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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：246/32  |  提交时间：2022/09/19
Traffic prediction  Spatial-temporal modeling  Meta-learning  Attention mechanism  Deep learning  
Hybrid Tree Guided PatchMatch and Quantizing Acceleration for Multiple Views Disparity Estimation 期刊论文
中国体视学与图像分析, 2021, 卷号: 26, 期号: 1, 页码: 47-61
作者:  Jiguang,Zhang;  Shibiao,Xu;  Xiaopeng,Zhang
Adobe PDF(1711Kb)  |  收藏  |  浏览/下载：159/22  |  提交时间：2022/04/07
stereo matching  multiple views  disparity estimation  hybrid tree  patch match  
You Only Search Once: Single Shot Neural Architecture Search via Direct Sparse Optimization 期刊论文
IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE, 2021, 卷号: 43, 期号: 9, 页码: 2891-2904
作者:  Zhang, Xinbang;  Huang, Zehao;  Wang, Naiyan;  Xiang, Shiming;  Pan, Chunhong
Adobe PDF(1271Kb)  |  收藏  |  浏览/下载：302/59  |  提交时间：2021/11/02
Computer architecture  Optimization  Learning (artificial intelligence)  Task analysis  Acceleration  Evolutionary computation  Convolution  Neural architecture search(NAS)  convolution neural network  sparse optimization  
Local-Aggregation Graph Networks 期刊论文
IEEE Transactions on Pattern Analysis and Machine Intelligence, 2020, 卷号: 42, 期号: 11, 页码: 2874-2886
作者:  Jianlong Chang;  Lingfeng Wang;  Gaofeng Meng;  Shiming Xiang;  Chunhong Pan
浏览  |  Adobe PDF(3090Kb)  |  收藏  |  浏览/下载：262/92  |  提交时间：2020/10/20
Local-aggregation function  local-aggregation graph neural network  non-Euclidean structured signal  
Triplet Adversarial Domain Adaptation for Pixel-Level Classification of VHR Remote Sensing Images 期刊论文
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, 2020, 卷号: 58, 期号: 5, 页码: 3558-3573
作者:  Yan, Liang;  Fan, Bin;  Liu, Hongmin;  Huo, Chunlei;  Xiang, Shiming;  Pan, Chunhong
Adobe PDF(6348Kb)  |  收藏  |  浏览/下载：368/71  |  提交时间：2020/06/22
Domain adaptation (DA)  pixel-level classification  self-training  triplet adversarial learning  very high resolution (VHR)