paper_id uint32 0 2.87k | title stringlengths 15 149 | authors listlengths 1 69 | cvf_url stringlengths 94 199 | pdf_url stringlengths 95 200 | supp_url stringlengths 100 148 ⌀ | arxiv_id stringlengths 10 10 ⌀ | bibtex large_stringlengths 285 1.82k | abstract large_stringlengths 547 2.44k | embedding listlengths 768 768 |
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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 | 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 ... | 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 dive... | [
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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 | 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 = ... | 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 m... | [
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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 L... | 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... | [
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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 | 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 ... | 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 vi... | [
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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 | 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 ... | 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 d... | [
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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 Recognit... | 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-spe... | [
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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 = {... | 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... | [
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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},
... | 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 con... | [
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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 | 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... | 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 representa... | [
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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 | 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 fo... | 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 cont... | [
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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 | 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 Com... | 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-d... | [
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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 | 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 = {Proceeding... | 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 issu... | [
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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 Visi... | 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 annotat... | [
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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 | 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)},
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2,814 | Query Efficient Black-Box Visual Prompting with Subspace Learning | [
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"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 C... | 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 signifi... | [
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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 | 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 = ... | 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 ... | [
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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 | 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)}... | 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-... | [
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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 | 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 ... | 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 adaptabi... | [
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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 = {Proceed... | 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 g... | [
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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 Visio... | 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 thes... | [
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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 | 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-Mem... | 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 infere... | [
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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 | 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... | 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 cap... | [
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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 Recogn... | 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 ster... | [
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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 = {Proceedin... | 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 insuffi... | [
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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 | 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 Patte... | 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 ha... | [
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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 | 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 Pa... | 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 co... | [
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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)},... | 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 deepfa... | [
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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 | 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)},
... | 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 a... | [
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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 = {... | 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 limitat... | [
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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 | 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 ... | 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... | [
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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 | 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)},
... | 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 support... | [
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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 ... | 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 au... | [
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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 | 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},
... | 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-t... | [
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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 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 e... | [
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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 an... | 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 de... | [
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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 = {20... | 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... | [
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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 | 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 (... | 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 rec... | [
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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 ... | 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 ... | [
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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},
y... | 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 learn... | [
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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 (... | 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... | [
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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 | 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 = {Jun... | 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 worki... | [
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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 | 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 ... | 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... | [
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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 | 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 = ... | 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 ... | [
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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 Patte... | 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 comprehens... | [
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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 | 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 Co... | 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 se... | [
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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 | 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 = {25... | 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 (e... | [
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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 | 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}... | 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 ... | [
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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 Recognit... | 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 i... | [
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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 Compute... | 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 ... | [
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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 | 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 a... | 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 ... | [
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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... | 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... | [
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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 | 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 Recognit... | 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... | [
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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 | 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 = {Proceeding... | 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 ... | [
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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 | 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... | 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... | [
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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 | 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 = {46... | 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 d... | [
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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 | 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 Mult... | 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 ... | [
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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 =... | 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 ... | [
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2,857 | StarGen: A Spatiotemporal Autoregression Framework with Video Diffusion Model for Scalable and Controllable Scene Generation | [
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"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 | 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 Framew... | 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, w... | [
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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 Pat... | 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 ... | [
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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 = {20... | 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 (EGDeblur... | [
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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 | 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 ... | 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 o... | [
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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 C... | 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, sho... | [
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2,862 | Consistency-aware Self-Training for Iterative-based Stereo Matching | [
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"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 | 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 ... | 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 t... | [
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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)},
m... | 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 ... | [
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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 | 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... | 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... | [
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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 | 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... | 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-qu... | [
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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 an... | 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 introd... | [
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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 | 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 Patter... | 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... | [
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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 | 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)}... | 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 freque... | [
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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},
pa... | 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 ... | [
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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 | 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: Struct... | 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 momen... | [
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... |
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