of Aerospace Engineering, Xiamen University"]


title: Q-Eval-100K: Evaluating Visual Quality and Alignment Level for Text-to-Vision Content
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 10621-10631
affiliations: ["Shanghai Jiao Tong University", "Meituan"]
cities: ["Shanghai", "Beijing"]
countries: ["China", "China"]
oral: yes
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Zhang_Q-Eval-100K_Evaluating_Visual_Quality_and_Alignment_Level_for_Text-to-Vision_Content_CVPR_2025_paper.html

authors:
Zicheng Zhang, Tengchuan Kou, Shushi Wang, Chunyi Li, Wei Sun, Wei Wang, Xiaoyu Li, Zongyu Wang, Xuezhi Cao, Xiongkuo Min, Xiaohong Liu, Guangtao Zhai

abstract:
Evaluating text-to-vision content hinges on two crucial aspects: **visual quality** and **alignment**. While significant progress has been made in developing objective models to assess these dimensions, the performance of such models heavily relies on the scale and quality of human annotations. According to **Scaling Law**, increasing the number of human-labeled instances follows a predictable pattern that enhances the performance of evaluation models.Therefore, we introduce a comprehensive dataset designed to **E**valuate **V**isual quality and **A**lignment **L**evel for text-to-vision content (**Q-EVAL-100K**), featuring the largest collection of human-labeled Mean Opinion Scores (MOS) for the mentioned two aspects.The **Q-EVAL-100K** dataset encompasses both text-to-image and text-to-video models, with 960K human annotations specifically focused on visual quality and alignment for 100K instances (60K images and 40K videos). Leveraging this dataset with context prompt, we propose **Q-Eval-Score**, a unified model capable of evaluating both visual quality and alignment with special improvements for handling long-text prompt alignment.Experimental results indicate that the proposed **Q-Eval-Score** achieves superior performance on both visual quality and alignment, with strong generalization capabilities across other benchmarks. These findings highlight the significant value of the **Q-EVAL-100K** dataset. **The data and code will be released** to help promote the generation models.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Zhang_Q-Eval-100K_Evaluating_Visual_Quality_and_Alignment_Level_for_Text-to-Vision_Content_CVPR_2025_paper.pdf

bibtex:
@InProceedings{Zhang_2025_CVPR,
    author    = {Zhang, Zicheng and Kou, Tengchuan and Wang, Shushi and Li, Chunyi and Sun, Wei and Wang, Wei and Li, Xiaoyu and Wang, Zongyu and Cao, Xuezhi and Min, Xiongkuo and Liu, Xiaohong and Zhai, Guangtao},
    title     = {Q-Eval-100K: Evaluating Visual Quality and Alignment Level for Text-to-Vision Content},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {10621-10631}
}

raw_affiliation:
[{"affiliation": "Shanghai Jiao Tong University", "city": "Shanghai", "country": "China"}, {"affiliation": "Meituan", "city": "Beijing", "country": "China"}]


title: Dual Focus-Attention Transformer for Robust Point Cloud Registration
authors: Kexue Fu, Mingzhi Yuan, Changwei Wang, Weiguang Pang, Jing Chi, Manning Wang, Longxiang Gao
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 11769-11778
cities: ["Jinan", "Shanghai", "Shanghai", "Jinan", "Jinan"]
countries: ["China", "China", "China", "China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Fu_Dual_Focus-Attention_Transformer_for_Robust_Point_Cloud_Registration_CVPR_2025_paper.html

abstract:
Recently, coarse-to-fine methods for point cloud registration have achieved great success, but few works deeply explore the impact of feature interaction at both coarse and fine scales. By visualizing attention scores and correspondences, we find that existing methods fail to achieve effective feature aggregation at the two scales during the feature interaction. To tackle this issue, we propose a Dual Focus-Attention Transformer framework, which only focuses on points relevant to the current point for feature interaction, avoiding interactions with irrelevant points. For the coarse scale, we design a superpoint focus-attention transformer guided by sparse keypoints, which are selected from the neighborhood of superpoints. For the fine scale, we only perform feature interaction between the point sets that belong to the same superpoint. Experiments show that our method achieve the state-of-the-art performance on three standard benchmarks. The code and pre-trained models will be available at Github.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Fu_Dual_Focus-Attention_Transformer_for_Robust_Point_Cloud_Registration_CVPR_2025_paper.pdf

bibtex:
@InProceedings{Fu_2025_CVPR,
    author    = {Fu, Kexue and Yuan, Mingzhi and Wang, Changwei and Pang, Weiguang and Chi, Jing and Wang, Manning and Gao, Longxiang},
    title     = {Dual Focus-Attention Transformer for Robust Point Cloud Registration},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {11769-11778}
}

raw_affiliation:
[{"affiliation": "Key Laboratory of Computing Power Network and Information Security, Ministry of Education, Shandong Computer Science Center (National Supercomputer Center in Jinan), Qilu University of Technology (Shandong Academy of Sciences)", "city": "Jinan", "country": "China"}, {"affiliation": "Digital Medical Research Center, School of Basic Medical Sciences, Fudan University", "city": "Shanghai", "country": "China"}, {"affiliation": "Shanghai Key Lab of Medical Image Computing and Computer Assisted Intervention", "city": "Shanghai", "country": "China"}, {"affiliation": "Shandong Provincial Key Laboratory of Computing Power Internet and Service Computing, Shandong Fundamental Research Center for Computer Science", "city": "Jinan", "country": "China"}, {"affiliation": "School of Computer Science and Technology, Shandong University of Finance and Economics", "city": "Jinan", "country": "China"}]

affiliations:
["Key Laboratory of Computing Power Network and Information Security, Ministry of Education, Shandong Computer Science Center (National Supercomputer Center in Jinan), Qilu University of Technology (Shandong Academy of Sciences)", "Digital Medical Research Center, School of Basic Medical Sciences, Fudan University", "Shanghai Key Lab of Medical Image Computing and Computer Assisted Intervention", "Shandong Provincial Key Laboratory of Computing Power Internet and Service Computing, Shandong Fundamental Research Center for Computer Science", "School of Computer Science and Technology, Shandong University of Finance and Economics"]


title: Forming Auxiliary High-confident Instance-level Loss to Promote Learning from Label Proportions
authors: Tianhao Ma, Han Chen, Juncheng Hu, Yungang Zhu, Ximing Li
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 20592-20601
cities: ["Changchun", "Changchun"]
countries: ["China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Ma_Forming_Auxiliary_High-confident_Instance-level_Loss_to_Promote_Learning_from_Label_CVPR_2025_paper.html

abstract:
Learning from label proportions (LLP), i.e. a challenging weakly-supervised learning task, aims to train a classifier by using bags of instances and the proportions of classes within bags, rather than annotated labels for each instance. Beyond the traditional bag-level loss, the mainstream methodology of LLP is to incorporate an auxiliary instance-level loss with pseudo-labels formed by predictions. Unfortunately, we empirically observed that the pseudo-labels are often inaccurate and even meaningless, especially for the scenarios with large bag sizes, hurting the classifier induction. To alleviate this problem, we suggest a novel LLP method, namely Learning Label Proportions with Auxiliary High-confident Instance-level Loss (L^2P-AHIL). Specifically, we propose a dual entropy-based weight (DEW) method to adaptively measure the confidences of pseudo-labels. It simultaneously emphasizes accurate predictions at the bag level and avoids smoothing predictions, which tend to be meaningless. We then form high-confident instance-level loss with DEW, and jointly optimize it with the bag-level loss in a self-training manner. The experimental results on benchmark datasets show that L^2P-AHIL can surpass the existing baseline methods, and the performance gain can be more significant as the bag size increases. The implementation of our method is available at https://github.com/TianhaoMa5/LLP-AHIL.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Ma_Forming_Auxiliary_High-confident_Instance-level_Loss_to_Promote_Learning_from_Label_CVPR_2025_paper.pdf

bibtex:
@InProceedings{Ma_2025_CVPR,
    author    = {Ma, Tianhao and Chen, Han and Hu, Juncheng and Zhu, Yungang and Li, Ximing},
    title     = {Forming Auxiliary High-confident Instance-level Loss to Promote Learning from Label Proportions},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {20592-20601}
}

raw_affiliation:
[{"affiliation": "College of Computer Science and Technology, Jilin University, China", "city": "Changchun", "country": "China"}, {"affiliation": "Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, China", "city": "Changchun", "country": "China"}]

affiliations:
["College of Computer Science and Technology, Jilin University, China", "Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, China"]


title: Progress-Aware Video Frame Captioning
authors: Zihui Xue, Joungbin An, Xitong Yang, Kristen Grauman
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 13639-13650
raw_affiliation: [{"affiliation": "University of Texasat Austin", "city": "Austin", "country": "USA"}]
affiliations: ["University of Texasat Austin"]
cities: ["Austin"]
countries: ["USA"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Xue_Progress-Aware_Video_Frame_Captioning_CVPR_2025_paper.html

abstract:
While image captioning provides isolated descriptions for individual images, and video captioning offers one single narrative for an entire video clip, our work explores an important middle ground: progress-aware video captioning at the frame level. This novel task aims to generate temporally fine-grained captions that not only accurately describe each frame but also capture the subtle progression of actions throughout a video sequence. Despite the strong capabilities of existing leading vision language models, they often struggle to discern the nuances of frame-wise differences. To address this, we propose ProgressCaptioner, a captioning model designed to capture the fine-grained temporal dynamics within an action sequence. Alongside, we develop the FrameCap dataset to support training and the FrameCapEval benchmark to assess caption quality. The results demonstrate that ProgressCaptioner significantly surpasses leading captioning models, producing precise captions that accurately capture action progression and set a new standard for temporal precision in video captioning. Finally, we showcase practical applications of our approach, specifically in aiding keyframe selection and advancing video understanding, highlighting its broad utility.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Xue_Progress-Aware_Video_Frame_Captioning_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Xue_Progress-Aware_Video_Frame_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Xue_2025_CVPR,
    author    = {Xue, Zihui and An, Joungbin and Yang, Xitong and Grauman, Kristen},
    title     = {Progress-Aware Video Frame Captioning},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {13639-13650}
}


title: SMTPD: A New Benchmark for Temporal Prediction of Social Media Popularity
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 18847-18857
affiliations: ["Hangzhou Dianzi University", "Tianjin University", "Peking University"]
cities: ["Hangzhou", "Tianjin", "Beijing"]
countries: ["China", "China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Xu_SMTPD_A_New_Benchmark_for_Temporal_Prediction_of_Social_Media_CVPR_2025_paper.html

authors:
Yijie Xu, Bolun Zheng, Wei Zhu, Hangjia Pan, Yuchen Yao, Ning Xu, Anan Liu, Quan Zhang, Chenggang Yan

abstract:
Social media popularity prediction task aims to predict the popularity of posts on social media platforms, which has a positive driving effect on application scenarios such as content optimization, digital marketing and online advertising. Though many studies have made significant progress, few of them pay much attention to the integration between popularity prediction with temporal alignment. In this paper, with exploring YouTube's multilingual and multi-modal content, we construct a new social media temporal popularity prediction benchmark, namely SMTPD, and suggest a baseline framework for temporal popularity prediction. Through data analysis and experiments, we verify that temporal alignment and early popularity play crucial roles in social media popularity prediction for not only deepening the understanding of temporal dynamics of popularity in social media but also offering a suggestion about developing more effective prediction models in this field. Code is available at https://github.com/zhuwei321/SMTPD

