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2,800 | SapiensID: Foundation for Human Recognition | [
"Minchul Kim",
"Dingqiang Ye",
"Yiyang Su",
"Feng Liu",
"Xiaoming Liu"
] | https://openaccess.thecvf.com/content/CVPR2025/html/Kim_SapiensID_Foundation_for_Human_Recognition_CVPR_2025_paper.html | https://openaccess.thecvf.com/content/CVPR2025/papers/Kim_SapiensID_Foundation_for_Human_Recognition_CVPR_2025_paper.pdf | https://openaccess.thecvf.com/content/CVPR2025/supplemental/Kim_SapiensID_Foundation_for_CVPR_2025_supplemental.pdf | http://arxiv.org/abs/2504.04708 | @InProceedings{Kim_2025_CVPR,
author = {Kim, Minchul and Ye, Dingqiang and Su, Yiyang and Liu, Feng and Liu, Xiaoming},
title = {SapiensID: Foundation for Human Recognition},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {13937-13947}
} | Existing human recognition systems often rely on separate, specialized models for face and body analysis, limiting their effectiveness in real-world scenarios where pose, visibility, and context vary widely. This paper introduces SapiensID, a unified model that bridges this gap, achieving robust performance across diverse settings. SapiensID introduces (i) Retina Patch (RP), a dynamic patch generation scheme that adapts to subject scale and ensures consistent tokenization of regions of interest; (ii) Semantic Attention Head (SAH), an attention mechanism that learns pose-invariant representations by pooling features around key body parts; and (iii) a masked recognition model (MRM) that learns from variable token length. To facilitate training, we introduce WebBody4M, a large-scale dataset capturing diverse poses and scale variations. Extensive experiments demonstrate that SapiensID achieves state-of-the-art results on various body ReID benchmarks, outperforming specialized models in both short-term and long-term scenarios while remaining competitive with dedicated face recognition systems. Furthermore, SapiensID establishes a strong baseline for the newly introduced challenge of Cross Pose-Scale ReID, demonstrating its ability to generalize to complex, real-world conditions.The dataset, code and models will be released. | [
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2,801 | MIDI: Multi-Instance Diffusion for Single Image to 3D Scene Generation | [
"Zehuan Huang",
"Yuan-Chen Guo",
"Xingqiao An",
"Yunhan Yang",
"Yangguang Li",
"Zi-Xin Zou",
"Ding Liang",
"Xihui Liu",
"Yan-Pei Cao",
"Lu Sheng"
] | https://openaccess.thecvf.com/content/CVPR2025/html/Huang_MIDI_Multi-Instance_Diffusion_for_Single_Image_to_3D_Scene_Generation_CVPR_2025_paper.html | https://openaccess.thecvf.com/content/CVPR2025/papers/Huang_MIDI_Multi-Instance_Diffusion_for_Single_Image_to_3D_Scene_Generation_CVPR_2025_paper.pdf | https://openaccess.thecvf.com/content/CVPR2025/supplemental/Huang_MIDI_Multi-Instance_Diffusion_CVPR_2025_supplemental.pdf | http://arxiv.org/abs/2412.03558 | @InProceedings{Huang_2025_CVPR,
author = {Huang, Zehuan and Guo, Yuan-Chen and An, Xingqiao and Yang, Yunhan and Li, Yangguang and Zou, Zi-Xin and Liang, Ding and Liu, Xihui and Cao, Yan-Pei and Sheng, Lu},
title = {MIDI: Multi-Instance Diffusion for Single Image to 3D Scene Generation},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {23646-23657}
} | This paper introduces MIDI, a novel paradigm for compositional 3D scene generation from a single image. Unlike existing methods that rely on reconstruction or retrieval techniques or recent approaches that employ multi-stage object-by-object generation, MIDI extends pre-trained image-to-3D object generation models to multi-instance diffusion models, enabling the simultaneous generation of multiple 3D instances with accurate spatial relationships and high generalizability. At its core, MIDI incorporates a novel multi-instance attention mechanism, that effectively captures inter-object interactions and spatial coherence directly within the generation process, without the need for complex multi-step processes. The method utilizes partial object images and global scene context as inputs, directly modeling object completion during 3D generation. During training, we effectively supervise the interactions between 3D instances using a limited amount of scene-level data, while incorporating single-object data for regularization, thereby maintaining the pre-trained generalization ability. MIDI demonstrates state-of-the-art performance in image-to-scene generation, validated through evaluations on synthetic data, real-world scene data, and stylized scene images generated by text-to-image diffusion models. | [
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2,802 | S4-Driver: Scalable Self-Supervised Driving Multimodal Large Language Model with Spatio-Temporal Visual Representation | [
"Yichen Xie",
"Runsheng Xu",
"Tong He",
"Jyh-Jing Hwang",
"Katie Luo",
"Jingwei Ji",
"Hubert Lin",
"Letian Chen",
"Yiren Lu",
"Zhaoqi Leng",
"Dragomir Anguelov",
"Mingxing Tan"
] | https://openaccess.thecvf.com/content/CVPR2025/html/Xie_S4-Driver_Scalable_Self-Supervised_Driving_Multimodal_Large_Language_Model_with_Spatio-Temporal_CVPR_2025_paper.html | https://openaccess.thecvf.com/content/CVPR2025/papers/Xie_S4-Driver_Scalable_Self-Supervised_Driving_Multimodal_Large_Language_Model_with_Spatio-Temporal_CVPR_2025_paper.pdf | https://openaccess.thecvf.com/content/CVPR2025/supplemental/Xie_S4-Driver_Scalable_Self-Supervised_CVPR_2025_supplemental.pdf | null | @InProceedings{Xie_2025_CVPR,
author = {Xie, Yichen and Xu, Runsheng and He, Tong and Hwang, Jyh-Jing and Luo, Katie and Ji, Jingwei and Lin, Hubert and Chen, Letian and Lu, Yiren and Leng, Zhaoqi and Anguelov, Dragomir and Tan, Mingxing},
title = {S4-Driver: Scalable Self-Supervised Driving Multimodal Large Language Model with Spatio-Temporal Visual Representation},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {1622-1632}
} | The latest advancements in multi-modal large language models (MLLMs) have spurred a strong renewed interest in end-to-end motion planning approaches for autonomous driving. Many end-to-end approaches rely on human annotations to learn intermediate perception and prediction tasks, while purely self-supervised approaches--which directly learn from sensor inputs to generate planning trajectories without human annotations--often underperform the state of the art. We observe a key gap in the input representation space: end-to-end approaches built on MLLMs are often pretrained with reasoning tasks in 2D image space rather than the native 3D space in which autonomous vehicles plan. To this end, we propose S4-Driver, a scalable self-supervised motion planning algorithm with spatio-temporal visual representation, based on the popular PaLI multimodal large language model. S4-Driver uses a novel sparse volume strategy to seamlessly transform the strong visual representation of MLLMs from perspective view to 3D space without the need to finetune the vision encoder. This representation aggregates multi-view and multi-frame visual inputs and enables better prediction of planning trajectories in 3D space. To validate our method, we run experiments on both nuScenes and Waymo Open Motion Dataset (with in-house camera data). Results show that S4-Driver performs favorably against existing supervised multi-task approaches while requiring no human annotations. It also demonstrates great scalability when pretrained on large volumes of unannotated driving logs. | [
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] |
2,803 | Extrapolating and Decoupling Image-to-Video Generation Models: Motion Modeling is Easier Than You Think | [
"Jie Tian",
"Xiaoye Qu",
"Zhenyi Lu",
"Wei Wei",
"Sichen Liu",
"Yu Cheng"
] | https://openaccess.thecvf.com/content/CVPR2025/html/Tian_Extrapolating_and_Decoupling_Image-to-Video_Generation_Models_Motion_Modeling_is_Easier_CVPR_2025_paper.html | https://openaccess.thecvf.com/content/CVPR2025/papers/Tian_Extrapolating_and_Decoupling_Image-to-Video_Generation_Models_Motion_Modeling_is_Easier_CVPR_2025_paper.pdf | https://openaccess.thecvf.com/content/CVPR2025/supplemental/Tian_Extrapolating_and_Decoupling_CVPR_2025_supplemental.pdf | http://arxiv.org/abs/2503.00948 | @InProceedings{Tian_2025_CVPR,
author = {Tian, Jie and Qu, Xiaoye and Lu, Zhenyi and Wei, Wei and Liu, Sichen and Cheng, Yu},
title = {Extrapolating and Decoupling Image-to-Video Generation Models: Motion Modeling is Easier Than You Think},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {12512-12521}
} | Image-to-Video (I2V) generation aims to synthesize a video clip according to a given image and condition (e.g., text). The key challenge of this task lies in simultaneously generating natural motions while preserving the original appearance of the images. However, current I2V diffusion models (I2V-DMs) often produce videos with limited motion degrees or exhibit uncontrollable motion that conflicts with the textual condition. To address these limitations, we propose a novel Extrapolating and Decoupling framework, which introduces model merging techniques to the I2V domain for the first time. Specifically, our framework consists of three separate stages: (1) Starting with a base I2V-DM, we explicitly inject the textual condition into the temporal module using a lightweight, learnable adapter and fine-tune the integrated model to improve motion controllability. (2) We introduce a training-free extrapolation strategy to amplify the dynamic range of the motion, effectively reversing the fine-tuning process to enhance the motion degree significantly. (3) With the above two-stage models excelling in motion controllability and degree, we decouple the relevant parameters associated with each type of motion ability and inject them into the base I2V-DM. Since the I2V-DM handles different levels of motion controllability and dynamics at various denoising time steps, we adjust the motion-aware parameters accordingly over time. Extensive qualitative and quantitative experiments have been conducted to demonstrate the superiority of our framework over existing methods. | [
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2,804 | FreeCloth: Free-form Generation Enhances Challenging Clothed Human Modeling | [
"Hang Ye",
"Xiaoxuan Ma",
"Hai Ci",
"Wentao Zhu",
"Yizhou Wang"
] | https://openaccess.thecvf.com/content/CVPR2025/html/Ye_FreeCloth_Free-form_Generation_Enhances_Challenging_Clothed_Human_Modeling_CVPR_2025_paper.html | https://openaccess.thecvf.com/content/CVPR2025/papers/Ye_FreeCloth_Free-form_Generation_Enhances_Challenging_Clothed_Human_Modeling_CVPR_2025_paper.pdf | https://openaccess.thecvf.com/content/CVPR2025/supplemental/Ye_FreeCloth_Free-form_Generation_CVPR_2025_supplemental.zip | http://arxiv.org/abs/2411.19942 | @InProceedings{Ye_2025_CVPR,
author = {Ye, Hang and Ma, Xiaoxuan and Ci, Hai and Zhu, Wentao and Wang, Yizhou},
title = {FreeCloth: Free-form Generation Enhances Challenging Clothed Human Modeling},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {15987-15997}
} | Achieving realistic animated human avatars requires accurate modeling of pose-dependent clothing deformations. Existing learning-based methods heavily rely on the Linear Blend Skinning (LBS) of minimally-clothed human models like SMPL to model deformation. However, they struggle to handle loose clothing, such as long dresses, where the canonicalization process becomes ill-defined when the clothing is far from the body, leading to disjointed and fragmented results. To overcome this limitation, we propose FreeCloth, a novel hybrid framework to model challenging clothed humans. Our core idea is to use dedicated strategies to model different regions, depending on whether they are close to or distant from the body. Specifically, we segment the human body into three categories: unclothed, deformed, and generated. We simply replicate unclothed regions that require no deformation. For deformed regions close to the body, we leverage LBS to handle the deformation. As for the generated regions, which correspond to loose clothing areas, we introduce a novel free-form, part-aware generator to model them, as they are less affected by movements. This free-form generation paradigm brings enhanced flexibility and expressiveness to our hybrid framework, enabling it to capture the intricate geometric details of challenging loose clothing, such as skirts and dresses. Experimental results on the benchmark dataset featuring loose clothing demonstrate that FreeCloth achieves state-of-the-art performance with superior visual fidelity and realism, particularly in the most challenging cases. | [
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2,805 | ABC-Former: Auxiliary Bimodal Cross-domain Transformer with Interactive Channel Attention for White Balance | [
"Yu-Cheng Chiu",
"Guan-Rong Chen",
"Zihao Chen",
"Yan-Tsung Peng"
] | https://openaccess.thecvf.com/content/CVPR2025/html/Chiu_ABC-Former_Auxiliary_Bimodal_Cross-domain_Transformer_with_Interactive_Channel_Attention_for_CVPR_2025_paper.html | https://openaccess.thecvf.com/content/CVPR2025/papers/Chiu_ABC-Former_Auxiliary_Bimodal_Cross-domain_Transformer_with_Interactive_Channel_Attention_for_CVPR_2025_paper.pdf | https://openaccess.thecvf.com/content/CVPR2025/supplemental/Chiu_ABC-Former_Auxiliary_Bimodal_CVPR_2025_supplemental.pdf | null | @InProceedings{Chiu_2025_CVPR,
author = {Chiu, Yu-Cheng and Chen, Guan-Rong and Chen, Zihao and Peng, Yan-Tsung},
title = {ABC-Former: Auxiliary Bimodal Cross-domain Transformer with Interactive Channel Attention for White Balance},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {21258-21266}
} | The primary goal of white balance (WB) for sRGB images is to correct inaccurate color temperatures, ensuring that images display natural, neutral colors. While existing WB methods yield reasonable results, their effectiveness is limited. They either focus solely on global color adjustments applied before the camera-specific image signal processing pipeline or rely on end-to-end models that generate WB outputs without accounting for global color trends, leading to suboptimal correction. To address these limitations, we propose an Auxiliary Bimodal Cross-domain Transformer (ABC-Former) that enhances WB correction by leveraging complementary knowledge from global color information from CIELab and RGB histograms alongside sRGB inputs. By introducing an Interactive Channel Attention (ICA) module to facilitate cross-modality global knowledge transfer, ABC-Former achieves more precise WB correction. Experimental results on benchmark WB datasets show that ABC-Former performs favorably against state-of-the-art WB methods. The source code is available at https://github.com/ytpeng-aimlab/ABC-Former. | [
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2,806 | Science-T2I: Addressing Scientific Illusions in Image Synthesis | [
"Jialuo Li",
"Wenhao Chai",
"Xingyu Fu",
"Haiyang Xu",
"Saining Xie"
] | https://openaccess.thecvf.com/content/CVPR2025/html/Li_Science-T2I_Addressing_Scientific_Illusions_in_Image_Synthesis_CVPR_2025_paper.html | https://openaccess.thecvf.com/content/CVPR2025/papers/Li_Science-T2I_Addressing_Scientific_Illusions_in_Image_Synthesis_CVPR_2025_paper.pdf | https://openaccess.thecvf.com/content/CVPR2025/supplemental/Li_Science-T2I_Addressing_Scientific_CVPR_2025_supplemental.pdf | null | @InProceedings{Li_2025_CVPR,
author = {Li, Jialuo and Chai, Wenhao and Fu, Xingyu and Xu, Haiyang and Xie, Saining},
title = {Science-T2I: Addressing Scientific Illusions in Image Synthesis},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {2734-2744}
} | We present a novel approach to integrating scientific knowledge into generative models, enhancing their realism and consistency in image synthesis. First, we introduce Science-T2I, an expert-annotated adversarial dataset comprising adversarial 20k image pairs with 9k prompts, covering wide distinct scientific knowledge categories. Leveraging Science-T2I, we present SciScore, an end-to-end reward model that refines the assessment of generated images based on scientific knowledge, which is achieved by augmenting both the scientific comprehension and visual capabilities of pre-trained CLIP model. Additionally, based on Science-T2I, we propose a two-stage training framework, comprising a supervised fine-tuning phase and a masked online fine-tuning phase, to incorporate scientific knowledge into existing generative models. Through comprehensive experiments, we demonstrate the effectiveness of our framework in establishing new standards for evaluating the scientific realism of generated content. Specifically, SciScore attains performance comparable to human-level, demonstrating a 5% improvement similar to evaluations conducted by experienced human evaluators. Furthermore, by applying our proposed fine-tuning method to FLUX, we achieve a performance enhancement exceeding 50% on SciScore. | [
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2,807 | Fingerprinting Denoising Diffusion Probabilistic Models | [
"Huan Teng",
"Yuhui Quan",
"Chengyu Wang",
"Jun Huang",
"Hui Ji"
] | https://openaccess.thecvf.com/content/CVPR2025/html/Teng_Fingerprinting_Denoising_Diffusion_Probabilistic_Models_CVPR_2025_paper.html | https://openaccess.thecvf.com/content/CVPR2025/papers/Teng_Fingerprinting_Denoising_Diffusion_Probabilistic_Models_CVPR_2025_paper.pdf | https://openaccess.thecvf.com/content/CVPR2025/supplemental/Teng_Fingerprinting_Denoising_Diffusion_CVPR_2025_supplemental.pdf | null | @InProceedings{Teng_2025_CVPR,
author = {Teng, Huan and Quan, Yuhui and Wang, Chengyu and Huang, Jun and Ji, Hui},
title = {Fingerprinting Denoising Diffusion Probabilistic Models},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {28811-28820}
} | Diffusion models, especially denoising diffusion probabilistic models (DDPMs), are prevalent tools in generative AI, making their intellectual property (IP) protection increasingly important. Most existing IP protection methods for DDPMs are invasive, e.g., model watermarking, which alter model parameters and raise concerns about performance degradation, also with requirement for extra computational resources for retraining or fine-tuning. In this paper, we propose the first non-invasive fingerprinting scheme for DDPMs, requiring no parameter changes or fine-tuning, and keeping generation quality intact. We introduce a discriminative and robust fingerprint latent space based on the well-designed "crossing route" of noisy samples that span the performance border-zone of DDPMs, with only black-box access required for the diffusion denoiser in ownership verification. Extensive experiments demonstrate that our fingerprinting approach enjoys both robustness against the often-seen attacks and distinctiveness on various DDPMs, providing an alternative for protecting DDPMs' IP rights without compromising their performance or integrity. | [
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] |
2,808 | MoST: Efficient Monarch Sparse Tuning for 3D Representation Learning | [
"Xu Han",
"Yuan Tang",
"Jinfeng Xu",
"Xianzhi Li"
] | https://openaccess.thecvf.com/content/CVPR2025/html/Han_MoST_Efficient_Monarch_Sparse_Tuning_for_3D_Representation_Learning_CVPR_2025_paper.html | https://openaccess.thecvf.com/content/CVPR2025/papers/Han_MoST_Efficient_Monarch_Sparse_Tuning_for_3D_Representation_Learning_CVPR_2025_paper.pdf | null | http://arxiv.org/abs/2503.18368 | @InProceedings{Han_2025_CVPR,
author = {Han, Xu and Tang, Yuan and Xu, Jinfeng and Li, Xianzhi},
title = {MoST: Efficient Monarch Sparse Tuning for 3D Representation Learning},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {6584-6594}
} | We introduce Monarch Sparse Tuning (MoST), the first reparameterization-based parameter-efficient fine-tuning (PEFT) method tailored for 3D representation learning. Unlike existing adapter-based and prompt-tuning 3D PEFT methods, MoST introduces no additional inference overhead and is compatible with many 3D representation learning backbones. At its core, we present a new family of structured matrices for 3D point clouds, Point Monarch, which can capture local geometric features of irregular points while offering high expressiveness. MoST reparameterizes the dense update weight matrices as our sparse Point Monarch matrices, significantly reducing parameters while retaining strong performance. Experiments on various backbones show that MoST is simple, effective, and highly generalizable. It captures local features in point clouds, achieving state-of-the-art results on multiple benchmarks, e.g., 97.5% acc. on ScanObjectNN (PB_50_RS) and 96.2% on ModelNet40 classification, while it can also combine with other matrix decompositions (e.g., Low-rank, Kronecker) to further reduce parameters. | [
