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Domain Adaptation by Class Centroid Matching and Local Manifold Self-Learning 期刊论文
IEEE TRANSACTIONS ON IMAGE PROCESSING, 2020, 卷号: 29, 页码: 9703-9718
作者:  Tian, Lei;  Tang, Yongqiang;  Hu, Liangchen;  Ren, Zhida;  Zhang, Wensheng
Adobe PDF(3443Kb)  |  收藏  |  浏览/下载：350/72  |  提交时间：2021/01/06
Domain adaptation  class centroid matching  local manifold self-learning  
Skeleton-Based Action Recognition With Multi-Stream Adaptive Graph Convolutional Networks 期刊论文
IEEE TRANSACTIONS ON IMAGE PROCESSING, 2020, 期号: 29, 页码: 9532-9545
作者:  Shi, Lei;  Zhang, Yifan;  Cheng, Jian;  Lu, Hanqing
Adobe PDF(2849Kb)  |  收藏  |  浏览/下载：386/150  |  提交时间：2020/11/05
Skeleton-based action recognition, graph convolutional network, adaptive graph, multi-stream network.  
Improve Person Re-Identification With Part Awareness Learning 期刊论文
IEEE TRANSACTIONS ON IMAGE PROCESSING, 2020, 卷号: 29, 页码: 7468-7481
作者:  Huang, Houjing;  Yang, Wenjie;  Lin, Jinbin;  Huang, Guan;  Xu, Jiamiao;  Wang, Guoli;  Chen, Xiaotang;  Huang, Kaiqi
Adobe PDF(3927Kb)  |  收藏  |  浏览/下载：323/57  |  提交时间：2020/08/31
Person re-identification  part awareness  part segmentation  multi-task learning  
Tensor Multi-Task Learning for Person Re-Identification 期刊论文
IEEE TRANSACTIONS ON IMAGE PROCESSING, 2020, 卷号: 29, 期号: 1, 页码: 2463-2477
作者:  Zhang, Zhizhong;  Xie, Yuan;  Zhang, Wensheng;  Tang, Yongqiang;  Tian, Qi
Adobe PDF(1444Kb)  |  收藏  |  浏览/下载：356/60  |  提交时间：2020/03/30
Cameras  Task analysis  Measurement  Visualization  Training  Computational modeling  Person re-identification  multi-task learning  tensor optimization  
Deep Unbiased Embedding Transfer for Zero-Shot Learning 期刊论文
IEEE TRANSACTIONS ON IMAGE PROCESSING, 2020, 卷号: 29, 期号: 29, 页码: 1958-1971
作者:  Jia, Zhen;  Zhang, Zhang;  Wang, Liang;  Shan, Caifeng;  Tan, Tieniu
Adobe PDF(3428Kb)  |  收藏  |  浏览/下载：408/67  |  提交时间：2020/03/30
Visualization  Feature extraction  Semantics  Training  Seals  Prototypes  Indexes  Zero-shot learning  image classification  projection domain shift  convolutional neural network  generative adversarial network