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Deep Reinforcement Learning With Part-Aware Exploration Bonus in Video Games 期刊论文
IEEE TRANSACTIONS ON GAMES, 2022, 卷号: 14, 期号: 4, 页码: 644-653
作者:  Xu, Pei;  Yin, Qiyue;  Zhang, Junge;  Huang, Kaiqi
Adobe PDF(1480Kb)  |  收藏  |  浏览/下载：269/68  |  提交时间：2023/02/22
Deep learning  exploration  reinforcement learning  video game  
Holographic Feature Learning of Egocentric-Exocentric Videos for Multi-Domain Action Recognition 期刊论文
IEEE TRANSACTIONS ON MULTIMEDIA, 2022, 卷号: 24, 页码: 2273-2286
作者:  Huang, Yi;  Yang, Xiaoshan;  Gao, Junyun;  Xu, Changsheng
Adobe PDF(2409Kb)  |  收藏  |  浏览/下载：287/61  |  提交时间：2022/07/25
Videos  Feature extraction  Visualization  Task analysis  Computational modeling  Target recognition  Prototypes  Egocentric videos  exocentric videos  holographic feature  multi-domain  action recognition  
Learning a Robust Part-Aware Monocular 3D Human Pose Estimator via Neural Architecture Search 期刊论文
INTERNATIONAL JOURNAL OF COMPUTER VISION, 2021, 页码: 20
作者:  Chen, Zerui;  Huang, Yan;  Yu, Hongyuan;  Wang, Liang
收藏  |  浏览/下载：168/0  |  提交时间：2021/12/28
Monocular 3D human pose estimation  Heterogeneous human body parts  Neural architecture search  
Richly Activated Graph Convolutional Network for Robust Skeleton-Based Action Recognition 期刊论文
IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY, 2021, 卷号: 31, 期号: 5, 页码: 1915-1925
作者:  Song, Yi-Fan;  Zhang, Zhang;  Shan, Caifeng;  Wang, Liang
Adobe PDF(3381Kb)  |  收藏  |  浏览/下载：337/55  |  提交时间：2021/06/15
Skeleton  Robustness  Noise measurement  Three-dimensional displays  Degradation  Standards  Feature extraction  Action recognition  skeleton  activation map  graph convolutional network  occlusion  jittering  
End -to -end video text detection with online tracking 期刊论文
PATTERN RECOGNITION, 2021, 卷号: 113, 页码: 12
作者:  Yu, Hongyuan;  Huang, Yan;  Pi, Lihong;  Zhang, Chengquan;  Li, Xuan;  Wang, Liang
Adobe PDF(4997Kb)  |  收藏  |  浏览/下载：290/54  |  提交时间：2021/05/06
End-to-end  Video text detection  Online tracking  
Proxy Graph Matching with Proximal Matching Networks 会议论文
, 线上远程会议, 2021-2-7
作者:  Tan HR(檀昊儒);  Wang C(王闯);  Wu ST(吴思彤);  Wang TQ(王铁强);  Zhang XY(张煦尧);  Liu CL(刘成林)
Adobe PDF(423Kb)  |  收藏  |  浏览/下载：226/59  |  提交时间：2021/07/01
Graph Matching  Combinatorial Optimization  Deep Learning  
Universal adversarial perturbations against object detection 期刊论文
PATTERN RECOGNITION, 2021, 卷号: 110, 期号: 无, 页码: 107584
作者:  Li, Debang;  Zhang, Junge;  Huang, Kaiqi
Adobe PDF(4553Kb)  |  收藏  |  浏览/下载：285/35  |  提交时间：2021/01/06
Adversarial examples  Object detection  Universal adversarial perturbation  
FA-GAN: Face Augmentation GAN for Deformation-Invariant Face Recognition 期刊论文
IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY, 2021, 卷号: 16, 期号: 0, 页码: 2341-2355
作者:  Luo, Mandi;  Cao, Jie;  Ma, Xin;  Zhang, Xiaoyu;  He, Ran
Adobe PDF(4742Kb)  |  收藏  |  浏览/下载：302/52  |  提交时间：2021/04/21
Face recognition  Strain  Geometry  Frequency division multiplexing  Training  Task analysis  Semantics  Face augmentation  deformation-invariant face recognition  face disentanglement  graph convolutional networks  
Partial NIR-VIS Heterogeneous Face Recognition With Automatic Saliency Search 期刊论文
IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY, 2021, 卷号: 16, 页码: 5003-5017
作者:  Luo, Mandi;  Ma, Xin;  Li, Zhihang;  Cao, Jie;  He, Ran
Adobe PDF(4346Kb)  |  收藏  |  浏览/下载：242/53  |  提交时间：2021/12/28
Face recognition  Task analysis  Visualization  Image recognition  Lighting  Feature extraction  Training  Heterogeneous face recognition  near infrared-visible matching  information bottleneck  neural architecture search  
Learning Aligned Image-Text Representations Using Graph Attentive Relational Network 期刊论文
IEEE TRANSACTIONS ON IMAGE PROCESSING, 2021, 期号: 30, 页码: 1840-1852
作者:  Jing, Ya;  Wang, Wei;  Wang, Liang;  Tan, Tieniu
Adobe PDF(4532Kb)  |  收藏  |  浏览/下载：299/49  |  提交时间：2021/03/08
Graph neural networks  Visualization  Semantics  Task analysis  Feature extraction  Annotations  Recurrent neural networks  Image-text matching  cross-modal retrieval  person search  graph neural network