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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：261/34  |  提交时间：2022/09/19
Traffic prediction  Spatial-temporal modeling  Meta-learning  Attention mechanism  Deep learning  
TVGCN: Time-variant graph convolutional network for traffic forecasting 期刊论文
NEUROCOMPUTING, 2022, 卷号: 471, 页码: 118-129
作者:  Wang, Yuhu;  Fang, Shen;  Zhang, Chunxia;  Xiang, Shiming;  Pan, Chunhong
Adobe PDF(2050Kb)  |  收藏  |  浏览/下载：219/5  |  提交时间：2022/06/06
Spatial-temporal correlation  Graph convolutional network  Traffic forecasting  
MS-Net: Multi-Source Spatio-Temporal Network for Traffic Flow Prediction 期刊论文
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS, 2021, 卷号: 23, 期号: 7, 页码: 14
作者:  Fang, Shen;  Prinet, Veronique;  Chang, Jianlong;  Werman, Michael;  Zhang, Chunxia;  Xiang, Shiming;  Pan, Chunhong
收藏  |  浏览/下载：244/0  |  提交时间：2022/01/27
Feature extraction  Convolution  Predictive models  Data models  Correlation  Roads  Kernel  Graph convolution  deep attention mechanism  traffic network  traffic flow prediction  artificial intelligence  deep learning  
Weakly Semantic Guided Action Recognition 期刊论文
IEEE TRANSACTIONS ON MULTIMEDIA, 2019, 卷号: 21, 期号: 10, 页码: 2504-2517
作者:  Yu, Tingzhao;  Wang, Lingfeng;  Da, Cheng;  Gu, Huxiang;  Xiang, Shiming;  Pan, Chunhong
浏览  |  Adobe PDF(18774Kb)  |  收藏  |  浏览/下载：459/114  |  提交时间：2019/05/15
Semantic guided module  action recognition  cross domain  3D convolution  attention model  
Semantic labeling in very high resolution images via a self-cascaded convolutional neural network 期刊论文
ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING, 2018, 卷号: 145, 期号: 1, 页码: 78-95
作者:  Liu, Yongcheng;  Fan, Bin;  Wang, Lingfeng;  Bai, Jun;  Xiang, Shiming;  Pan, Chunhong
浏览  |  Adobe PDF(1679Kb)  |  收藏  |  浏览/下载：518/107  |  提交时间：2019/01/08
Semantic labeling  Convolutional neural networks (CNNs)  Multi-scale contexts  End-to-end