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Richly Activated Graph Convolutional Network for Robust Skeleton-Based Action Recognition 期刊论文
IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY, 2021, 卷号: 31, 期号: 5, 页码: 1915-1925
作者:  Song, Yi-Fan;  Zhang, Zhang;  Shan, Caifeng;  Wang, Liang
Adobe PDF(3381Kb)  |  收藏  |  浏览/下载：369/56  |  提交时间：2021/06/15
Skeleton  Robustness  Noise measurement  Three-dimensional displays  Degradation  Standards  Feature extraction  Action recognition  skeleton  activation map  graph convolutional network  occlusion  jittering  
End -to -end video text detection with online tracking 期刊论文
PATTERN RECOGNITION, 2021, 卷号: 113, 页码: 12
作者:  Yu, Hongyuan;  Huang, Yan;  Pi, Lihong;  Zhang, Chengquan;  Li, Xuan;  Wang, Liang
Adobe PDF(4997Kb)  |  收藏  |  浏览/下载：312/57  |  提交时间：2021/05/06
End-to-end  Video text detection  Online tracking  
Controllable Multi-Attribute Editing of High-Resolution Face Images 期刊论文
IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY, 2021, 卷号: 16, 期号: 0, 页码: 1410-1423
作者:  Deng, Qiyao;  Li, Qi;  Cao, Jie;  Liu, Yunfan;  Sun, Zhenan
Adobe PDF(8442Kb)  |  收藏  |  浏览/下载：275/34  |  提交时间：2021/03/02
Faces  Image resolution  Face recognition  Wavelet transforms  Generative adversarial networks  Computational modeling  Face attribute editing  Face synthesis  Generative adversarial network  
High-fidelity View Synthesis for Light Field Imaging With Extended Pseudo 4DCNN 期刊论文
IEEE TRANSACTIONS ON COMPUTATIONAL IMAGING, 2020, 卷号: 6, 页码: 830-842
作者:  Wang, Yunlong;  Liu, Fei;  Zhang, Kunbo;  Wang, Zilei;  Sun, Zhenan;  Tan, Tieniu
Adobe PDF(7054Kb)  |  收藏  |  浏览/下载：188/36  |  提交时间：2020/09/28
View synthesis  light field reconstruction  end-to-end  structure preserving  extended pseudo 4DCNN  
A hierarchical contextual attention-based network for sequential recommendation 期刊论文
NEUROCOMPUTING, 2019, 卷号: 358, 页码: 141-149
作者:  Cui, Qiang;  Wu, Shu;  Huang, Yan;  Wang, Liang
浏览  |  Adobe PDF(545Kb)  |  收藏  |  浏览/下载：276/28  |  提交时间：2019/05/09
Sequential recommendation  Recurrent neural network  Short-term interest  Context  Attention mechanism