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2,809 | Re-thinking Temporal Search for Long-Form Video Understanding | [
"Jinhui Ye",
"Zihan Wang",
"Haosen Sun",
"Keshigeyan Chandrasegaran",
"Zane Durante",
"Cristobal Eyzaguirre",
"Yonatan Bisk",
"Juan Carlos Niebles",
"Ehsan Adeli",
"Li Fei-Fei",
"Jiajun Wu",
"Manling Li"
] | https://openaccess.thecvf.com/content/CVPR2025/html/Ye_Re-thinking_Temporal_Search_for_Long-Form_Video_Understanding_CVPR_2025_paper.html | https://openaccess.thecvf.com/content/CVPR2025/papers/Ye_Re-thinking_Temporal_Search_for_Long-Form_Video_Understanding_CVPR_2025_paper.pdf | https://openaccess.thecvf.com/content/CVPR2025/supplemental/Ye_Re-thinking_Temporal_Search_CVPR_2025_supplemental.pdf | http://arxiv.org/abs/2504.02259 | @InProceedings{Ye_2025_CVPR,
author = {Ye, Jinhui and Wang, Zihan and Sun, Haosen and Chandrasegaran, Keshigeyan and Durante, Zane and Eyzaguirre, Cristobal and Bisk, Yonatan and Niebles, Juan Carlos and Adeli, Ehsan and Fei-Fei, Li and Wu, Jiajun and Li, Manling},
title = {Re-thinking Temporal Search for Long-Form Video Understanding},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {8579-8591}
} | Efficient understanding of long-form videos remains a significant challenge in computer vision. In this work, we revisit temporal search paradigms for long-form video understanding, studying a fundamental issue pertaining to all state-of-the-art (SOTA) long-context vision-language models (VLMs). In particular, our contributions are two-fold: **First**, we formulate temporal search as a **Long Video Haystack** problem, i.e., finding a minimal set of relevant frames (typically one to five) among tens of thousands of frames from real-world long videos given specific queries. To validate our formulation, we create **LV-Haystack**, the first benchmark containing 3,874 human-annotated instances with fine-grained evaluation metrics for assessing keyframe search quality and computational efficiency. Experimental results on LV-Haystack highlight a significant research gap in temporal search capabilities, with SOTA keyframe selection methods achieving only 2.1% temporal F1 score on the LVBench subset.**Next**, inspired by visual search in images, we re-think temporal searching and propose a lightweight keyframe searching framework, T^* , which casts the expensive temporal search as a spatial search problem. T^* leverages superior visual localization capabilities typically used in images and introduces an adaptive zooming-in mechanism that operates across both temporal and spatial dimensions. Our extensive experiments show that when integrated with existing methods, T^* significantly improves SOTA long-form video understanding performance. Specifically, under an inference budget of 32 frames, T^* improves GPT-4o's performance from 50.5% to **53.1%** and LLaVA-OneVision-72B's performance from 56.5% to **62.4%** on LongVideoBench XL subset. Our PyTorch code, benchmark dataset and models are included in the Supplementary material. | [
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] |
2,810 | InstanceGaussian: Appearance-Semantic Joint Gaussian Representation for 3D Instance-Level Perception | [
"Haijie Li",
"Yanmin Wu",
"Jiarui Meng",
"Qiankun Gao",
"Zhiyao Zhang",
"Ronggang Wang",
"Jian Zhang"
] | https://openaccess.thecvf.com/content/CVPR2025/html/Li_InstanceGaussian_Appearance-Semantic_Joint_Gaussian_Representation_for_3D_Instance-Level_Perception_CVPR_2025_paper.html | https://openaccess.thecvf.com/content/CVPR2025/papers/Li_InstanceGaussian_Appearance-Semantic_Joint_Gaussian_Representation_for_3D_Instance-Level_Perception_CVPR_2025_paper.pdf | https://openaccess.thecvf.com/content/CVPR2025/supplemental/Li_InstanceGaussian_Appearance-Semantic_Joint_CVPR_2025_supplemental.pdf | http://arxiv.org/abs/2411.19235 | @InProceedings{Li_2025_CVPR,
author = {Li, Haijie and Wu, Yanmin and Meng, Jiarui and Gao, Qiankun and Zhang, Zhiyao and Wang, Ronggang and Zhang, Jian},
title = {InstanceGaussian: Appearance-Semantic Joint Gaussian Representation for 3D Instance-Level Perception},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {14078-14088}
} | 3D scene understanding is vital for applications in autonomous driving, robotics, and augmented reality. However, scene understanding based on 3D Gaussian Splatting faces three key challenges: (i) an imbalance between appearance and semantics, (ii) inconsistencies in object boundaries, and (iii) difficulties with top-down instance segmentation. To address these challenges, we propose InstanceGaussian, a method that jointly learns appearance and semantic features while adaptively aggregating instances. Our contributions are as follows: (i) a new Semantic-Scaffold-GS representation to improve feature representation and boundary delineation, (ii) a progressive training strategy for enhanced stability and segmentation, and (iii) a category-agnostic, bottom-up instance aggregation approach for better segmentation. Experimental results demonstrate that our approach achieves state-of-the-art performance in category-agnostic, open-vocabulary 3D point-level segmentation, validating the effectiveness of our proposed method. | [
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2,811 | When Domain Generalization meets Generalized Category Discovery: An Adaptive Task-Arithmetic Driven Approach | [
"Vaibhav Rathore",
"Shubhranil B",
"Saikat Dutta",
"Sarthak Mehrotra",
"Zsolt Kira",
"Biplab Banerjee"
] | https://openaccess.thecvf.com/content/CVPR2025/html/Rathore_When_Domain_Generalization_meets_Generalized_Category_Discovery_An_Adaptive_Task-Arithmetic_CVPR_2025_paper.html | https://openaccess.thecvf.com/content/CVPR2025/papers/Rathore_When_Domain_Generalization_meets_Generalized_Category_Discovery_An_Adaptive_Task-Arithmetic_CVPR_2025_paper.pdf | https://openaccess.thecvf.com/content/CVPR2025/supplemental/Rathore_When_Domain_Generalization_CVPR_2025_supplemental.pdf | http://arxiv.org/abs/2503.14897 | @InProceedings{Rathore_2025_CVPR,
author = {Rathore, Vaibhav and B, Shubhranil and Dutta, Saikat and Mehrotra, Sarthak and Kira, Zsolt and Banerjee, Biplab},
title = {When Domain Generalization meets Generalized Category Discovery: An Adaptive Task-Arithmetic Driven Approach},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {4905-4915}
} | Generalized Class Discovery (GCD) clusters base and novel classes in a target domain, using supervision from a source domain with only base classes. Current methods often falter with distribution shifts and typically require access to target data during training, which can sometimes be impractical. To address this issue, we introduce the novel paradigm of Domain Generalization in GCD (DG-GCD), where only source data is available for training, while the target domain--with a distinct data distribution--remains unseen until inference. To this end, our solution, DG2CD-Net, aims to construct a domain-independent, discriminative embedding space for GCD. The core innovation is an episodic training strategy that enhances cross-domain generalization by adapting a base model on tasks derived from source and synthetic domains generated by a foundation model. Each episode focuses on a cross-domain GCD task, diversifying task setups over episodes and combining open-set domain adaptation with a novel margin loss and representation learning for optimizing the feature space progressively. To capture the effects of fine-tunings on the base model, we extend task arithmetic by adaptively weighting the local task vectors concerning the fine-tuned models based on their GCD performance on a validation distribution. This episodic update mechanism boosts the adaptability of the base model to unseen targets. Experiments across three datasets confirm that DG2CD-Net outperforms existing GCD methods customized for DG-GCD | [
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2,812 | CSC-PA: Cross-image Semantic Correlation via Prototype Attentions for Single-network Semi-supervised Breast Tumor Segmentation | [
"Zhenhui Ding",
"Guilian Chen",
"Qin Zhang",
"Huisi Wu",
"Jing Qin"
] | https://openaccess.thecvf.com/content/CVPR2025/html/Ding_CSC-PA_Cross-image_Semantic_Correlation_via_Prototype_Attentions_for_Single-network_Semi-supervised_CVPR_2025_paper.html | https://openaccess.thecvf.com/content/CVPR2025/papers/Ding_CSC-PA_Cross-image_Semantic_Correlation_via_Prototype_Attentions_for_Single-network_Semi-supervised_CVPR_2025_paper.pdf | https://openaccess.thecvf.com/content/CVPR2025/supplemental/Ding_CSC-PA_Cross-image_Semantic_CVPR_2025_supplemental.pdf | null | @InProceedings{Ding_2025_CVPR,
author = {Ding, Zhenhui and Chen, Guilian and Zhang, Qin and Wu, Huisi and Qin, Jing},
title = {CSC-PA: Cross-image Semantic Correlation via Prototype Attentions for Single-network Semi-supervised Breast Tumor Segmentation},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {15632-15641}
} | Accurate automatic breast ultrasound (BUS) image segmentation is essential for early breast cancer screening and diagnosis. However, it remains challenging owing to (1) breast lesions of various scale and shape, (2) ambiguous boundaries caused by speckle noise and artifacts, and (3) the scarcity of high-quality annotations. Most existing semi-supervised methods employ the mean-teacher architecture, which merely learns semantic information within a single image and heavily relies on the performance of the teacher model. Therefore, we present a novel cross-image semantic correlation semi-supervised framework, named CSC-PA, to improve the performance of BUS image segmentation. CSC-PA is trained based on a single network, which integrates a foreground prototype attention (FPA) and an edge prototype attention (EPA). Specifically, FPA transfers complementary foreground information for more stable and complete lesion segmentation. On the other hand, EPA enhances edge features of lesions by using edge prototype, where an adaptive edge container is proposed to store global edge features and generate the edge prototype. Additionally, we introduce a pixel affinity loss (PAL) to exploit previously ignored contextual correlation in supervision, which further improves performance on edges. Extensive experiments on two benchmark BUS datasets demonstrate that our model outperforms other state-of-the-art methods under different partition protocols. Codes are available at https://github.com/shdkdh/CSC-PA. | [
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2,813 | BIP3D: Bridging 2D Images and 3D Perception for Embodied Intelligence | [
"Xuewu Lin",
"Tianwei Lin",
"Lichao Huang",
"Hongyu Xie",
"Zhizhong Su"
] | https://openaccess.thecvf.com/content/CVPR2025/html/Lin_BIP3D_Bridging_2D_Images_and_3D_Perception_for_Embodied_Intelligence_CVPR_2025_paper.html | https://openaccess.thecvf.com/content/CVPR2025/papers/Lin_BIP3D_Bridging_2D_Images_and_3D_Perception_for_Embodied_Intelligence_CVPR_2025_paper.pdf | https://openaccess.thecvf.com/content/CVPR2025/supplemental/Lin_BIP3D_Bridging_2D_CVPR_2025_supplemental.pdf | http://arxiv.org/abs/2411.14869 | @InProceedings{Lin_2025_CVPR,
author = {Lin, Xuewu and Lin, Tianwei and Huang, Lichao and Xie, Hongyu and Su, Zhizhong},
title = {BIP3D: Bridging 2D Images and 3D Perception for Embodied Intelligence},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {9007-9016}
} | In embodied intelligence systems, a key component is 3D perception algorithm, which enables agents to understand their surrounding environments. Previous algorithms primarily rely on point cloud, which, despite offering precise geometric information, still constrain perception performance due to inherent sparsity, noise, and data scarcity. In this work, we introduce a novel image-centric 3D perception model, BIP3D, which leverages expressive image features with explicit 3D position encoding to overcome the limitations of point-centric methods. Specifically, we leverage pre-trained 2D vision foundation models to enhance semantic understanding, and introduce a spatial enhancer module to improve spatial understanding. Together, these modules enable BIP3D to achieve multi-view, multi-modal feature fusion and end-to-end 3D perception. In our experiments, BIP3D outperforms current state-of-the-art results on the EmbodiedScan benchmark, achieving improvements of 5.69% in the 3D detection task and 15.25% in the 3D visual grounding task. Code has been released at https://github.com/HorizonRobotics/BIP3D. | [
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2,814 | Query Efficient Black-Box Visual Prompting with Subspace Learning | [
"Zhaogeng Liu",
"Haozhen Zhang",
"Hualin Zhang",
"Xingchen Li",
"Wanli Shi",
"Bin Gu",
"Yi Chang"
] | https://openaccess.thecvf.com/content/CVPR2025/html/Liu_Query_Efficient_Black-Box_Visual_Prompting_with_Subspace_Learning_CVPR_2025_paper.html | https://openaccess.thecvf.com/content/CVPR2025/papers/Liu_Query_Efficient_Black-Box_Visual_Prompting_with_Subspace_Learning_CVPR_2025_paper.pdf | null | null | @InProceedings{Liu_2025_CVPR,
author = {Liu, Zhaogeng and Zhang, Haozhen and Zhang, Hualin and Li, Xingchen and Shi, Wanli and Gu, Bin and Chang, Yi},
title = {Query Efficient Black-Box Visual Prompting with Subspace Learning},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {4322-4331}
} | Visual Prompt Learning (VPL) has emerged as a powerful strategy for harnessing the capabilities of large-scale pre-trained models (PTMs) to tackle specific downstream tasks. However, the opaque nature of PTMs in many real-world applications has led to a growing interest in gradient-free approaches within VPL. A significant challenge with existing black-box VPL methods lies in the high dimensionality of visual prompts, which necessitates considerable API queries for tuning, thereby impacting efficiency. To address this issue, we propose a novel query-efficient framework for black-box visual prompting, designed to generate input-dependent visual prompts efficiently for large-scale black-box PTMs. Our framework is built upon the insight of reparameterizing prompts using neural networks, improving the typical pre-training-fine-tuning paradigm through the subspace learning strategy to maximize efficiency and adaptability from both the perspective of initial weights and parameter dimensionality. This tuning intrinsically optimizes low-dimensional representations within the well-learned subspace, enabling the efficient adaptation of the network to downstream tasks. Our approach significantly reduces the necessity for substantial API queries to PTMs, presenting an efficient method for leveraging large-scale black-box PTMs in visual prompting tasks. Most experimental results across various benchmarks demonstrate the effectiveness of our method, showcasing substantial reductions in the number of required API queries to PTMs while maintaining or even enhancing performance on downstream tasks. | [
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2,815 | VisionPAD: A Vision-Centric Pre-training Paradigm for Autonomous Driving | [
"Haiming Zhang",
"Wending Zhou",
"Yiyao Zhu",
"Xu Yan",
"Jiantao Gao",
"Dongfeng Bai",
"Yingjie Cai",
"Bingbing Liu",
"Shuguang Cui",
"Zhen Li"
] | https://openaccess.thecvf.com/content/CVPR2025/html/Zhang_VisionPAD_A_Vision-Centric_Pre-training_Paradigm_for_Autonomous_Driving_CVPR_2025_paper.html | https://openaccess.thecvf.com/content/CVPR2025/papers/Zhang_VisionPAD_A_Vision-Centric_Pre-training_Paradigm_for_Autonomous_Driving_CVPR_2025_paper.pdf | https://openaccess.thecvf.com/content/CVPR2025/supplemental/Zhang_VisionPAD_A_Vision-Centric_CVPR_2025_supplemental.pdf | http://arxiv.org/abs/2411.14716 | @InProceedings{Zhang_2025_CVPR,
author = {Zhang, Haiming and Zhou, Wending and Zhu, Yiyao and Yan, Xu and Gao, Jiantao and Bai, Dongfeng and Cai, Yingjie and Liu, Bingbing and Cui, Shuguang and Li, Zhen},
title = {VisionPAD: A Vision-Centric Pre-training Paradigm for Autonomous Driving},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {17165-17175}
} | This paper introduces VisionPAD, a novel self-supervised pre-training paradigm designed for vision-centric algorithms in autonomous driving. In contrast to previous approaches that employ neural rendering with explicit depth supervision, VisionPAD utilizes more efficient 3D Gaussian Splatting to reconstruct multi-view representations using only images as supervision. Specifically, we introduce a self-supervised method for voxel velocity estimation. By warping voxels to adjacent frames and supervising the rendered outputs, the model effectively learns motion cues in the sequential data. Furthermore, we adopt a multi-frame photometric consistency approach to enhance geometric perception. It projects adjacent frames to the current frame based on rendered depths and relative poses, boosting the 3D geometric representation through pure image supervision. Extensive experiments on autonomous driving datasets demonstrate that VisionPAD significantly improves performance in 3D object detection, occupancy prediction and map segmentation, surpassing state-of-the-art pre-training strategies by a considerable margin. | [
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2,816 | Detecting Adversarial Data Using Perturbation Forgery | [
"Qian Wang",
"Chen Li",
"Yuchen Luo",
"Hefei Ling",
"Shijuan Huang",
"Ruoxi Jia",
"Ning Yu"
] | https://openaccess.thecvf.com/content/CVPR2025/html/Wang_Detecting_Adversarial_Data_Using_Perturbation_Forgery_CVPR_2025_paper.html | https://openaccess.thecvf.com/content/CVPR2025/papers/Wang_Detecting_Adversarial_Data_Using_Perturbation_Forgery_CVPR_2025_paper.pdf | https://openaccess.thecvf.com/content/CVPR2025/supplemental/Wang_Detecting_Adversarial_Data_CVPR_2025_supplemental.pdf | http://arxiv.org/abs/2405.16226 | @InProceedings{Wang_2025_CVPR,
author = {Wang, Qian and Li, Chen and Luo, Yuchen and Ling, Hefei and Huang, Shijuan and Jia, Ruoxi and Yu, Ning},
title = {Detecting Adversarial Data Using Perturbation Forgery},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {13917-13926}
} | As a defense strategy against adversarial attacks, adversarial detection aims to identify and filter out adversarial data from the data flow based on discrepancies in distribution and noise patterns between natural and adversarial data. Although previous detection methods achieve high performance in detecting gradient-based adversarial attacks, new attacks based on generative models with imbalanced and anisotropic noise patterns evade detection. Even worse, the significant inference time overhead and limited performance against unseen attacks make existing techniques impractical for real-world use. In this paper, we explore the proximity relationship among adversarial noise distributions and demonstrate the existence of an open covering for these distributions. By training on the open covering of adversarial noise distributions, a detector with strong generalization performance against various types of unseen attacks can be developed. Based on this insight, we heuristically propose Perturbation Forgery, which includes noise distribution perturbation, sparse mask generation, and pseudo-adversarial data production, to train an adversarial detector capable of detecting any unseen gradient-based, generative-based, and physical adversarial attacks. Comprehensive experiments conducted on multiple general and facial datasets, with a wide spectrum of attacks, validate the strong generalization of our method. | [
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2,817 | CoA: Towards Real Image Dehazing via Compression-and-Adaptation | [
"Long Ma",
"Yuxin Feng",
"Yan Zhang",
"Jinyuan Liu",
"Weimin Wang",
"Guang-Yong Chen",
"Chengpei Xu",
"Zhuo Su"
] | https://openaccess.thecvf.com/content/CVPR2025/html/Ma_CoA_Towards_Real_Image_Dehazing_via_Compression-and-Adaptation_CVPR_2025_paper.html | https://openaccess.thecvf.com/content/CVPR2025/papers/Ma_CoA_Towards_Real_Image_Dehazing_via_Compression-and-Adaptation_CVPR_2025_paper.pdf | null | http://arxiv.org/abs/2504.05590 | @InProceedings{Ma_2025_CVPR,
author = {Ma, Long and Feng, Yuxin and Zhang, Yan and Liu, Jinyuan and Wang, Weimin and Chen, Guang-Yong and Xu, Chengpei and Su, Zhuo},
title = {CoA: Towards Real Image Dehazing via Compression-and-Adaptation},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {11197-11206}
