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Meta Graph Transformer: A Novel Framework for Spatial-Temporal Traffic Prediction 期刊论文
NEUROCOMPUTING, 2022, 卷号: 491, 页码: 544-563
作者:  Ye, Xue;  Fang, Shen;  Sun, Fang;  Zhang, Chunxia;  Xiang, Shiming
Adobe PDF(3491Kb)  |  收藏  |  浏览/下载：260/34  |  提交时间：2022/09/19
Traffic prediction  Spatial-temporal modeling  Meta-learning  Attention mechanism  Deep learning  
Kernel-Weighted Graph Convolutional Network: A Deep Learning Approach for Traffic Forecasting 会议论文
, Beijing, China, 2018-08
作者:  Zhang Qi;  Jin Qizhao;  Chang Jianlong;  Xiang Shiming;  Pan Chunhong
Adobe PDF(4041Kb)  |  收藏  |  浏览/下载：198/37  |  提交时间：2021/05/31
3D PostureNet: A unified framework for skeleton-based posture recognition 期刊论文
PATTERN RECOGNITION LETTERS, 2020, 卷号: 140, 期号: 140, 页码: 143-149
作者:  Liu, Jianbo;  Wang, Ying;  Liu, Yongcheng;  Xiang, Shiming;  Pan, Chunhong
Adobe PDF(1997Kb)  |  收藏  |  浏览/下载：287/41  |  提交时间：2021/03/02
Human posture recognition  Static hand gesture recognition  Skeleton-based  3D convolutional neural network  
Learning graph structure via graph convolutional networks 期刊论文
PATTERN RECOGNITION, 2019, 卷号: 95, 期号: -, 页码: 308-318
作者:  Zhang, Qi;  Chang, Jianlong;  Meng, Gaofeng;  Xu, Shibiao;  Xiang, Shiming;  Pan, Chunhong
浏览  |  Adobe PDF(2475Kb)  |  收藏  |  浏览/下载：481/111  |  提交时间：2019/12/16
Deep learning  Graph convolutional neural networks  Graph structure learning  Changeable kernel sizes  
Blind image quality assessment via learnable attention-based pooling 期刊论文
PATTERN RECOGNITION, 2019, 卷号: 91, 页码: 332-344
作者:  Gu, Jie;  Meng, Gaofeng;  Xiang, Shiming;  Pan, Chunhong
浏览  |  Adobe PDF(3081Kb)  |  收藏  |  浏览/下载：515/192  |  提交时间：2019/05/15
Image quality assessment  Perceptual image quality  Visual attention  Convolutional neural network  Learnable pooling  
Pseudo low rank video representation 期刊论文
PATTERN RECOGNITION, 2019, 卷号: 85, 期号: 1, 页码: 50-59
作者:  Yu, Tingzhao;  Wang, Lingfeng;  Guo, Chaoxu;  Gu, Huxiang;  Xiang, Shiming;  Pan, Chunhong
浏览  |  Adobe PDF(1456Kb)  |  收藏  |  浏览/下载：632/188  |  提交时间：2019/01/08
Pseudo low rank  Data driven  Low resolution  Action recognition