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CASIA OpenIR
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Efficient Remote Sensing Image Super-Resolution via Lightweight Diffusion Models 期刊论文
IEEE Geoscience and Remote Sensing Letters, 2024, 卷号: 21, 页码: 1-5
作者:  An T(安泰);  Xue B(薛斌);  Huo CL(霍春雷);  Xiang SM(向世明);  Pan CH(潘春洪)
Adobe PDF(30422Kb)  |  收藏  |  浏览/下载：126/27  |  提交时间：2024/01/17
Remote sensing super-resolution  lightweight diffusion models  cross-attention mechanism  satellite imagery  
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：154/0  |  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：246/32  |  提交时间：2022/09/19
Traffic prediction  Spatial-temporal modeling  Meta-learning  Attention mechanism  Deep learning  
Task-aware adaptive attention learning for few-shot semantic segmentation 期刊论文
NEUROCOMPUTING, 2022, 卷号: 494, 页码: 104-115
作者:  Mao, Binjie;  Wang, Lingfeng;  Xiang, Shiming;  Pan, Chunhong
Adobe PDF(3903Kb)  |  收藏  |  浏览/下载：301/70  |  提交时间：2022/09/19
Few-shot semantic segmentation  Adaptive feature learning  Attention mechanism  Task-aware  
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：200/0  |  提交时间：2022/06/06
Spatial-temporal correlation  Graph convolutional network  Traffic forecasting  
HENet: Head-Level Ensemble Network for Very High Resolution Remote Sensing Images Semantic Segmentation 期刊论文
IEEE GEOSCIENCE AND REMOTE SENSING LETTERS, 2022, 卷号: 19, 页码: 5
作者:  Cao, Yong;  Huo, Chunlei;  Xu, Nuo;  Zhang, Xin;  Xiang, Shiming;  Pan, Chunhong
Adobe PDF(1478Kb)  |  收藏  |  浏览/下载：248/1  |  提交时间：2022/06/06
Head  Computational modeling  Semantics  Image segmentation  Feature extraction  Correlation  Mathematical models  Cooperative learning (CL)  ensemble learning  semantic segmentation  
Decoupled Representation Learning for Character Glyph Synthesis 期刊论文
IEEE Transactions on Multimedia, 2021, 卷号: 2021, 期号: 2021, 页码: 1-13
作者:  Xiyan Liu;  Gaofeng Meng;  Jianlong Chang;  Ruiguang Hu;  Shiming Xiang;  Chunhong Pan
Adobe PDF(4588Kb)  |  收藏  |  浏览/下载：195/48  |  提交时间：2022/01/24
Character glyph synthesis  Decoupled representation  generative adversarial networks  
DATA: Differentiable ArchiTecture Approximation With Distribution Guided Sampling 期刊论文
IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE, 2021, 卷号: 43, 期号: 9, 页码: 2905-2920
作者:  Zhang, Xinbang;  Chang, Jianlong;  Guo, Yiwen;  Meng, Gaofeng;  Xiang, Shiming;  Lin, Zhouchen;  Pan, Chunhong
Adobe PDF(1346Kb)  |  收藏  |  浏览/下载：317/51  |  提交时间：2021/11/02
Computer architecture  Search problems  Optimization  Task analysis  Bridges  Binary codes  Estimation  Neural architecture search(NAS)  ensemble gumbel-softmax  distribution guided sampling  
Geometric Rectification of Document Images using Adversarial Gated Unwarping Network 期刊论文
Pattern Recognition, 2020, 卷号: 108, 期号: 108, 页码: 1-13
作者:  Xiyan Liu;  Gaofeng MENG;  Bin FAN;  Shiming Xiang;  Chunhong PAN
浏览  |  Adobe PDF(7916Kb)  |  收藏  |  浏览/下载：283/90  |  提交时间：2020/10/20
Distorted document image  Geometric rectification  Gated module  Deep learning  
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)