} | Learning-based image dehazing algorithms have shown remarkable success in synthetic domains. However, real image dehazing is still in suspense due to computational resource constraints and the diversity of real-world scenes. Therefore, there is an urgent need for an algorithm that excels in both efficiency and adaptability to address real image dehazing effectively. This work proposes a Compression-and-Adaptation (CoA) computational flow to tackle these challenges from a divide-and-conquer perspective. First, model compression is performed in the synthetic domain to develop a compact dehazing parameter space, satisfying efficiency demands. Then, a bilevel adaptation in the real domain is introduced to be fearless in unknown real environments by aggregating the synthetic dehazing capabilities during the learning process. Leveraging a succinct design free from additional constraints, our CoA exhibits domain-irrelevant stability and model-agnostic flexibility, effectively bridging the model chasm between synthetic and real domains to further improve its practical utility. Extensive evaluations and analyses underscore the approach's superiority and effectiveness. The code is publicly available at https://github.com/fyxnl/COA. | [
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2,818 | NightAdapter: Learning a Frequency Adapter for Generalizable Night-time Scene Segmentation | [
"Qi Bi",
"Jingjun Yi",
"Huimin Huang",
"Hao Zheng",
"Haolan Zhan",
"Yawen Huang",
"Yuexiang Li",
"Xian Wu",
"Yefeng Zheng"
] | https://openaccess.thecvf.com/content/CVPR2025/html/Bi_NightAdapter_Learning_a_Frequency_Adapter_for_Generalizable_Night-time_Scene_Segmentation_CVPR_2025_paper.html | https://openaccess.thecvf.com/content/CVPR2025/papers/Bi_NightAdapter_Learning_a_Frequency_Adapter_for_Generalizable_Night-time_Scene_Segmentation_CVPR_2025_paper.pdf | https://openaccess.thecvf.com/content/CVPR2025/supplemental/Bi_NightAdapter_Learning_a_CVPR_2025_supplemental.pdf | null | @InProceedings{Bi_2025_CVPR,
author = {Bi, Qi and Yi, Jingjun and Huang, Huimin and Zheng, Hao and Zhan, Haolan and Huang, Yawen and Li, Yuexiang and Wu, Xian and Zheng, Yefeng},
title = {NightAdapter: Learning a Frequency Adapter for Generalizable Night-time Scene Segmentation},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {23838-23849}
} | Night-time scene segmentation is a critical yet challenging task in the real-world applications, primarily due to the complicated lighting conditions. However, existing methods lack sufficient generalization ability to unseen nigh-time scenes with varying illumination.In light of this issue, we focus on investigating generalizable paradigms for night-time scene segmentation and propose an efficient fine-tuning scheme, dubbed as \texttt NightAdapter , alleviating the domain gap across various scenes.Interestingly, different properties embedded in the day-time and night-time features can be characterized by the bands after discrete sine transformation, which can be categorized into illumination-sensitive/-insensitive bands.Hence, our \texttt NightAdapter is powered by two appealing designs: (1) Illumination-Insensitive Band Adaptation that provides a foundation for understanding the prior, enhancing the robustness to illumination shifts; (2) Illumination-Sensitive Band Adaptation that fine-tunes the randomized frequency bands, mitigating the domain gap between the day-time and various night-time scenes. As a consequence, illumination-insensitive enhancement improves the domain invariance, while illumination-sensitive diminution strengthens the domain shift between different scenes.\texttt NightAdapter yields significant improvements over the state-of-the-art methods under various day-to-night, night-to-night, and in-domain night segmentation experiments.We will release our code. | [
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2,819 | UMFN: Unified Multi-Domain Face Normalization for Joint Cross-domain Prototype Learning and Heterogeneous Face Recognition | [
"Meng Pang",
"Wenjun Zhang",
"Nanrun Zhou",
"Shengbo Chen",
"Hong Rao"
] | https://openaccess.thecvf.com/content/CVPR2025/html/Pang_UMFN_Unified_Multi-Domain_Face_Normalization_for_Joint_Cross-domain_Prototype_Learning_CVPR_2025_paper.html | https://openaccess.thecvf.com/content/CVPR2025/papers/Pang_UMFN_Unified_Multi-Domain_Face_Normalization_for_Joint_Cross-domain_Prototype_Learning_CVPR_2025_paper.pdf | null | null | @InProceedings{Pang_2025_CVPR,
author = {Pang, Meng and Zhang, Wenjun and Zhou, Nanrun and Chen, Shengbo and Rao, Hong},
title = {UMFN: Unified Multi-Domain Face Normalization for Joint Cross-domain Prototype Learning and Heterogeneous Face Recognition},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {29299-29308}
} | Face normalization aims to enhance the robustness and effectiveness of face recognition systems by mitigating intra-personal variations in expressions, poses, occlusions, illuminations, and domains. Existing methods face limitations in handling multiple variations and adapting to cross-domain scenarios. To address these challenges, we propose a novel Unified Multi-Domain Face Normalization Network (UMFN) model, which can process face images with various types of facial variations from different domains, and reconstruct frontal, neutral-expression facial prototypes in the target domain. As an unsupervised domain adaptation model, UMFN facilitates concurrent training on multiple datasets across domains and demonstrates strong prototype reconstruction capabilities. Notably, UMFN serves as a joint prototype and feature learning framework, enabling the simultaneous extraction of domain-agnostic identity features through a decoupling mapping network and a feature domain classifier for adversarial training. Moreover, we design an efficient Heterogeneous Face Recognition (HFR) network that fuses domain-agnostic and identity-discriminative features for HFR, and introduce contrastive learning to enhance identity recognition accuracy. Empirical studies on diverse cross-domain face datasets validate the effectiveness of our proposed method. | [
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] |
2,820 | TopV: Compatible Token Pruning with Inference Time Optimization for Fast and Low-Memory Multimodal Vision Language Model | [
"Cheng Yang",
"Yang Sui",
"Jinqi Xiao",
"Lingyi Huang",
"Yu Gong",
"Chendi Li",
"Jinghua Yan",
"Yu Bai",
"Ponnuswamy Sadayappan",
"Xia Hu",
"Bo Yuan"
] | https://openaccess.thecvf.com/content/CVPR2025/html/Yang_TopV_Compatible_Token_Pruning_with_Inference_Time_Optimization_for_Fast_CVPR_2025_paper.html | https://openaccess.thecvf.com/content/CVPR2025/papers/Yang_TopV_Compatible_Token_Pruning_with_Inference_Time_Optimization_for_Fast_CVPR_2025_paper.pdf | https://openaccess.thecvf.com/content/CVPR2025/supplemental/Yang_TopV_Compatible_Token_CVPR_2025_supplemental.pdf | http://arxiv.org/abs/2503.18278 | @InProceedings{Yang_2025_CVPR,
author = {Yang, Cheng and Sui, Yang and Xiao, Jinqi and Huang, Lingyi and Gong, Yu and Li, Chendi and Yan, Jinghua and Bai, Yu and Sadayappan, Ponnuswamy and Hu, Xia and Yuan, Bo},
title = {TopV: Compatible Token Pruning with Inference Time Optimization for Fast and Low-Memory Multimodal Vision Language Model},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {19803-19813}
} | Vision-Language Models (VLMs) demand substantial computational resources during inference, largely due to the extensive visual input tokens for representing visual information. Previous studies have noted that visual tokens tend to receive less attention than text tokens, suggesting their lower importance during inference and potential for pruning. However, their methods encounter several chal- lenges: reliance on greedy heuristic criteria for token im- portance and incompatibility with FlashAttention and KV cache. To address these issues, we introduce TopV, a com- patible TOken Pruning with inference Time Optimization for fast and low-memory VLM, achieving efficient pruning without additional training or fine-tuning. Instead of re- lying on attention scores, we formulate token pruning as an optimization problem, accurately identifying important visual tokens while remaining compatible with FlashAtten- tion. Additionally, since we only perform this pruning once during the prefilling stage, it effectively reduces KV cache size. Our optimization framework incorporates a visual- aware cost function considering factors such as Feature Similarity, Relative Spatial Distance, and Absolute Central Distance, to measure the importance of each source visual token, enabling effective pruning of low-importance tokens. Extensive experiments demonstrate that our method outperforms previous token pruning methods, validating the effectiveness and efficiency of our approach. | [
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2,821 | Improving Autoregressive Visual Generation with Cluster-Oriented Token Prediction | [
"Teng Hu",
"Jiangning Zhang",
"Ran Yi",
"Jieyu Weng",
"Yabiao Wang",
"Xianfang Zeng",
"Zhucun Xue",
"Lizhuang Ma"
] | https://openaccess.thecvf.com/content/CVPR2025/html/Hu_Improving_Autoregressive_Visual_Generation_with_Cluster-Oriented_Token_Prediction_CVPR_2025_paper.html | https://openaccess.thecvf.com/content/CVPR2025/papers/Hu_Improving_Autoregressive_Visual_Generation_with_Cluster-Oriented_Token_Prediction_CVPR_2025_paper.pdf | https://openaccess.thecvf.com/content/CVPR2025/supplemental/Hu_Improving_Autoregressive_Visual_CVPR_2025_supplemental.pdf | http://arxiv.org/abs/2501.00880 | @InProceedings{Hu_2025_CVPR,
author = {Hu, Teng and Zhang, Jiangning and Yi, Ran and Weng, Jieyu and Wang, Yabiao and Zeng, Xianfang and Xue, Zhucun and Ma, Lizhuang},
title = {Improving Autoregressive Visual Generation with Cluster-Oriented Token Prediction},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {9351-9360}
} | Employing LLMs for visual generation has recently become a research focus. However, the existing methods primarily transfer the LLM architecture to visual generation but rarely investigate the fundamental differences between language and vision. This oversight may lead to suboptimal utilization of visual generation capabilities within the LLM framework. In this paper, we explore the characteristics of visual embedding space under the LLM framework and discover that the correlation between visual embeddings can help achieve more stable and robust generation results. We present **IAR**, an **I**mproved **A**uto**R**egressive Visual Generation Method that enhances the training efficiency and generation quality of LLM-based visual generation models. Firstly, we propose a Codebook Rearrangement strategy that uses balanced k-means clustering algorithm to rearrange the visual codebook into clusters, ensuring high similarity among visual features within each cluster. Leveraging the rearranged codebook, we propose a Cluster-oriented Cross-entropy Loss that guides the model to correctly predict the cluster where the token is located. This approach ensures that even if the model predicts the wrong token index, there is a high probability the predicted token is located in the correct cluster, which significantly enhances the generation quality and robustness. Extensive experiments demonstrate that our method consistently enhances the model training efficiency and performance from 100M to 1.4B, reducing the training time by half while achieving the same FID. Additionally, our approach can be applied to various LLM-based visual generation models and adheres to the scaling law, providing a promising direction for future research in LLM-based visual generation. | [
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2,822 | Learned Binocular-Encoding Optics for RGBD Imaging Using Joint Stereo and Focus Cues | [
"Yuhui Liu",
"Liangxun Ou",
"Qiang Fu",
"Hadi Amata",
"Wolfgang Heidrich",
"Yifan Peng"
] | https://openaccess.thecvf.com/content/CVPR2025/html/Liu_Learned_Binocular-Encoding_Optics_for_RGBD_Imaging_Using_Joint_Stereo_and_CVPR_2025_paper.html | https://openaccess.thecvf.com/content/CVPR2025/papers/Liu_Learned_Binocular-Encoding_Optics_for_RGBD_Imaging_Using_Joint_Stereo_and_CVPR_2025_paper.pdf | https://openaccess.thecvf.com/content/CVPR2025/supplemental/Liu_Learned_Binocular-Encoding_Optics_CVPR_2025_supplemental.pdf | null | @InProceedings{Liu_2025_CVPR,
author = {Liu, Yuhui and Ou, Liangxun and Fu, Qiang and Amata, Hadi and Heidrich, Wolfgang and Peng, Yifan},
title = {Learned Binocular-Encoding Optics for RGBD Imaging Using Joint Stereo and Focus Cues},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {15833-15842}
} | Extracting high-fidelity RGBD information from two-dimensional (2D) images is essential for various visual computing applications. Stereo imaging, as a reliable passive imaging technique for obtaining three-dimensional (3D) scene information, has benefited greatly from deep learning advancements. However, existing stereo depth estimation algorithms struggle to perceive high-frequency information and resolve high-resolution depth maps in realistic camera settings with large depth variations. These algorithms commonly neglect the hardware parameter configuration, limiting the potential for achieving optimal solutions solely through software-based design strategies.This work presents a hardware-software co-designed RGBD imaging framework that leverages both stereo and focus cues to reconstruct texture-rich color images along with detailed depth maps over a wide depth range. A pair of rank-2 parameterized diffractive optical elements (DOEs) is employed to encode perpendicular complementary information optically during stereo acquisitions. Additionally, we employ an IGEV-UNet-fused neural network tailored to the proposed rank-2 encoding for stereo matching and image reconstruction. Through prototyping a stereo camera with customized DOEs, our deep stereo imaging paradigm has demonstrated superior performance over existing monocular and stereo imaging systems in both image PSNR by 2.96 dB gain and depth accuracy in high-frequency details across distances from 0.67 to 8 meters. | [
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2,823 | Dual-view X-ray Detection: Can AI Detect Prohibited Items from Dual-view X-ray Images like Humans? | [
"Renshuai Tao",
"Haoyu Wang",
"Yuzhe Guo",
"Hairong Chen",
"Li Zhang",
"Xianglong Liu",
"Yunchao Wei",
"Yao Zhao"
] | https://openaccess.thecvf.com/content/CVPR2025/html/Tao_Dual-view_X-ray_Detection_Can_AI_Detect_Prohibited_Items_from_Dual-view_CVPR_2025_paper.html | https://openaccess.thecvf.com/content/CVPR2025/papers/Tao_Dual-view_X-ray_Detection_Can_AI_Detect_Prohibited_Items_from_Dual-view_CVPR_2025_paper.pdf | null | null | @InProceedings{Tao_2025_CVPR,
author = {Tao, Renshuai and Wang, Haoyu and Guo, Yuzhe and Chen, Hairong and Zhang, Li and Liu, Xianglong and Wei, Yunchao and Zhao, Yao},
title = {Dual-view X-ray Detection: Can AI Detect Prohibited Items from Dual-view X-ray Images like Humans?},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {10338-10347}
} | To detect prohibited items in challenging categories, human inspectors typically rely on images from two distinct views (vertical and side). Can AI detect prohibited items from dual-view X-ray images in the same way humans do? Existing X-ray datasets often suffer from limitations, such as single-view imaging or insufficient sample diversity. To address these gaps, we introduce the Large-scale Dual-view X-ray (LDXray), which consists of 353,646 instances across 12 categories, providing a diverse and comprehensive resource for training and evaluating models. To emulate human intelligence in dual-view detection, we propose the Auxiliary-view Enhanced Network (AENet), a novel detection framework that leverages both the main and auxiliary views of the same object. The main-view pipeline focuses on detecting common categories, while the auxiliary-view pipeline handles more challenging categories using "expert models" learned from the main view. Extensive experiments on the LDXray dataset demonstrate that the dual-view mechanism significantly enhances detection performance, e.g., achieving improvements of up to +24.7%for the challenging category of umbrellas. Furthermore, our results show that AENet exhibits strong generalization across seven different detection models. | [
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2,824 | LUCAS: Layered Universal Codec Avatars | [
"Di Liu",
"Teng Deng",
"Giljoo Nam",
"Yu Rong",
"Stanislav Pidhorskyi",
"Junxuan Li",
"Jason Saragih",
"Dimitris N. Metaxas",
"Chen Cao"
] | https://openaccess.thecvf.com/content/CVPR2025/html/Liu_LUCAS_Layered_Universal_Codec_Avatars_CVPR_2025_paper.html | https://openaccess.thecvf.com/content/CVPR2025/papers/Liu_LUCAS_Layered_Universal_Codec_Avatars_CVPR_2025_paper.pdf | https://openaccess.thecvf.com/content/CVPR2025/supplemental/Liu_LUCAS_Layered_Universal_CVPR_2025_supplemental.zip | http://arxiv.org/abs/2502.19739 | @InProceedings{Liu_2025_CVPR,
author = {Liu, Di and Deng, Teng and Nam, Giljoo and Rong, Yu and Pidhorskyi, Stanislav and Li, Junxuan and Saragih, Jason and Metaxas, Dimitris N. and Cao, Chen},
title = {LUCAS: Layered Universal Codec Avatars},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {21127-21137}
} | Photorealistic 3D head avatar reconstruction faces critical challenges in modeling dynamic face-hair interactions and achieving cross-identity generalization, particularly during expressions and head movements. We present LUCAS, a novel Universal Prior Model (UPM) for codec avatar modeling that disentangles face and hair through a layered representation. Unlike previous UPMs that treat hair as an integral part of the head, our approach separates the modeling of the hairless head and hair into distinct branches. LUCAS is the first to introduce a mesh-based UPM, facilitating real-time rendering on devices. LUCAS can be integrated with Gaussian Splatting to enhance visual fidelity, a feature particularly beneficial for rendering complex hairstyles. Experimental results indicate that LUCAS outperforms existing single-mesh and Gaussian-based avatar models in both quantitative and qualitative assessments, including evaluations on held-out subjects in zero-shot driving scenarios. LUCAS demonstrates superior dynamic performance in managing head pose changes, expression transfer, and hairstyle variations, thereby advancing the state-of-the-art in 3D head avatar reconstruction. | [
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2,825 | MobilePortrait: Real-Time One-Shot Neural Head Avatars on Mobile Devices | [
"Jianwen Jiang",
"Gaojie Lin",
"Zhengkun Rong",
"Chao Liang",
"Yongming Zhu",
"Jiaqi Yang",
"Tianyun Zhong"
] | https://openaccess.thecvf.com/content/CVPR2025/html/Jiang_MobilePortrait_Real-Time_One-Shot_Neural_Head_Avatars_on_Mobile_Devices_CVPR_2025_paper.html | https://openaccess.thecvf.com/content/CVPR2025/papers/Jiang_MobilePortrait_Real-Time_One-Shot_Neural_Head_Avatars_on_Mobile_Devices_CVPR_2025_paper.pdf | https://openaccess.thecvf.com/content/CVPR2025/supplemental/Jiang_MobilePortrait_Real-Time_One-Shot_CVPR_2025_supplemental.zip | http://arxiv.org/abs/2407.05712 | @InProceedings{Jiang_2025_CVPR,
author = {Jiang, Jianwen and Lin, Gaojie and Rong, Zhengkun and Liang, Chao and Zhu, Yongming and Yang, Jiaqi and Zhong, Tianyun},
title = {MobilePortrait: Real-Time One-Shot Neural Head Avatars on Mobile Devices},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {15920-15929}
} | Existing neural head avatars methods have achieved significant progress in the image quality and motion range of portrait animation. However, these methods prioritize effectiveness over computational overhead. This paper presents MobilePortrait, a lightweight one-shot neural head avatars method that reduces learning complexity by integrating external knowledge into both the motion modeling and image synthesis, enabling real-time inference on mobile devices. Specifically, We introduce a mixed keypoint representation of explicit and implicit keypoints for precise motion modeling and equip it with precomputed visual features to enhance both facial and background synthesis. With these designs, our model can achieve state-of-the-art performance using UNet with low FLOPs as the backbone, requiring less than 1/10 the computational demand. It has been validated to reach speeds of over 50 FPS on mobile devices and support both video and audio-driven inputs. | [
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2,826 | D^3: Scaling Up Deepfake Detection by Learning from Discrepancy | [
"Yongqi Yang",
"Zhihao Qian",
"Ye Zhu",
"Olga Russakovsky",
"Yu Wu"
] | https://openaccess.thecvf.com/content/CVPR2025/html/Yang_D3_Scaling_Up_Deepfake_Detection_by_Learning_from_Discrepancy_CVPR_2025_paper.html | https://openaccess.thecvf.com/content/CVPR2025/papers/Yang_D3_Scaling_Up_Deepfake_Detection_by_Learning_from_Discrepancy_CVPR_2025_paper.pdf | https://openaccess.thecvf.com/content/CVPR2025/supplemental/Yang_D3_Scaling_Up_CVPR_2025_supplemental.pdf | null | @InProceedings{Yang_2025_CVPR,
