中国科学院机构知识库网格
Chinese Academy of Sciences Institutional Repositories Grid
A Hybrid of Hard and Soft Attention for Person Re-Identification

文献类型:会议论文

作者Li Xuesong; Liu Yating; Wang Kunfeng; Yan Yong; Wang Fei-Yue
出版日期2020-02
会议日期22-24 Nov. 2019
会议地点Hangzhou, China
关键词person re-identification attention model computer vision deep learning
DOI10.1109/CAC48633.2019.8997406
英文摘要

Existing pedestrian re-identification methods based on deep learning have achieved good results under constrained conditions. However, there exist some challenges including large human pose variations, viewpoint changes, severe occlusions and imprecise detection of persons. So we present a Hard/Soft hybrid Attention Network (HSAN) that combines pose information and attention mechanism to deal with the challenges. Our model includes two main parts: Pose-guided Hard Attention (PHA) and Regional Soft Attention (RSA). PHA uses the keypoints generated by pose estimation to enhance the foreground information, and RSA is learned to eliminate the background clutter. We extract reliable features and locate discriminative regions by using these two modules to handle occlusions, pose changes and background noises. We conduct a lot of experiments on public datasets including DukeMTMC-ReID, Market-1501, and CUHK03, and the results show that our method achieves stateof-the-art performance. 

会议录出版者IEEE
语种英语
源URL[http://ir.ia.ac.cn/handle/173211/39061]  
专题自动化研究所_复杂系统管理与控制国家重点实验室_先进控制与自动化团队
作者单位1.The State Key Laboratory for Management and Control of Complex Systems, Institute of Automation, Chinese Academy of Sciences
2.University of Chinese Academy of Sciences
推荐引用方式
GB/T 7714
Li Xuesong,Liu Yating,Wang Kunfeng,et al. A Hybrid of Hard and Soft Attention for Person Re-Identification[C]. 见:. Hangzhou, China. 22-24 Nov. 2019.

入库方式: OAI收割

来源:自动化研究所

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