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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4821-4830 订阅
Understanding ReLU Network Robustness Through Test Set Certification Performance
Understanding ReLU Network Robustness Through Test Set Certi...
收藏 引用
ieee computer Society conference on computer vision and pattern recognition workshops (CVPRW)
作者: Nicola Franco Jeanette Miriam Lorenz Karsten Roscher Stephan Günnemann Fraunhofer Institute for Cognitive Systems IKS Munich Germany Dept. of Computer Science & Munich Data Science Institute Technical Univ. of Munich Germany
Neural networks can be vulnerable to small changes in input within their learning distribution, and this vulnerability increases for distributional shifts or input completely outside their training distribution. To en... 详细信息
来源: 评论
Improving Weakly Supervised Visual Grounding by Contrastive Knowledge Distillation
Improving Weakly Supervised Visual Grounding by Contrastive ...
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ieee/cvf conference on computer vision and pattern recognition (CVPR)
作者: Wang, Liwei Huang, Jing Li, Yin Xu, Kun Yang, Zhengyuan Yu, Dong Chinese Univ Hong Kong Hong Kong Peoples R China Univ Illinois Champaign IL USA Univ Wisconsin Madison Madison WI USA Tencent AI Lab Bellevue WA USA Univ Rochester Rochester NY 14627 USA
Weakly supervised phrase grounding aims at learning region-phrase correspondences using only image-sentence pairs. A major challenge thus lies in the missing links between image regions and sentence phrases during tra... 详细信息
来源: 评论
Transferable Semantic Augmentation for Domain Adaptation
Transferable Semantic Augmentation for Domain Adaptation
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ieee/cvf conference on computer vision and pattern recognition (CVPR)
作者: Li, Shuang Xie, Mixue Gong, Kaixiong Liu, Chi Harold Wang, Yulin Li, Wei Beijing Inst Technol Beijing Peoples R China Tsinghua Univ Beijing Peoples R China Inceptio Tech Fremont CA USA
Domain adaptation has been widely explored by transferring the knowledge from a label-rich source domain to a related but unlabeled target domain. Most existing domain adaptation algorithms attend to adapting feature ... 详细信息
来源: 评论
Bottom-Up Human Pose Estimation Via Disentangled Keypoint Regression
Bottom-Up Human Pose Estimation Via Disentangled Keypoint Re...
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ieee/cvf conference on computer vision and pattern recognition (CVPR)
作者: Geng, Zigang Sun, Ke Xiao, Bin Zhang, Zhaoxiang Wang, Jingdong Univ Sci & Technol China Beijing Peoples R China Chinese Acad Sci Univ Chinese Acad Sci Inst Automat Ctr Artificial Intelligence & RobotHKISI Beijing Peoples R China Microsoft Corp Redmond WA 98052 USA
In this paper, we are interested in the bottom-up paradigm of estimating human poses from an image. We study the dense keypoint regression framework that is previously inferior to the keypoint detection and grouping f... 详细信息
来源: 评论
Improving Unsupervised Image Clustering With Robust Learning
Improving Unsupervised Image Clustering With Robust Learning
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ieee/cvf conference on computer vision and pattern recognition (CVPR)
作者: Park, Sungwon Han, Sungwon Kim, Sundong Kim, Danu Park, Sungkyu Hong, Seunghoon Cha, Meeyoung Korea Adv Inst Sci & Technol Sch Comp Daejeon South Korea Inst for Basic Sci Korea Data Sci Grp Daejeon South Korea
Unsupervised image clustering methods often introduce alternative objectives to indirectly train the model and are subject to faulty predictions and overconfident results. To overcome these challenges, the current res... 详细信息
来源: 评论
PhD Learning: Learning with Pompeiu-hausdorff Distances for Video-based Vehicle Re-Identification
PhD Learning: Learning with Pompeiu-hausdorff Distances for ...
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ieee/cvf conference on computer vision and pattern recognition (CVPR)
作者: Zhao, Jianan Qi, Fengliang Ren, Guangyu Xu, Lin Shanghai Em Data Technol Co Ltd Shanghai Peoples R China Imperial Coll London London England
Vehicle re-identification (re-ID) is of great significance to urban operation, management, security and has gained more attention in recent years. However, two critical challenges in vehicle re-ID have primarily been ... 详细信息
来源: 评论
AdderSR: Towards Energy Efficient Image Super-Resolution
AdderSR: Towards Energy Efficient Image Super-Resolution
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ieee/cvf conference on computer vision and pattern recognition (CVPR)
作者: Song, Dehua Wang, Yunhe Chen, Hanting Xu, Chang Xu, Chunjing Tao, Dacheng Huawei Technol Noahs Ark Lab Shenzhen Peoples R China Peking Univ Beijing Peoples R China Univ Sydney Sydney NSW Australia
This paper studies the single image super-resolution problem using adder neural networks (AdderNets). Compared with convolutional neural networks, AdderNets utilize additions to calculate the output features thus avoi... 详细信息
来源: 评论
Lite-HRNet: A Lightweight High-Resolution Network
Lite-HRNet: A Lightweight High-Resolution Network
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ieee/cvf conference on computer vision and pattern recognition (CVPR)
作者: Yu, Changqian Xiao, Bin Gao, Changxin Yuan, Lu Zhang, Lei Sang, Nong Wang, Jingdong Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Key Lab Image Proc & Intelligent Control Huazhong Peoples R China Microsoft Redmond WA 98052 USA
We present an efficient high-resolution network, Lite-HRNet, for human pose estimation. We start by simply applying the efficient shuffle block in ShuffleNet to HRNet (high-resolution network), yielding stronger perfo... 详细信息
来源: 评论
Bridging the Visual Gap: Wide-Range Image Blending
Bridging the Visual Gap: Wide-Range Image Blending
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ieee/cvf conference on computer vision and pattern recognition (CVPR)
作者: Chia-Ni Lu Ya-Chu Chang Wei-Chen Chiu Natl Chiao Tung Univ NCTU MediaTek NCTU Res Ctr Hsinchu Taiwan
In this paper we propose a new problem scenario in image processing, wide-range image blending, which aims to smoothly merge two different input photos into a panorama by generating novel image content for the interme... 详细信息
来源: 评论
Transferable Query Selection for Active Domain Adaptation
Transferable Query Selection for Active Domain Adaptation
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ieee/cvf conference on computer vision and pattern recognition (CVPR)
作者: Fu, Bo Cao, Zhangjie Wang, Jianmin Long, Mingsheng Tsinghua Univ Sch Software BNRist Beijing Peoples R China
Unsupervised domain adaptation (UDA) enables transferring knowledge from a related source domain to a fully unlabeled target domain. Despite the significant advances in UDA, the performance gap remains quite large bet... 详细信息
来源: 评论