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Xu_SMTPD_A_New_Benchmark_for_Temporal_Prediction_of_Social_Media_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Xu_SMTPD_A_New_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Xu_2025_CVPR,
    author    = {Xu, Yijie and Zheng, Bolun and Zhu, Wei and Pan, Hangjia and Yao, Yuchen and Xu, Ning and Liu, Anan and Zhang, Quan and Yan, Chenggang},
    title     = {SMTPD: A New Benchmark for Temporal Prediction of Social Media Popularity},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {18847-18857}
}

raw_affiliation:
[{"affiliation": "Hangzhou Dianzi University", "city": "Hangzhou", "country": "China"}, {"affiliation": "Tianjin University", "city": "Tianjin", "country": "China"}, {"affiliation": "Peking University", "city": "Beijing", "country": "China"}]


title: Enhancing Dance-to-Music Generation via Negative Conditioning Latent Diffusion Model
authors: Changchang Sun, Gaowen Liu, Charles Fleming, Yan Yan
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 8321-8330
affiliations: ["University of Illinois Chicago", "Cisco Research"]
cities: ["Chicago", "Unknown"]
countries: ["USA", "Unknown"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Sun_Enhancing_Dance-to-Music_Generation_via_Negative_Conditioning_Latent_Diffusion_Model_CVPR_2025_paper.html

abstract:
Conditional diffusion models have gained increasing attention since their impressive results for cross-modal synthesis, where the strong alignment between conditioning input and generated output can be achieved by training a time-conditioned U-Net augmented with cross-attention mechanism. In this paper, we focus on the problem of generating music synchronized with rhythmic visual cues of the given dance video. Considering that bi-directional guidance is more beneficial for training a diffusion model, we propose to enhance the quality of generated music and its synchronization with dance videos by adopting both positive rhythmic information and negative ones (PN-Diffusion) as conditions, where a dual diffusion and reverse processes is devised. Specifically, to train a sequential multi-modal U-Net structure, PN-Diffusion consists of a noise prediction objective for positive conditioning and an additional noise prediction objective for negative conditioning. To accurately define and select both positive and negative conditioning, we ingeniously utilize temporal correlations in dance videos, capturing positive and negative rhythmic cues by playing them forward and backward, respectively. Through subjective and objective evaluations of input-output correspondence in terms of dance-music beat alignment and the quality of generated music, experimental results on the AIST++ and TikTok dance video datasets demonstrate that our model outperforms SOTA dance-to-music generation models.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Sun_Enhancing_Dance-to-Music_Generation_via_Negative_Conditioning_Latent_Diffusion_Model_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Sun_Enhancing_Dance-to-Music_Generation_CVPR_2025_supplemental.zip

bibtex:
@InProceedings{Sun_2025_CVPR,
    author    = {Sun, Changchang and Liu, Gaowen and Fleming, Charles and Yan, Yan},
    title     = {Enhancing Dance-to-Music Generation via Negative Conditioning Latent Diffusion Model},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {8321-8330}
}

raw_affiliation:
[{"affiliation": "University of Illinois Chicago", "city": "Chicago", "country": "USA"}, {"affiliation": "Cisco Research", "city": "Unknown", "country": "Unknown"}]


title: Neuro-Symbolic Evaluation of Text-to-Video Models using Formal Verification
authors: S P Sharan, Minkyu Choi, Sahil Shah, Harsh Goel, Mohammad Omama, Sandeep Chinchali
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 8395-8405
affiliations: ["The University of Texas at Austin"]
cities: ["Austin"]
countries: ["United States"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Sharan_Neuro-Symbolic_Evaluation_of_Text-to-Video_Models_using_Formal_Verification_CVPR_2025_paper.html

abstract:
Recent advancements in text-to-video models such as Sora, Gen-3, MovieGen, and CogVideoX are pushing the boundaries of synthetic video generation, with adoption seen in fields like robotics, autonomous driving, and entertainment. As these models become prevalent, various metrics and benchmarks have emerged to evaluate the quality of the generated videos. However, these metrics emphasize visual quality and smoothness, neglecting temporal fidelity and text-to-video alignment, which are crucial for safety-critical applications. To address this gap, we introduce NeuS-V, a novel synthetic video evaluation metric that rigorously assesses text-to-video alignment using neuro-symbolic formal verification techniques. Our approach first converts the prompt into a formally defined Temporal Logic (TL) specification and translates the generated video into an automaton representation. Then, it evaluates the text-to-video alignment by formally checking the video automaton against the TL specification. Furthermore, we present a dataset of temporally extended prompts to evaluate state-of-the-art video generation models against our benchmark. We find that NeuS-V demonstrates a higher correlation by over 5x with human evaluations when compared to existing metrics. Our evaluation further reveals that current video generation models perform poorly on these temporally complex prompts, highlighting the need for future work in improving text-to-video generation capabilities. We open-source our benchmark, code, and dataset at https://utaustin-swarmlab.github.io/neusv.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Sharan_Neuro-Symbolic_Evaluation_of_Text-to-Video_Models_using_Formal_Verification_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Sharan_Neuro-Symbolic_Evaluation_of_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Sharan_2025_CVPR,
    author    = {Sharan, S P and Choi, Minkyu and Shah, Sahil and Goel, Harsh and Omama, Mohammad and Chinchali, Sandeep},
    title     = {Neuro-Symbolic Evaluation of Text-to-Video Models using Formal Verification},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {8395-8405}
}

raw_affiliation:
[{"affiliation": "The University of Texas at Austin", "city": "Austin", "country": "United States"}]


title: Spherical Manifold Guided Diffusion Model for Panoramic Image Generation
authors: Xiancheng Sun, Mai Xu, Shengxi Li, Senmao Ma, Xin Deng, Lai Jiang, Gang Shen
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 5824-5834
affiliations: ["Beihang University", "China Tower"]
cities: ["Beijing", "Beijing"]
countries: ["China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Sun_Spherical_Manifold_Guided_Diffusion_Model_for_Panoramic_Image_Generation_CVPR_2025_paper.html

abstract:
Panoramic image essentially acts as a pivotal role in emerging virtual reality and augmented reality scenarios; however, the generation of panoramic images are essentially challenging due to the intrinsic spherical geometry and spherical distortions caused by equirectangular projection (ERP). To address this, we start from the very basics of S^2 manifold inherent to panoramic images, and propose a novel spherical manifold convolution (SMConv) on S^2 manifold. Based on the SMConv operation, we propose a spherical manifold guided diffusion (SMGD) model for text-conditioned panoramic image generation, which can well accommodate the spherical geometry during generation. We further develop a novel evaluation method by calculating grouped Frechet inception distance (FID) on cube-map projections, which can well reflect the quality of generated panoramic images, compared to existing methods that randomly crop ERP-distorted content. Experiment results demonstrate that our SMGD model achieves the state-of-the-art generation quality and accuracy, whilst retaining the shortest sampling time in the text-conditioned panoramic image generation task. Codes are publicly available at https://github.com/chronos123/SMGD.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Sun_Spherical_Manifold_Guided_Diffusion_Model_for_Panoramic_Image_Generation_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Sun_Spherical_Manifold_Guided_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Sun_2025_CVPR,
    author    = {Sun, Xiancheng and Xu, Mai and Li, Shengxi and Ma, Senmao and Deng, Xin and Jiang, Lai and Shen, Gang},
    title     = {Spherical Manifold Guided Diffusion Model for Panoramic Image Generation},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {5824-5834}
}

raw_affiliation:
[{"affiliation": "Beihang University", "city": "Beijing", "country": "China"}, {"affiliation": "China Tower", "city": "Beijing", "country": "China"}]


title: Learning on Model Weights using Tree Experts
authors: Eliahu Horwitz, Bar Cavia, Jonathan Kahana, Yedid Hoshen
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 20468-20478
raw_affiliation: [{"affiliation": "The Hebrew University of Jerusalem", "city": "Jerusalem", "country": "Israel"}]
affiliations: ["The Hebrew University of Jerusalem"]
cities: ["Jerusalem"]
countries: ["Israel"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Horwitz_Learning_on_Model_Weights_using_Tree_Experts_CVPR_2025_paper.html

abstract:
The number of publicly available models is rapidly increasing, yet most remain undocumented. Users looking for suitable models for their tasks must first determine what each model does. Training machine learning models to infer missing documentation directly from model weights is challenging, as these weights often contain significant variation unrelated to model functionality (denoted nuisance). Here, we identify a key property of real-world models: most public models belong to a small set of Model Trees, where all models within a tree are fine-tuned from a common ancestor (e.g., a foundation model). Importantly, we find that within each tree there is less nuisance variation between models. Concretely, while learning across Model Trees requires complex architectures, even a linear classifier trained on a single model layer often works within trees. While effective, these linear classifiers are computationally expensive, especially when dealing with larger models that have many parameters. To address this, we introduce Probing Experts (ProbeX), a theoretically motivated and lightweight method. Notably, ProbeX is the first probing method specifically designed to learn from the weights of a single hidden model layer. We demonstrate the effectiveness of ProbeX by predicting the categories in a model's training dataset based only on its weights. Excitingly, ProbeX can map the weights of Stable Diffusion into a weight-language embedding space, enabling model search via text, i.e., zero-shot model classification.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Horwitz_Learning_on_Model_Weights_using_Tree_Experts_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Horwitz_Learning_on_Model_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Horwitz_2025_CVPR,
    author    = {Horwitz, Eliahu and Cavia, Bar and Kahana, Jonathan and Hoshen, Yedid},
    title     = {Learning on Model Weights using Tree Experts},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {20468-20478}
}


title: Rethinking Query-based Transformer for Continual Image Segmentation
authors: Yuchen Zhu, Cheng Shi, Dingyou Wang, Jiajin Tang, Zhengxuan Wei, Yu Wu, Guanbin Li, Sibei Yang
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 4595-4606
cities: ["Shanghai", "Guangzhou", "Wuhan"]
countries: ["China", "China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Zhu_Rethinking_Query-based_Transformer_for_Continual_Image_Segmentation_CVPR_2025_paper.html

abstract:
Class-incremental/Continual image segmentation (CIS) aims to train an image segmenter in stages, where the set of available categories differs at each stage. To leverage the built-in objectness of query-based transformers, which mitigates catastrophic forgetting of mask proposals, current methods often decouple mask generation from the continual learning process. This study, however, identifies two key issues with decoupled frameworks: loss of plasticity and heavy reliance on input data order. To address these, we conduct an in-depth investigation of the built-in objectness and find that highly aggregated image features provide a shortcut for queries to generate masks through simple feature alignment. Based on this, we propose SimCIS, a simple yet powerful baseline for CIS. Its core idea is to directly select image features for query assignment, ensuring "perfect alignment" to preserve objectness, while simultaneously allowing queries to select new classes to promote plasticity. To further combat catastrophic forgetting of categories, we introduce cross-stage consistency in selection and an innovative "visual query"-based replay mechanism. Experiments demonstrate that SimCIS consistently outperforms state-of-the-art methods across various segmentation tasks, settings, splits, and input data orders. All models and codes will be made publicly available at https://github.com/SooLab/SimCIS.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Zhu_Rethinking_Query-based_Transformer_for_Continual_Image_Segmentation_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Zhu_Rethinking_Query-based_Transformer_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Zhu_2025_CVPR,
    author    = {Zhu, Yuchen and Shi, Cheng and Wang, Dingyou and Tang, Jiajin and Wei, Zhengxuan and Wu, Yu and Li, Guanbin and Yang, Sibei},
    title     = {Rethinking Query-based Transformer for Continual Image Segmentation},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {4595-4606}
}

raw_affiliation:
[{"affiliation": "School of Information Science and Technology, ShanghaiTech University", "city": "Shanghai", "country": "China"}, {"affiliation": "Sun Yat-sen University", "city": "Guangzhou", "country": "China"}, {"affiliation": "Wuhan University", "city": "Wuhan", "country": "China"}]

affiliations:
["School of Information Science and Technology, ShanghaiTech University", "Sun Yat-sen University", "Wuhan University"]


title: Image Reconstruction from Readout-Multiplexed Single-Photon Detector Arrays
authors: Shashwath Bharadwaj, Ruangrawee Kitichotkul, Akshay Agarwal, Vivek K Goyal
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 11406-11415
raw_affiliation: [{"affiliation": "Boston University", "city": "Boston", "country": "USA"}]
affiliations: ["Boston University"]
cities: ["Boston"]
countries: ["USA"]
highlight: yes
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Bharadwaj_Image_Reconstruction_from_Readout-Multiplexed_Single-Photon_Detector_Arrays_CVPR_2025_paper.html

abstract:
Readout multiplexing is a promising solution to overcome hardware limitations and data bottlenecks in imaging with single-photon detectors. Conventional multiplexed readout processing creates an upper bound on photon counts at a very fine time scale, where frames with multiple detected photons must either be discarded or allowed to introduce significant bias. We formulate multiphoton coincidence resolution as an inverse imaging problem and introduce a solution framework to probabilistically resolve the spatial locations of photon incidences. Specifically, we develop a theoretical abstraction of row--column multiplexing and a model of photon events that make readouts ambiguous. Using this, we propose a novel estimator that spatially resolves up to four coincident photons. Monte Carlo simulations show that our proposed method increases the peak signal-to-noise ratio (PSNR) of reconstruction by 3 to 4 dB compared to conventional methods under optimal incident flux conditions. Additionally, this method reduces the required number of readout frames to achieve the same mean-squared error as other methods by a factor of  4. Finally, our method matches the Cramer-Rao bound for detection probability estimation for a wider range of incident flux values compared to conventional methods. While demonstrated for a specific detector type and readout architecture, this method can be extended to more general multiplexing with different detector models.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Bharadwaj_Image_Reconstruction_from_Readout-Multiplexed_Single-Photon_Detector_Arrays_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Bharadwaj_Image_Reconstruction_from_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Bharadwaj_2025_CVPR,
    author    = {Bharadwaj, Shashwath and Kitichotkul, Ruangrawee and Agarwal, Akshay and Goyal, Vivek K},
    title     = {Image Reconstruction from Readout-Multiplexed Single-Photon Detector Arrays},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {11406-11415}
}


title: Towards Smart Point-and-Shoot Photography
authors: Jiawan Li, Fei Zhou, Zhipeng Zhong, Jiongzhi Lin, Guoping Qiu
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 28242-28251
cities: ["Shenzhen", "Guangdong", "Guangdong", "Shenzhen", "Loughborough", "Nottingham", "Unknown"]
countries: ["China", "China", "China", "China", "UK", "UK", "Unknown"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Li_Towards_Smart_Point-and-Shoot_Photography_CVPR_2025_paper.html