author = {Yang, Yongqi and Qian, Zhihao and Zhu, Ye and Russakovsky, Olga and Wu, Yu},
title = {D{\textasciicircum}3: Scaling Up Deepfake Detection by Learning from Discrepancy},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {23850-23859}
} | The boom of Generative AI brings opportunities entangled with risks and concerns. Existing literature emphasizes the generalization capability of deepfake detection on unseen generators, significantly promoting the detector's ability to identify more universal artifacts. This work seeks a step toward a universal deepfake detection system with better generalization and robustness. We do so by first scaling up the existing detection task setup from the one-generator to multiple-generators in training, during which we disclose two challenges presented in prior methodological designs and demonstrate the divergence of detectors' performance. Specifically, we reveal that the current methods tailored for training on one specific generator either struggle to learn comprehensive artifacts from multiple generators or sacrifice their fitting ability for seen generators (i.e., In-Domain (ID) performance) to exchange the generalization for unseen generators (i.e., Out-Of-Domain (OOD) performance). To tackle the above challenges, we propose our Discrepancy Deepfake Detector (D3) framework, whose core idea is to deconstruct the universal artifacts from multiple generators by introducing a parallel network branch that takes a distorted image feature as an extra discrepancy signal and supplements its original counterpart. Extensive scaled-up experiments demonstrate the effectiveness of D3, achieving 5.3% accuracy improvement in the OOD testing compared to the current SOTA methods while maintaining the ID performance. The source code will be updated in our GitHub repository: https://github.com/BigAandSmallq/D3. | [
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2,827 | Jailbreaking the Non-Transferable Barrier via Test-Time Data Disguising | [
"Yongli Xiang",
"Ziming Hong",
"Lina Yao",
"Dadong Wang",
"Tongliang Liu"
] | https://openaccess.thecvf.com/content/CVPR2025/html/Xiang_Jailbreaking_the_Non-Transferable_Barrier_via_Test-Time_Data_Disguising_CVPR_2025_paper.html | https://openaccess.thecvf.com/content/CVPR2025/papers/Xiang_Jailbreaking_the_Non-Transferable_Barrier_via_Test-Time_Data_Disguising_CVPR_2025_paper.pdf | https://openaccess.thecvf.com/content/CVPR2025/supplemental/Xiang_Jailbreaking_the_Non-Transferable_CVPR_2025_supplemental.pdf | http://arxiv.org/abs/2503.17198 | @InProceedings{Xiang_2025_CVPR,
author = {Xiang, Yongli and Hong, Ziming and Yao, Lina and Wang, Dadong and Liu, Tongliang},
title = {Jailbreaking the Non-Transferable Barrier via Test-Time Data Disguising},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {30671-30681}
} | Non-transferable learning (NTL) has been proposed to protect model intellectual property (IP) by creating a "non-transferable barrier" to restrict generalization from authorized to unauthorized domains. Recently, well-designed attack, which restores the unauthorized-domain performance by fine-tuning NTL models on few authorized samples, highlights the security risks of NTL-based applications. However, such attack requires modifying model weights, thus being invalid in the black-box scenario. This raises a critical question: can we trust the security of NTL models deployed as black-box systems? In this work, we reveal the first loophole of black-box NTL models by proposing a novel attack method (dubbed as JailNTL) to jailbreak the non-transferable barrier through test-time data disguising, The main idea of JailNTL is to disguise unauthorized data so it can be identified as authorized by the NTL model, thereby bypassing the non-transferable barrier without modifying the NTL model weights. Specifically, JailNTL encourages unauthorized-domain disguising in two levels, including: (i) data-intrinsic disguising (DID) for eliminating domain discrepancy and preserving class-related content at the input-level, and (ii) model-guided disguising (MGD) for mitigating output-level statistics difference of the NTL model. Empirically, when attacking state-of-the-art (SOTA) NTL models in the black-box scenario, JailNTL achieves an accuracy increase of up to 55.7% in the unauthorized domain by using only 1% authorized samples, largely exceeding existing SOTA white-box attacks. Code is released at https://github.com/tmllab/2025_CVPR_JailNTL. | [
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2,828 | Light3R-SfM: Towards Feed-forward Structure-from-Motion | [
"Sven Elflein",
"Qunjie Zhou",
"Laura Leal-Taixé"
] | https://openaccess.thecvf.com/content/CVPR2025/html/Elflein_Light3R-SfM_Towards_Feed-forward_Structure-from-Motion_CVPR_2025_paper.html | https://openaccess.thecvf.com/content/CVPR2025/papers/Elflein_Light3R-SfM_Towards_Feed-forward_Structure-from-Motion_CVPR_2025_paper.pdf | https://openaccess.thecvf.com/content/CVPR2025/supplemental/Elflein_Light3R-SfM_Towards_Feed-forward_CVPR_2025_supplemental.pdf | null | @InProceedings{Elflein_2025_CVPR,
author = {Elflein, Sven and Zhou, Qunjie and Leal-Taix\'e, Laura},
title = {Light3R-SfM: Towards Feed-forward Structure-from-Motion},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {16774-16784}
} | We present Light3R-SfM, a feed-forward, end-to-end learnable framework for efficient large-scale Structure-from-Motion (SfM) from unconstrained image collections. Unlike existing SfM solutions that rely on costly matching and global optimization to achieve accurate 3D reconstructions, Light3R-SfM addresses this limitation through a novel latent global alignment module. This module replaces traditional global optimization with a learnable attention mechanism, effectively capturing multi-view constraints across images for robust and precise camera pose estimation. Light3R-SfM constructs a sparse scene graph via retrieval-score-guided shortest path tree to dramatically reduce memory usage and computational overhead compared to the naive approach. Extensive experiments demonstrate that Light3R-SfM achieves competitive accuracy while significantly reducing runtime, making it ideal for 3D reconstruction tasks in real-world applications with a runtime constraint. | [
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] |
2,829 | Robotic Visual Instruction | [
"Yanbang Li",
"Ziyang Gong",
"Haoyang Li",
"Xiaoqi Huang",
"Haolan Kang",
"Guangping Bai",
"Xianzheng Ma"
] | https://openaccess.thecvf.com/content/CVPR2025/html/Li_Robotic_Visual_Instruction_CVPR_2025_paper.html | https://openaccess.thecvf.com/content/CVPR2025/papers/Li_Robotic_Visual_Instruction_CVPR_2025_paper.pdf | https://openaccess.thecvf.com/content/CVPR2025/supplemental/Li_Robotic_Visual_Instruction_CVPR_2025_supplemental.pdf | http://arxiv.org/abs/2505.00693 | @InProceedings{Li_2025_CVPR,
author = {Li, Yanbang and Gong, Ziyang and Li, Haoyang and Huang, Xiaoqi and Kang, Haolan and Bai, Guangping and Ma, Xianzheng},
title = {Robotic Visual Instruction},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {12155-12165}
} | Recently, natural language has been the primary medium for human-robot interaction. However, its inherent lack of spatial precision for robotic control introduces challenges such as ambiguity and verbosity. To address these limitations, we introduce the ***Robotic Visual Instruction (RoVI)***, a novel paradigm to guide robotic tasks through an object-centric, hand-drawn symbolic representation. RoVI effectively encodes spatial-temporal information into human-interpretable visual instructions through 2D sketches, utilizing arrows, circles, colors, and numbers to direct 3D robotic manipulation. To enable robots to understand RoVI better and generate precise actions based on RoVI, we present ***Visual Instruction Embodied Workflow (VIEW)***, a pipeline formulated for RoVI-conditioned policies. This approach leverages Vision-Language Models (VLMs) to interpret RoVI inputs, decode spatial and temporal constraints from 2D pixel space via keypoint extraction, and then transform them into executable 3D action sequences. We additionally curate a specialized dataset of 15K instances to fine-tune small VLMs for edge deployment, enabling them to effectively learn RoVI capabilities. Our approach is rigorously validated across 11 novel tasks in both real and simulated environments, demonstrating significant generalization capability. Notably, VIEW achieves an 87.5% success rate in real-world scenarios involving unseen tasks that feature multi-step actions, with disturbances, and trajectory-following requirements. Code and Datasets in this paper will be released soon. | [
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2,830 | Solving Instance Detection from an Open-World Perspective | [
"Qianqian Shen",
"Yunhan Zhao",
"Nahyun Kwon",
"Jeeeun Kim",
"Yanan Li",
"Shu Kong"
] | https://openaccess.thecvf.com/content/CVPR2025/html/Shen_Solving_Instance_Detection_from_an_Open-World_Perspective_CVPR_2025_paper.html | https://openaccess.thecvf.com/content/CVPR2025/papers/Shen_Solving_Instance_Detection_from_an_Open-World_Perspective_CVPR_2025_paper.pdf | https://openaccess.thecvf.com/content/CVPR2025/supplemental/Shen_Solving_Instance_Detection_CVPR_2025_supplemental.pdf | http://arxiv.org/abs/2503.00359 | @InProceedings{Shen_2025_CVPR,
author = {Shen, Qianqian and Zhao, Yunhan and Kwon, Nahyun and Kim, Jeeeun and Li, Yanan and Kong, Shu},
title = {Solving Instance Detection from an Open-World Perspective},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {9901-9910}
} | Instance detection (InsDet) aims to localize specific object instances within a novel scene imagery based on given visual references. Technically, it requires proposal detection to identify all possible object instances, followed by instance-level matching to pinpoint the ones of interest. Its open-world nature supports its broad applications from robotics to AR/VR but also presents significant challenges: methods must generalize to unknown testing data distributions because (1) the testing scene imagery is unseen during training, and (2) there are domain gaps between visual references and detected proposals. Existing methods tackle these challenges by synthesizing diverse training examples or utilizing off-the-shelf foundation models (FMs). However, they only partially capitalize the available open-world information. In contrast, we approach InsDet from an Open-World perspective, introducing our method IDOW. We find that, while pretrained FMs yield high recall in instance detection, they are not specifically optimized for instance-level feature matching. Therefore, we adapt pretrained FMs for improved instance-level matching using open-world data. Our approach incorporates metric learning along with novel data augmentations, which sample distractors as negative examples and synthesize novel-view instances to enrich the visual references. Extensive experiments demonstrate that our method significantly outperforms prior works, achieving >10 AP over previous results on two recently released challenging benchmark datasets in both conventional and novel instance detection settings. | [
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2,831 | Percept, Memory, and Imagine: World Feature Simulating for Open-Domain Unknown Object Detection | [
"Aming Wu",
"Cheng Deng"
] | https://openaccess.thecvf.com/content/CVPR2025/html/Wu_Percept_Memory_and_Imagine_World_Feature_Simulating_for_Open-Domain_Unknown_CVPR_2025_paper.html | https://openaccess.thecvf.com/content/CVPR2025/papers/Wu_Percept_Memory_and_Imagine_World_Feature_Simulating_for_Open-Domain_Unknown_CVPR_2025_paper.pdf | https://openaccess.thecvf.com/content/CVPR2025/supplemental/Wu_Percept_Memory_and_CVPR_2025_supplemental.pdf | null | @InProceedings{Wu_2025_CVPR,
author = {Wu, Aming and Deng, Cheng},
title = {Percept, Memory, and Imagine: World Feature Simulating for Open-Domain Unknown Object Detection},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {4682-4691}
} | To accelerate the safe deployment of object detectors, we focus on reducing the impact of both covariate and semantic shifts. And we consider a realistic yet challenging scenario, namely Open-Domain Unknown Object Detection (ODU-OD), which aims to detect unknown objects in unseen target domains without accessing any auxiliary data. Towards ODU-OD, it is feasible to learn a robust discriminative boundary by synthesizing virtual features. Generally, perception, memory, and imagination are three essential capacities for human beings. Through multi-level perception and rich memory about known objects, the characteristics of unknown objects can be imagined sufficiently, enhancing the ability of discriminating known from unknown objects. Inspired by this idea, an approach of World Feature Simulation (WFS) is proposed, mainly consisting of a multi-level perception, memory recorder, and unknown-feature generator. Specifically, after extracting the features of the input, we separately employ a Mamba and Graph Network to obtain the global-level and connective-level representations. Next, a codebook containing multiple learnable codewords is defined to preserve fragmented memory of known objects. Meanwhile, we perform a modulated operation on the memory to form the imagination bank involving unknown characteristics. Finally, to alleviate the impact of lacking supervision data, based on the multi-level representation and imagination bank, a dedicated unknown-feature generator is designed to recurrently synthesize outlier features deviating from in-distribution (ID) objects. The significant performance gains on four different detection tasks demonstrate the superiorities of our method. The code will be released at https://github.com/AmingWu/WFS. | [
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] |
2,832 | Efficient Depth Estimation for Unstable Stereo Camera Systems on AR Glasses | [
"Yongfan Liu",
"Hyoukjun Kwon"
] | https://openaccess.thecvf.com/content/CVPR2025/html/Liu_Efficient_Depth_Estimation_for_Unstable_Stereo_Camera_Systems_on_AR_CVPR_2025_paper.html | https://openaccess.thecvf.com/content/CVPR2025/papers/Liu_Efficient_Depth_Estimation_for_Unstable_Stereo_Camera_Systems_on_AR_CVPR_2025_paper.pdf | https://openaccess.thecvf.com/content/CVPR2025/supplemental/Liu_Efficient_Depth_Estimation_CVPR_2025_supplemental.pdf | http://arxiv.org/abs/2411.10013 | @InProceedings{Liu_2025_CVPR,
author = {Liu, Yongfan and Kwon, Hyoukjun},
title = {Efficient Depth Estimation for Unstable Stereo Camera Systems on AR Glasses},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {6252-6261}
} | Stereo depth estimation is a fundamental component in augmented reality (AR), which requires low latency for real-time processing. However, preprocessing such as rectification and non-ML computations such as cost volume require significant amount of latency exceeding that of an ML model itself, which hinders the real-time processing required by AR. Therefore, we develop alternative approaches to the rectification and cost volume that consider ML acceleration (GPU and NPUs) in recent hardware. For pre-processing, we eliminate it by introducing homography matrix prediction network with a rectification positional encoding (RPE), which delivers both low latency and robustness to unrectified images. For cost volume, we replace it with a group-pointwise convolution-based operator and approximation of cosine similarity based on layernorm and dot product. Based on our approaches, we develop MultiHeadDepth (replacing cost volume) and HomoDepth (MultiHeadDepth + removing pre-processing) models. MultiHeadDepth provides 11.8-30.3% improvements in accuracy and 22.9-25.2% reduction in latency compared to a state-of-the-art depth estimation model for AR glasses from industry. HomoDepth, which can directly process unrectified images, reduces the end-to-end latency by 44.5%. We also introduce a multi-task learning method to handle misaligned stereo inputs on HomoDepth, which reduces the AbsRel error by 10.0-24.3%. The overall results demonstrate the efficacy of our approaches, which not only reduce the inference latency but also improve the model performance. Our code is available at https://github.com/UCI-ISA-Lab/MultiHeadDepth-HomoDepth | [
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2,833 | 3D-GRAND: A Million-Scale Dataset for 3D-LLMs with Better Grounding and Less Hallucination | [
"Jianing Yang",
"Xuweiyi Chen",
"Nikhil Madaan",
"Madhavan Iyengar",
"Shengyi Qian",
"David F. Fouhey",
"Joyce Chai"
] | https://openaccess.thecvf.com/content/CVPR2025/html/Yang_3D-GRAND_A_Million-Scale_Dataset_for_3D-LLMs_with_Better_Grounding_and_CVPR_2025_paper.html | https://openaccess.thecvf.com/content/CVPR2025/papers/Yang_3D-GRAND_A_Million-Scale_Dataset_for_3D-LLMs_with_Better_Grounding_and_CVPR_2025_paper.pdf | https://openaccess.thecvf.com/content/CVPR2025/supplemental/Yang_3D-GRAND_A_Million-Scale_CVPR_2025_supplemental.pdf | null | @InProceedings{Yang_2025_CVPR,
author = {Yang, Jianing and Chen, Xuweiyi and Madaan, Nikhil and Iyengar, Madhavan and Qian, Shengyi and Fouhey, David F. and Chai, Joyce},
title = {3D-GRAND: A Million-Scale Dataset for 3D-LLMs with Better Grounding and Less Hallucination},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {29501-29512}
} | The integration of language and 3D perception is crucial for embodied agents and robots that comprehend and interact with the physical world. While large language models (LLMs) have demonstrated impressive language understanding and generation capabilities, their adaptation to 3D environments (3D-LLMs) remains in its early stages. A primary challenge is a lack of large-scale datasets with dense grounding between language and 3D scenes. We introduce 3D-GRAND, a pioneering large-scale dataset comprising 40,087 household scenes paired with 6.2 million densely-grounded scene-language instructions. Our results show that instruction tuning with 3D-GRAND significantly enhances grounding capabilities and reduces hallucinations in 3D-LLMs. As part of our contributions, we propose a comprehensive benchmark 3D-POPE to systematically evaluate hallucination in 3D-LLMs, enabling fair comparisons of models. Our experiments highlight a scaling effect between dataset size and 3D-LLM performance, emphasizing the importance of large-scale 3D-text datasets for embodied AI research. Our results demonstrate early signals for effective sim-to-real transfer, indicating that models trained on large synthetic data can perform well on real-world 3D scans. Through 3D-GRAND and 3D-POPE, we aim to equip the embodied AI community with resources and insights to lead to more reliable and better-grounded 3D-LLMs. | [
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2,834 | LiDAR-RT: Gaussian-based Ray Tracing for Dynamic LiDAR Re-simulation | [
"Chenxu Zhou",
"Lvchang Fu",
"Sida Peng",
"Yunzhi Yan",
"Zhanhua Zhang",
"Yong Chen",
"Jiazhi Xia",
"Xiaowei Zhou"
] | https://openaccess.thecvf.com/content/CVPR2025/html/Zhou_LiDAR-RT_Gaussian-based_Ray_Tracing_for_Dynamic_LiDAR_Re-simulation_CVPR_2025_paper.html | https://openaccess.thecvf.com/content/CVPR2025/papers/Zhou_LiDAR-RT_Gaussian-based_Ray_Tracing_for_Dynamic_LiDAR_Re-simulation_CVPR_2025_paper.pdf | https://openaccess.thecvf.com/content/CVPR2025/supplemental/Zhou_LiDAR-RT_Gaussian-based_Ray_CVPR_2025_supplemental.pdf | null | @InProceedings{Zhou_2025_CVPR,
author = {Zhou, Chenxu and Fu, Lvchang and Peng, Sida and Yan, Yunzhi and Zhang, Zhanhua and Chen, Yong and Xia, Jiazhi and Zhou, Xiaowei},
title = {LiDAR-RT: Gaussian-based Ray Tracing for Dynamic LiDAR Re-simulation},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {1538-1548}
} | This paper targets the challenge of real-time LiDAR re-simulation in dynamic driving scenarios. Recent approaches utilize neural radiance fields combined with the physical modeling of LiDAR sensors to achieve high-fidelity re-simulation results. Unfortunately, these methods face limitations due to high computational demands in large-scale scenes and cannot perform real-time LiDAR rendering. To overcome these constraints, we propose LiDAR-RT, a novel framework that supports real-time, physically accurate LiDAR re-simulation for driving scenes. Our primary contribution is the development of an efficient and effective rendering pipeline, which integrates Gaussian primitives and hardware-accelerated ray tracing technology. Specifically, we model the physical properties of LiDAR sensors using Gaussian primitives with learnable parameters and incorporate scene graphs to handle scene dynamics. Building upon this scene representation, our framework first constructs a bounding volume hierarchy (BVH), then casts rays for each pixel and generates novel LiDAR views through a differentiable rendering algorithm. Importantly, our framework supports realistic rendering with flexible scene editing operations and various sensor configurations. Extensive experiments across multiple public benchmarks demonstrate that our method outperforms state-of-the-art methods in terms of rendering quality and efficiency. Codes will be released to ensure reproducibility. | [
