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检索条件"任意字段=IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops"
12859 条 记 录,以下是4861-4870 订阅
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ieee/cvf conference on computer vision and pattern recognition (CVPR)
作者: Lu, Mandy Zhao, Qingyu Zhang, Jiequan Pohl, Kilian M. Li Fei-Fei Niebles, Juan Carlos Adeli, Ehsan Stanford Univ Stanford CA 94305 USA
Batch Normalization (BN) and its variants have delivered tremendous success in combating the covariate shift induced by the training step of deep learning methods. While these techniques normalize the feature distribu... 详细信息
来源: 评论
3PSDF: Three-Pole Signed Distance Function for Learning Surfaces with Arbitrary Topologies
3PSDF: Three-Pole Signed Distance Function for Learning Surf...
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ieee/cvf conference on computer vision and pattern recognition (CVPR)
作者: Chen, Weikai Lin, Cheng Li, Weiyang Yang, Bo Tencent Games Digital Content Technol Ctr Shenzhen Peoples R China
Recent advances in learning 3D shapes using neural implicit functions have achieved impressive results by breaking the previous barrier of resolution and diversity for varying topologies. However, most of such approac... 详细信息
来源: 评论
Neural Reprojection Error: Merging Feature Learning and Camera Pose Estimation
Neural Reprojection Error: Merging Feature Learning and Came...
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ieee/cvf conference on computer vision and pattern recognition (CVPR)
作者: Germain, Hugo Lepetit, Vincent Bourmaud, Guillaume Univ Gustave Eiffel Ecole Ponts LIGM CNRS Marne La Vallee France Univ Bordeaux IMS Bordeaux INP CNRS Bordeaux France
Absolute camera pose estimation is usually addressed by sequentially solving two distinct subproblems: First a feature matching problem that seeks to establish putative 2D-3D correspondences, and then a Perspective-n-... 详细信息
来源: 评论
Dense Contrastive Learning for Self-Supervised Visual Pre-Training
Dense Contrastive Learning for Self-Supervised Visual Pre-Tr...
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ieee/cvf conference on computer vision and pattern recognition (CVPR)
作者: Wang, Xinlong Zhang, Rufeng Shen, Chunhua Kong, Tao Li, Lei Univ Adelaide Adelaide SA Australia Tongji Univ Shanghai Peoples R China ByteDance AI Lab Beijing Peoples R China
To date, most existing self-supervised learning methods are designed and optimized for image classification. These pre-trained models can be sub-optimal for dense prediction tasks due to the discrepancy between image-... 详细信息
来源: 评论
Efficient Deep Embedded Subspace Clustering
Efficient Deep Embedded Subspace Clustering
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ieee/cvf conference on computer vision and pattern recognition (CVPR)
作者: Cai, Jinyu Fan, Jicong Guo, Wenzhong Wang, Shiping Zhang, Yunhe Zhang, Zhao Fuzhou Univ Coll Comp & Data Sci Fuzhou Peoples R China Chinese Univ Hong Kong Shenzhen Sch Data Sci Hong Kong Peoples R China Shenzhen Res Inst Big Data Shenzhen Peoples R China Hefei Univ Technol Hefei Peoples R China
Recently deep learning methods have shown significant progress in data clustering tasks. Deep clustering methods (including distance-based methods and subspace-based methods) integrate clustering and feature learning ... 详细信息
来源: 评论
Fine-Grained Object Classification via Self-Supervised Pose Alignment
Fine-Grained Object Classification via Self-Supervised Pose ...
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ieee/cvf conference on computer vision and pattern recognition (CVPR)
作者: Yang, Xuhui Wang, Yaowei Chen, Ke Xu, Yong Tian, Yonghong Peng Cheng Lab Shenzhen Peoples R China South China Univ Technol Guangzhou Peoples R China China Commun & Comp Network Lab Guangdong Guangzhou Peoples R China
Semantic patterns of fine-grained objects are determined by subtle appearance difference of local parts, which thus inspires a number of part-based methods. However, due to uncontrollable object poses in images, disti... 详细信息
来源: 评论
Language-driven Grasp Detection
Language-driven Grasp Detection
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ieee/cvf conference on computer vision and pattern recognition (CVPR)
作者: An Dinh Vuong Minh Nhat Vu Baoru Huang Nghia Nguyen Hieu Le Thieu Vo Anh Nguyen FPT Software AI Ctr Hanoi Vietnam TU Wien Automat Control Inst Vienna Austria Imperial Coll London London England Ton Duc Thang Univ Ho Chi Minh City Vietnam Univ Liverpool Liverpool Merseyside England
Grasp detection is a persistent and intricate challenge with various industrial applications. Recently, many methods and datasets have been proposed to tackle the grasp detection problem. However, most of them do not ... 详细信息
来源: 评论
Regressive Domain Adaptation for Unsupervised Keypoint Detection
Regressive Domain Adaptation for Unsupervised Keypoint Detec...
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ieee/cvf conference on computer vision and pattern recognition (CVPR)
作者: Jiang, Junguang Ji, Yifei Wang, Ximei Liu, Yufeng Wang, Jianmin Long, Mingsheng Tsinghua Univ Sch Software BNRist Beijing Peoples R China Kuaishou Technol Y Tech Beijing Peoples R China
Domain adaptation (DA) aims at transferring knowledge from a labeled source domain to an unlabeled target domain. Though many DA theories and algorithms have been proposed, most of them are tailored into classificatio... 详细信息
来源: 评论
VideoGrounding-DINO: Towards Open-Vocabulary Spatio-Temporal Video Grounding
VideoGrounding-DINO: Towards Open-Vocabulary Spatio-Temporal...
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ieee/cvf conference on computer vision and pattern recognition (CVPR)
作者: Wasim, Syed Talal Naseer, Muzammal Khan, Salman Yang, Ming-Hsuan Khan, Fahad Shahbaz Mohamed Bin Zayed Univ AI Abu Dhabi U Arab Emirates Australian Natl Univ Canberra Australia Univ Calif Merced Merced CA USA Google Res Mountain View CA USA Linkoping Univ Linkoping Sweden
Video grounding aims to localize a spatio-temporal section in a video corresponding to an input text query. This paper addresses a critical limitation in current video grounding methodologies by introducing an Open-Vo... 详细信息
来源: 评论
ActiveZero: Mixed Domain Learning for Active Stereovision with Zero Annotation
ActiveZero: Mixed Domain Learning for Active Stereovision wi...
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ieee/cvf conference on computer vision and pattern recognition (CVPR)
作者: Liu, Isabella Yang, Edward Tao, Jianyu Chen, Rui Zhang, Xiaoshuai Ran, Qing Liu, Zhu Su, Hao Univ Calif San Diego San Diego CA 92103 USA Tsinghua Univ Beijing Peoples R China Alibaba DAMO Acad Hangzhou Peoples R China
Traditional depth sensors generate accurate real world depth estimates that surpass even the most advanced learning approaches trained only on simulation domains. Since ground truth depth is readily available in the s... 详细信息
来源: 评论