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Relational graph neural network for situation recognition 期刊论文
Pattern Recognition, 2020, 期号: 108, 页码: 107544
作者:  Jing Y(荆雅);  Wang JB(王君波);  Wang W(王威);  Wang L(王亮);  Tan TN(谭铁牛)
Adobe PDF(3098Kb)  |  收藏  |  浏览/下载：180/40  |  提交时间：2021/06/07
Situation recognition  Relationship modeling  Graph neural network  Reinforcement learning  
Classification of Severe and Critical Covid-19 Using Deep Learning and Radiomics 期刊论文
IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS, 2020, 卷号: 24, 期号: 12, 页码: 3585-3594
作者:  Li, Cong;  Dong, Di;  Li, Liang;  Gong, Wei;  Li, Xiaohu;  Bai, Yan;  Wang, Meiyun;  Hu, Zhenhua;  Zha, Yunfei;  Tian, Jie
Adobe PDF(2325Kb)  |  收藏  |  浏览/下载：346/56  |  提交时间：2021/03/02
COVID-19  radiomics  deep learning  computed tomography (CT)  
A Deep Learning Prognosis Model Help Alert for COVID-19 Patients at High-Risk of Death: A Multi-Center Study 期刊论文
IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS, 2020, 卷号: 24, 期号: 12, 页码: 3576-3584
作者:  Meng, Lingwei;  Dong, Di;  Li, Liang;  Niu, Meng;  Bai, Yan;  Wang, Meiyun;  Qiu, Xiaoming;  Zha, Yunfei;  Tian, Jie
Adobe PDF(4120Kb)  |  收藏  |  浏览/下载：326/54  |  提交时间：2021/03/02
COVID-19  Computed tomography  Lung  Hospitals  Biomedical imaging  Training  Coronavirus disease 2019 (COVID-19)  prognosis  computed tomography  deep learning  artificial intelligence  
Robust Object Tracking via Information Theoretic Measures 期刊论文
International Journal of Automation and Computing, 2020, 卷号: 17, 期号: 5, 页码: 652-666
作者:  Wei-Ning Wang;  Qi Li;  Liang Wang
Adobe PDF(1855Kb)  |  收藏  |  浏览/下载：162/20  |  提交时间：2021/02/22
Object tracking  information theoretic measures  correntropy  template update  robust to complex noises.  
Manipulating Template Pixels for Model Adaptation of Siamese Visual Tracking 期刊论文
IEEE SIGNAL PROCESSING LETTERS, 2020, 卷号: 27, 页码: 1690-1694
作者:  Li, Zhenbang;  Li, Bing;  Gao, Jin;  Li, Liang;  Hu, Weiming
收藏  |  浏览/下载：204/0  |  提交时间：2021/01/07
Adaptation models  Target tracking  Task analysis  Visualization  Feature extraction  Object tracking  Training  Model adaptation  siamese networks  visual tracking  
Spatial Preserved Graph Convolution Networks for Person Re-identification 期刊论文
ACM TRANSACTIONS ON MULTIMEDIA COMPUTING COMMUNICATIONS AND APPLICATIONS, 2020, 卷号: 16, 期号: 1, 页码: 14
作者:  Li, Zhaoju;  Zhou, Zongwei;  Jiang, Nan;  Han, Zhenjun;  Xing, Junliang;  Jiao, Jianbin
Adobe PDF(937Kb)  |  收藏  |  浏览/下载：222/40  |  提交时间：2021/01/06
Person re-identification  graph convolution  feature embedding  
Enhanced Motor Imagery Based Brain- Computer Interface via FES and VR for Lower Limbs 期刊论文
IEEE TRANSACTIONS ON NEURAL SYSTEMS AND REHABILITATION ENGINEERING, 2020, 卷号: 28, 期号: 8, 页码: 1846-1855
作者:  Ren, Shixin;  Wang, Weiqun;  Hou, Zeng-Guang;  Liang, Xu;  Wang, Jiaxing;  Shi, Weiguo
Adobe PDF(1893Kb)  |  收藏  |  浏览/下载：300/51  |  提交时间：2020/09/07
Brain computer interface  functional electrical stimulation (FES)  virtual reality  enhanced motor imagery  rehabilitation training  
Robust Object Tracking via Information Theoretic Measures 期刊论文
International Journal of Automation and Computing, 2020, 期号: 17, 页码: 1
作者:  Wang, Weining;  Li, Qi;  Wang, Liang
浏览  |  Adobe PDF(9045Kb)  |  收藏  |  浏览/下载：188/54  |  提交时间：2020/09/03
Object tracking, information theoretic measures, correntropy, template update, robust to complex noises  
Skeleton-based action recognition with hierarchical spatial reasoning and temporal stack learning network 期刊论文
PATTERN RECOGNITION, 2020, 卷号: 107, 期号: 107511, 页码: 12
作者:  Si, Chenyang;  Jing, Ya;  Wang, Wei;  Wang, Liang;  Tan, Tieniu
Adobe PDF(2378Kb)  |  收藏  |  浏览/下载：357/67  |  提交时间：2020/08/31
Skeleton-based action recognition  Hierarchical spatial reasoning  Temporal stack learning  Clip-based incremental loss  
Distractor-aware discrimination learning for online multiple object tracking 期刊论文
PATTERN RECOGNITION, 2020, 期号: 107, 页码: 10
作者:  Zhou, Zongwei;  Luo, Wenhan;  Wang, Qiang;  Xing, Junliang;  Hu, Weiming
Adobe PDF(840Kb)  |  收藏  |  浏览/下载：391/111  |  提交时间：2020/08/31
Multi-object tracking  Distractor-aware discrimination learning  Relational attention learning