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] |
2,835 | Generative Zero-Shot Composed Image Retrieval | [
"Lan Wang",
"Wei Ao",
"Vishnu Naresh Boddeti",
"Ser-Nam Lim"
] | https://openaccess.thecvf.com/content/CVPR2025/html/Wang_Generative_Zero-Shot_Composed_Image_Retrieval_CVPR_2025_paper.html | https://openaccess.thecvf.com/content/CVPR2025/papers/Wang_Generative_Zero-Shot_Composed_Image_Retrieval_CVPR_2025_paper.pdf | https://openaccess.thecvf.com/content/CVPR2025/supplemental/Wang_Generative_Zero-Shot_Composed_CVPR_2025_supplemental.pdf | null | @InProceedings{Wang_2025_CVPR,
author = {Wang, Lan and Ao, Wei and Boddeti, Vishnu Naresh and Lim, Ser-Nam},
title = {Generative Zero-Shot Composed Image Retrieval},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {29690-29700}
} | Composed Image Retrieval (CIR) is a vision-language task utilizing queries comprising images and textual descriptions to achieve precise image retrieval. This task seeks to find images that are visually similar to a reference image while incorporating specific changes or features described textually (visual delta). CIR enables a more flexible and user-specific retrieval by bridging visual data with verbal instructions. This paper introduces a novel generative method that augments Composed Image Retrieval by Composed Image Generation (CIG) to provide pseudo-target images. CIG utilizes a textual inversion network to map reference images into semantic word space, which generates pseudo-target images in combination with textual descriptions. These images serve as additional visual information, significantly improving the accuracy and relevance of retrieved images when integrated into existing retrieval frameworks. Experiments conducted across multiple CIR datasets and several baseline methods demonstrate improvements in retrieval performance, which shows the potential of our approach as an effective add-on for existing composed image retrieval. | [
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2,836 | Large-Scale Text-to-Image Model with Inpainting is a Zero-Shot Subject-Driven Image Generator | [
"Chaehun Shin",
"Jooyoung Choi",
"Heeseung Kim",
"Sungroh Yoon"
] | https://openaccess.thecvf.com/content/CVPR2025/html/Shin_Large-Scale_Text-to-Image_Model_with_Inpainting_is_a_Zero-Shot_Subject-Driven_Image_CVPR_2025_paper.html | https://openaccess.thecvf.com/content/CVPR2025/papers/Shin_Large-Scale_Text-to-Image_Model_with_Inpainting_is_a_Zero-Shot_Subject-Driven_Image_CVPR_2025_paper.pdf | https://openaccess.thecvf.com/content/CVPR2025/supplemental/Shin_Large-Scale_Text-to-Image_Model_CVPR_2025_supplemental.pdf | http://arxiv.org/abs/2411.15466 | @InProceedings{Shin_2025_CVPR,
author = {Shin, Chaehun and Choi, Jooyoung and Kim, Heeseung and Yoon, Sungroh},
title = {Large-Scale Text-to-Image Model with Inpainting is a Zero-Shot Subject-Driven Image Generator},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {7986-7996}
} | Subject-driven text-to-image generation aims to produce images of a new subject within a desired context by accurately capturing both the visual characteristics of the subject and the semantic content of a text prompt. Traditional methods rely on time- and resource-intensive fine-tuning for subject alignment, while recent zero-shot approaches leverage on-the-fly image prompting, often sacrificing subject alignment. In this paper, we introduce Diptych Prompting, a novel zero-shot approach that reinterprets as an inpainting task with precise subject alignment by leveraging the emergent property of diptych generation in large-scale text-to-image models. Diptych Prompting arranges an incomplete diptych with the reference image in the left panel, and performs text-conditioned inpainting on the right panel. We further prevent unwanted content leakage by removing the background in the reference image and improve fine-grained details in the generated subject by enhancing attention weights between the panels during inpainting. Experimental results confirm that our approach significantly outperforms zero-shot image prompting methods, resulting in images that are visually preferred by users. Additionally, our method supports not only subject-driven generation but also stylized image generation and subject-driven image editing, demonstrating versatility across diverse image generation applications. | [
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] |
2,837 | MASt3R-SLAM: Real-Time Dense SLAM with 3D Reconstruction Priors | [
"Riku Murai",
"Eric Dexheimer",
"Andrew J. Davison"
] | https://openaccess.thecvf.com/content/CVPR2025/html/Murai_MASt3R-SLAM_Real-Time_Dense_SLAM_with_3D_Reconstruction_Priors_CVPR_2025_paper.html | https://openaccess.thecvf.com/content/CVPR2025/papers/Murai_MASt3R-SLAM_Real-Time_Dense_SLAM_with_3D_Reconstruction_Priors_CVPR_2025_paper.pdf | https://openaccess.thecvf.com/content/CVPR2025/supplemental/Murai_MASt3R-SLAM_Real-Time_Dense_CVPR_2025_supplemental.pdf | null | @InProceedings{Murai_2025_CVPR,
author = {Murai, Riku and Dexheimer, Eric and Davison, Andrew J.},
title = {MASt3R-SLAM: Real-Time Dense SLAM with 3D Reconstruction Priors},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {16695-16705}
} | We present a real-time monocular dense SLAM system designed bottom-up from MASt3R, a two-view 3D reconstruction and matching prior. Equipped with this strong prior, our system is robust on in-the-wild video sequences despite making no assumption on a fixed or parametric camera model beyond a unique camera centre. We introduce efficient methods for pointmap matching, camera tracking and local fusion, graph construction and loop closure, and second-order global optimisation. With known calibration, a simple modification to the system achieves state-of-the-art performance across various benchmarks. Altogether, we propose a plug-and-play monocular SLAM system capable of producing globally consistent poses and dense geometry while operating at 15 FPS. | [
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2,838 | Flow-NeRF: Joint Learning of Geometry, Poses, and Dense Flow within Unified Neural Representations | [
"Xunzhi Zheng",
"Dan Xu"
] | https://openaccess.thecvf.com/content/CVPR2025/html/Zheng_Flow-NeRF_Joint_Learning_of_Geometry_Poses_and_Dense_Flow_within_CVPR_2025_paper.html | https://openaccess.thecvf.com/content/CVPR2025/papers/Zheng_Flow-NeRF_Joint_Learning_of_Geometry_Poses_and_Dense_Flow_within_CVPR_2025_paper.pdf | https://openaccess.thecvf.com/content/CVPR2025/supplemental/Zheng_Flow-NeRF_Joint_Learning_CVPR_2025_supplemental.pdf | null | @InProceedings{Zheng_2025_CVPR,
author = {Zheng, Xunzhi and Xu, Dan},
title = {Flow-NeRF: Joint Learning of Geometry, Poses, and Dense Flow within Unified Neural Representations},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {993-1002}
} | Learning accurate scene reconstruction without pose priors in neural radiance fields is challenging due to inherent geometric ambiguity. Recent development either relies on correspondence priors for regularization or uses off-the-shelf flow estimators to derive analytical poses. However, the potential for jointly learning scene geometry, camera poses, and dense flow within a unified neural representation remains largely unexplored. In this paper, we present Flow-NeRF, a unified framework that simultaneously optimizes scene geometry, camera poses, and dense optical flow all on-the-fly. To enable the learning of dense flow within the neural radiance field, we design and build a bijective mapping for flow estimation, conditioned on pose. To make the scene reconstruction benefit from the flow estimation, we develop an effective feature enhancement mechanism to pass canonical space features to world space representations, significantly enhancing scene geometry. We validate our model across four important tasks, i.e., novel view synthesis, depth estimation, camera pose prediction, and dense optical flow estimation, using several datasets. Our approach surpasses previous methods in almost all metrics for novel-view view synthesis and depth estimation and yields both qualitatively sound and quantitatively accurate novel-view flow. Our project page is https://zhengxunzhi.github.io/flownerf/. | [
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2,839 | Viewpoint Rosetta Stone: Unlocking Unpaired Ego-Exo Videos for View-invariant Representation Learning | [
"Mi Luo",
"Zihui Xue",
"Alex Dimakis",
"Kristen Grauman"
] | https://openaccess.thecvf.com/content/CVPR2025/html/Luo_Viewpoint_Rosetta_Stone_Unlocking_Unpaired_Ego-Exo_Videos_for_View-invariant_Representation_CVPR_2025_paper.html | https://openaccess.thecvf.com/content/CVPR2025/papers/Luo_Viewpoint_Rosetta_Stone_Unlocking_Unpaired_Ego-Exo_Videos_for_View-invariant_Representation_CVPR_2025_paper.pdf | https://openaccess.thecvf.com/content/CVPR2025/supplemental/Luo_Viewpoint_Rosetta_Stone_CVPR_2025_supplemental.pdf | null | @InProceedings{Luo_2025_CVPR,
author = {Luo, Mi and Xue, Zihui and Dimakis, Alex and Grauman, Kristen},
title = {Viewpoint Rosetta Stone: Unlocking Unpaired Ego-Exo Videos for View-invariant Representation Learning},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {15802-15812}
} | Egocentric and exocentric perspectives of human action differ significantly, yet overcoming this extreme viewpoint gap is critical for applications in augmented reality and robotics. We propose ViewpointRosetta, an approach that unlocks large-scale unpaired ego and exo video data to learn clip-level viewpoint-invariant video representations. Our framework introduces (1) a diffusion-based Rosetta Stone Translator (RST), which, leveraging a moderate amount of synchronized multi-view videos, serves as a translator in feature space to decipher the alignments between unpaired ego and exo data, and (2) a dual encoder that aligns unpaired data representations through contrastive learning with RST-based synthetic feature augmentation and soft alignment. To evaluate the learned features in a standardized setting, we construct a new cross-view benchmark using Ego-Exo4D, covering cross-view retrieval, action recognition, and skill assessment. Our framework demonstrates superior cross-view understanding compared to previous view-invariant learning and egocentric video representation learning approaches, and opens the door to bringing vast amounts of traditional third-person video to bear on the more nascent first-person setting. | [
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2,840 | Cross-modal Information Flow in Multimodal Large Language Models | [
"Zhi Zhang",
"Srishti Yadav",
"Fengze Han",
"Ekaterina Shutova"
] | https://openaccess.thecvf.com/content/CVPR2025/html/Zhang_Cross-modal_Information_Flow_in_Multimodal_Large_Language_Models_CVPR_2025_paper.html | https://openaccess.thecvf.com/content/CVPR2025/papers/Zhang_Cross-modal_Information_Flow_in_Multimodal_Large_Language_Models_CVPR_2025_paper.pdf | https://openaccess.thecvf.com/content/CVPR2025/supplemental/Zhang_Cross-modal_Information_Flow_CVPR_2025_supplemental.pdf | http://arxiv.org/abs/2411.18620 | @InProceedings{Zhang_2025_CVPR,
author = {Zhang, Zhi and Yadav, Srishti and Han, Fengze and Shutova, Ekaterina},
title = {Cross-modal Information Flow in Multimodal Large Language Models},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {19781-19791}
} | The recent advancements in auto-regressive multimodal large language models (MLLMs) have demonstrated promising progress for vision-language tasks. While there exists a variety of studies investigating the processing of linguistic information within large language models, little is currently known about the inner working mechanism of MLLMs and how linguistic and visual information interact within these models. In this study, we aim to fill this gap by examining the information flow between different modalities---language and vision---in MLLMs, focusing on visual question answering. Specifically, given an image-question pair as input, we investigate where in the model and how the visual and linguistic information are combined to generate the final prediction. Conducting experiments with a series of models from the LLaVA series, we find that there are two distinct stages in the process of integration of the two modalities. In the lower layers, the model first transfers the more general visual features of the whole image into the representations of (linguistic) question tokens. In the middle layers, it once again transfers visual information about specific objects relevant to the question to the respective token positions of the question. Finally, in the higher layers, the resulting multimodal representation is propagated to the last position of the input sequence for the final prediction. Overall, our findings provide a new and comprehensive perspective on the spatial and functional aspects of image and language processing in the MLLMs, thereby facilitating future research into multimodal information localization and editing. Our code and collected dataset are released here: https://github.com/FightingFighting/cross-modal-information-flow-in-MLLM.git. | [
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2,841 | Consistent and Controllable Image Animation with Motion Diffusion Models | [
"Xin Ma",
"Yaohui Wang",
"Gengyun Jia",
"Xinyuan Chen",
"Tien-Tsin Wong",
"Yuan-Fang Li",
"Cunjian Chen"
] | https://openaccess.thecvf.com/content/CVPR2025/html/Ma_Consistent_and_Controllable_Image_Animation_with_Motion_Diffusion_Models_CVPR_2025_paper.html | https://openaccess.thecvf.com/content/CVPR2025/papers/Ma_Consistent_and_Controllable_Image_Animation_with_Motion_Diffusion_Models_CVPR_2025_paper.pdf | https://openaccess.thecvf.com/content/CVPR2025/supplemental/Ma_Consistent_and_Controllable_CVPR_2025_supplemental.pdf | http://arxiv.org/abs/2407.15642 | @InProceedings{Ma_2025_CVPR,
author = {Ma, Xin and Wang, Yaohui and Jia, Gengyun and Chen, Xinyuan and Wong, Tien-Tsin and Li, Yuan-Fang and Chen, Cunjian},
title = {Consistent and Controllable Image Animation with Motion Diffusion Models},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {7288-7298}
} | Diffusion models have achieved significant progress in the task of image animation due to their powerful generative capabilities. However, preserving appearance consistency to the static input image, and avoiding abrupt motion change in the generated animation, remains challenging. In this paper, we introduce Cinemo, a novel image animation approach that aims to achieve better appearance consistency and motion smoothness. The core of Cinemo is to focus on learning the distribution of motion residuals, rather than directly predicting frames as in existing diffusion models. During the inference, we further mitigate the sudden motion changes in the generated video by introducing a novel DCT-based noise refinement strategy. To counteract the over-smoothing of motion, we introduce a dynamics degree control design for better control of the magnitude of motion. Altogether, these strategies enable Cinemo to produce highly consistent, smooth, and motion-controllable results. Extensive experiments compared with several state-of-the-art methods demonstrate the effectiveness and superiority of our proposed approach. In the end, we also demonstrate how our model can be applied for motion transfer or video editing of any given video. | [
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2,842 | Towards Better Alignment: Training Diffusion Models with Reinforcement Learning Against Sparse Rewards | [
"Zijing Hu",
"Fengda Zhang",
"Long Chen",
"Kun Kuang",
"Jiahui Li",
"Kaifeng Gao",
"Jun Xiao",
"Xin Wang",
"Wenwu Zhu"
] | https://openaccess.thecvf.com/content/CVPR2025/html/Hu_Towards_Better_Alignment_Training_Diffusion_Models_with_Reinforcement_Learning_Against_CVPR_2025_paper.html | https://openaccess.thecvf.com/content/CVPR2025/papers/Hu_Towards_Better_Alignment_Training_Diffusion_Models_with_Reinforcement_Learning_Against_CVPR_2025_paper.pdf | https://openaccess.thecvf.com/content/CVPR2025/supplemental/Hu_Towards_Better_Alignment_CVPR_2025_supplemental.pdf | http://arxiv.org/abs/2503.11240 | @InProceedings{Hu_2025_CVPR,
author = {Hu, Zijing and Zhang, Fengda and Chen, Long and Kuang, Kun and Li, Jiahui and Gao, Kaifeng and Xiao, Jun and Wang, Xin and Zhu, Wenwu},
title = {Towards Better Alignment: Training Diffusion Models with Reinforcement Learning Against Sparse Rewards},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {23604-23614}
} | Diffusion models have achieved remarkable success in text-to-image generation. However, their practical applications are hindered by the misalignment between generated images and corresponding text prompts. To tackle this issue, reinforcement learning (RL) has been considered for diffusion model fine-tuning. Yet, RL's effectiveness is limited by the challenge of sparse reward, where feedback is only available at the end of the generation process. This makes it difficult to identify which actions during the denoising process contribute positively to the final generated image, potentially leading to ineffective or unnecessary denoising policies. To this end, this paper presents a novel RL-based framework that addresses the sparse reward problem when training diffusion models. Our framework, named \text B ^2\text -DiffuRL , employs two strategies: **B**ackward progressive training and **B**ranch-based sampling. For one thing, backward progressive training focuses initially on the final timesteps of the denoising process and gradually extends the training interval to earlier timesteps, easing the learning difficulty associated with sparse rewards. For another, we perform branch-based sampling for each training interval. By comparing the samples within the same branch, we can identify how much the policies of the current training interval contribute to the final image, which helps to learn effective policies instead of unnecessary ones. \text B ^2\text -DiffuRL is compatible with existing optimization algorithms. Extensive experiments demonstrate the effectiveness of \text B ^2\text -DiffuRL in improving prompt-image alignment and maintaining diversity in generated images. The code for this work is available. | [
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2,843 | Spatial457: A Diagnostic Benchmark for 6D Spatial Reasoning of Large Mutimodal Models | [
"Xingrui Wang",
"Wufei Ma",
"Tiezheng Zhang",
"Celso M de Melo",
"Jieneng Chen",
"Alan Yuille"
] | https://openaccess.thecvf.com/content/CVPR2025/html/Wang_Spatial457_A_Diagnostic_Benchmark_for_6D_Spatial_Reasoning_of_Large_CVPR_2025_paper.html | https://openaccess.thecvf.com/content/CVPR2025/papers/Wang_Spatial457_A_Diagnostic_Benchmark_for_6D_Spatial_Reasoning_of_Large_CVPR_2025_paper.pdf | https://openaccess.thecvf.com/content/CVPR2025/supplemental/Wang_Spatial457_A_Diagnostic_CVPR_2025_supplemental.pdf | null | @InProceedings{Wang_2025_CVPR,
author = {Wang, Xingrui and Ma, Wufei and Zhang, Tiezheng and de Melo, Celso M and Chen, Jieneng and Yuille, Alan},
title = {Spatial457: A Diagnostic Benchmark for 6D Spatial Reasoning of Large Mutimodal Models},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {24669-24679}
} | Although large multimodal models (LMMs) have demonstrated remarkable capabilities in visual scene interpretation and reasoning, their capacity for complex and precise 3-dimensional spatial reasoning remains uncertain. Existing benchmarks focus predominantly on 2D spatial understanding and lack a framework to comprehensively evaluate 6D spatial reasoning across varying complexities.To address this limitation, we present Spatial457, a scalable and unbiased synthetic dataset designed with 4 key spatial components: multi-object recognition, 2D and 3D spatial relationships, and 3D orientation. Spatial457 supports a cascading evaluation structure, offering 7 question types across 5 difficulty levels that progress from basic single-object recognition to our newly proposed complex 6D spatial reasoning tasks.We evaluated various large multimodal models (LMMs) on Spatial457, observing a general decline in performance as task complexity increases, particularly in 3D reasoning and 6D spatial tasks. To quantify these challenges, we introduce the Relative Performance Dropping Rate (RPDR), highlighting key weaknesses in 3D reasoning capabilities. Leveraging the unbiased attribute design of our dataset, we also uncover prediction biases across different attributes, with similar patterns observed in real-world image settings. | [
