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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
浏览  |  Adobe PDF(2849Kb)  |  收藏  |  浏览/下载：372/148  |  提交时间：2020/11/05
Skeleton-based action recognition, graph convolutional network, adaptive graph, multi-stream network.  
Attention Guided Multiple Source and Target Domain Adaptation 期刊论文
IEEE TRANSACTIONS ON IMAGE PROCESSING, 2020, 期号: 30, 页码: 892-906
作者:  Wang, Yuxi;  Zhang, Zhaoxiang;  Hao, Wangli;  Song, Chunfeng
Adobe PDF(3460Kb)  |  收藏  |  浏览/下载：272/41  |  提交时间：2021/03/02
Semantics  Task analysis  Generators  Generative adversarial networks  Feature extraction  Visualization  Meteorology  Domain adaptation  multiple source and target domains  attention  
Multi-Domain Image-to-Image Translation via a Unified Circular Framework 期刊论文
IEEE TRANSACTIONS ON IMAGE PROCESSING, 2020, 期号: 30, 页码: 670-684
作者:  Wang, Yuxi;  Zhang, Zhaoxiang;  Hao, Wangli;  Song, Chunfeng
Adobe PDF(3399Kb)  |  收藏  |  浏览/下载：312/57  |  提交时间：2021/03/02
Task analysis  Semantics  Visualization  Generative adversarial networks  Generators  Feature extraction  Meteorology  Image-to-image transfer  sharing knowledge module  multiple domain pairs  GANs  
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：326/64  |  提交时间：2021/01/06
Domain adaptation  class centroid matching  local manifold self-learning  
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：303/54  |  提交时间：2020/08/31
Person re-identification  part awareness  part segmentation  multi-task learning