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CASIA OpenIR
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MsIFT: Multi-Source Image Fusion Transformer 期刊论文
REMOTE SENSING, 2022, 卷号: 14, 期号: 16, 页码: 19
作者:  Zhang, Xin;  Jiang, Hangzhi;  Xu, Nuo;  Ni, Lei;  Huo, Chunlei;  Pan, Chunhong
Adobe PDF(4788Kb)  |  收藏  |  浏览/下载：316/83  |  提交时间：2022/11/14
transformer  multi-source image fusion  non-local  
Learning adversarial point-wise domain alignment for stereo matching 期刊论文
NEUROCOMPUTING, 2022, 卷号: 491, 页码: 564-574
作者:  Zhang, Chenghao;  Meng, Gaofeng;  Xu, Richard Yi Da;  Xiang, Shiming;  Pan, Chunhong
Adobe PDF(3885Kb)  |  收藏  |  浏览/下载：303/53  |  提交时间：2022/09/19
Stereo Matching  Domain adaptation  Point-wise linear transformation  Adversarial 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：293/69  |  提交时间：2022/09/19
Few-shot semantic segmentation  Adaptive feature learning  Attention mechanism  Task-aware  
Monocular contextual constraint for stereo matching with adaptive weights assignment 期刊论文
IMAGE AND VISION COMPUTING, 2022, 卷号: 121, 页码: 10
作者:  Zhang, Chenghao;  Meng, Gaofeng;  Su, Bing;  Xiang, Shiming;  Pan, Chunhong
Adobe PDF(3669Kb)  |  收藏  |  浏览/下载：264/41  |  提交时间：2022/06/10
Deep learning  Stereo matching  Monocular contextual constraint  Adaptive weights assignment  
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：192/48  |  提交时间：2022/01/24
Character glyph synthesis  Decoupled representation  generative adversarial networks  
Multitask Learning for Ship Detection From Synthetic Aperture Radar Images 期刊论文
IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING, 2021, 卷号: 14, 页码: 8048-8062
作者:  Zhang, Xin;  Huo, Chunlei;  Xu, Nuo;  Jiang, Hangzhi;  Cao, Yong;  Ni, Lei;  Pan, Chunhong
Adobe PDF(12575Kb)  |  收藏  |  浏览/下载：275/44  |  提交时间：2021/11/03
Task analysis  Feature extraction  Radar polarimetry  Object detection  Marine vehicles  Detectors  Synthetic aperture radar  Multitask learning  synthetic aperture radar (SAR)  SAR ship detection  
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：296/58  |  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  
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：265/35  |  提交时间：2021/03/02
Human posture recognition  Static hand gesture recognition  Skeleton-based  3D convolutional neural network  
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
Adobe PDF(3090Kb)  |  收藏  |  浏览/下载：258/91  |  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：358/68  |  提交时间：2020/06/22
Domain adaptation (DA)  pixel-level classification  self-training  triplet adversarial learning  very high resolution (VHR)