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] |
2,844 | Omnidirectional Multi-Object Tracking | [
"Kai Luo",
"Hao Shi",
"Sheng Wu",
"Fei Teng",
"Mengfei Duan",
"Chang Huang",
"Yuhang Wang",
"Kaiwei Wang",
"Kailun Yang"
] | https://openaccess.thecvf.com/content/CVPR2025/html/Luo_Omnidirectional_Multi-Object_Tracking_CVPR_2025_paper.html | https://openaccess.thecvf.com/content/CVPR2025/papers/Luo_Omnidirectional_Multi-Object_Tracking_CVPR_2025_paper.pdf | https://openaccess.thecvf.com/content/CVPR2025/supplemental/Luo_Omnidirectional_Multi-Object_Tracking_CVPR_2025_supplemental.pdf | http://arxiv.org/abs/2503.04565 | @InProceedings{Luo_2025_CVPR,
author = {Luo, Kai and Shi, Hao and Wu, Sheng and Teng, Fei and Duan, Mengfei and Huang, Chang and Wang, Yuhang and Wang, Kaiwei and Yang, Kailun},
title = {Omnidirectional Multi-Object Tracking},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {21959-21969}
} | Panoramic imagery, with its 360deg field of view, offers comprehensive information to support Multi-Object Tracking (MOT) in capturing spatial and temporal relationships of surrounding objects. However, most MOT algorithms are tailored for pinhole images with limited views, impairing their effectiveness in panoramic settings. Additionally, panoramic image distortions, such as resolution loss, geometric deformation, and uneven lighting, hinder direct adaptation of existing MOT methods, leading to significant performance degradation. To address these challenges, we propose OmniTrack, an omnidirectional MOT framework that incorporates Tracklet Management to introduce temporal cues, FlexiTrack Instances for object localization and association, and the CircularStatE Module to alleviate image and geometric distortions. This integration enables tracking in panoramic field-of-view scenarios, even under rapid sensor motion. To mitigate the lack of panoramic MOT datasets, we introduce the QuadTrack dataset--a comprehensive panoramic dataset collected by a quadruped robot, featuring diverse challenges such as panoramic fields of view, intense motion, and complex environments. Extensive experiments on the public JRDB dataset and the newly introduced QuadTrack benchmark demonstrate the state-of-the-art performance of the proposed framework. OmniTrack achieves a HOTA score of 26.92% on JRDB, representing an improvement of 3.43%, and further achieves 23.45% on QuadTrack, surpassing the baseline by 6.81%. The established dataset and source code are available at https://github.com/xifen523/OmniTrack. | [
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] |
2,845 | Potential Field Based Deep Metric Learning | [
"Shubhang Bhatnagar",
"Narendra Ahuja"
] | https://openaccess.thecvf.com/content/CVPR2025/html/Bhatnagar_Potential_Field_Based_Deep_Metric_Learning_CVPR_2025_paper.html | https://openaccess.thecvf.com/content/CVPR2025/papers/Bhatnagar_Potential_Field_Based_Deep_Metric_Learning_CVPR_2025_paper.pdf | https://openaccess.thecvf.com/content/CVPR2025/supplemental/Bhatnagar_Potential_Field_Based_CVPR_2025_supplemental.zip | http://arxiv.org/abs/2405.18560 | @InProceedings{Bhatnagar_2025_CVPR,
author = {Bhatnagar, Shubhang and Ahuja, Narendra},
title = {Potential Field Based Deep Metric Learning},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {25549-25559}
} | Deep metric learning (DML) involves training a network to learn a semantically meaningful representation space. Many current approaches mine n-tuples of examples and model interactions within each tuplets. We present a novel, compositional DML model that instead of in tuples, represents the influence of each example (embedding) by a continuous potential field, and superposes the fields to obtain their combined global potential field. We use attractive/repulsive potential fields to represent interactions among embeddings from images of the same/different classes. Contrary to typical learning methods, where mutual influence of samples is proportional to their distance, we enforce reduction in such influence with distance, leading to a decaying field. We show that such decay helps improve performance on real world datasets with large intra-class variations and label noise. Like other proxy-based methods, we also use proxies to succinctly represent sub-populations of examples. We evaluate our method on three standard DML benchmarks- Cars-196, CUB-200-2011, and SOP datasets where it outperforms state-of-the-art baselines. | [
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2,846 | Enhancing Vision-Language Compositional Understanding with Multimodal Synthetic Data | [
"Haoxin Li",
"Boyang Li"
] | https://openaccess.thecvf.com/content/CVPR2025/html/Li_Enhancing_Vision-Language_Compositional_Understanding_with_Multimodal_Synthetic_Data_CVPR_2025_paper.html | https://openaccess.thecvf.com/content/CVPR2025/papers/Li_Enhancing_Vision-Language_Compositional_Understanding_with_Multimodal_Synthetic_Data_CVPR_2025_paper.pdf | https://openaccess.thecvf.com/content/CVPR2025/supplemental/Li_Enhancing_Vision-Language_Compositional_CVPR_2025_supplemental.pdf | http://arxiv.org/abs/2503.01167 | @InProceedings{Li_2025_CVPR,
author = {Li, Haoxin and Li, Boyang},
title = {Enhancing Vision-Language Compositional Understanding with Multimodal Synthetic Data},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {24849-24861}
} | Paired image-text data with subtle variations in-between (e.g., people holding surfboards vs. people holding shovels) hold the promise of producing Vision-Language Models with proper compositional understanding. Synthesizing such training data from generative models is a highly coveted prize due to the reduced cost of data collection. However, synthesizing training images for compositional learning presents three challenges: (1) efficiency in generating large quantities of images, (2) text alignment between the generated image and the caption in the exact place of the subtle change, and (3) image fidelity in ensuring sufficient similarity with the original real images in all other places. We propose SPARCL (Synthetic Perturbations for Advancing Robust Compositional Learning), which integrates image feature injection into a fast text-to-image generative model, followed by an image style transfer step, to meet the three challenges. Further, to cope with any residual issues of text alignment, we propose an adaptive margin loss to filter out potentially incorrect synthetic samples and focus the learning on informative hard samples. Evaluation on four compositional understanding benchmarks demonstrates that SPARCL significantly improves the compositionality of CLIP, boosting the average accuracy of the CLIP base model by over 8% across all benchmarks and outperforming state-of-the-art methods by 2% on three benchmarks. | [
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2,847 | Directional Label Diffusion Model for Learning from Noisy Labels | [
"Senyu Hou",
"Gaoxia Jiang",
"Jia Zhang",
"Shangrong Yang",
"Husheng Guo",
"Yaqing Guo",
"Wenjian Wang"
] | https://openaccess.thecvf.com/content/CVPR2025/html/Hou_Directional_Label_Diffusion_Model_for_Learning_from_Noisy_Labels_CVPR_2025_paper.html | https://openaccess.thecvf.com/content/CVPR2025/papers/Hou_Directional_Label_Diffusion_Model_for_Learning_from_Noisy_Labels_CVPR_2025_paper.pdf | https://openaccess.thecvf.com/content/CVPR2025/supplemental/Hou_Directional_Label_Diffusion_CVPR_2025_supplemental.pdf | null | @InProceedings{Hou_2025_CVPR,
author = {Hou, Senyu and Jiang, Gaoxia and Zhang, Jia and Yang, Shangrong and Guo, Husheng and Guo, Yaqing and Wang, Wenjian},
title = {Directional Label Diffusion Model for Learning from Noisy Labels},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {25738-25748}
} | In image classification, the label quality of training data critically influences model generalization, especially for deep neural networks (DNNs). Traditionally, learning from noisy labels (LNL) can improve the generalization of DNNs through complex architectures or a series of robust techniques, but its performance improvement is limited by the discriminative paradigm. Unlike traditional ways, we resolve the LNL problems from the perspective of robust label generation, based on diffusion models within the generative paradigm. To expand the diffusion model into a robust classifier that explicitly accommodates more noise knowledge, we propose a Directional Label Diffusion (DLD) model. It disentangles the diffusion process into two paths, i.e., directional diffusion and random diffusion. Specifically, directional diffusion simulates the corruption of true labels into a directed noise distribution, prioritizing the removal of likely noise, whereas random diffusion introduces inherent randomness to support label recovery. This architecture enable DLD to gradually infer labels from an initial random state, interpretably diverging from the specified noise distribution. To adapt the model to diverse noisy environments, we design a low-cost label pre-correction method that automatically supplies more accurate label information to the diffusion model, without requiring manual intervention or additional iterations. Our approach outperforms state-of-the-art methods on both simulated and real-world noisy datasets. Code is available at https://github.com/SenyuHou/DLD. | [
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2,848 | AA-CLIP: Enhancing Zero-Shot Anomaly Detection via Anomaly-Aware CLIP | [
"Wenxin Ma",
"Xu Zhang",
"Qingsong Yao",
"Fenghe Tang",
"Chenxu Wu",
"Yingtai Li",
"Rui Yan",
"Zihang Jiang",
"S.Kevin Zhou"
] | https://openaccess.thecvf.com/content/CVPR2025/html/Ma_AA-CLIP_Enhancing_Zero-Shot_Anomaly_Detection_via_Anomaly-Aware_CLIP_CVPR_2025_paper.html | https://openaccess.thecvf.com/content/CVPR2025/papers/Ma_AA-CLIP_Enhancing_Zero-Shot_Anomaly_Detection_via_Anomaly-Aware_CLIP_CVPR_2025_paper.pdf | https://openaccess.thecvf.com/content/CVPR2025/supplemental/Ma_AA-CLIP_Enhancing_Zero-Shot_CVPR_2025_supplemental.pdf | null | @InProceedings{Ma_2025_CVPR,
author = {Ma, Wenxin and Zhang, Xu and Yao, Qingsong and Tang, Fenghe and Wu, Chenxu and Li, Yingtai and Yan, Rui and Jiang, Zihang and Zhou, S.Kevin},
title = {AA-CLIP: Enhancing Zero-Shot Anomaly Detection via Anomaly-Aware CLIP},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {4744-4754}
} | Anomaly detection (AD) identifies outliers for applications like defect and lesion detection. While CLIP shows promise for zero-shot AD tasks due to its strong generalization capabilities, its inherent Anomaly-Unawareness leads to limited discrimination between normal and abnormal features. To address this problem, we propose Anomaly-Aware CLIP (AA-CLIP), which enhances CLIP's anomaly discrimination ability in both text and visual spaces while preserving its generalization capability. AA-CLIP is achieved through a straightforward yet effective two-stage approach: it first creates anomaly-aware text anchors to differentiate normal and abnormal semantics clearly, then aligns patch-level visual features with these anchors for precise anomaly localization. This two-stage strategy, with the help of residual adapters, gradually adapts CLIP in a controlled manner, achieving effective AD while maintaining CLIP's class knowledge. Extensive experiments validate AA-CLIP as a resource-efficient solution for zero-shot AD tasks, achieving state-of-the-art results in industrial and medical applications. The code is available at https://github.com/Mwxinnn/AA-CLIP. | [
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2,849 | HybridGS: Decoupling Transients and Statics with 2D and 3D Gaussian Splatting | [
"Jingyu Lin",
"Jiaqi Gu",
"Lubin Fan",
"Bojian Wu",
"Yujing Lou",
"Renjie Chen",
"Ligang Liu",
"Jieping Ye"
] | https://openaccess.thecvf.com/content/CVPR2025/html/Lin_HybridGS_Decoupling_Transients_and_Statics_with_2D_and_3D_Gaussian_CVPR_2025_paper.html | https://openaccess.thecvf.com/content/CVPR2025/papers/Lin_HybridGS_Decoupling_Transients_and_Statics_with_2D_and_3D_Gaussian_CVPR_2025_paper.pdf | https://openaccess.thecvf.com/content/CVPR2025/supplemental/Lin_HybridGS_Decoupling_Transients_CVPR_2025_supplemental.zip | http://arxiv.org/abs/2412.03844 | @InProceedings{Lin_2025_CVPR,
author = {Lin, Jingyu and Gu, Jiaqi and Fan, Lubin and Wu, Bojian and Lou, Yujing and Chen, Renjie and Liu, Ligang and Ye, Jieping},
title = {HybridGS: Decoupling Transients and Statics with 2D and 3D Gaussian Splatting},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {788-797}
} | Generating high-quality novel view renderings of 3D Gaussian Splatting (3DGS) in scenes featuring transient objects is challenging. We propose a novel hybrid representation, termed as HybridGS, using 2D Gaussians for transient objects per image and maintaining traditional 3D Gaussians for the whole static scenes. 3DGS is suited for modeling static scenes that assume multi-view consistency, but the transient objects appear occasionally and do not adhere to the assumption, thus we model them as planar objects from a single view by 2D Gaussians. Our novel representation decomposes the scene from the perspective of fundamental viewpoint consistency. Additionally, we present a multi-view supervision method for 3DGS that leverages information from co-visible regions, further enhancing the distinctions between the transients and statics. Then, we propose a straightforward yet effective multi-stage training strategy to ensure robust training and view synthesis. Experiments on benchmarks show our state-of-the-art performance of novel view synthesis in indoor and outdoor scenes, even in the presence of distracting elements. Project page: https://gujiaqivadin.github.io/hybridgs/ | [
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2,850 | Keyframe-Guided Creative Video Inpainting | [
"Yuwei Guo",
"Ceyuan Yang",
"Anyi Rao",
"Chenlin Meng",
"Omer Bar-Tal",
"Shuangrui Ding",
"Maneesh Agrawala",
"Dahua Lin",
"Bo Dai"
] | https://openaccess.thecvf.com/content/CVPR2025/html/Guo_Keyframe-Guided_Creative_Video_Inpainting_CVPR_2025_paper.html | https://openaccess.thecvf.com/content/CVPR2025/papers/Guo_Keyframe-Guided_Creative_Video_Inpainting_CVPR_2025_paper.pdf | https://openaccess.thecvf.com/content/CVPR2025/supplemental/Guo_Keyframe-Guided_Creative_Video_CVPR_2025_supplemental.pdf | null | @InProceedings{Guo_2025_CVPR,
author = {Guo, Yuwei and Yang, Ceyuan and Rao, Anyi and Meng, Chenlin and Bar-Tal, Omer and Ding, Shuangrui and Agrawala, Maneesh and Lin, Dahua and Dai, Bo},
title = {Keyframe-Guided Creative Video Inpainting},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {13009-13020}
} | Video inpainting, which aims to fill missing regions with visually coherent content, has emerged as a crucial technique for creative applications such as editing. While existing approaches achieve visual consistency or text-guided generation, they often struggle to balance coherence and creative diversity. In this work, we introduce VideoRepainter, a two-stage framework that allows users to inpaint a keyframe using established image-level techniques, then propagate the changes to other frames. Our approach can leverage state-of-the-art image models for keyframe manipulation, thereby easing the burden of the video-inpainting process. To this end, we integrate an image-to-video model with a symmetric condition mechanism to address ambiguity caused by direct mask downsampling. We further explore efficient strategies for mask synthesis and parameter tuning to reduce costs in data processing and model training. Evaluations demonstrate our method achieves superior results in both visual fidelity and content diversity compared to existing approaches, providing a practical solution for creative video manipulation. | [
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2,851 | Channel Consistency Prior and Self-Reconstruction Strategy Based Unsupervised Image Deraining | [
"Guanglu Dong",
"Tianheng Zheng",
"Yuanzhouhan Cao",
"Linbo Qing",
"Chao Ren"
] | https://openaccess.thecvf.com/content/CVPR2025/html/Dong_Channel_Consistency_Prior_and_Self-Reconstruction_Strategy_Based_Unsupervised_Image_Deraining_CVPR_2025_paper.html | https://openaccess.thecvf.com/content/CVPR2025/papers/Dong_Channel_Consistency_Prior_and_Self-Reconstruction_Strategy_Based_Unsupervised_Image_Deraining_CVPR_2025_paper.pdf | https://openaccess.thecvf.com/content/CVPR2025/supplemental/Dong_Channel_Consistency_Prior_CVPR_2025_supplemental.pdf | http://arxiv.org/abs/2503.18703 | @InProceedings{Dong_2025_CVPR,
author = {Dong, Guanglu and Zheng, Tianheng and Cao, Yuanzhouhan and Qing, Linbo and Ren, Chao},
title = {Channel Consistency Prior and Self-Reconstruction Strategy Based Unsupervised Image Deraining},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {7469-7479}
} | Recently, deep image deraining models based on paired datasets have made a series of remarkable progress. However, they cannot be well applied in real-world applications due to the difficulty of obtaining real paired datasets and the poor generalization performance. In this paper, we propose a novel Channel Consistency Prior and Self-Reconstruction Strategy Based Unsupervised Image Deraining framework, CSUD, to tackle the aforementioned challenges. During training with unpaired data, CSUD is capable of generating high-quality pseudo clean and rainy image pairs which are used to enhance the performance of deraining network. Specifically, to preserve more image background details while transferring rain streaks from rainy images to the unpaired clean images, we propose a novel Channel Consistency Loss (CCLoss) by introducing the Channel Consistency Prior (CCP) of rain streaks into training process, thereby ensuring that the generated pseudo rainy images closely resemble the real ones. Furthermore, we propose a novel Self-Reconstruction (SR) strategy to alleviate the redundant information transfer problem of the generator, further improving the deraining performance and the generalization capability of our method. Extensive experiments on multiple synthetic and real-world datasets demonstrate that the deraining performance of CSUD surpasses other state-of-the-art unsupervised methods and CSUD exhibits superior generalization capability. Code is available at https://github.com/GuangluDong0728/CSUD. | [
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2,852 | MobileMamba: Lightweight Multi-Receptive Visual Mamba Network | [
"Haoyang He",
"Jiangning Zhang",
"Yuxuan Cai",
"Hongxu Chen",
"Xiaobin Hu",
"Zhenye Gan",
"Yabiao Wang",
"Chengjie Wang",
"Yunsheng Wu",
"Lei Xie"
] | https://openaccess.thecvf.com/content/CVPR2025/html/He_MobileMamba_Lightweight_Multi-Receptive_Visual_Mamba_Network_CVPR_2025_paper.html | https://openaccess.thecvf.com/content/CVPR2025/papers/He_MobileMamba_Lightweight_Multi-Receptive_Visual_Mamba_Network_CVPR_2025_paper.pdf | https://openaccess.thecvf.com/content/CVPR2025/supplemental/He_MobileMamba_Lightweight_Multi-Receptive_CVPR_2025_supplemental.zip | http://arxiv.org/abs/2411.15941 | @InProceedings{He_2025_CVPR,
author = {He, Haoyang and Zhang, Jiangning and Cai, Yuxuan and Chen, Hongxu and Hu, Xiaobin and Gan, Zhenye and Wang, Yabiao and Wang, Chengjie and Wu, Yunsheng and Xie, Lei},
title = {MobileMamba: Lightweight Multi-Receptive Visual Mamba Network},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {4497-4507}
} | Previous research on lightweight models has primarily focused on CNNs and Transformer-based designs. CNNs, with their local receptive fields, struggle to capture long-range dependencies, while Transformers, despite their global modeling capabilities, are limited by quadratic computational complexity in high-resolution scenarios. Recently, state-space models have gained popularity in the visual domain due to their linear computational complexity. Despite their low FLOPs, current lightweight Mamba-based models exhibit suboptimal throughput. In this work, we propose the MobileMamba framework, which balances efficiency and performance. We design a three-stage network to enhance inference speed significantly. At a fine-grained level, we introduce the Multi-Receptive Field Feature Interaction (MRFFI) module, comprising the Long-Range Wavelet Transform-Enhanced Mamba (WTE-Mamba), Efficient Multi-Kernel Depthwise Deconvolution (MK-DeConv), and Eliminate Redundant Identity components. This module integrates multi-receptive field information and enhances high-frequency detail extraction. Additionally, we employ training and testing strategies to further improve performance and efficiency. MobileMamba achieves up to 83.6% on Top-1, surpassing existing state-of-the-art methods which is maximum x21| faster than LocalVim on GPU. Extensive experiments on high-resolution downstream tasks demonstrate that MobileMamba surpasses current efficient models, achieving an optimal balance between speed and accuracy. | [