abstract:
Hundreds of millions of people routinely take photos using their smartphones as point and shoot (PAS) cameras, yet very few would have the photography skills to compose a good shot of a scene. While traditional PAS cameras have built-infunctions to ensure a photo is well focused and has the right brightness, they cannot tell the users how to compose the best shot of a scene. In this paper, we present a first of its kind smart point and shoot (SPAS) system to help users to take good photos. Our SPAS proposes to help users to compose a good shot of a scene by automatically guiding the users to adjust the camera pose live on the scene. We first constructed a large dataset containing 320K images with camera pose information from 4000 scenes. We then developed an innovative CLIP-based Composition Quality Assessment (CCQA) model to assign pseudo labels to these images. The CCQA introduces a unique learnable text embedding technique to learn continuous word embeddings capable of discerning subtle visual quality differences in the range covered by five levels of quality description words  bad, poor, fair, good, perfect . And finally we have developed a camera pose adjustment model (CPAM) which first determines if the current view can be further improved and if so it outputs the adjust suggestion in the form of two camera pose adjustment angles. The two tasks of CPAM make decisions in a sequential manner and each involves different sets of training samples, we have developed a mixture-of-experts model with a gated loss function to train the CPAM in an end-to-end manner. We will present extensive results to demonstrate the performances of our SPAS system using publicly available image composition datasets.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Li_Towards_Smart_Point-and-Shoot_Photography_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Li_Towards_Smart_Point-and-Shoot_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Li_2025_CVPR,
    author    = {Li, Jiawan and Zhou, Fei and Zhong, Zhipeng and Lin, Jiongzhi and Qiu, Guoping},
    title     = {Towards Smart Point-and-Shoot Photography},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {28242-28251}
}

raw_affiliation:
[{"affiliation": "Shenzhen University", "city": "Shenzhen", "country": "China"}, {"affiliation": "Guangdong Provincial Key Laboratory of Intelligent Information Processing", "city": "Guangdong", "country": "China"}, {"affiliation": "Guangdong-Hong Kong Joint Laboratory for Big Data Imaging and Communication", "city": "Guangdong", "country": "China"}, {"affiliation": "Shenzhen Key Laboratory of Digital Creative Technology", "city": "Shenzhen", "country": "China"}, {"affiliation": "Loughborough University", "city": "Loughborough", "country": "UK"}, {"affiliation": "University of Nottingham", "city": "Nottingham", "country": "UK"}, {"affiliation": "Everimaging Ltd", "city": "Unknown", "country": "Unknown"}]

affiliations:
["Shenzhen University", "Guangdong Provincial Key Laboratory of Intelligent Information Processing", "Guangdong-Hong Kong Joint Laboratory for Big Data Imaging and Communication", "Shenzhen Key Laboratory of Digital Creative Technology", "Loughborough University", "University of Nottingham", "Everimaging Ltd"]


title: SlideChat: A Large Vision-Language Assistant for Whole-Slide Pathology Image Understanding
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 5134-5143
cities: ["Shanghai", "Xiamen", "Shanghai", "Stanford", "Melbourne"]
countries: ["China", "China", "China", "USA", "Australia"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Chen_SlideChat_A_Large_Vision-Language_Assistant_for_Whole-Slide_Pathology_Image_Understanding_CVPR_2025_paper.html

authors:
Ying Chen, Guoan Wang, Yuanfeng Ji, Yanjun Li, Jin Ye, Tianbin Li, Ming Hu, Rongshan Yu, Yu Qiao, Junjun He

abstract:
Despite the progress made by multimodal large language models (MLLMs) in computational pathology, they remain limited by a predominant focus on patch-level analysis, missing essential contextual information at the whole-slide level. The lack of large-scale instruction datasets and the gigapixel scale of whole slide images (WSIs) pose significant developmental challenges.  In this paper, we present SlideChat, the first vision-language assistant capable of understanding gigapixel whole-slide images, exhibiting excellent multimodal conversational capability and response complex instruction across diverse pathology scenarios. To support its development, we created SlideInstruction, the largest instruction-following dataset for WSIs consisting of 4.2K WSI captions and 176K VQA pairs with multiple categories. Furthermore, we propose SlideBench, a multimodal benchmark that incorporates captioning and VQA tasks to assess SlideChat's capabilities in various settings such as microscopy, diagnosis and clinical. Compared to both general and specialized MLLMs, SlideChat exhibits exceptional  capabilities, achieving state-of-the-art performance on 18 of 22 tasks. For example, it achieved an overall accuracy of 81.17% on SlideBench-VQA (TCGA), and 54.15% on SlideBench-VQA (BCNB). Our code, data, and model is publicly accessible at https://uni-medical.github.io/SlideChat.github.io.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Chen_SlideChat_A_Large_Vision-Language_Assistant_for_Whole-Slide_Pathology_Image_Understanding_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Chen_SlideChat_A_Large_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Chen_2025_CVPR,
    author    = {Chen, Ying and Wang, Guoan and Ji, Yuanfeng and Li, Yanjun and Ye, Jin and Li, Tianbin and Hu, Ming and Yu, Rongshan and Qiao, Yu and He, Junjun},
    title     = {SlideChat: A Large Vision-Language Assistant for Whole-Slide Pathology Image Understanding},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {5134-5143}
}

raw_affiliation:
[{"affiliation": "Shanghai AI Laboratory", "city": "Shanghai", "country": "China"}, {"affiliation": "Xiamen University", "city": "Xiamen", "country": "China"}, {"affiliation": "East China Normal University", "city": "Shanghai", "country": "China"}, {"affiliation": "Stanford University", "city": "Stanford", "country": "USA"}, {"affiliation": "Monash University", "city": "Melbourne", "country": "Australia"}]

affiliations:
["Shanghai AI Laboratory", "Xiamen University", "East China Normal University", "Stanford University", "Monash University"]


title: Prototype-Based Image Prompting for Weakly Supervised Histopathological Image Segmentation
authors: Qingchen Tang, Lei Fan, Maurice Pagnucco, Yang Song
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 30271-30280
raw_affiliation: [{"affiliation": "University of New South Wales", "city": "Sydney", "country": "Australia"}]
affiliations: ["University of New South Wales"]
cities: ["Sydney"]
countries: ["Australia"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Tang_Prototype-Based_Image_Prompting_for_Weakly_Supervised_Histopathological_Image_Segmentation_CVPR_2025_paper.html

abstract:
Weakly supervised image segmentation with image-level labels has drawn attention due to the high cost of pixel-level annotations. Traditional methods using Class Activation Maps (CAMs) often highlight only the most discriminative regions, leading to incomplete masks. Recent approaches that introduce textual information struggle with histopathological images due to inter-class homogeneity and intra-class heterogeneity. In this paper, we propose a prototype-based image prompting framework for histopathological image segmentation. It constructs an image bank from the training set using clustering, extracting multiple prototype features per class to capture intra-class heterogeneity. By designing a matching loss between input features and class-specific prototypes using contrastive learning, our method addresses inter-class homogeneity and guides the model to generate more accurate CAMs. Experiments on four datasets (LUAD-HistoSeg, BCSS-WSSS, GCSS, and BCSS) show that our method outperforms existing weakly supervised segmentation approaches, setting new benchmarks in histopathological image segmentation.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Tang_Prototype-Based_Image_Prompting_for_Weakly_Supervised_Histopathological_Image_Segmentation_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Tang_Prototype-Based_Image_Prompting_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Tang_2025_CVPR,
    author    = {Tang, Qingchen and Fan, Lei and Pagnucco, Maurice and Song, Yang},
    title     = {Prototype-Based Image Prompting for Weakly Supervised Histopathological Image Segmentation},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {30271-30280}
}


title: Towards Transformer-Based Aligned Generation with Self-Coherence Guidance
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 18455-18464
affiliations: ["Zhejiang University", "Huawei Noah\u2019s Ark Lab"]
cities: ["Hangzhou", "Shenzhen"]
countries: ["China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Wang_Towards_Transformer-Based_Aligned_Generation_with_Self-Coherence_Guidance_CVPR_2025_paper.html

authors:
Shulei Wang, Wang Lin, Hai Huang, Hanting Wang, Sihang Cai, WenKang Han, Tao Jin, Jingyuan Chen, Jiacheng Sun, Jieming Zhu, Zhou Zhao

abstract:
We introduce a novel, training-free approach for enhancing alignment in Transformer-based Text-Guided Diffusion Models (TGDMs). Existing TGDMs often struggle to generate semantically aligned images, particularly when dealing with complex text prompts or multi-concept attribute binding challenges. Previous U-Net-based methods primarily optimized the latent space, but their direct application to Transformer-based architectures has shown limited effectiveness. Our method addresses these challenges by directly optimizing cross-attention maps during the generation process. Specifically, we introduce Self-Coherence Guidance, a method that dynamically refines attention maps using masks derived from previous denoising steps, ensuring precise alignment without additional training. To validate our approach, we constructed more challenging benchmarks for evaluating coarse-grained attribute binding, fine-grained attribute binding, and style binding. Experimental results demonstrate the superior performance of our method, significantly surpassing other state-of-the-art methods across all evaluated tasks. Our code is available at https://scg-diffusion.github.io/scg-diffusion.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Wang_Towards_Transformer-Based_Aligned_Generation_with_Self-Coherence_Guidance_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Wang_Towards_Transformer-Based_Aligned_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Wang_2025_CVPR,
    author    = {Wang, Shulei and Lin, Wang and Huang, Hai and Wang, Hanting and Cai, Sihang and Han, WenKang and Jin, Tao and Chen, Jingyuan and Sun, Jiacheng and Zhu, Jieming and Zhao, Zhou},
    title     = {Towards Transformer-Based Aligned Generation with Self-Coherence Guidance},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {18455-18464}
}

raw_affiliation:
[{"affiliation": "Zhejiang University", "city": "Hangzhou", "country": "China"}, {"affiliation": "Huawei Noah\u2019s Ark Lab", "city": "Shenzhen", "country": "China"}]


title: Accurate Scene Text Recognition with Efficient Model Scaling and Cloze Self-Distillation
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 14516-14526
affiliations: ["Samsung R&D Institute UK", "Samsung Electronics"]
cities: ["London", "Seoul"]
countries: ["UK", "South Korea"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Maracani_Accurate_Scene_Text_Recognition_with_Efficient_Model_Scaling_and_Cloze_CVPR_2025_paper.html

authors:
Andrea Maracani, Savas Ozkan, Sijun Cho, Hyowon Kim, Eunchung Noh, Jeongwon Min, Cho Jung Min, Dookun Park, Mete Ozay

abstract:
Scaling architectures have been proven effective for improving Scene Text Recognition (STR), but the individual contribution of vision encoder and text decoder scaling remain under-explored. In this work, we present an in-depth empirical analysis and demonstrate that, contrary to previous observations, scaling the decoder yields significant performance gains, always exceeding those achieved by encoder scaling alone. We also identify label noise as a key challenge in STR, particularly in real-world data, which can limit the effectiveness of STR models. To address this, we propose Cloze Self-Distillation (CSD), a method that mitigates label noise by distilling a student model from context-aware soft predictions and pseudolabels generated by a teacher model. Additionally, we enhance the decoder architecture by introducing differential cross-attention for STR. Our methodology achieves state-of-the-art performance on 10 out of 11 benchmarks using only real data, while significantly reducing the parameter size and computational costs.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Maracani_Accurate_Scene_Text_Recognition_with_Efficient_Model_Scaling_and_Cloze_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Maracani_Accurate_Scene_Text_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Maracani_2025_CVPR,
    author    = {Maracani, Andrea and Ozkan, Savas and Cho, Sijun and Kim, Hyowon and Noh, Eunchung and Min, Jeongwon and Min, Cho Jung and Park, Dookun and Ozay, Mete},
    title     = {Accurate Scene Text Recognition with Efficient Model Scaling and Cloze Self-Distillation},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {14516-14526}
}

raw_affiliation:
[{"affiliation": "Samsung R&D Institute UK", "city": "London", "country": "UK"}, {"affiliation": "Samsung Electronics", "city": "Seoul", "country": "South Korea"}]


authors: Sang-Jun Park, Keun-Soo Heo, Dong-Hee Shin, Young-Han Son, Ji-Hye Oh, Tae-Eui Kam
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 15580-15589
affiliations: ["Department of Artificial Intelligence, Korea University"]
cities: ["Seoul"]
countries: ["South Korea"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Park_DART_Disease-aware_Image-Text_Alignment_and_Self-correcting_Re-alignment_for_Trustworthy_Radiology_CVPR_2025_paper.html

title:
DART: Disease-aware Image-Text Alignment and Self-correcting Re-alignment for Trustworthy Radiology Report Generation

