CASIA OpenIR

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:
Encoding-decoding Network With Pyramid Self-attention Module for Retinal Vessel Segmentation 期刊论文
International Journal of Automation and Computing, 2021, 卷号: 18, 期号: 6, 页码: 973-980
作者:  Cong-Zhong Wu;  Jun Sun;  Jing Wang;  Liang-Feng Xu;  Shu Zhan
Adobe PDF(1416Kb)  |  收藏  |  浏览/下载:190/45  |  提交时间:2021/11/26
Retina vessel segmentation  deep learning  U-Net  attention mechanism  medical image  
Improved Network for Face Recognition Based on Feature Super Resolution Method 期刊论文
International Journal of Automation and Computing, 2021, 卷号: 18, 期号: 6, 页码: 915-925
作者:  Ling-Yi Xu;  Zoran Gajic
Adobe PDF(1989Kb)  |  收藏  |  浏览/下载:202/46  |  提交时间:2021/11/26
Face recognition  feature super resolution  multiple-branch network  deep learning  convolutional neural networks  
Supervised and Semi-supervised Methods for Abdominalm Organ Segmentation: A Review 期刊论文
International Journal of Automation and Computing, 2021, 卷号: 18, 期号: 6, 页码: 887-914
作者:  Isaac Baffour Senkyire;  Zhe Liu
Adobe PDF(1308Kb)  |  收藏  |  浏览/下载:249/51  |  提交时间:2021/11/26
Abdominal organ, supervised segmentation  semi-supervised segmentation  evaluation metrics  image segmentation  machine learning  
Contrastive Self-supervised Representation Learning Using Synthetic Data 期刊论文
International Journal of Automation and Computing, 2021, 卷号: 18, 期号: 4, 页码: 556-567
作者:  Dong-Yu She;  Kun Xu
Adobe PDF(993Kb)  |  收藏  |  浏览/下载:198/47  |  提交时间:2021/07/20
Self-supervised learning  contrastive learning  synthetic image  convolutional neural network  representation learning  
Learning Deep RGBT Representations for Robust Person Re-identification 期刊论文
International Journal of Automation and Computing, 2021, 卷号: 18, 期号: 3, 页码: 443-456
作者:  Ai-Hua Zheng;  Zi-Han Chen;  Cheng-Long Li;  Jin Tang;  Bin Luo
Adobe PDF(1832Kb)  |  收藏  |  浏览/下载:287/63  |  提交时间:2021/05/24
Person re-identification (Re-ID)  thermal infrared  generative networks  attention  deep learning  
2D and 3D Palmprint and Palm Vein Recognition Based on Neural Architecture Search 期刊论文
International Journal of Automation and Computing, 2021, 卷号: 18, 期号: 3, 页码: 377-409
作者:  Wei Jia;  Wei Xia;  Yang Zhao;  Hai Min;  Yan-Xiang Chen
Adobe PDF(15758Kb)  |  收藏  |  浏览/下载:337/48  |  提交时间:2021/05/24
Performance evaluation  neural architecture search  biometrics  palmprint  palm vein  deep learning  
Deep Audio-Visual Learning: A Survey 期刊论文
International Journal of Automation and Computing, 2021, 卷号: 18, 期号: 3, 页码: 351-376
作者:  Hao Zhu;  Man-Di Luo;  Rui Wang;  Ai-Hua Zheng;  Ran He
Adobe PDF(1864Kb)  |  收藏  |  浏览/下载:218/45  |  提交时间:2021/05/24
Deep audio-visual learning  audio-visual separation and localization  correspondence learning  generative models  representation learning  
Advances in Deep Learning Methods for Visual Tracking: Literature Review and Fundamentals 期刊论文
International Journal of Automation and Computing, 2021, 卷号: 18, 期号: 3, 页码: 311-333
作者:  Xiao-Qin Zhang;  Run-Hua Jiang;  Chen-Xiang Fan;  Tian-Yu Tong;  Tao Wang Peng-Cheng Huang
Adobe PDF(1787Kb)  |  收藏  |  浏览/下载:287/44  |  提交时间:2021/05/24
Deep learning  visual tracking  data-invariant  data-adaptive  general components  
Computational Intelligence in Remote Sensing Image Registration: A survey 期刊论文
International Journal of Automation and Computing, 2021, 卷号: 18, 期号: 1, 页码: 1-17
作者:  Yue Wu;  Jun-Wei Liu;  Chen-Zhuo Zhu;  Zhuang-Fei Bai;  Qi-Guang Miao;  Wen-Ping Ma;  Mao-Guo Gong
浏览  |  Adobe PDF(995Kb)  |  收藏  |  浏览/下载:281/73  |  提交时间:2021/02/23
Computational intelligence  evolutionary computation  neural network  deep learning  remote sensing image registration  
Deep Learning Based Single Image Super-resolution: A Survey 期刊论文
International Journal of Automation and Computing, 2019, 卷号: 16, 期号: 4, 页码: 413-426
作者:  Viet Khanh Ha;  Jin-Chang Ren;  Xin-Ying Xu;  Sophia Zhao;  Gang Xie;  Valentin Masero;  Amir Hussain
浏览  |  Adobe PDF(1389Kb)  |  收藏  |  浏览/下载:141/36  |  提交时间:2021/02/22
Image super-resolution  convolutional neural network  high-resolution image  low-resolution image  deep learning.