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2,853 | EdgeTAM: On-Device Track Anything Model | [
"Chong Zhou",
"Chenchen Zhu",
"Yunyang Xiong",
"Saksham Suri",
"Fanyi Xiao",
"Lemeng Wu",
"Raghuraman Krishnamoorthi",
"Bo Dai",
"Chen Change Loy",
"Vikas Chandra",
"Bilge Soran"
] | https://openaccess.thecvf.com/content/CVPR2025/html/Zhou_EdgeTAM_On-Device_Track_Anything_Model_CVPR_2025_paper.html | https://openaccess.thecvf.com/content/CVPR2025/papers/Zhou_EdgeTAM_On-Device_Track_Anything_Model_CVPR_2025_paper.pdf | https://openaccess.thecvf.com/content/CVPR2025/supplemental/Zhou_EdgeTAM_On-Device_Track_CVPR_2025_supplemental.zip | http://arxiv.org/abs/2501.07256 | @InProceedings{Zhou_2025_CVPR,
author = {Zhou, Chong and Zhu, Chenchen and Xiong, Yunyang and Suri, Saksham and Xiao, Fanyi and Wu, Lemeng and Krishnamoorthi, Raghuraman and Dai, Bo and Loy, Chen Change and Chandra, Vikas and Soran, Bilge},
title = {EdgeTAM: On-Device Track Anything Model},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {13832-13842}
} | On top of Segment Anything Model (SAM), SAM 2 further extends its capability from image to video inputs through a memory bank mechanism and obtains a remarkable performance compared with previous methods, making it a foundation model for video segmentation task. In this paper, we aim at making SAM 2 much more efficient so that it even runs on mobile devices while maintaining a comparable performance. Despite several works optimizing SAM for better efficiency, we find they are not sufficient for SAM 2 because they all focus on compressing the image encoder, while our benchmark shows that the newly introduced memory attention blocks are also the latency bottleneck. Given this observation, we propose EdgeTAM, which leverages a novel 2D Spatial Perceiver to reduce the computational cost. In particular, the proposed 2D Spatial Perceiver encodes the densely stored frame-level memories with a lightweight Transformer that contains a fixed set of learnable queries. Given that video segmentation is a dense prediction task, we find preserving the spatial structure of the memories is essential so that the queries are split into global-level and patch-level groups. We also propose a distillation pipeline that further improves the performance without inference overhead. As a result, EdgeTAM achieves 87.7, 70.0, 72.3, and 71.7 J&F on DAVIS 2017, MOSE, SA-V val, and SA-V test, while running at 16 FPS on iPhone 15 Pro Max. The code and models are available at https://github.com/facebookresearch/EdgeTAM. | [
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2,854 | SimLTD: Simple Supervised and Semi-Supervised Long-Tailed Object Detection | [
"Phi Vu Tran"
] | https://openaccess.thecvf.com/content/CVPR2025/html/Tran_SimLTD_Simple_Supervised_and_Semi-Supervised_Long-Tailed_Object_Detection_CVPR_2025_paper.html | https://openaccess.thecvf.com/content/CVPR2025/papers/Tran_SimLTD_Simple_Supervised_and_Semi-Supervised_Long-Tailed_Object_Detection_CVPR_2025_paper.pdf | null | http://arxiv.org/abs/2412.20047 | @InProceedings{Tran_2025_CVPR,
author = {Tran, Phi Vu},
title = {SimLTD: Simple Supervised and Semi-Supervised Long-Tailed Object Detection},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {4672-4681}
} | While modern visual recognition systems have made significant advancements, many continue to struggle with the open problem of learning from few exemplars. This paper focuses on the task of object detection in the setting where object classes follow a natural long-tailed distribution. Existing methods for long-tailed detection resort to external ImageNet labels to augment the low-shot training instances. However, such dependency on a large labeled database has limited utility in practical scenarios. We propose a versatile and scalable approach to leverage optional unlabeled images, which are easy to collect without the burden of human annotations. Our SimLTD framework is straightforward and intuitive, and consists of three simple steps: (1) pre-training on abundant head classes; (2) transfer learning on scarce tail classes; and (3) fine-tuning on a sampled set of both head and tail classes. Our approach can be viewed as an improved head-to-tail model transfer paradigm without the added complexities of meta-learning or knowledge distillation, as was required in past research. By harnessing supplementary unlabeled images, without extra image labels, SimLTD establishes new record results on the challenging LVIS v1 benchmark across both supervised and semi-supervised settings. | [
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2,855 | EarthDial: Turning Multi-sensory Earth Observations to Interactive Dialogues | [
"Sagar Soni",
"Akshay Dudhane",
"Hiyam Debary",
"Mustansar Fiaz",
"Muhammad Akhtar Munir",
"Muhammad Sohail Danish",
"Paolo Fraccaro",
"Campbell D Watson",
"Levente J Klein",
"Fahad Shahbaz Khan",
"Salman Khan"
] | https://openaccess.thecvf.com/content/CVPR2025/html/Soni_EarthDial_Turning_Multi-sensory_Earth_Observations_to_Interactive_Dialogues_CVPR_2025_paper.html | https://openaccess.thecvf.com/content/CVPR2025/papers/Soni_EarthDial_Turning_Multi-sensory_Earth_Observations_to_Interactive_Dialogues_CVPR_2025_paper.pdf | https://openaccess.thecvf.com/content/CVPR2025/supplemental/Soni_EarthDial_Turning_Multi-sensory_CVPR_2025_supplemental.pdf | http://arxiv.org/abs/2412.15190 | @InProceedings{Soni_2025_CVPR,
author = {Soni, Sagar and Dudhane, Akshay and Debary, Hiyam and Fiaz, Mustansar and Munir, Muhammad Akhtar and Danish, Muhammad Sohail and Fraccaro, Paolo and Watson, Campbell D and Klein, Levente J and Khan, Fahad Shahbaz and Khan, Salman},
title = {EarthDial: Turning Multi-sensory Earth Observations to Interactive Dialogues},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {14303-14313}
} | Automated analysis of vast Earth observation data via interactive Vision-Language Models (VLMs) can unlock new opportunities for environmental monitoring, disaster response, and resource management. Existing generic VLMs do not perform well on Remote Sensing data, while the recent Geo-spatial VLMs remain restricted to a fixed resolution and few sensor modalities. In this paper, we introduce EarthDial, a conversational assistant specifically designed for Earth Observation (EO) data, transforming complex, multi-sensory Earth observations into interactive, natural language dialogues. EarthDial supports multispectral, multi-temporal, and multi-resolution imagery, enabling a wide range of remote sensing tasks, including classification, detection, captioning, question answering, visual reasoning, and visual grounding. To achieve this, we introduce an extensive instruction tuning dataset comprising over 11.11M instruction pairs covering RGB, Synthetic Aperture Radar (SAR), and multispectral modalities such as Near-Infrared (NIR) and infrared. Furthermore, EarthDial handles bi-temporal and multi-temporal sequence analysis for applications like change detection. Our extensive experimental results on 44 downstream datasets demonstrate that EarthDial outperforms existing generic and domain specific models, achieving better generalization across various EO tasks. Our source codes and pre-trained models are at https://github.com/hiyamdebary/EarthDial. | [
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] |
2,856 | Learning Endogenous Attention for Incremental Object Detection | [
"Xiang Song",
"Yuhang He",
"Jingyuan Li",
"Qiang Wang",
"Yihong Gong"
] | https://openaccess.thecvf.com/content/CVPR2025/html/Song_Learning_Endogenous_Attention_for_Incremental_Object_Detection_CVPR_2025_paper.html | https://openaccess.thecvf.com/content/CVPR2025/papers/Song_Learning_Endogenous_Attention_for_Incremental_Object_Detection_CVPR_2025_paper.pdf | https://openaccess.thecvf.com/content/CVPR2025/supplemental/Song_Learning_Endogenous_Attention_CVPR_2025_supplemental.pdf | null | @InProceedings{Song_2025_CVPR,
author = {Song, Xiang and He, Yuhang and Li, Jingyuan and Wang, Qiang and Gong, Yihong},
title = {Learning Endogenous Attention for Incremental Object Detection},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {30354-30364}
} | In this paper, we focus on a challenging Incremental Object Detection (IOD) problem. Existing IOD methods follow an image-to-annotation alignment paradigm, which attempts to complete the annotations for old categories and subsequently learns both new and old categories in new tasks. This paradigm inherently introduces missing/redundant/inaccurate annotations of old categories, resulting in a suboptimal performance. Instead, we propose a novel annotation-to-instance alignment IOD paradigm and develop a corresponding method named Learning Endogenous Attention (LEA). Inspired by the human brain, LEA enables the model to focus on annotated task-specific objects, while ignoring irrelevant ones, thus solving the annotation incomplete problem in IOD. Concretely, our LEA consists of Endogenous Attention Modules (EAMs) and an Energy-based Task Modulator (ETM). During training, we add the dedicated EAMs for each new task and train them to focus on the new categories. During testing, ETM predicts task IDs using energy functions, directing the model to detect task-specific objects. The detection results corresponding to all task IDs are combined as the final output, thereby alleviating the catastrophic forgetting of old knowledge. Extensive experiments on COCO 2017 and Pascal VOC 2007 demonstrate the effectiveness of our method. | [
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2,857 | StarGen: A Spatiotemporal Autoregression Framework with Video Diffusion Model for Scalable and Controllable Scene Generation | [
"Shangjin Zhai",
"Zhichao Ye",
"Jialin Liu",
"Weijian Xie",
"Jiaqi Hu",
"Zhen Peng",
"Hua Xue",
"Danpeng Chen",
"Xiaomeng Wang",
"Lei Yang",
"Nan Wang",
"Haomin Liu",
"Guofeng Zhang"
] | https://openaccess.thecvf.com/content/CVPR2025/html/Zhai_StarGen_A_Spatiotemporal_Autoregression_Framework_with_Video_Diffusion_Model_for_CVPR_2025_paper.html | https://openaccess.thecvf.com/content/CVPR2025/papers/Zhai_StarGen_A_Spatiotemporal_Autoregression_Framework_with_Video_Diffusion_Model_for_CVPR_2025_paper.pdf | https://openaccess.thecvf.com/content/CVPR2025/supplemental/Zhai_StarGen_A_Spatiotemporal_CVPR_2025_supplemental.zip | http://arxiv.org/abs/2501.05763 | @InProceedings{Zhai_2025_CVPR,
author = {Zhai, Shangjin and Ye, Zhichao and Liu, Jialin and Xie, Weijian and Hu, Jiaqi and Peng, Zhen and Xue, Hua and Chen, Danpeng and Wang, Xiaomeng and Yang, Lei and Wang, Nan and Liu, Haomin and Zhang, Guofeng},
title = {StarGen: A Spatiotemporal Autoregression Framework with Video Diffusion Model for Scalable and Controllable Scene Generation},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {26822-26833}
} | Recent advances in large reconstruction and generative models have significantly improved scene reconstruction and novel view generation. However, due to compute limitations, each inference with these large models is confined to a small area, making long-range consistent scene generation challenging. To address this, we propose StarGen, a novel framework that employs a pre-trained video diffusion model in an autoregressive manner for long-range scene generation. The generation of each video clip is conditioned on the 3D warping of spatially adjacent images and the temporally overlapping image from previously generated clips, improving spatiotemporal consistency in long-range scene generation with precise pose control. The spatiotemporal condition is compatible with various input conditions, facilitating diverse tasks, including sparse view interpolation, perpetual view generation, and layout-conditioned city generation. Quantitative and qualitative evaluations demonstrate StarGen's superior scalability, fidelity, and pose accuracy compared to state-of-the-art methods. | [
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2,858 | HyperSeg: Hybrid Segmentation Assistant with Fine-grained Visual Perceiver | [
"Cong Wei",
"Yujie Zhong",
"Haoxian Tan",
"Yong Liu",
"Jie Hu",
"Dengjie Li",
"Zheng Zhao",
"Yujiu Yang"
] | https://openaccess.thecvf.com/content/CVPR2025/html/Wei_HyperSeg_Hybrid_Segmentation_Assistant_with_Fine-grained_Visual_Perceiver_CVPR_2025_paper.html | https://openaccess.thecvf.com/content/CVPR2025/papers/Wei_HyperSeg_Hybrid_Segmentation_Assistant_with_Fine-grained_Visual_Perceiver_CVPR_2025_paper.pdf | https://openaccess.thecvf.com/content/CVPR2025/supplemental/Wei_HyperSeg_Hybrid_Segmentation_CVPR_2025_supplemental.pdf | null | @InProceedings{Wei_2025_CVPR,
author = {Wei, Cong and Zhong, Yujie and Tan, Haoxian and Liu, Yong and Hu, Jie and Li, Dengjie and Zhao, Zheng and Yang, Yujiu},
title = {HyperSeg: Hybrid Segmentation Assistant with Fine-grained Visual Perceiver},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {8931-8941}
} | This paper aims to address universal segmentation for image and video perception with the strong reasoning ability empowered by Visual Large Language Models (VLLMs). Despite significant progress in current unified segmentation methods, limitations in adaptation to both image and video scenarios, as well as the complex reasoning segmentation, make it difficult for them to handle various challenging instructions and achieve an accurate understanding of fine-grained vision-language correlations. We propose HyperSeg, the first VLLM-based universal segmentation model for pixel-level image and video perception, encompassing generic segmentation tasks and more complex reasoning perception tasks requiring powerful reasoning abilities and world knowledge. Besides, to fully leverage the recognition capabilities of VLLMs and the fine-grained visual information, HyperSeg incorporates hybrid entity recognition and fine-grained visual perceiver modules for various segmentation tasks. Combined with the temporal adapter, HyperSeg achieves a comprehensive understanding of temporal information. Experimental results validate the effectiveness of our insights in resolving universal image and video segmentation tasks, including the more complex reasoning perception tasks. Our code is available at https://github.com/congvvc/HyperSeg. | [
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2,859 | Diffusion-based Event Generation for High-Quality Image Deblurring | [
"Xinan Xie",
"Qing Zhang",
"Wei-Shi Zheng"
] | https://openaccess.thecvf.com/content/CVPR2025/html/Xie_Diffusion-based_Event_Generation_for_High-Quality_Image_Deblurring_CVPR_2025_paper.html | https://openaccess.thecvf.com/content/CVPR2025/papers/Xie_Diffusion-based_Event_Generation_for_High-Quality_Image_Deblurring_CVPR_2025_paper.pdf | https://openaccess.thecvf.com/content/CVPR2025/supplemental/Xie_Diffusion-based_Event_Generation_CVPR_2025_supplemental.pdf | null | @InProceedings{Xie_2025_CVPR,
author = {Xie, Xinan and Zhang, Qing and Zheng, Wei-Shi},
title = {Diffusion-based Event Generation for High-Quality Image Deblurring},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {2194-2203}
} | While event-based deblurring have demonstrated impressive results, they are impractical for consumer photos captured by cell phones and digital cameras that are not equipped with the event sensor. To address this problem, we in this paper propose a novel deblurring framework called Event Generation Deblurring (EGDeblurring), which allows to effectively deblur an image by generating event guidance describing the motion information using a diffusion model. Specifically, we design a motion prior generation diffusion model and a feature extractor to produce prior information beneficial for deblurring, rather than generating the raw event representation. In order to achieve effective fusion of motion prior information with blurry images and produce high-quality results, we develop a regression deblurring network embedded with a dual-attention channel fusion block. Experiments on multiple datasets demonstrate that our method outperforms state-of-the-art image deblurring methods. Our code is available at https://github.com/XinanXie/EGDeblurring https://github.com/XinanXie/EGDeblurring. | [
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] |
2,860 | Video Summarization with Large Language Models | [
"Min Jung Lee",
"Dayoung Gong",
"Minsu Cho"
] | https://openaccess.thecvf.com/content/CVPR2025/html/Lee_Video_Summarization_with_Large_Language_Models_CVPR_2025_paper.html | https://openaccess.thecvf.com/content/CVPR2025/papers/Lee_Video_Summarization_with_Large_Language_Models_CVPR_2025_paper.pdf | https://openaccess.thecvf.com/content/CVPR2025/supplemental/Lee_Video_Summarization_with_CVPR_2025_supplemental.pdf | http://arxiv.org/abs/2504.11199 | @InProceedings{Lee_2025_CVPR,
author = {Lee, Min Jung and Gong, Dayoung and Cho, Minsu},
title = {Video Summarization with Large Language Models},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {18981-18991}
} | The exponential increase in video content poses significant challenges in terms of efficient navigation, search, and retrieval, thus requiring advanced video summarization techniques. Existing video summarization methods, which heavily rely on visual features and temporal dynamics, often fail to capture the semantics of video content, resulting in incomplete or incoherent summaries. To tackle the challenge, we propose a new video summarization framework that leverages the capabilities of recent Large Language Models (LLMs), expecting that the knowledge learned from massive data enables LLMs to evaluate video frames in a manner that better aligns with diverse semantics and human judgments, effectively addressing the inherent subjectivity in defining keyframes. Our method, dubbed LLM-based Video Summarization (LLMVS), translates video frames into a sequence of captions using a Muti-modal Large Language Model (M-LLM) and then assesses the importance of each frame using an LLM, based on the captions in its local context. These local importance scores are refined through a global attention mechanism in the entire context of video captions, ensuring that our summaries effectively reflect both the details and the overarching narrative. Our experimental results demonstrate the superiority of the proposed method over existing ones in standard benchmarks, highlighting the potential of LLMs in the processing of multimedia content. | [
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] |
2,861 | Sketchtopia: A Dataset and Foundational Agents for Benchmarking Asynchronous Multimodal Communication with Iconic Feedback | [
"Mohd Hozaifa Khan",
"Ravi Kiran Sarvadevabhatla"
] | https://openaccess.thecvf.com/content/CVPR2025/html/Khan_Sketchtopia_A_Dataset_and_Foundational_Agents_for_Benchmarking_Asynchronous_Multimodal_CVPR_2025_paper.html | https://openaccess.thecvf.com/content/CVPR2025/papers/Khan_Sketchtopia_A_Dataset_and_Foundational_Agents_for_Benchmarking_Asynchronous_Multimodal_CVPR_2025_paper.pdf | https://openaccess.thecvf.com/content/CVPR2025/supplemental/Khan_Sketchtopia_A_Dataset_CVPR_2025_supplemental.pdf | null | @InProceedings{Khan_2025_CVPR,
author = {Khan, Mohd Hozaifa and Sarvadevabhatla, Ravi Kiran},
title = {Sketchtopia: A Dataset and Foundational Agents for Benchmarking Asynchronous Multimodal Communication with Iconic Feedback},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {18176-18186}
} | We introduce Sketchtopia, a large-scale dataset and AI framework designed to explore goal-driven, multimodal communication through asynchronous interactions in a Pictionary-inspired setup. Sketchtopia captures natural human interactions, including freehand sketches, open-ended guesses, and iconic feedback gestures, showcasing the complex dynamics of cooperative communication under constraints. It features over 20K gameplay sessions from 916 players, capturing 263K sketches, 10K erases, 56K guesses and 19.4K iconic feedbacks. We introduce multimodal foundational agents with capabilities for generative sketching, guess generation and asynchronous communication. Our dataset also includes 800 human-agent sessions for benchmarking the agents. We introduce novel metrics to characterize collaborative success, responsiveness to feedback and inter-agent asynchronous communication. Sketchtopia pushes the boundaries of multimodal AI, establishing a new benchmark for studying asynchronous, goal-oriented interactions between humans and AI agents. The dataset can be found at https://sketchtopia25.github.io/ | [
