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Global Instance Tracking: Locating Target More Like Humans 期刊论文
IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE, 2023, 卷号: 45, 期号: 1, 页码: 576-592
作者:  Hu, Shiyu;  Zhao, Xin;  Huang, Lianghua;  Huang, Kaiqi
Adobe PDF(15055Kb)  |  收藏  |  浏览/下载：229/54  |  提交时间：2023/02/22
Global instance tracking  single object tracking  benchmark dataset  performance evaluation  human tracking ability  
Nonlinear Asymmetric Multi-Valued Hashing 期刊论文
IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE, 2019, 卷号: 41, 期号: 11, 页码: 2660-2676
作者:  Da, Cheng;  Meng, Gaofeng;  Xiang, Shiming;  Ding, Kun;  Xu, Shibiao;  Yang, Qing;  Pan, Chunhong
浏览  |  Adobe PDF(2583Kb)  |  收藏  |  浏览/下载：500/117  |  提交时间：2018/10/07
Asymmetric hashing  multi-valued embeddings  binary sparse representation  nonlinear transformation  
Salient Object Detection via Structured Matrix Decomposition 期刊论文
IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE, 2017, 卷号: 39, 期号: 4, 页码: 818-832
作者:  Peng, Houwen;  Li, Bing;  Ling, Haibin;  Hu, Weiming;  Xiong, Weihua;  Maybank, Stephen J.
Adobe PDF(3573Kb)  |  收藏  |  浏览/下载：471/178  |  提交时间：2017/05/05
Salient Object Detection  Matrix Decomposition  Low Rank  Structured Sparsity  Subspace Learning  
Semi-Supervised Tensor-Based Graph Embedding Learning and Its Application to Visual Discriminant Tracking 期刊论文
IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE, 2017, 卷号: 39, 期号: 1, 页码: 172-188
作者:  Hu, Weiming;  Gao, Jin;  Xing, Junliang;  Zhang, Chao;  Maybank, Stephen;  Weiming Hu
浏览  |  Adobe PDF(7133Kb)  |  收藏  |  浏览/下载：450/137  |  提交时间：2016/04/13
Discriminant Tracking  Tensor Samples  Semi-supervised Learning  Graph Embedding Space  
Robust Structured Subspace Learning for Data Representation 期刊论文
IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE, 2015, 卷号: 37, 期号: 10, 页码: 2085-2098
作者:  Li, Zechao;  Liu, Jing;  Tang, Jinhui;  Lu, Hanqing
浏览  |  Adobe PDF(525Kb)  |  收藏  |  浏览/下载：964/418  |  提交时间：2015/10/13
Data Representation  Latent Subspace  Image Understanding  Feature Learning  Structure Preserving