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PokerNet: Expanding Features Cheaply via Depthwise Convolutions 期刊论文
International Journal of Automation and Computing, 2021, 卷号: 18, 期号: 3, 页码: 432-442
作者:  Wei Tang;  Yan Huang;  Liang Wang
Adobe PDF(1163Kb)  |  收藏  |  浏览/下载：214/32  |  提交时间：2021/05/24
Deep learning  depthwise convolution  lightweight deep model  model compression  model acceleration  
Dynamic Event-triggered Control and Estimation: A Survey 期刊论文
International Journal of Automation and Computing, 2021, 卷号: 18, 期号: 6, 页码: 857-886
作者:  Xiaohua Ge;  Qing-Long Han;  Xian-Ming Zhang;  Derui Ding
Adobe PDF(3887Kb)  |  收藏  |  浏览/下载：190/36  |  提交时间：2021/11/26
Networked systems  dynamic event-triggered control  dynamic event-triggered estimation  dynamic event-triggered mechanisms  vehicle active suspension system  water distribution and supply system  
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：191/36  |  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：259/38  |  提交时间：2021/05/24
Deep learning  visual tracking  data-invariant  data-adaptive  general components  
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：177/41  |  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：252/49  |  提交时间：2021/05/24
Person re-identification (Re-ID)  thermal infrared  generative networks  attention  deep learning  
Hybrid Approach to Document Anomaly Detection: An Application to Facilitate RPA in Title Insurance 期刊论文
International Journal of Automation and Computing, 2021, 卷号: 18, 期号: 1, 页码: 55-72
作者:  Abhijit Guha;  Debabrata Samanta
浏览  |  Adobe PDF(1485Kb)  |  收藏  |  浏览/下载：190/53  |  提交时间：2021/02/23
Anomaly detection  title insurance  autoencoder  one-class support vector machine (OSVM)  term frequency – inverse document frequency (TF-IDF)  robotic process automation  dimensionality reduction  
Knowing Your Dog Breed: Identifying a Dog Breed with Deep Learning 期刊论文
International Journal of Automation and Computing, 2021, 卷号: 18, 期号: 1, 页码: 45-54
作者:  Punyanuch Borwarnginn;  Worapan Kusakunniran;  Sarattha Karnjanapreechakorn;  Kittikhun Thongkanchorn
浏览  |  Adobe PDF(1149Kb)  |  收藏  |  浏览/下载：187/67  |  提交时间：2021/02/23
Computer vision  deep learning  dog breed classification  transfer learning  image augmentation  
A Performance Evaluation of Classic Convolutional Neural Networks for 2D and 3D Palmprint and Palm Vein Recognition 期刊论文
International Journal of Automation and Computing, 2021, 卷号: 18, 期号: 1, 页码: 18-44
作者:  Wei Jia;  Jian Gao;  Wei Xia;  Yang Zhao;  Hai Min;  Jing-Ting Lu
浏览  |  Adobe PDF(2680Kb)  |  收藏  |  浏览/下载：212/76  |  提交时间：2021/02/23
Performance evaluation  convolutional neural network (CNN)  biometrics  palmprint  palm vein  deep learning  
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：208/47  |  提交时间：2021/11/26
Abdominal organ, supervised segmentation  semi-supervised segmentation  evaluation metrics  image segmentation  machine learning