abstract:
The automatic generation of radiology reports has emerged as a promising solution to reduce a time-consuming task and accurately capture critical disease-relevant findings in X-ray images. Previous approaches for radiology report generation have shown impressive performance. However, there remains significant potential to improve accuracy by ensuring that retrieved reports contain disease-relevant findings similar to those in the X-ray images and by refining generated reports. In this study, we propose a Disease-aware image-text Alignment and self-correcting Re-alignment for Trustworthy radiology report generation (DART) framework. In the first stage, we generate initial reports based on image-to-text retrieval with disease-matching, embedding both images and texts in a shared embedding space through contrastive learning. This approach ensures the retrieval of reports with similar disease-relevant findings that closely align with the input X-ray images. In the second stage, we further enhance the initial reports by introducing a self-correction module that re-aligns them with the X-ray images. Our proposed framework achieves state-of-the-art results on two widely used benchmarks, surpassing previous approaches in both report generation and clinical efficacy metrics, thereby enhancing the trustworthiness of radiology reports.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Park_DART_Disease-aware_Image-Text_Alignment_and_Self-correcting_Re-alignment_for_Trustworthy_Radiology_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Park_DART_Disease-aware_Image-Text_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Park_2025_CVPR,
    author    = {Park, Sang-Jun and Heo, Keun-Soo and Shin, Dong-Hee and Son, Young-Han and Oh, Ji-Hye and Kam, Tae-Eui},
    title     = {DART: Disease-aware Image-Text Alignment and Self-correcting Re-alignment for Trustworthy Radiology Report Generation},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {15580-15589}
}

raw_affiliation:
[{"affiliation": "Department of Artificial Intelligence, Korea University", "city": "Seoul", "country": "South Korea"}]


title: On the Consistency of Video Large Language Models in Temporal Comprehension
authors: Minjoon Jung, Junbin Xiao, Byoung-Tak Zhang, Angela Yao
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 13713-13722
cities: ["Singapore", "Seoul"]
countries: ["Singapore", "South Korea"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Jung_On_the_Consistency_of_Video_Large_Language_Models_in_Temporal_CVPR_2025_paper.html

abstract:
Video large language models (Video-LLMs) can temporally ground language queries and retrieve video moments. Yet, such temporal comprehension capabilities are neither well-studied nor understood. So we conduct a study on prediction consistency -- a key indicator for robustness and trustworthiness of temporal grounding. After the model identifies an initial moment within the video content, we apply a series of probes to check if the model's responses align with this initial grounding as an indicator of reliable comprehension. Our results reveal that current Video-LLMs are sensitive to variations in video contents, language queries, and task settings, unveiling severe deficiencies in maintaining consistency. We further explore common prompting and instruction-tuning methods as potential solutions, but find that their improvements are often unstable. To that end, we propose event temporal verification tuning that explicitly accounts for consistency, and demonstrate significant improvements for both grounding and consistency. Our data and code are open-sourced at https://github.com/minjoong507/Consistency-of-Video-LLM.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Jung_On_the_Consistency_of_Video_Large_Language_Models_in_Temporal_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Jung_On_the_Consistency_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Jung_2025_CVPR,
    author    = {Jung, Minjoon and Xiao, Junbin and Zhang, Byoung-Tak and Yao, Angela},
    title     = {On the Consistency of Video Large Language Models in Temporal Comprehension},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {13713-13722}
}

raw_affiliation:
[{"affiliation": "Department of Computer Science, National University of Singapore", "city": "Singapore", "country": "Singapore"}, {"affiliation": "Interdisciplinary Program in Arti\ufb01cial Intelligence, Seoul National University", "city": "Seoul", "country": "South Korea"}]

affiliations:
["Department of Computer Science, National University of Singapore", "Interdisciplinary Program in Arti\ufb01cial Intelligence, Seoul National University"]


title: Mitigating the Human-Robot Domain Discrepancy in Visual Pre-training for Robotic Manipulation
authors: Jiaming Zhou, Teli Ma, Kun-Yu Lin, Zifan Wang, Ronghe Qiu, Junwei Liang
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 22551-22561
cities: ["Guangzhou", "Guangzhou", "Hong Kong"]
countries: ["China", "China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Zhou_Mitigating_the_Human-Robot_Domain_Discrepancy_in_Visual_Pre-training_for_Robotic_CVPR_2025_paper.html

abstract:
Learning generalizable visual representations across different embodied environments is essential for effective robotic manipulation in real-world scenarios. However, the limited scale and diversity of robot demonstration data pose a significant challenge. Recent research has explored leveraging large-scale human activity data for pre-training, but the substantial morphological differences between humans and robots introduce a significant human-robot domain discrepancy, hindering the generalization of these models to downstream manipulation tasks. To overcome this, we propose a novel adaptation paradigm that leverages readily available paired human-robot video data to bridge the domain gap. Our method employs a human-robot contrastive alignment loss to align the semantics of human and robot videos, adapting pre-trained models to the robot domain in a parameter-efficient manner. Experiments on 20 simulated tasks across two different benchmarks and five real-world tasks demonstrate significant improvements. These results span both single-task and language-conditioned multi-task settings, evaluated using two different pre-trained models. Compared to existing pre-trained models, our adaptation method improves the average success rate by over 7% across multiple tasks on both simulated benchmarks and real-world evaluations.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Zhou_Mitigating_the_Human-Robot_Domain_Discrepancy_in_Visual_Pre-training_for_Robotic_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Zhou_Mitigating_the_Human-Robot_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Zhou_2025_CVPR,
    author    = {Zhou, Jiaming and Ma, Teli and Lin, Kun-Yu and Wang, Zifan and Qiu, Ronghe and Liang, Junwei},
    title     = {Mitigating the Human-Robot Domain Discrepancy in Visual Pre-training for Robotic Manipulation},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {22551-22561}
}

raw_affiliation:
[{"affiliation": "AI Thrust, The Hong Kong University of Science and Technology (Guangzhou)", "city": "Guangzhou", "country": "China"}, {"affiliation": "Sun Yat-sen University", "city": "Guangzhou", "country": "China"}, {"affiliation": "The Hong Kong University of Science and Technology", "city": "Hong Kong", "country": "China"}]

affiliations:
["AI Thrust, The Hong Kong University of Science and Technology (Guangzhou)", "Sun Yat-sen University", "The Hong Kong University of Science and Technology"]


title: Less is More: Efficient Model Merging with Binary Task Switch
authors: Biqing Qi, Fangyuan Li, Zhen Wang, Junqi Gao, Dong Li, Peng Ye, Bowen Zhou
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 15265-15274
cities: ["Shanghai", "Harbin", "Harbin", "Beijing"]
countries: ["China", "China", "China", "China"]
highlight: yes
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Qi_Less_is_More_Efficient_Model_Merging_with_Binary_Task_Switch_CVPR_2025_paper.html

abstract:
As an effective approach to equip models with multi-task capabilities without additional training, model merging has garnered significant attention. However, existing merging methods face challenges of redundant parameter conflicts and the excessive storage burden of fine-tuned parameters. In this work, through controlled experiments, we reveal that for fine-tuned task vectors, only those parameters with magnitudes above a certain threshold contribute positively to the task, exhibiting a pulse-like characteristic. We then attempt leveraging this pulse-like characteristic to binarize the task vectors and reduce storage overhead. Further controlled experiments show that the binarized task vectors incur almost no decrease in fine-tuning and merging performance, and even exhibit stronger performance improvements as the proportion of redundant parameters increases. Based on these insights, we propose Task Switch (T-Switch), which decomposes task vectors into three components: 1) an activation switch instantiated by a binarized mask vector, 2) a polarity switch instantiated by a binarized sign vector, and 3) a scaling knob instantiated by a scalar coefficient. By storing task vectors in a binarized form, T-Switch alleviates parameter conflicts while ensuring efficient task parameter storage. Furthermore, to enable automated switch combination in T-Switch, we further introduce Auto-Switch, which enables training-free switch combination via retrieval from a small query set. Experiments indicate that our methods achieve significant performance improvements over existing baselines, requiring only 1-3% of the storage space of full-precision parameters.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Qi_Less_is_More_Efficient_Model_Merging_with_Binary_Task_Switch_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Qi_Less_is_More_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Qi_2025_CVPR,
    author    = {Qi, Biqing and Li, Fangyuan and Wang, Zhen and Gao, Junqi and Li, Dong and Ye, Peng and Zhou, Bowen},
    title     = {Less is More: Efficient Model Merging with Binary Task Switch},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {15265-15274}
}

raw_affiliation:
[{"affiliation": "Shanghai Artificial Intelligence Laboratory", "city": "Shanghai", "country": "China"}, {"affiliation": "Department of Control Science and Engineering, Harbin Institute of Technology", "city": "Harbin", "country": "China"}, {"affiliation": "School of Mathematics, Harbin Institute of Technology", "city": "Harbin", "country": "China"}, {"affiliation": "Department of Electronic Engineering, Tsinghua University", "city": "Beijing", "country": "China"}]

affiliations:
["Shanghai Artificial Intelligence Laboratory", "Department of Control Science and Engineering, Harbin Institute of Technology", "School of Mathematics, Harbin Institute of Technology", "Department of Electronic Engineering, Tsinghua University"]


title: One-Minute Video Generation with Test-Time Training
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 17702-17711
affiliations: ["NVIDIA", "Stanford University", "UCSD", "UC Berkeley", "UT Austin"]
cities: ["Santa Clara", "Stanford", "San Diego", "Berkeley", "Austin"]
countries: ["USA", "USA", "USA", "USA", "USA"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Dalal_One-Minute_Video_Generation_with_Test-Time_Training_CVPR_2025_paper.html

authors:
Karan Dalal, Daniel Koceja, Jiarui Xu, Yue Zhao, Shihao Han, Ka Chun Cheung, Jan Kautz, Yejin Choi, Yu Sun, Xiaolong Wang

abstract:
Transformers today still struggle to generate one-minute videos because self-attention layers are inefficient for long context. Alternatives such as Mamba layers struggle to produce coherent scenes because their hidden states are small and less expressive. We experiment with Test-Time Training (TTT) layers, whose hidden states themselves can be neural networks, therefore larger and more expressive. Adding TTT layers into a pre-trained Transformer enables it to generate one-minute videos from text storyboards. We curate a dataset based on Tom and Jerry cartoons as a proof-of-concept benchmark. Compared to baselines such as Mamba 2, Gated DeltaNet, and sliding-window attention layers, TTT layers generate much more coherent videos that tell complete stories, leading by 34 Elo points in a human evaluation of 100 videos per method. Although promising, our results are still limited in physical realism, and the efficiency of our implementation can be further improved. Sample videos, code and annotations are available at: https://test-time-training.github.io/video-dit

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Dalal_One-Minute_Video_Generation_with_Test-Time_Training_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Dalal_One-Minute_Video_Generation_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Dalal_2025_CVPR,
    author    = {Dalal, Karan and Koceja, Daniel and Xu, Jiarui and Zhao, Yue and Han, Shihao and Cheung, Ka Chun and Kautz, Jan and Choi, Yejin and Sun, Yu and Wang, Xiaolong},
    title     = {One-Minute Video Generation with Test-Time Training},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {17702-17711}
}

raw_affiliation:
[{"affiliation": "NVIDIA", "city": "Santa Clara", "country": "USA"}, {"affiliation": "Stanford University", "city": "Stanford", "country": "USA"}, {"affiliation": "UCSD", "city": "San Diego", "country": "USA"}, {"affiliation": "UC Berkeley", "city": "Berkeley", "country": "USA"}, {"affiliation": "UT Austin", "city": "Austin", "country": "USA"}]


title: InteractionMap: Improving Online Vectorized HDMap Construction with Interaction
authors: Kuang Wu, Chuan Yang, Zhanbin Li
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 17176-17186
raw_affiliation: [{"affiliation": "Langge Technology", "city": "Beijing", "country": "China"}]
affiliations: ["Langge Technology"]
cities: ["Beijing"]
countries: ["China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Wu_InteractionMap_Improving_Online_Vectorized_HDMap_Construction_with_Interaction_CVPR_2025_paper.html

abstract:
Vectorized high-definition (HD) maps are essential for an autonomous driving system. Recently, state-of-the-art map vectorization methods are mainly based on DETR-like framework to generate HD maps in an end-to-end manner. In this paper, we propose InteractionMap, which improves previous map vectorization methods by fully leveraging local-to-global information interaction in both time and space. Firstly, we explore enhancing DETR-like detectors by explicit position relation prior from point-level to instance-level, since map elements contain strong shape priors. Secondly, we propose a key-frame-based hierarchical temporal fusion module, which interacts temporal information from local to global. Lastly, the separate classification branch and regression branch lead to the problem of misalignment in the output distribution. We interact semantic information with geometric information by introducing a novel geometric-aware classification loss in optimization and a geometric-aware matching cost in label assignment. InteractionMap achieves state-of-the-art performance on both nuScenes and Argoverse2 benchmarks.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Wu_InteractionMap_Improving_Online_Vectorized_HDMap_Construction_with_Interaction_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Wu_InteractionMap_Improving_Online_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Wu_2025_CVPR,
    author    = {Wu, Kuang and Yang, Chuan and Li, Zhanbin},
    title     = {InteractionMap: Improving Online Vectorized HDMap Construction with Interaction},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {17176-17186}
}


title: Text-guided Sparse Voxel Pruning for Efficient 3D Visual Grounding
authors: Wenxuan Guo, Xiuwei Xu, Ziwei Wang, Jianjiang Feng, Jie Zhou, Jiwen Lu
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 3666-3675
affiliations: ["Tsinghua University", "Nanyang Technological University"]
cities: ["Beijing", "Singapore"]
countries: ["China", "Singapore"]
highlight: yes
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Guo_Text-guided_Sparse_Voxel_Pruning_for_Efficient_3D_Visual_Grounding_CVPR_2025_paper.html

