VAC-Net: Visual Attention Consistency Network for Person Re-identification

Weidong Shi, Yunzhou Zhang, Shangdong Zhu, Yixiu Liu, Sonya Coleman, Dermot Kerr

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

1 Citation (Scopus)


Person re-identification (ReID) is a crucial aspect of recognising pedestrians across multiple surveillance cameras. Even though significant progress has been made in recent years, the viewpoint change and scale variations still affect model performance. In this paper, we observe that it is beneficial for the model to handle the above issues when boost the consistent feature extraction capability among different transforms (e.g., flipping and scaling) of the same image. To this end, we propose a visual attention consistency network (VAC-Net). Specifically, we propose Embedding Spatial Consistency (ESC) architecture with flipping, scaling and original forms of the same image as inputs to learn a consistent embedding space. Furthermore, we design an Input-Wise visual attention consistent loss (IW-loss) so that the class activation maps(CAMs) from the three transforms are aligned with each other to enforce their advanced semantic information remains consistent. Finally, we propose a Layer-Wise visual attention consistent loss (LW-loss) to further enforce the semantic information among different stages to be consistent with the CAMs within each branch. These two losses can effectively improve the model to address the viewpoint and scale variations. Experiments on the challenging Market-1501, DukeMTMC-reID, and MSMT17 datasets demonstrate the effectiveness of the proposed VAC-Net.
Original languageEnglish
Title of host publicationICMR '22: Proceedings of the 2022 International Conference on Multimedia Retrieval
PublisherAssociation for Computing Machinery
Number of pages8
ISBN (Print)978-1-4503-9238-9
Publication statusPublished (in print/issue) - 27 Jun 2022
EventICMR '22: International Conference on Multimedia Retrieval - Newark, United States
Duration: 27 Jun 202230 Jun 2022

Publication series

NameICMR 2022 - Proceedings of the 2022 International Conference on Multimedia Retrieval


ConferenceICMR '22: International Conference on Multimedia Retrieval
Abbreviated titleICMR '22
Country/TerritoryUnited States
Internet address

Bibliographical note

Funding Information:
This work is supported by National Natural Science Foundation of China (No. 61973066, 61471110), Foundation Project of National Key Laboratory (6142002301), and Fundamental Research Funds for the Central Universities (N172608005, N182608004).

Publisher Copyright:
© 2022 ACM.


  • person re-identification
  • scale variations
  • viewpoint change
  • visual attention


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