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2,862 | Consistency-aware Self-Training for Iterative-based Stereo Matching | [
"Jingyi Zhou",
"Peng Ye",
"Haoyu Zhang",
"Jiakang Yuan",
"Rao Qiang",
"Liu YangChenXu",
"Wu Cailin",
"Feng Xu",
"Tao Chen"
] | https://openaccess.thecvf.com/content/CVPR2025/html/Zhou_Consistency-aware_Self-Training_for_Iterative-based_Stereo_Matching_CVPR_2025_paper.html | https://openaccess.thecvf.com/content/CVPR2025/papers/Zhou_Consistency-aware_Self-Training_for_Iterative-based_Stereo_Matching_CVPR_2025_paper.pdf | null | http://arxiv.org/abs/2503.23747 | @InProceedings{Zhou_2025_CVPR,
author = {Zhou, Jingyi and Ye, Peng and Zhang, Haoyu and Yuan, Jiakang and Qiang, Rao and YangChenXu, Liu and Cailin, Wu and Xu, Feng and Chen, Tao},
title = {Consistency-aware Self-Training for Iterative-based Stereo Matching},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {16641-16650}
} | Iterative-based methods have become mainstream in stereo matching due to their high performance. However, these methods heavily rely on labeled data and face challenges with unlabeled real-world data. To this end, we propose a consistency-aware self-training framework for iterative-based stereo matching for the first time, leveraging real-world unlabeled data in a teacher-student manner. We first observe that regions with larger errors tend to exhibit more pronounced oscillation characteristics during model prediction.Based on this, we introduce a novel consistency-aware soft filtering module to evaluate the reliability of teacher-predicted pseudo-labels, which consists of a multi-resolution prediction consistency filter and an iterative prediction consistency filter to assess the prediction fluctuations of multiple resolutions and iterative optimization respectively. Further, we introduce a consistency-aware soft-weighted loss to adjust the weight of pseudo-labels accordingly, relieving the error accumulation and performance degradation problem due to incorrect pseudo-labels. Extensive experiments demonstrate that our method can improve the performance of various iterative-based stereo matching approaches in various scenarios. In particular, our method can achieve further enhancements over the current SOTA methods on several benchmark datasets. | [
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2,863 | MV-MATH: Evaluating Multimodal Math Reasoning in Multi-Visual Contexts | [
"Peijie Wang",
"Zhong-Zhi Li",
"Fei Yin",
"Dekang Ran",
"Cheng-Lin Liu"
] | https://openaccess.thecvf.com/content/CVPR2025/html/Wang_MV-MATH_Evaluating_Multimodal_Math_Reasoning_in_Multi-Visual_Contexts_CVPR_2025_paper.html | https://openaccess.thecvf.com/content/CVPR2025/papers/Wang_MV-MATH_Evaluating_Multimodal_Math_Reasoning_in_Multi-Visual_Contexts_CVPR_2025_paper.pdf | https://openaccess.thecvf.com/content/CVPR2025/supplemental/Wang_MV-MATH_Evaluating_Multimodal_CVPR_2025_supplemental.pdf | null | @InProceedings{Wang_2025_CVPR,
author = {Wang, Peijie and Li, Zhong-Zhi and Yin, Fei and Ran, Dekang and Liu, Cheng-Lin},
title = {MV-MATH: Evaluating Multimodal Math Reasoning in Multi-Visual Contexts},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {19541-19551}
} | Multimodal Large Language Models (MLLMs) have shown promising capabilities in mathematical reasoning within visual contexts across various datasets. However, most existing multimodal math benchmarks are limited to single-visual contexts, which diverges from the multi-visual scenarios commonly encountered in real-world mathematical applications. To address this gap, we introduce MV-MATH: a meticulously curated dataset of 2,009 high-quality mathematical problems. Each problem integrates multiple images interleaved with text, derived from authentic K-12 scenarios and enriched with detailed annotations. MV-MATH includes multiple-choice, free-form, and multi-step questions, covering 11 subject areas across 3 difficulty levels, and serves as a comprehensive and rigorous benchmark for assessing MLLMs' mathematical reasoning in multi-visual contexts. Through extensive experimentation, we observe that MLLMs encounter substantial challenges in multi-visual math tasks, with a considerable performance gap relative to human capabilities on MV-MATH. Furthermore, we analyze the performance and error patterns of various models, providing insights into MLLMs' mathematical reasoning capabilities within multi-visual settings. | [
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2,864 | Balanced Rate-Distortion Optimization in Learned Image Compression | [
"Yichi Zhang",
"Zhihao Duan",
"Yuning Huang",
"Fengqing Zhu"
] | https://openaccess.thecvf.com/content/CVPR2025/html/Zhang_Balanced_Rate-Distortion_Optimization_in_Learned_Image_Compression_CVPR_2025_paper.html | https://openaccess.thecvf.com/content/CVPR2025/papers/Zhang_Balanced_Rate-Distortion_Optimization_in_Learned_Image_Compression_CVPR_2025_paper.pdf | null | http://arxiv.org/abs/2502.20161 | @InProceedings{Zhang_2025_CVPR,
author = {Zhang, Yichi and Duan, Zhihao and Huang, Yuning and Zhu, Fengqing},
title = {Balanced Rate-Distortion Optimization in Learned Image Compression},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {2428-2438}
} | Learned image compression (LIC) using deep learning architectures has seen significant advancements, yet standard rate-distortion (R-D) optimization often encounters imbalanced updates due to diverse gradients of the rate and distortion objectives. This imbalance can lead to suboptimal optimization, where one objective dominates, thereby reducing overall compression efficiency. To address this challenge, we reformulate R-D optimization as a multi-objective optimization (MOO) problem and introduce two balanced R-D optimization strategies that adaptively adjust gradient updates to achieve more equitable improvements in both rate and distortion. The first proposed strategy utilizes a coarse-to-fine gradient descent approach along standard R-D optimization trajectories, making it particularly suitable for training LIC models from scratch. The second proposed strategy analytically addresses the reformulated optimization as a quadratic programming problem with an equality constraint, which is ideal for fine-tuning existing models. Experimental results demonstrate that both proposed methods enhance the R-D performance of LIC models, achieving around a 2% BD-Rate reduction with acceptable additional training cost, leading to a more balanced and efficient optimization process. Code will be available at https://gitlab.com/viper-purdue/Balanced-RD. | [
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2,865 | Bridge the Gap: From Weak to Full Supervision for Temporal Action Localization with PseudoFormer | [
"Ziyi Liu",
"Yangcen Liu"
] | https://openaccess.thecvf.com/content/CVPR2025/html/Liu_Bridge_the_Gap_From_Weak_to_Full_Supervision_for_Temporal_CVPR_2025_paper.html | https://openaccess.thecvf.com/content/CVPR2025/papers/Liu_Bridge_the_Gap_From_Weak_to_Full_Supervision_for_Temporal_CVPR_2025_paper.pdf | https://openaccess.thecvf.com/content/CVPR2025/supplemental/Liu_Bridge_the_Gap_CVPR_2025_supplemental.pdf | http://arxiv.org/abs/2504.14860 | @InProceedings{Liu_2025_CVPR,
author = {Liu, Ziyi and Liu, Yangcen},
title = {Bridge the Gap: From Weak to Full Supervision for Temporal Action Localization with PseudoFormer},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {8711-8720}
} | Weakly-supervised Temporal Action Localization (WTAL) has achieved notable success but still suffers from a lack of temporal annotations, leading to a performance and framework gap compared with fully-supervised methods. While recent approaches employ pseudo labels for training, three key challenges: generating high-quality pseudo labels, making full use of different priors, and optimizing training methods with noisy labels remain unresolved. Due to these perspectives, we propose PseudoFormer, a novel two-branch framework that bridges the gap between weakly and fully-supervised Temporal Action Localization (TAL). We first introduce RickerFusion, which maps all predicted action proposals to a global shared space to generate pseudo labels with better quality. Subsequently, we leverage both snippet-level and proposal-level labels with different priors from the weak branch to train the regression-based model in the full branch. Finally, the uncertainty mask and iterative refinement mechanism are applied for training with noisy pseudo labels. PseudoFormer achieves state-of-the-art WTAL results on the two commonly used benchmarks, THUMOS14 and ActivityNet1.3. Besides, extensive ablation studies demonstrate the contribution of each component of our method. | [
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2,866 | HomoGen: Enhanced Video Inpainting via Homography Propagation and Diffusion | [
"Ding Ding",
"Yueming Pan",
"Ruoyu Feng",
"Qi Dai",
"Kai Qiu",
"Jianmin Bao",
"Chong Luo",
"Zhenzhong Chen"
] | https://openaccess.thecvf.com/content/CVPR2025/html/Ding_HomoGen_Enhanced_Video_Inpainting_via_Homography_Propagation_and_Diffusion_CVPR_2025_paper.html | https://openaccess.thecvf.com/content/CVPR2025/papers/Ding_HomoGen_Enhanced_Video_Inpainting_via_Homography_Propagation_and_Diffusion_CVPR_2025_paper.pdf | https://openaccess.thecvf.com/content/CVPR2025/supplemental/Ding_HomoGen_Enhanced_Video_CVPR_2025_supplemental.pdf | null | @InProceedings{Ding_2025_CVPR,
author = {Ding, Ding and Pan, Yueming and Feng, Ruoyu and Dai, Qi and Qiu, Kai and Bao, Jianmin and Luo, Chong and Chen, Zhenzhong},
title = {HomoGen: Enhanced Video Inpainting via Homography Propagation and Diffusion},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {22953-22962}
} | In this paper, we present HomoGen, an enhanced video inpainting method based on homography propagation and diffusion models. HomoGen leverages homography registration to propagate contextual pixels as priors for generating missing content in corrupted videos. Unlike previous flow-based propagation methods, which introduce local distortions due to point-to-point optical flows, homography-induced artifacts are typically global structural distortions that preserve semantic integrity. To effectively utilize these priors for generation, we employ a video diffusion model that inherently prioritizes semantic information within the priors over pixel-level details. A content-adaptive control mechanism is proposed to scale and inject the priors into intermediate video latents during iterative denoising. In contrast to existing transformer-based networks that often suffer from artifacts within priors, leading to error accumulation and unrealistic results, our denoising diffusion network can smooth out artifacts and ensure natural outputs. Extensive experiments demonstrate the effectiveness of the proposed method qualitatively and quantitatively. | [
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2,867 | Generalized Few-shot 3D Point Cloud Segmentation with Vision-Language Model | [
"Zhaochong An",
"Guolei Sun",
"Yun Liu",
"Runjia Li",
"Junlin Han",
"Ender Konukoglu",
"Serge Belongie"
] | https://openaccess.thecvf.com/content/CVPR2025/html/An_Generalized_Few-shot_3D_Point_Cloud_Segmentation_with_Vision-Language_Model_CVPR_2025_paper.html | https://openaccess.thecvf.com/content/CVPR2025/papers/An_Generalized_Few-shot_3D_Point_Cloud_Segmentation_with_Vision-Language_Model_CVPR_2025_paper.pdf | https://openaccess.thecvf.com/content/CVPR2025/supplemental/An_Generalized_Few-shot_3D_CVPR_2025_supplemental.pdf | http://arxiv.org/abs/2503.16282 | @InProceedings{An_2025_CVPR,
author = {An, Zhaochong and Sun, Guolei and Liu, Yun and Li, Runjia and Han, Junlin and Konukoglu, Ender and Belongie, Serge},
title = {Generalized Few-shot 3D Point Cloud Segmentation with Vision-Language Model},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {16997-17007}
} | Generalized few-shot 3D point cloud segmentation (GFS-PCS) adapts models to new classes with few support samples while retaining base class segmentation. Existing GFS-PCS methods enhance prototypes via interacting with support or query features but remain limited by sparse knowledge from few-shot samples. Meanwhile, 3D vision-language models (3D VLMs), generalizing across open-world novel classes, contain rich but noisy novel class knowledge. In this work, we introduce a GFS-PCS framework that synergizes dense but noisy pseudo-labels from 3D VLMs with precise yet sparse few-shot samples to maximize the strengths of both, named GFS-VL. Specifically, we present a prototype-guided pseudo-label selection to filter low-quality regions, followed by an adaptive infilling strategy that combines knowledge from pseudo-label contexts and few-shot samples to adaptively label the filtered, unlabeled areas. Additionally, we design a novel-base mix strategy to embed few-shot samples into training scenes, preserving essential context for improved novel class learning. Moreover, recognizing the limited diversity in current GFS-PCS benchmarks, we introduce two challenging benchmarks with diverse novel classes for comprehensive generalization evaluation. Experiments validate the effectiveness of our framework across models and datasets. Our approach and benchmarks provide a solid foundation for advancing GFS-PCS in the real world. The code is at here. | [
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] |
2,868 | Do ImageNet-trained Models Learn Shortcuts? The Impact of Frequency Shortcuts on Generalization | [
"Shunxin Wang",
"Raymond Veldhuis",
"Nicola Strisciuglio"
] | https://openaccess.thecvf.com/content/CVPR2025/html/Wang_Do_ImageNet-trained_Models_Learn_Shortcuts_The_Impact_of_Frequency_Shortcuts_CVPR_2025_paper.html | https://openaccess.thecvf.com/content/CVPR2025/papers/Wang_Do_ImageNet-trained_Models_Learn_Shortcuts_The_Impact_of_Frequency_Shortcuts_CVPR_2025_paper.pdf | https://openaccess.thecvf.com/content/CVPR2025/supplemental/Wang_Do_ImageNet-trained_Models_CVPR_2025_supplemental.pdf | http://arxiv.org/abs/2503.03519 | @InProceedings{Wang_2025_CVPR,
author = {Wang, Shunxin and Veldhuis, Raymond and Strisciuglio, Nicola},
title = {Do ImageNet-trained Models Learn Shortcuts? The Impact of Frequency Shortcuts on Generalization},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {25198-25207}
} | Frequency shortcuts refer to specific frequency patterns that models heavily rely on for correct classification. Previous studies have shown that models trained on small image datasets often exploit such shortcuts, potentially impairing their generalization performance. However, existing methods for identifying frequency shortcuts require expensive computations and become impractical for analyzing models trained on large datasets. In this work, we propose the first approach to more efficiently analyze frequency shortcuts at a large scale. We show that both CNN and transformer models learn frequency shortcuts on ImageNet. We also expose that frequency shortcut solutions can yield good performance on out-of-distribution (OOD) test sets which largely retain texture information. However, these shortcuts, mostly aligned with texture patterns, hinder model generalization on rendition-based OOD test sets. These observations suggest that current OOD evaluations often overlook the impact of frequency shortcuts on model generalization. Future benchmarks could thus benefit from explicitly assessing and accounting for these shortcuts to build models that generalize across a broader range of OOD scenarios. | [
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2,869 | HORP: Human-Object Relation Priors Guided HOI Detection | [
"Pei Geng",
"Jian Yang",
"Shanshan Zhang"
] | https://openaccess.thecvf.com/content/CVPR2025/html/Geng_HORP_Human-Object_Relation_Priors_Guided_HOI_Detection_CVPR_2025_paper.html | https://openaccess.thecvf.com/content/CVPR2025/papers/Geng_HORP_Human-Object_Relation_Priors_Guided_HOI_Detection_CVPR_2025_paper.pdf | https://openaccess.thecvf.com/content/CVPR2025/supplemental/Geng_HORP_Human-Object_Relation_CVPR_2025_supplemental.pdf | null | @InProceedings{Geng_2025_CVPR,
author = {Geng, Pei and Yang, Jian and Zhang, Shanshan},
title = {HORP: Human-Object Relation Priors Guided HOI Detection},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {25325-25335}
} | Human-Object Interaction (HOI) detection aims to predict the <Human, Interaction, Object> triplets, where the core challenge lies in recognizing the interaction of each human-object pair. Despite recent progress thanks to more advanced model architectures, HOI performance remains unsatisfactory. In this work, we first perform some failure analysis and find that the accuracy of the no-interaction category is extremely low, largely hindering the improvement of overall performance. We further look into the error types and find the mis-classification between no-interaction and with-interaction ones can be handled by human-object relation priors. Specifically, to better distinguish no-interaction from direct interactions, we propose 3D location prior, which indicates the distance between human and object; as of no-interaction vs. indirect interactions, we propose gaze area prior, which denotes whether human can see the object or not. The above two types of human-object relation priors are represented by text and are combined with the original visual features, generating multi-modal cues for interaction recognition.Experimental results on the HICO-DET and V-COCO datasets demonstrate that our proposed human-object relation priors are effective and our method HORP surpasses previous methods under various settings and scenarios. In particular, the usage of our priors significantly enhances the model's recognition ability for the no-interaction category. | [
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2,870 | Building a Mind Palace: Structuring Environment-Grounded Semantic Graphs for Effective Long Video Analysis with LLMs | [
"Zeyi Huang",
"Yuyang Ji",
"Xiaofang Wang",
"Nikhil Mehta",
"Tong Xiao",
"Donghyun Lee",
"Sigmund Vanvalkenburgh",
"Shengxin Zha",
"Bolin Lai",
"Licheng Yu",
"Ning Zhang",
"Yong Jae Lee",
"Miao Liu"
] | https://openaccess.thecvf.com/content/CVPR2025/html/Huang_Building_a_Mind_Palace_Structuring_Environment-Grounded_Semantic_Graphs_for_Effective_CVPR_2025_paper.html | https://openaccess.thecvf.com/content/CVPR2025/papers/Huang_Building_a_Mind_Palace_Structuring_Environment-Grounded_Semantic_Graphs_for_Effective_CVPR_2025_paper.pdf | https://openaccess.thecvf.com/content/CVPR2025/supplemental/Huang_Building_a_Mind_CVPR_2025_supplemental.pdf | http://arxiv.org/abs/2501.04336 | @InProceedings{Huang_2025_CVPR,
author = {Huang, Zeyi and Ji, Yuyang and Wang, Xiaofang and Mehta, Nikhil and Xiao, Tong and Lee, Donghyun and Vanvalkenburgh, Sigmund and Zha, Shengxin and Lai, Bolin and Yu, Licheng and Zhang, Ning and Lee, Yong Jae and Liu, Miao},
title = {Building a Mind Palace: Structuring Environment-Grounded Semantic Graphs for Effective Long Video Analysis with LLMs},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {24169-24179}
} | Long-form video understanding with Large Vision Language Models is challenged by the need to analyze temporally dispersed yet spatially concentrated key moments within limited context windows. In this work, we introduce VideoMindPalace, a new framework inspired by the "Mind Palace", which organizes critical video moments into a topologically structured semantic graph. VideoMindPalace organizes key information through (i) hand-object tracking and interaction, (ii) clustered activity zones representing specific areas of recurring activities, and (iii) environment layout mapping, allowing natural language parsing by LLMs to provide grounded insights on spatio-temporal and 3D context. In addition, we propose the Video MindPalace Benchmark (VMB), to assess human-like reasoning, including spatial localization, temporal reasoning, and layout-aware sequential understanding. Evaluated on VMB and established video QA datasets, including EgoSchema, NExT-QA, IntentQA, and the Active Memories Benchmark, VideoMindPalace demonstrates notable gains in spatio-temporal coherence and human-aligned reasoning, advancing long-form video analysis capabilities in VLMs. | [
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