Reliable Part Guided Multiple Level Attention Learning for Person Re-Identification

Yanbing GENG, Yongjian LIAN, Shunmin YANG, Mingliang ZHOU, Jingchao CAO

Research output: Journal PublicationsJournal Article (refereed)peer-review

2 Citations (Scopus)


Person Re-ID is challenged by background clutter, body misalignment and part missing. In this paper, we propose a reliable part-based multiple levels attention deep network to learn multiple scales salience representation. In particular, person alignment and key point detection are sequentially carried out to locate three relative stable body components, then fused attention (FA) mode is designed to capture the fine-grained salient features from effective spatial of valuable channels of each part, regional attention mode is succeeded to weight the importance of different parts for highlighting the representative parts while suppressing the valueless ones. A late fusion-based multiple-task loss is finally adopted to further optimize the valuable feature representation. Experimental results demonstrate that the proposed method achieves state-of-the-art performances on three challenging benchmarks: Market-1501, DukeMTMC-reID and CUHK03.

Original languageEnglish
Article number2150246
JournalJournal of Circuits, Systems and Computers
Issue number13
Early online date25 May 2021
Publication statusPublished - Oct 2021
Externally publishedYes

Bibliographical note

Publisher Copyright:
© 2021 World Scientific Publishing Company.


  • fused attention
  • multiple level attention network
  • Person re-identification
  • regional attention
  • reliable part


Dive into the research topics of 'Reliable Part Guided Multiple Level Attention Learning for Person Re-Identification'. Together they form a unique fingerprint.

Cite this