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AutoMSNet: Multi-Source Spatio-Temporal Network via Automatic Neural Architecture Search for Traffic Flow Prediction 期刊论文
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS, 2022, 页码: 15
作者:  Fang, Shen;  Zhang, Chunxia;  Xiang, Shiming;  Pan, Chunhong
收藏  |  浏览/下载：140/0  |  提交时间：2023/02/22
Deep learning  neural architecture search  graph convolution  meta-learning  traffic flow prediction  
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：221/26  |  提交时间：2022/09/19
Traffic prediction  Spatial-temporal modeling  Meta-learning  Attention mechanism  Deep learning  
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：217/0  |  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  
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：254/34  |  提交时间：2021/03/02
Human posture recognition  Static hand gesture recognition  Skeleton-based  3D convolutional neural network  
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：433/112  |  提交时间：2019/05/15
Semantic guided module  action recognition  cross domain  3D convolution  attention model