abstract:
In this paper, we propose an efficient multi-level convolution architecture for 3D visual grounding. Conventional methods are difficult to meet the requirements of real-time inference due to the two-stage or point-based architecture. Inspired by the success of multi-level fully sparse convolutional architecture in 3D object detection, we aim to build a new 3D visual grounding framework following this technical route. However, as in 3D visual grounding task the 3D scene representation should be deeply interacted with text features, sparse convolution-based architecture is inefficient for this interaction due to the large amount of voxel features. To this end, we propose text-guided pruning (TGP) and completion-based addition (CBA) to deeply fuse 3D scene representation and text features in an efficient way by gradual region pruning and target completion. Specifically, TGP iteratively sparsifies the 3D scene representation and thus efficiently interacts the voxel features with text features by cross-attention. To mitigate the affect of pruning on delicate geometric information, CBA adaptively fixes the over-pruned region by voxel completion with negligible computational overhead. Compared with previous single-stage methods, our method achieves top inference speed and surpasses previous fastest method by 100% FPS. Our method also achieves state-of-the-art accuracy even compared with two-stage methods, with +1.13 lead of Acc@0.5 on ScanRefer, and +2.6 and +3.2 leads on NR3D and SR3D respectively. The code is available at https://github.com/GWxuan/TSP3D.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Guo_Text-guided_Sparse_Voxel_Pruning_for_Efficient_3D_Visual_Grounding_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Guo_Text-guided_Sparse_Voxel_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Guo_2025_CVPR,
    author    = {Guo, Wenxuan and Xu, Xiuwei and Wang, Ziwei and Feng, Jianjiang and Zhou, Jie and Lu, Jiwen},
    title     = {Text-guided Sparse Voxel Pruning for Efficient 3D Visual Grounding},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {3666-3675}
}

raw_affiliation:
[{"affiliation": "Tsinghua University", "city": "Beijing", "country": "China"}, {"affiliation": "Nanyang Technological University", "city": "Singapore", "country": "Singapore"}]


title: ROCKET-1: Mastering Open-World Interaction with Visual-Temporal Context Prompting
authors: Shaofei Cai, Zihao Wang, Kewei Lian, Zhancun Mu, Xiaojian Ma, Anji Liu, Yitao Liang
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 12122-12131
cities: ["Beijing", "Beijing", "Los Angeles", "Beijing"]
countries: ["China", "China", "USA", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Cai_ROCKET-1_Mastering_Open-World_Interaction_with_Visual-Temporal_Context_Prompting_CVPR_2025_paper.html

abstract:
Vision-language models (VLMs) have excelled in multimodal tasks, but adapting them to embodied decision-making in open-world environments presents challenges. One critical issue is bridging the gap between discrete entities in low-level observations and the abstract concepts required for effective planning. A common solution is building hierarchical agents, where VLMs serve as high-level reasoners that break down tasks into executable sub-tasks, typically specified using language. However, language suffers from the inability to communicate detailed spatial information. We propose visual-temporal context prompting, a novel communication protocol between VLMs and policy models. This protocol leverages object segmentation from past observations to guide policy-environment interactions. Using this approach, we train ROCKET-2, a low-level policy that predicts actions based on concatenated visual observations and segmentation masks, supported by real-time object tracking from SAM2. Our method unlocks the potential of VLMs, enabling them to tackle complex tasks that demand spatial reasoning.  Experiments in Minecraft show that our approach enables agents to achieve previously unattainable tasks, with a 76% absolute improvement in open-world interaction performance. Codes are available at https://craftjarvis.github.io/ROCKET-1.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Cai_ROCKET-1_Mastering_Open-World_Interaction_with_Visual-Temporal_Context_Prompting_CVPR_2025_paper.pdf

bibtex:
@InProceedings{Cai_2025_CVPR,
    author    = {Cai, Shaofei and Wang, Zihao and Lian, Kewei and Mu, Zhancun and Ma, Xiaojian and Liu, Anji and Liang, Yitao},
    title     = {ROCKET-1: Mastering Open-World Interaction with Visual-Temporal Context Prompting},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {12122-12131}
}

raw_affiliation:
[{"affiliation": "Institute for Artificial Intelligence, Peking University", "city": "Beijing", "country": "China"}, {"affiliation": "School of Intelligence Science and Technology, Peking University", "city": "Beijing", "country": "China"}, {"affiliation": "Computer Science Department, University of California, Los Angeles", "city": "Los Angeles", "country": "USA"}, {"affiliation": "Beijing Institute for General Artificial Intelligence (BIGAI)", "city": "Beijing", "country": "China"}]

affiliations:
["Institute for Artificial Intelligence, Peking University", "School of Intelligence Science and Technology, Peking University", "Computer Science Department, University of California, Los Angeles", "Beijing Institute for General Artificial Intelligence (BIGAI)"]


authors: Leonhard Sommer, Olaf Dünkel, Christian Theobalt, Adam Kortylewski
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 6468-6479
affiliations: ["University of Freiburg", "Max Planck Institute for Informatics"]
cities: ["Freiburg", "Saarbr\u00fccken"]
countries: ["Germany", "Germany"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Sommer_Common3D_Self-Supervised_Learning_of_3D_Morphable_Models_for_Common_Objects_CVPR_2025_paper.html

title:
Common3D: Self-Supervised Learning of 3D Morphable Models for Common Objects in Neural Feature Space

abstract:
3D morphable models (3DMMs) are a powerful tool to represent the possible shapes and appearances of an object category. Given a single test image, 3DMMs can be used to solve various tasks, such as predicting the 3D shape, pose, semantic correspondence, and instance segmentation of an object. Unfortunately, 3DMMs are only available for very few object categories that are of particular interest, like faces or human bodies, as they require a demanding 3D data acquisition and category-specific training process. In contrast, we introduce a new method, Common3D, that learns 3DMMs of common objects in a fully self-supervised manner from a collection of object-centric videos. For this purpose, our model represents objects as a learned 3D template mesh and a deformation field that is parameterized as an image-conditioned neural network. Different from prior works, Common3D represents the object appearance with neural features instead of RGB colors, which enables the learning of more generalizable representations through an abstraction from pixel intensities. Importantly, we train the appearance features using a contrastive objective by exploiting the correspondences defined through the deformable template mesh. This leads to higher quality correspondence features compared to related works and a significantly improved model performance at estimating 3D object pose and semantic correspondence. Common3D is the first completely self-supervised method that can solve various vision tasks in a zero-shot manner. github.com/GenIntel/common3d.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Sommer_Common3D_Self-Supervised_Learning_of_3D_Morphable_Models_for_Common_Objects_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Sommer_Common3D_Self-Supervised_Learning_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Sommer_2025_CVPR,
    author    = {Sommer, Leonhard and D\"unkel, Olaf and Theobalt, Christian and Kortylewski, Adam},
    title     = {Common3D: Self-Supervised Learning of 3D Morphable Models for Common Objects in Neural Feature Space},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {6468-6479}
}

raw_affiliation:
[{"affiliation": "University of Freiburg", "city": "Freiburg", "country": "Germany"}, {"affiliation": "Max Planck Institute for Informatics", "city": "Saarbr\u00fccken", "country": "Germany"}]


title: RLAIF-V: Open-Source AI Feedback Leads to Super GPT-4V Trustworthiness
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 19985-19995
cities: ["Beijing", "Singapore", "Harbin", "Hangzhou", "Shenzhen"]
countries: ["China", "Singapore", "China", "China", "China"]
highlight: yes
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Yu_RLAIF-V_Open-Source_AI_Feedback_Leads_to_Super_GPT-4V_Trustworthiness_CVPR_2025_paper.html

authors:
Tianyu Yu, Haoye Zhang, Qiming Li, Qixin Xu, Yuan Yao, Da Chen, Xiaoman Lu, Ganqu Cui, Yunkai Dang, Taiwen He, Xiaocheng Feng, Jun Song, Bo Zheng, Zhiyuan Liu, Tat-Seng Chua, Maosong Sun

abstract:
Traditional feedback learning for hallucination reduction relies on labor-intensive manual labeling or expensive proprietary models.This leaves the community without  foundational knowledge about how to build high-quality feedback with open-source MLLMs.In this work, we introduce RLAIF-V, a novel framework that aligns MLLMs in a fully open-source paradigm. RLAIF-V maximally explores open-source MLLMs from two perspectives, including high-quality feedback data generation for preference learning and self-feedback guidance for inference-time scaling.Extensive experiments on seven benchmarks in both automatic and human evaluation show that RLAIF-V substantially enhances the trustworthiness of models at both preference learning and inference time. RLAIF-V 7B reduces object hallucination by 80.7% and overall hallucination by 33.7%. Remarkably, RLAIF-V 12B further reveals the self-alignment potential of open-source MLLMs, where the  model can learn from feedback of itself to achieve super GPT-4V trustworthiness.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Yu_RLAIF-V_Open-Source_AI_Feedback_Leads_to_Super_GPT-4V_Trustworthiness_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Yu_RLAIF-V_Open-Source_AI_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Yu_2025_CVPR,
    author    = {Yu, Tianyu and Zhang, Haoye and Li, Qiming and Xu, Qixin and Yao, Yuan and Chen, Da and Lu, Xiaoman and Cui, Ganqu and Dang, Yunkai and He, Taiwen and Feng, Xiaocheng and Song, Jun and Zheng, Bo and Liu, Zhiyuan and Chua, Tat-Seng and Sun, Maosong},
    title     = {RLAIF-V: Open-Source AI Feedback Leads to Super GPT-4V Trustworthiness},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {19985-19995}
}

raw_affiliation:
[{"affiliation": "Department of Computer Science and Technology, Tsinghua University", "city": "Beijing", "country": "China"}, {"affiliation": "NExT++ Lab, School of Computing, National University of Singapore", "city": "Singapore", "country": "Singapore"}, {"affiliation": "Harbin Institute of Technology", "city": "Harbin", "country": "China"}, {"affiliation": "Taobao & Tmall Group of Alibaba", "city": "Hangzhou", "country": "China"}, {"affiliation": "Peng Cheng Laboratory", "city": "Shenzhen", "country": "China"}]

affiliations:
["Department of Computer Science and Technology, Tsinghua University", "NExT++ Lab, School of Computing, National University of Singapore", "Harbin Institute of Technology", "Taobao & Tmall Group of Alibaba", "Peng Cheng Laboratory"]


title: ECVC: Exploiting Non-Local Correlations in Multiple Frames for Contextual Video Compression
authors: Wei Jiang, Junru Li, Kai Zhang, Li Zhang
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 7331-7341
raw_affiliation: [{"affiliation": "Bytedance", "city": "Beijing", "country": "China"}]
affiliations: ["Bytedance"]
cities: ["Beijing"]
countries: ["China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Jiang_ECVC_Exploiting_Non-Local_Correlations_in_Multiple_Frames_for_Contextual_Video_CVPR_2025_paper.html

abstract:
In Learned Video Compression (LVC), improving inter prediction, such as enhancing temporal context mining and mitigating accumulated errors, is crucial for boosting rate-distortion performance. Existing LVCs mainly focus on mining the temporal movements while neglecting non-local correlations among frames. Additionally, current contextual video compression models use a single reference frame, which is insufficient for handling complex movements. To address these issues, we propose leveraging non-local correlations across multiple frames to enhance temporal priors, significantly boosting rate-distortion performance. To mitigate error accumulation, we introduce a partial cascaded fine-tuning strategy that supports fine-tuning on full-length sequences with constrained computational resources. This method reduces the train-test mismatch in sequence lengths and significantly decreases accumulated errors. Based on the proposed techniques, we present a video compression scheme ECVC. Experiments demonstrate that our ECVC achieves state-of-the-art performance, reducing 10.5% and 11.5% more bit-rates than previous SOTA method DCVC-FM over VTM-13.2 low delay B (LDB) under the intra period (IP) of 32 and -1, respectively. Our code is available at https://github.com/JiangWeibeta/ECVC.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Jiang_ECVC_Exploiting_Non-Local_Correlations_in_Multiple_Frames_for_Contextual_Video_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Jiang_ECVC_Exploiting_Non-Local_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Jiang_2025_CVPR,
    author    = {Jiang, Wei and Li, Junru and Zhang, Kai and Zhang, Li},
    title     = {ECVC: Exploiting Non-Local Correlations in Multiple Frames for Contextual Video Compression},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {7331-7341}
}


proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 2578-2588
affiliations: ["Princeton University", "Meta"]
cities: ["Princeton", "Menlo Park"]
countries: ["USA", "USA"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Wang_LinGen_Towards_High-Resolution_Minute-Length_Text-to-Video_Generation_with_Linear_Computational_Complexity_CVPR_2025_paper.html

title:
LinGen: Towards High-Resolution Minute-Length Text-to-Video Generation with Linear Computational Complexity

authors:
Hongjie Wang, Chih-Yao Ma, Yen-Cheng Liu, Ji Hou, Tao Xu, Jialiang Wang, Felix Juefei-Xu, Yaqiao Luo, Peizhao Zhang, Tingbo Hou, Peter Vajda, Niraj K. Jha, Xiaoliang Dai

