CASIA OpenIR
(本次检索基于用户作品认领结果)

浏览/检索结果: 共12条,第1-10条 帮助

限定条件                
已选(0)清除 条数/页:   排序方式:
DCAT: Dual Cross-Attention-Based Transformer for Change Detection 期刊论文
Remote Sensing, 2023, 卷号: 15, 期号: 9, 页码: 2395
作者:  Yuan Zhou;  Chunlei Huo;  Jiahang Zhu;  Leigang Huo;  Chunhong Pan
Adobe PDF(47919Kb)  |  收藏  |  浏览/下载:135/19  |  提交时间:2023/06/16
change detection  transformer  dual cross-attention  remote sensing  
Patch Loss: A generic multi-scale perceptual loss for single image super-resolution 期刊论文
Pattern Recognition, 2023, 卷号: 139, 页码: 109510
作者:  An T(安泰);  Mao BJ(毛彬杰);  Xue B(薛斌);  Huo CL(霍春雷);  Xiang SM(向世明);  Pan CH(潘春洪)
Adobe PDF(5876Kb)  |  收藏  |  浏览/下载:101/14  |  提交时间:2024/01/17
Single-image super-resolution  Multi-scale loss functions  Image visual perception  Perceptual metrics  
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:303/80  |  提交时间:2022/11/14
transformer  multi-source image fusion  non-local  
Multi-modal spatio-temporal meteorological forecasting with deep neural network 期刊论文
ISPRS Journal of Photogrammetry and Remote Sensing, 2022, 页码: 14
作者:  Xinbang Zhang;  Qizhao Jin;  Tingzhao Yu;  Shiming Xiang;  Qiuming Kuang;  Véronique Prinet;  Chunhong Pan
Adobe PDF(3735Kb)  |  收藏  |  浏览/下载:294/68  |  提交时间:2022/07/01
Meterological forecasting  Deep learning  Neural architecture search  AutoML  
TR-MISR: Multiimage super-resolution based on feature fusion with transformers 期刊论文
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2022, 卷号: 15, 页码: 1373-1388
作者:  An T(安泰);  Zhang X(张鑫);  Huo CL(霍春雷);  Xue B(薛斌);  Wang LF(汪凌峰);  Pan CH(潘春洪)
Adobe PDF(6058Kb)  |  收藏  |  浏览/下载:116/9  |  提交时间:2024/01/17
Deep learning  end-to-end networks  feature extraction and fusion  multiimage super-resolution (MISR)  remote sensing  transformers  
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:304/50  |  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  
CMT: Cross Mean Teacher Unsupervised Domain Adaptation for VHR Image Semantic Segmentation 期刊论文
IEEE Geoscience and Remote Sensing Letters, 2021, 卷号: 0, 期号: 0, 页码: 1-5
作者:  Liang Yan;  Bin Fan;  Shiming Xiang;  Chunhong Pan
Adobe PDF(1700Kb)  |  收藏  |  浏览/下载:159/35  |  提交时间:2021/06/15
Cross mean teacher (CMT)  self-training (ST)  semantic segmentation  unsupervised domain adaptation (UDA)  very-high-resolution (VHR) image  
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  
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:482/100  |  提交时间:2019/01/08
Semantic labeling  Convolutional neural networks (CNNs)  Multi-scale contexts  End-to-end  
Ensemble based deep networks for image super-resolution 期刊论文
PATTERN RECOGNITION, 2017, 卷号: 68, 期号: null, 页码: 191-198
作者:  Wang, Lingfeng;  Huang, Zehao;  Gong, Yongchao;  Pan, Chunhong
浏览  |  Adobe PDF(1948Kb)  |  收藏  |  浏览/下载:832/320  |  提交时间:2017/05/09
Super-resolution  Ensemble  Sparse Prior  Deep Networks