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Convolutional relation network for skeleton-based action recognition 期刊论文
NEUROCOMPUTING, 2019, 卷号: 370, 期号: 1, 页码: 109-117
作者:  Zhu, Jiagang;  Zou, Wei;  Zhu, Zheng;  Hu, Yiming
Adobe PDF(1981Kb)  |  收藏  |  浏览/下载：401/87  |  提交时间：2020/03/30
Action recognition  Skeleton  Deep learning  Joint interaction  Dilation  Attention  
Analyzing the dynamic sectoral influence in Chinese and American stock markets 期刊论文
PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS, 2019, 卷号: 536, 页码: 15
作者:  Tian, Hu;  Zheng, Xiaolong;  Zeng, Daniel Danjun
Adobe PDF(2694Kb)  |  收藏  |  浏览/下载：296/27  |  提交时间：2020/03/30
Sectoral influence  Multi-time scales  Causal network  Granger causality  Empirical mode decomposition  
Adversarial image generation by combining content and style 期刊论文
IET IMAGE PROCESSING, 2019, 卷号: 13, 期号: 14, 页码: 2716-2723
作者:  Liu, Songyan;  Zhao, Chaoyang;  Gao, Yunze;  Wang, Jinqiao;  Tang, Ming
Adobe PDF(4319Kb)  |  收藏  |  浏览/下载：403/112  |  提交时间：2020/03/30
image recognition  feature extraction  learning (artificial intelligence)  image texture  adversarial image generation  unique style  reference images  style feature extraction module  style specific image generation model  double-cycle training strategy  natural-content pairs  input natural images  style exchange  style-exchanged images  licence-plate image  handbags images  
Towards Real-Time Advancement of Underwater Visual Quality With GAN 期刊论文
IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS, 2019, 卷号: 66, 期号: 12, 页码: 9350-9359
作者:  Chen, Xingyu;  Yu, Junzhi;  Kong, Shihan;  Wu, Zhengxing;  Fang, Xi;  Wen, Li
Adobe PDF(4984Kb)  |  收藏  |  浏览/下载：428/140  |  提交时间：2019/12/16
Generative adversarial networks (GAN) image restoration  machine learning  underwater vision  
Exploring uncertainty in pseudo-label guided unsupervised domain adaptation 期刊论文
PATTERN RECOGNITION, 2019, 卷号: 96, 页码: 11
作者:  Liang, Jian;  He, Ran;  Sun, Zhenan;  Tan, Tieniu
收藏  |  浏览/下载：308/0  |  提交时间：2019/12/16
Unsupervised domain adaptation  Pseudo labeling  Feature transformation  Progressive learning  Transfer learning  
GaitNet: An end-to-end network for gait based human identification 期刊论文
PATTERN RECOGNITION, 2019, 卷号: 96, 期号: 106988, 页码: 11
作者:  Song, Chunfeng;  Huang, Yongzhen;  Huang, Yan;  Jia, Ning;  Wang, Liang
Adobe PDF(3015Kb)  |  收藏  |  浏览/下载：589/158  |  提交时间：2019/12/16
Gait recognition  Video-based human identification  End-to-end CNN  Joint learning  
Surface Defects Detection Based on Adaptive Multiscale Image Collection and Convolutional Neural Networks 期刊论文
IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT, 2019, 卷号: 68, 期号: 12, 页码: 4787-4797
作者:  Sun, Jia;  Wang, Peng;  Luo, Yong-Kang;  Li, Wanyi
Adobe PDF(4530Kb)  |  收藏  |  浏览/下载：256/80  |  提交时间：2020/03/30
Inspection  Surface treatment  Training  Metals  Task analysis  Instruments  Visualization  Adaptive multiscale image collection (AMIC)  convolutional neural networks (CNNs)  image classification  surface inspection  
A Fast Surface Defect Detection Method Based on Background Reconstruction 期刊论文
INTERNATIONAL JOURNAL OF PRECISION ENGINEERING AND MANUFACTURING, 2019, 卷号: 21, 期号: 3, 页码: 363-375
作者:  Lv, Chengkan;  Zhang, Zhengtao;  Shen, Fei;  Zhang, Feng;  Su, Hu
Adobe PDF(2471Kb)  |  收藏  |  浏览/下载：339/45  |  提交时间：2020/03/30
Defect detection  Unsupervised learning  Background reconstruction  
Recognizing Profile Faces by Imagining Frontal View 期刊论文
INTERNATIONAL JOURNAL OF COMPUTER VISION, 2019, 页码: 19
作者:  Zhao, Jian;  Xing, Junliang;  Xiong, Lin;  Yan, Shuicheng;  Feng, Jiashi
收藏  |  浏览/下载：212/0  |  提交时间：2020/03/30
Pose-invariant face recognition  Face frontalization  Cross-domain adversarial learning  Meta-learning  Learning to learn  Enforced cross-entropy optimization  Generative adversarial networks  
Global and Local Consistent Wavelet-Domain Age Synthesis 期刊论文
IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY, 2019, 卷号: 14, 期号: 11, 页码: 2943-2957
作者:  Li, Peipei;  Hu, Yibo;  He, Ran;  Sun, Zhenan
Adobe PDF(10667Kb)  |  收藏  |  浏览/下载：334/34  |  提交时间：2019/12/16
Age synthesis  wavelet transform  generative adversarial network  global and local features