abstract:
Text-to-video generation enhances content creation but is highly computationally intensive: The computational cost of Diffusion Transformers (DiTs) scales quadratically in the number of pixels.  This makes minute-length video generation extremely expensive, limiting most existing models to generating videos of only 10-20 seconds length. We propose a Linear-complexity text-to-video Generation (LinGen) framework whose cost scales linearly in the number of pixels. For the first time, LinGen enables high-resolution minute-length video generation on a single GPU without compromising quality. It replaces the computationally-dominant and quadratic-complexity block, self-attention, with a linear-complexity block called MATE, which consists of an MA-branch and a TE-branch. The MA-branch targets short-to-long-range correlations, combining a bidirectional Mamba2 block with our token rearrangement method, Rotary Major Scan, and our review tokens developed for long video generation. The TE-branch is a novel TEmporal Swin Attention block that focuses on temporal correlations between adjacent tokens and medium-range tokens. The MATE block addresses the adjacency preservation issue of Mamba and improves the consistency of generated videos significantly. Experimental results show that  LinGen outperforms DiT (with a 75.6% win rate) in video quality with up to 15x(11.5x) FLOPs (latency) reduction.  Furthermore, both automatic metrics and human evaluation demonstrate our LinGen-4B yields comparable video quality to state-of-the-art models (with a 50.5%, 52.1%, 49.1% win rate with respect to Gen-3, LumaLabs, and Kling, respectively). This paves the way to hour-length movie generation and real-time interactive video generation. Project website: https://lineargen.github.io/.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Wang_LinGen_Towards_High-Resolution_Minute-Length_Text-to-Video_Generation_with_Linear_Computational_Complexity_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Wang_LinGen_Towards_High-Resolution_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Wang_2025_CVPR,
    author    = {Wang, Hongjie and Ma, Chih-Yao and Liu, Yen-Cheng and Hou, Ji and Xu, Tao and Wang, Jialiang and Juefei-Xu, Felix and Luo, Yaqiao and Zhang, Peizhao and Hou, Tingbo and Vajda, Peter and Jha, Niraj K. and Dai, Xiaoliang},
    title     = {LinGen: Towards High-Resolution Minute-Length Text-to-Video Generation with Linear Computational Complexity},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {2578-2588}
}

raw_affiliation:
[{"affiliation": "Princeton University", "city": "Princeton", "country": "USA"}, {"affiliation": "Meta", "city": "Menlo Park", "country": "USA"}]


proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 11135-11145
affiliations: ["Korea University", "Yonsei University", "Hanhwa Systems"]
cities: ["Seoul", "Seoul", "Seoul"]
countries: ["South Korea", "South Korea", "South Korea"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Lee_EditSplat_Multi-View_Fusion_and_Attention-Guided_Optimization_for_View-Consistent_3D_Scene_CVPR_2025_paper.html

title:
EditSplat: Multi-View Fusion and Attention-Guided Optimization for View-Consistent 3D Scene Editing with 3D Gaussian Splatting

authors:
Dong In Lee, Hyeongcheol Park, Jiyoung Seo, Eunbyung Park, Hyunje Park, Ha Dam Baek, Sangheon Shin, Sangmin Kim, Sangpil Kim

abstract:
Recent advancements in 3D editing have highlighted the potential of text-driven methods in real-time, user-friendly AR/VR applications. However, current methods rely on 2D diffusion models without adequately considering multi-view information, resulting in multi-view inconsistency. While 3D Gaussian Splatting (3DGS) significantly improves rendering quality and speed, its 3D editing process encounters difficulties with inefficient optimization, as pre-trained Gaussians retain excessive source information, hindering optimization. To address these limitations, we propose EditSplat, a novel 3D editing framework that integrates Multi-view Fusion Guidance (MFG) and Attention-Guided Trimming (AGT). Our MFG ensures multi-view consistency by incorporating essential multi-view information into the diffusion process, leveraging classifier-free guidance from the text-to-image diffusion model and the geometric properties of 3DGS. Additionally, our AGT leverages the explicit representation of 3DGS to selectively prune and optimize 3D Gaussians, enhancing optimization efficiency and enabling precise, semantically rich local edits. Through extensive qualitative and quantitative evaluations, EditSplat achieves superior multi-view consistency and editing quality over existing methods, significantly enhancing overall efficiency.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Lee_EditSplat_Multi-View_Fusion_and_Attention-Guided_Optimization_for_View-Consistent_3D_Scene_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Lee_EditSplat_Multi-View_Fusion_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Lee_2025_CVPR,
    author    = {Lee, Dong In and Park, Hyeongcheol and Seo, Jiyoung and Park, Eunbyung and Park, Hyunje and Baek, Ha Dam and Shin, Sangheon and Kim, Sangmin and Kim, Sangpil},
    title     = {EditSplat: Multi-View Fusion and Attention-Guided Optimization for View-Consistent 3D Scene Editing with 3D Gaussian Splatting},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {11135-11145}
}

raw_affiliation:
[{"affiliation": "Korea University", "city": "Seoul", "country": "South Korea"}, {"affiliation": "Yonsei University", "city": "Seoul", "country": "South Korea"}, {"affiliation": "Hanhwa Systems", "city": "Seoul", "country": "South Korea"}]


title: SpatialCLIP: Learning 3D-aware Image Representations from Spatially Discriminative Language
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 29656-29666
affiliations: ["Zhejiang University", "The University of Hong Kong"]
cities: ["Hangzhou", "Hong Kong"]
countries: ["China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Wang_SpatialCLIP_Learning_3D-aware_Image_Representations_from_Spatially_Discriminative_Language_CVPR_2025_paper.html

authors:
Zehan Wang, Sashuai Zhou, Shaoxuan He, Haifeng Huang, Lihe Yang, Ziang Zhang, Xize Cheng, Shengpeng Ji, Tao Jin, Hengshuang Zhao, Zhou Zhao

abstract:
Contrastive Language-Image Pre-training (CLIP) learns robust visual models through language supervision, making it a crucial visual encoding technique for various applications. However, CLIP struggles with comprehending spatial concepts in images, potentially restricting the spatial intelligence of CLIP-based AI systems. In this work, we propose SpatialCLIP, an enhanced version of CLIP with better spatial understanding capabilities. To capture the intricate 3D spatial relationships in images, we improve both "visual model" and "language supervision" of CLIP. Specifically, we design 3D-inspired ViT to replace the standard ViT in CLIP. By lifting 2D image tokens into 3D space and incorporating design insights from point cloud networks, our visual model gains greater potential for spatial perception. Meanwhile, captions with accurate and detailed spatial information are very rare. To explore better language supervision for spatial understanding, we re-caption images and perturb their spatial phrases as negative descriptions, which compels the visual model to seek spatial cues to distinguish these hard negative captions. With the enhanced visual model, we introduce SpatialLLaVA, following the same LLaVA-1.5 training protocol, to investigate the importance of visual representations for MLLM's spatial intelligence. Furthermore, we create SpatialBench, a benchmark specifically designed to evaluate CLIP and MLLM in spatial reasoning. SpatialCLIP and SpatialLLaVA achieve substantial performance improvements, demonstrating stronger capabilities in spatial perception and reasoning, while maintaining comparable results on general-purpose benchmarks.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Wang_SpatialCLIP_Learning_3D-aware_Image_Representations_from_Spatially_Discriminative_Language_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Wang_SpatialCLIP_Learning_3D-aware_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Wang_2025_CVPR,
    author    = {Wang, Zehan and Zhou, Sashuai and He, Shaoxuan and Huang, Haifeng and Yang, Lihe and Zhang, Ziang and Cheng, Xize and Ji, Shengpeng and Jin, Tao and Zhao, Hengshuang and Zhao, Zhou},
    title     = {SpatialCLIP: Learning 3D-aware Image Representations from Spatially Discriminative Language},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {29656-29666}
}

raw_affiliation:
[{"affiliation": "Zhejiang University", "city": "Hangzhou", "country": "China"}, {"affiliation": "The University of Hong Kong", "city": "Hong Kong", "country": "China"}]


title: Mono2Stereo: A Benchmark and Empirical Study for Stereo Conversion
authors: Songsong Yu, Yuxin Chen, Zhongang Qi, Zeke Xie, Yifan Wang, Lijun Wang, Ying Shan, Huchuan Lu
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 21847-21856
affiliations: ["DLUT", "ARC Lab, Tencent PCG", "HKUST(GZ)"]
cities: ["Dalian", "Shenzhen", "Guangzhou"]
countries: ["China", "China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Yu_Mono2Stereo_A_Benchmark_and_Empirical_Study_for_Stereo_Conversion_CVPR_2025_paper.html

abstract:
With the rapid proliferation of 3D devices and the shortage of 3D content, stereo conversion is attracting increasing attention. Recent works introduce pretrained Diffusion Models (DMs) into this task. However, due to the scarcity of large-scale training data and comprehensive benchmarks, the optimal methodologies for employing DMs in stereo conversion and the accurate evaluation of stereo effects remain largely unexplored. In this work, we introduce the Mono2Stereo dataset, providing high-quality training data and benchmark to support in-depth exploration of stereo conversion. With this dataset, we conduct an empirical study that yields two primary findings. 1) The differences between the left and right views are subtle, yet existing metrics consider overall pixels, failing to concentrate on regions critical to stereo effects. 2) Mainstream methods adopt either one-stage left-to-right generation or warp-and-inpaint pipeline, facing challenges of degraded stereo effect and image distortion respectively. Based on these findings, we introduce a new evaluation metric, Stereo Intersection-over-Union, which prioritizes disparity and achieves a high correlation with human judgments on stereo effect. Moreover, we propose a strong baseline model, harmonizing the stereo effect and image quality simultaneously, and notably surpassing current mainstream methods. Our code and data will be open-sourced to promote further research in stereo conversion.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Yu_Mono2Stereo_A_Benchmark_and_Empirical_Study_for_Stereo_Conversion_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Yu_Mono2Stereo_A_Benchmark_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Yu_2025_CVPR,
    author    = {Yu, Songsong and Chen, Yuxin and Qi, Zhongang and Xie, Zeke and Wang, Yifan and Wang, Lijun and Shan, Ying and Lu, Huchuan},
    title     = {Mono2Stereo: A Benchmark and Empirical Study for Stereo Conversion},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {21847-21856}
}

raw_affiliation:
[{"affiliation": "DLUT", "city": "Dalian", "country": "China"}, {"affiliation": "ARC Lab, Tencent PCG", "city": "Shenzhen", "country": "China"}, {"affiliation": "HKUST(GZ)", "city": "Guangzhou", "country": "China"}]


title: Towards Open-Vocabulary Audio-Visual Event Localization
authors: Jinxing Zhou, Dan Guo, Ruohao Guo, Yuxin Mao, Jingjing Hu, Yiran Zhong, Xiaojun Chang, Meng Wang
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 8362-8371
affiliations: ["MBZUAI", "HFUT", "PKU", "NWPU", "OpenNLPLab", "USTC"]
cities: ["Abu Dhabi", "Hefei", "Beijing", "Xi'an", "Unknown", "Hefei"]
countries: ["UAE", "China", "China", "China", "China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Zhou_Towards_Open-Vocabulary_Audio-Visual_Event_Localization_CVPR_2025_paper.html

abstract:
The Audio-Visual Event Localization (AVEL) task aims to temporally locate and classify video events that are both audible and visible.Most research in this field assumes a closed-set setting, which restricts these models' ability to handle test data containing event categories absent (unseen) during training. Recently, a few studies have explored AVEL in an open-set setting, enabling the recognition of unseen events as "unknown", but without providing category-specific semantics.In this paper, we advance the field by introducing the Open-Vocabulary Audio-Visual Event Localization (OV-AVEL) problem, which requires localizing audio-visual events and predicting explicit categories for both seen and unseen data at inference.To address this new task, we propose the OV-AVEBench dataset, comprising 24,800 videos across 67 real-life audio-visual scenes (seen:unseen = 46:21), each with manual segment-level annotation.We also establish three evaluation metrics for this task.Moreover, we investigate two baseline approaches, one training-free and one using a further fine-tuning paradigm.Specifically, we utilize the unified multimodal space from the pretrained ImageBind model to extract audio, visual, and textual (event classes) features.The training-free baseline then determines predictions by comparing the consistency of audio-text and visual-text feature similarities.The fine-tuning baseline incorporates lightweight temporal layers to encode temporal relations within the audio and visual modalities, using OV-AVEBench training data for model fine-tuning.We evaluate these baselines on the proposed OV-AVEBench dataset and discuss potential directions for future work in this new field.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Zhou_Towards_Open-Vocabulary_Audio-Visual_Event_Localization_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Zhou_Towards_Open-Vocabulary_Audio-Visual_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Zhou_2025_CVPR,
    author    = {Zhou, Jinxing and Guo, Dan and Guo, Ruohao and Mao, Yuxin and Hu, Jingjing and Zhong, Yiran and Chang, Xiaojun and Wang, Meng},
    title     = {Towards Open-Vocabulary Audio-Visual Event Localization},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {8362-8371}
}

raw_affiliation:
[{"affiliation": "MBZUAI", "city": "Abu Dhabi", "country": "UAE"}, {"affiliation": "HFUT", "city": "Hefei", "country": "China"}, {"affiliation": "PKU", "city": "Beijing", "country": "China"}, {"affiliation": "NWPU", "city": "Xi'an", "country": "China"}, {"affiliation": "OpenNLPLab", "city": "Unknown", "country": "China"}, {"affiliation": "USTC", "city": "Hefei", "country": "China"}]


title: One-shot 3D Object Canonicalization based on Geometric and Semantic Consistency
authors: Li Jin, Yujie Wang, Wenzheng Chen, Qiyu Dai, Qingzhe Gao, Xueying Qin, Baoquan Chen
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 16850-16859
cities: ["Jinan", "Beijing", "Beijing", "Chapel Hill", "Beijing", "Beijing", "Beijing"]
countries: ["China", "China", "China", "USA", "China", "China", "China"]
highlight: yes
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Jin_One-shot_3D_Object_Canonicalization_based_on_Geometric_and_Semantic_Consistency_CVPR_2025_paper.html

abstract:
3D object canonicalization is a fundamental task, essential for various downstream tasks. Existing methods rely on either cumbersome manual processes or priors learned from extensive, per-category training samples. Real-world datasets, however, often exhibit long-tail distributions, challenging existing learning-based methods, especially in categories with limited samples. We address this by introducing the first one-shot category-level object canonicalization framework that operates under arbitrary poses, requiring only a single canonical model as a reference (the "prior model") for each category. To canonicalize any object, our framework first extracts semantic cues with large language models (LLMs) and vision-language models (VLMs) to establish correspondences with the prior model. We introduce a novel joint energy function to enforce geometric and semantic consistency, aligning object orientations precisely despite significant shape variations. Moreover, we adopt a support-plane strategy to reduce search space for initial poses and utilize a semantic relationship map to select the canonical pose from multiple hypotheses. Extensive experiments on multiple datasets demonstrate that our framework achieves state-of-the-art performance and validates key design choices. Using our framework, we create the Canonical Objaverse Dataset (COD), canonicalizing 32K samples in the Objaverse-LVIS dataset, underscoring the effectiveness of our framework on handling large-scale datasets. Project page at https://jinli998.github.io/One-shot_3D_Object_Canonicalization/

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Jin_One-shot_3D_Object_Canonicalization_based_on_Geometric_and_Semantic_Consistency_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Jin_One-shot_3D_Object_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Jin_2025_CVPR,
    author    = {Jin, Li and Wang, Yujie and Chen, Wenzheng and Dai, Qiyu and Gao, Qingzhe and Qin, Xueying and Chen, Baoquan},
    title     = {One-shot 3D Object Canonicalization based on Geometric and Semantic Consistency},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {16850-16859}
}

raw_affiliation:
[{"affiliation": "School of Software, Shandong University", "city": "Jinan", "country": "China"}, {"affiliation": "State Key Laboratory of General Artificial Intelligence, Peking University", "city": "Beijing", "country": "China"}, {"affiliation": "School of Intelligence Science and Technology, Peking University", "city": "Beijing", "country": "China"}, {"affiliation": "UNC Chapel Hill", "city": "Chapel Hill", "country": "USA"}, {"affiliation": "Wangxuan Institue of Computer Technology, Peking University", "city": "Beijing", "country": "China"}, {"affiliation": "State Key Laboratory of Multimedia Information Processing, Peking University, Beijing, P.R. China", "city": "Beijing", "country": "China"}, {"affiliation": "Engineering Research Center of Digital Media Technology, Ministry of Education, P.R. China", "city": "Beijing", "country": "China"}]

affiliations:
["School of Software, Shandong University", "State Key Laboratory of General Artificial Intelligence, Peking University", "School of Intelligence Science and Technology, Peking University", "UNC Chapel Hill", "Wangxuan Institue of Computer Technology, Peking University", "State Key Laboratory of Multimedia Information Processing, Peking University, Beijing, P.R. China", "Engineering Research Center of Digital Media Technology, Ministry of Education, P.R. China"]


title: Inst3D-LMM: Instance-Aware 3D Scene Understanding with Multi-modal Instruction Tuning
authors: Hanxun Yu, Wentong Li, Song Wang, Junbo Chen, Jianke Zhu
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 14147-14157
affiliations: ["Zhejiang University", "Nanjing University of Aeronautics and Astronautics", "Udeer.ai"]
cities: ["Hangzhou", "Nanjing", "Beijing"]
countries: ["China", "China", "China"]
highlight: yes
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Yu_Inst3D-LMM_Instance-Aware_3D_Scene_Understanding_with_Multi-modal_Instruction_Tuning_CVPR_2025_paper.html

abstract:
Despite encouraging progress in 3D scene understanding, it remains challenging to develop an effective Large Multi-modal Model (LMM) that is capable of understanding and reasoning in complex 3D environments. Most previous methods typically encode 3D point and 2D image features separately, neglecting interactions between 2D semantics and 3D object properties, as well as the spatial relationships within the 3D environment. This limitation not only hinders comprehensive representations of 3D scene, but also compromises training and inference efficiency. To address these challenges, we propose a unified Instance-aware 3D Large Multi-modal Model (Inst3D-LMM) to deal with multiple 3D scene understanding tasks simultaneously. To obtain the fine-grained instance-level visual tokens, we first introduce a novel Multi-view Cross-Modal Fusion (MCMF) module to inject the multi-view 2D semantics into their corresponding 3D geometric features. For scene-level relation-aware tokens, we further present a 3D Instance Spatial Relation (3D-ISR) module to capture the intricate pairwise spatial relationships among objects. Additionally, we perform end-to-end multi-task instruction tuning simultaneously without the subsequent task-specific fine-tuning. Extensive experiments demonstrate that our approach outperforms the state-of-the-art methods across 3D scene understanding, reasoning and grounding tasks. Source code is available at: https://github.com/hanxunyu/Inst3D-LMM.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Yu_Inst3D-LMM_Instance-Aware_3D_Scene_Understanding_with_Multi-modal_Instruction_Tuning_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Yu_Inst3D-LMM_Instance-Aware_3D_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Yu_2025_CVPR,
    author    = {Yu, Hanxun and Li, Wentong and Wang, Song and Chen, Junbo and Zhu, Jianke},
    title     = {Inst3D-LMM: Instance-Aware 3D Scene Understanding with Multi-modal Instruction Tuning},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {14147-14157}
}

raw_affiliation:
[{"affiliation": "Zhejiang University", "city": "Hangzhou", "country": "China"}, {"affiliation": "Nanjing University of Aeronautics and Astronautics", "city": "Nanjing", "country": "China"}, {"affiliation": "Udeer.ai", "city": "Beijing", "country": "China"}]


title: S2Gaussian: Sparse-View Super-Resolution 3D Gaussian Splatting
authors: Yecong Wan, Mingwen Shao, Yuanshuo Cheng, Wangmeng Zuo
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 711-721
cities: ["Qingdao", "Harbin"]
countries: ["China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Wan_S2Gaussian_Sparse-View_Super-Resolution_3D_Gaussian_Splatting_CVPR_2025_paper.html

abstract:
In this paper, we aim ambitiously for a realistic yet challenging problem, namely, how to reconstruct high-quality 3D scenes from sparse low-resolution views that simultaneously suffer from deficient perspectives and contents. Whereas existing methods only deal with either sparse views or low-resolution observations, they fail to handle such hybrid and complicated scenarios. To this end, we propose a novel Sparse-view Super-resolution 3D Gaussian Splatting framework, dubbed S2Gaussian, that can reconstruct structure-accurate and detail-faithful 3D scenes with only sparse and low-resolution views. The S2Gaussian operates in a two-stage fashion. In the first stage, we initially optimize a low-resolution Gaussian representation with depth regularization and densify it to initialize the high-resolution Gaussians through a tailored Gaussian Shuffle operation. In the second stage, we refine the high-resolution Gaussians with the super-resolved images generated from both original sparse views and pseudo-views rendered by the low-resolution Gaussians. In which a customized blur-free inconsistency modeling scheme and a 3D robust optimization strategy are elaborately designed to mitigate multi-view inconsistency and eliminate erroneous updates caused by imperfect supervision. Extensive experiments demonstrate superior results and in particular establishing new state-of-the-art performances with more consistent geometry and finer details. Project Page https://jeasco.github.io/S2Gaussian/.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Wan_S2Gaussian_Sparse-View_Super-Resolution_3D_Gaussian_Splatting_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Wan_S2Gaussian_Sparse-View_Super-Resolution_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Wan_2025_CVPR,
    author    = {Wan, Yecong and Shao, Mingwen and Cheng, Yuanshuo and Zuo, Wangmeng},
    title     = {S2Gaussian: Sparse-View Super-Resolution 3D Gaussian Splatting},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {711-721}
}

raw_affiliation:
[{"affiliation": "Shandong Key Laboratory of Intelligent Oil & Gas Industrial Software, Qingdao Institute of Software, College of Computer Science and Technology, China University of Petroleum (East China)", "city": "Qingdao", "country": "China"}, {"affiliation": "Faculty of Computing, Harbin Institute of Technology", "city": "Harbin", "country": "China"}]

affiliations:
["Shandong Key Laboratory of Intelligent Oil & Gas Industrial Software, Qingdao Institute of Software, College of Computer Science and Technology, China University of Petroleum (East China)", "Faculty of Computing, Harbin Institute of Technology"]


title: HIIF: Hierarchical Encoding based Implicit Image Function for Continuous Super-resolution
authors: Yuxuan Jiang, Ho Man Kwan, Tianhao Peng, Ge Gao, Fan Zhang, Xiaoqing Zhu, Joel Sole, David Bull
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 2289-2299
affiliations: ["University of Bristol", "Netflix Inc."]
cities: ["Bristol", "Unknown"]
countries: ["UK", "USA"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Jiang_HIIF_Hierarchical_Encoding_based_Implicit_Image_Function_for_Continuous_Super-resolution_CVPR_2025_paper.html

abstract:
Recent advances in implicit neural representations (INRs) have shown significant promise in modeling visual signals for various low-vision tasks including image super-resolution (ISR). INR-based ISR methods typically learn continuous representations, providing flexibility for generating high-resolution images at any desired scale from their low-resolution counterparts. However, existing INR-based ISR methods utilize multi-layer perceptrons for parameterization in the network; this does not take account of the hierarchical structure existing in local sampling points and hence constrains the representation capability. In this paper, we propose a new Hierarchical encoding based Implicit Image Function for continuous image super-resolution, HIIF, which leverages a novel hierarchical positional encoding that enhances the local implicit representation, enabling it to capture fine details at multiple scales. Our approach also embeds a multi-head linear attention mechanism within the implicit attention network by taking additional non-local information into account. Our experiments show that, when integrated with different backbone encoders, HIIF outperforms the state-of-the-art continuous image super-resolution methods by up to 0.17dB in PSNR. The source code of HIIF will be made publicly available at www.github.com.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Jiang_HIIF_Hierarchical_Encoding_based_Implicit_Image_Function_for_Continuous_Super-resolution_CVPR_2025_paper.pdf

bibtex:
@InProceedings{Jiang_2025_CVPR,
    author    = {Jiang, Yuxuan and Kwan, Ho Man and Peng, Tianhao and Gao, Ge and Zhang, Fan and Zhu, Xiaoqing and Sole, Joel and Bull, David},
    title     = {HIIF: Hierarchical Encoding based Implicit Image Function for Continuous Super-resolution},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {2289-2299}
}

raw_affiliation:
[{"affiliation": "University of Bristol", "city": "Bristol", "country": "UK"}, {"affiliation": "Netflix Inc.", "city": "Unknown", "country": "USA"}]


title: Motion Prompting: Controlling Video Generation with Motion Trajectories
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 1-12
affiliations: ["Google DeepMind", "University of Michigan", "Brown University"]
cities: ["Mountain View", "Ann Arbor", "Providence"]
countries: ["USA", "USA", "USA"]
oral: yes
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Geng_Motion_Prompting_Controlling_Video_Generation_with_Motion_Trajectories_CVPR_2025_paper.html

authors:
Daniel Geng, Charles Herrmann, Junhwa Hur, Forrester Cole, Serena Zhang, Tobias Pfaff, Tatiana Lopez-Guevara, Yusuf Aytar, Michael Rubinstein, Chen Sun, Oliver Wang, Andrew Owens, Deqing Sun

abstract:
Motion control is crucial for generating expressive and compelling video content; however, most existing video generation models rely mainly on text prompts for control, which struggle to capture the nuances of dynamic actions and temporal compositions. To this end, we train a video generation model conditioned on spatio-temporally sparse or dense motion trajectories. In contrast to prior motion conditioning work, this flexible representation can encode any number of trajectories, object-specific or global scene motion, and temporally sparse motion; due to its flexibility we refer to this conditioning as motion prompts. While users may directly specify sparse trajectories, we also show how to translate high-level user requests into detailed, semi-dense motion prompts, a process we term motion prompt expansion. We demonstrate the versatility of our approach through various applications, including camera and object motion control, "interacting" with an image, motion transfer, and image editing. Our results showcase emergent behaviors, such as realistic physics, suggesting the potential of motion prompts for probing video models and interacting with future generative world models. Finally, we evaluate quantitatively, conduct a human study, and demonstrate strong performance.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Geng_Motion_Prompting_Controlling_Video_Generation_with_Motion_Trajectories_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Geng_Motion_Prompting_Controlling_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Geng_2025_CVPR,
    author    = {Geng, Daniel and Herrmann, Charles and Hur, Junhwa and Cole, Forrester and Zhang, Serena and Pfaff, Tobias and Lopez-Guevara, Tatiana and Aytar, Yusuf and Rubinstein, Michael and Sun, Chen and Wang, Oliver and Owens, Andrew and Sun, Deqing},
    title     = {Motion Prompting: Controlling Video Generation with Motion Trajectories},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {1-12}
}

raw_affiliation:
[{"affiliation": "Google DeepMind", "city": "Mountain View", "country": "USA"}, {"affiliation": "University of Michigan", "city": "Ann Arbor", "country": "USA"}, {"affiliation": "Brown University", "city": "Providence", "country": "USA"}]


title: VERA: Explainable Video Anomaly Detection via Verbalized Learning of Vision-Language Models
authors: Muchao Ye, Weiyang Liu, Pan He
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 8679-8688
cities: ["Iowa City", "T\u00fcbingen", "Auburn"]
countries: ["USA", "Germany", "USA"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Ye_VERA_Explainable_Video_Anomaly_Detection_via_Verbalized_Learning_of_Vision-Language_CVPR_2025_paper.html

abstract:
The rapid advancement of vision-language models (VLMs) has established a new paradigm in video anomaly detection (VAD): leveraging VLMs to simultaneously detect anomalies and provide comprehendible explanations for the decisions. Existing work in this direction often assumes the complex reasoning required for VAD exceeds the capabilities of pretrained VLMs. Consequently, these approaches either incorporate specialized reasoning modules during inference or rely on instruction tuning datasets through additional training to adapt VLMs for VAD. However, such strategies often incur substantial computational costs or data annotation overhead. To address these challenges in explainable VAD, we introduce a verbalized learning framework named VERA that enables VLMs to perform  VAD without model parameter modifications. Specifically, VERA automatically decomposes the complex reasoning required for VAD into reflections on simpler, more focused guiding questions capturing distinct abnormal patterns. It treats these reflective questions as learnable parameters and optimizes them through data-driven verbal interactions between learner and optimizer VLMs, using coarsely labeled training data. During inference, VERA embeds the learned questions into model prompts to guide VLMs in generating segment-level anomaly scores, which are then refined into frame-level scores via the fusion of scene and temporal contexts. Experimental results on challenging benchmarks demonstrate that the learned questions of VERA are highly adaptable, significantly improving both detection performance and explainability of VLMs for VAD.

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

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Ye_VERA_Explainable_Video_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Ye_2025_CVPR,
    author    = {Ye, Muchao and Liu, Weiyang and He, Pan},
    title     = {VERA: Explainable Video Anomaly Detection via Verbalized Learning of Vision-Language Models},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {8679-8688}
}

raw_affiliation:
[{"affiliation": "The University of Iowa", "city": "Iowa City", "country": "USA"}, {"affiliation": "Max Planck Institute for Intelligent Systems, T\u00a8ubingen", "city": "T\u00fcbingen", "country": "Germany"}, {"affiliation": "Auburn University", "city": "Auburn", "country": "USA"}]

affiliations:
["The University of Iowa", "Max Planck Institute for Intelligent Systems, T\u00a8ubingen", "Auburn University"]


title: ProHOC: Probabilistic Hierarchical Out-of-Distribution Classification via Multi-Depth Networks
authors: Erik Wallin, Fredrik Kahl, Lars Hammarstrand
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 20612-20621
affiliations: ["Saab AB", "Chalmers University of Technology"]
cities: ["Stockholm", "Gothenburg"]
countries: ["Sweden", "Sweden"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Wallin_ProHOC_Probabilistic_Hierarchical_Out-of-Distribution_Classification_via_Multi-Depth_Networks_CVPR_2025_paper.html

abstract:
Out-of-distribution (OOD) detection in deep learning has traditionally been framed as a binary task, where samples are either classified as belonging to the known classes or marked as OOD, with little attention given to the semantic relationships between OOD samples and the in-distribution (ID) classes. We propose a framework for detecting and classifying OOD samples in a given class hierarchy. Specifically, we aim to predict OOD data to their correct internal nodes of the class hierarchy, whereas the known ID classes should be predicted as their corresponding leaf nodes. Our approach leverages the class hierarchy to create a probabilistic model and we implement this model by using networks trained for ID classification at multiple hierarchy depths. We conduct experiments on three datasets with predefined class hierarchies and show the effectiveness of our method. Our code is available at https://github.com/walline/prohoc.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Wallin_ProHOC_Probabilistic_Hierarchical_Out-of-Distribution_Classification_via_Multi-Depth_Networks_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Wallin_ProHOC_Probabilistic_Hierarchical_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Wallin_2025_CVPR,
    author    = {Wallin, Erik and Kahl, Fredrik and Hammarstrand, Lars},
    title     = {ProHOC: Probabilistic Hierarchical Out-of-Distribution Classification via Multi-Depth Networks},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {20612-20621}
}

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


title: CCIN: Compositional Conflict Identification and Neutralization for Composed Image Retrieval
authors: Likai Tian, Jian Zhao, Zechao Hu, Zhengwei Yang, Hao Li, Lei Jin, Zheng Wang, Xuelong Li
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 3974-3983
cities: ["Wuhan", "Unknown", "Xi'an", "Beijing"]
countries: ["China", "China", "China", "China"]
highlight: yes
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Tian_CCIN_Compositional_Conflict_Identification_and_Neutralization_for_Composed_Image_Retrieval_CVPR_2025_paper.html

abstract:
Composed Image Retrieval (CIR) is a multi-modal task that seeks to retrieve target images by harmonizing a reference image with a modified instruction. A key challenge in CIR lies in compositional conflicts between the reference image (e.g., blue, long sleeve) and the modified instruction (e.g., grey, short sleeve). Previous works attempt to mitigate such conflicts through feature-level manipulation, commonly employing learnable masks to obscure conflicting features within the reference image. However, the inherent complexity of feature spaces poses significant challenges in precise conflict neutralization, thereby leading to uncontrollable results. To this end, this paper proposes the Compositional Conflict Identification and Neutralization (CCIN) framework, which sequentially identifies and neutralizes compositional conflicts for effective CIR. Specifically, CCIN comprises two core modules: 1) Compositional Conflict Identification module, which utilizes LLM-based analysis to identify specific conflicting attributes, and 2) Compositional Conflict Neutralization module, which first generates a kept instruction to preserve non-conflicting attributes, then neutralizes conflicts under collaborative guidance of both the kept and modified instructions. Extensive experiments demonstrate the superiority of CCIN over the state-of-the-arts. Code repository: https://github.com/LikaiTian/CCIN.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Tian_CCIN_Compositional_Conflict_Identification_and_Neutralization_for_Composed_Image_Retrieval_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Tian_CCIN_Compositional_Conflict_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Tian_2025_CVPR,
    author    = {Tian, Likai and Zhao, Jian and Hu, Zechao and Yang, Zhengwei and Li, Hao and Jin, Lei and Wang, Zheng and Li, Xuelong},
    title     = {CCIN: Compositional Conflict Identification and Neutralization for Composed Image Retrieval},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {3974-3983}
}

raw_affiliation:
[{"affiliation": "National Engineering Research Center for Multimedia Software, School of Computer Science, Wuhan University", "city": "Wuhan", "country": "China"}, {"affiliation": "Institute of Artificial Intelligence (TeleAI), China Telecom", "city": "Unknown", "country": "China"}, {"affiliation": "School of Artificial Intelligence, Optics and Electronics (iOPEN), Northwestern Polytechnical University", "city": "Xi'an", "country": "China"}, {"affiliation": "Beijing University of Posts and Telecommunications", "city": "Beijing", "country": "China"}]

affiliations:
["National Engineering Research Center for Multimedia Software, School of Computer Science, Wuhan University", "Institute of Artificial Intelligence (TeleAI), China Telecom", "School of Artificial Intelligence, Optics and Electronics (iOPEN), Northwestern Polytechnical University", "Beijing University of Posts and Telecommunications"]


authors: Songlong Xing, Zhengyu Zhao, Nicu Sebe
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 15172-15182
affiliations: ["University of Trento, Italy", "Xi\u2019an Jiaotong University, China"]
cities: ["Trento", "Xi'an"]
countries: ["Italy", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Xing_CLIP_is_Strong_Enough_to_Fight_Back_Test-time_Counterattacks_towards_CVPR_2025_paper.html

title:
CLIP is Strong Enough to Fight Back: Test-time Counterattacks towards Zero-shot Adversarial Robustness of CLIP

abstract:
Despite its prevalent use in image-text matching tasks in a zero-shot manner, CLIP has been shown to be highly vulnerable to adversarial perturbations added onto images. Recent studies propose to finetune the vision encoder of CLIP with adversarial samples generated on the fly, and show improved robustness against adversarial attacks on a spectrum of downstream datasets, a property termed as zero-shot robustness. In this paper, we show that malicious perturbations that seek to maximise the classification loss lead to `falsely stable' images, and propose to leverage the pre-trained vision encoder of CLIP to counterattack such adversarial images during inference to achieve robustness. Our paradigm is simple and training-free, providing the first method to defend CLIP from adversarial attacks at test time, which is orthogonal to existing methods aiming to boost zero-shot adversarial robustness of CLIP. We conduct experiments across 16 classification datasets, and demonstrate stable and consistent gains compared to test-time defence methods adapted from existing adversarial robustness studies that do not rely on external networks, without noticeably impairing performance on clean images. We also show that our paradigm can be employed on CLIP models that have been adversarially finetuned to further enhance their robustness at test time. Our code will be released.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Xing_CLIP_is_Strong_Enough_to_Fight_Back_Test-time_Counterattacks_towards_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Xing_CLIP_is_Strong_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Xing_2025_CVPR,
    author    = {Xing, Songlong and Zhao, Zhengyu and Sebe, Nicu},
    title     = {CLIP is Strong Enough to Fight Back: Test-time Counterattacks towards Zero-shot Adversarial Robustness of CLIP},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {15172-15182}
}

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


title: OverLoCK: An Overview-first-Look-Closely-next ConvNet with Context-Mixing Dynamic Kernels
authors: Meng Lou, Yizhou Yu
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 128-138
affiliations: ["School of Computing and Data Science, The University of Hong Kong"]
cities: ["Hong Kong"]
countries: ["China"]
oral: yes
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Lou_OverLoCK_An_Overview-first-Look-Closely-next_ConvNet_with_Context-Mixing_Dynamic_Kernels_CVPR_2025_paper.html

abstract:
Top-down attention plays a crucial role in the human vision system, wherein the brain initially obtains a rough overview of a scene to discover salient cues (i.e., overview first), followed by a more careful finer-grained examination (i.e., look closely next). However, modern ConvNets remain confined to a pyramid structure that successively downsamples the feature map for receptive field expansion, neglecting this crucial biomimetic principle. We present OverLoCK, the first pure ConvNet backbone architecture that explicitly incorporates a top-down attention mechanism. Unlike pyramid backbone networks, our design features a branched architecture with three synergistic sub-networks: 1) a Base-Net that encodes low/mid-level features; 2) a lightweight Overview-Net that generates dynamic top-down attention through coarse global context modeling (i.e., overview first); and 3) a robust Focus-Net that performs finer-grained perception guided by top-down attention (i.e., look closely next). To fully unleash the power of top-down attention, we further propose a novel context-mixing dynamic convolution (ContMix) that effectively models long-range dependencies while preserving inherent local inductive biases even when the input resolution increases, addressing critical limitations in existing convolutions. Our OverLoCK exhibits a notable performance improvement over existing methods. For instance, OverLoCK-T achieves a Top-1 accuracy of 84.2%, significantly surpassing ConvNeXt-B while using only around one-third of the FLOPs/parameters. On object detection, our OverLoCK-S clearly surpasses MogaNet-B by 1% in AP^b. On semantic segmentation, our OverLoCK-T remarkably improves UniRepLKNet-T by 1.7% in mIoU. Code is publicly available at https://rb.gy/wit4jh.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Lou_OverLoCK_An_Overview-first-Look-Closely-next_ConvNet_with_Context-Mixing_Dynamic_Kernels_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Lou_OverLoCK_An_Overview-first-Look-Closely-next_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Lou_2025_CVPR,
    author    = {Lou, Meng and Yu, Yizhou},
    title     = {OverLoCK: An Overview-first-Look-Closely-next ConvNet with Context-Mixing Dynamic Kernels},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {128-138}
}

raw_affiliation:
[{"affiliation": "School of Computing and Data Science, The University of Hong Kong", "city": "Hong Kong", "country": "China"}]


