research-article

More is better: Multi-source Dynamic Parsing Attention for Occluded Person Re-identification

Authors:

Jian ZhangAuthors Info & Claims

MM '22: Proceedings of the 30th ACM International Conference on Multimedia

Pages 6840 - 6849

https://doi.org/10.1145/3503161.3547819

Published: 10 October 2022 Publication History

Abstract

Occluded person re-identification (re-ID) has been a long-standing challenge in surveillance systems. Most existing methods tackle this challenge by aligning spatial features of human parts according to external semantic cues, which are inferred from the off-the-shelf semantic models (e.g. human parsing and pose estimation). However, there is a significant domain gap between the images in re-ID datasets and the images used for training the semantic models, such that inevitably making those semantic cues unreliable and deteriorating the re-ID performance. Multi-source knowledge ensemble has been proved to be effective for domain adaptation. Inspired by this, we propose a multi-source dynamic parsing attention (MSDPA) mechanism that leverages knowledge learned from different source datasets to generate reliable semantic cues and dynamically integrate and adapt them in a self-supervised manner by attention mechanism. Specifically, we first design a parsing embedding module (PEM) to integrate and embed the multi-source semantic cues into the patch tokens through a voting procedure. To further exploit correlations among body parts with similar semantics, we design a dynamic parsing attention block (DPAB) to guide the patch sequences aggregation by prior attentions which are dynamically generated from human parsing results. Extensive experiments over occluded, partial, and holistic re-ID datasets show that the MSDPA achieves superior re-ID performance consistently and outperforms the state-of-the-art methods by large margins on occluded datasets.

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Cited By

Tan ZZhang GTan ZTiwari PWang YYang Y(2025) Former: Fusion of Camera-specific Class Activation Map matters for occluded person re-identification Information Fusion10.1016/j.inffus.2025.103011(103011)Online publication date: Feb-2025
https://doi.org/10.1016/j.inffus.2025.103011
Zhang LCheng SWang L(2025)Label-guided diversified learning model for occluded person re-identificationExpert Systems with Applications10.1016/j.eswa.2025.126745272(126745)Online publication date: May-2025
https://doi.org/10.1016/j.eswa.2025.126745
Xiang CWu XHe JYuan ZHe T(2024)Person in Uniforms Re-IdentificationACM Transactions on Multimedia Computing, Communications, and Applications10.1145/370383921:2(1-23)Online publication date: 11-Nov-2024
https://dl.acm.org/doi/10.1145/3703839
Show More Cited By

Index Terms

More is better: Multi-source Dynamic Parsing Attention for Occluded Person Re-identification
1. Computing methodologies
  1. Artificial intelligence
    1. Computer vision
      1. Computer vision problems
        Object identification

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cover image ACM Conferences

MM '22: Proceedings of the 30th ACM International Conference on Multimedia

October 2022

7537 pages

ISBN:9781450392037

DOI:10.1145/3503161

General Chairs:
João Magalhães
NOVA University of Lisbon, Portugal
,
Alberto del Bimbo
University of Florence, Italy
,
Shin'ichi Satoh
National Institute of Informatics, Japan
,
Nicu Sebe
University of Trento, Italy
,
Program Chairs:
Xavier Alameda-Pineda
Inria, Grenoble, France
,
Qin Jin
Renmin University of China, China
,
Vincent Oria
New Jersey Institute of Technology, USA
,
Laura Toni
University College London, UK

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Published: 10 October 2022

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Shenzhen General Research Project

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MM '22

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MM '22: The 30th ACM International Conference on Multimedia

October 10 - 14, 2022

Lisboa, Portugal

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Overall Acceptance Rate 2,145 of 8,556 submissions, 25%

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Cited By

Tan ZZhang GTan ZTiwari PWang YYang Y(2025) Former: Fusion of Camera-specific Class Activation Map matters for occluded person re-identification Information Fusion10.1016/j.inffus.2025.103011(103011)Online publication date: Feb-2025
https://doi.org/10.1016/j.inffus.2025.103011
Zhang LCheng SWang L(2025)Label-guided diversified learning model for occluded person re-identificationExpert Systems with Applications10.1016/j.eswa.2025.126745272(126745)Online publication date: May-2025
https://doi.org/10.1016/j.eswa.2025.126745
Xiang CWu XHe JYuan ZHe T(2024)Person in Uniforms Re-IdentificationACM Transactions on Multimedia Computing, Communications, and Applications10.1145/370383921:2(1-23)Online publication date: 11-Nov-2024
https://dl.acm.org/doi/10.1145/3703839
Cui CHuang SSong WDing PZhang MWang DCai JKankanhalli MPrabhakaran BBoll SSubramanian RZheng LSingh VCesar PXie LXu D(2024)ProFD: Prompt-Guided Feature Disentangling for Occluded Person Re-IdentificationProceedings of the 32nd ACM International Conference on Multimedia10.1145/3664647.3680958(1583-1592)Online publication date: 28-Oct-2024
https://dl.acm.org/doi/10.1145/3664647.3680958
Qin LWang ZYan RTang H(2024)Attention-Based Deep Spiking Neural Networks for Temporal Credit Assignment ProblemsIEEE Transactions on Neural Networks and Learning Systems10.1109/TNNLS.2023.324017635:8(10301-10311)Online publication date: Aug-2024
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Li YLiu YZhang HZhao CWei ZMiao D(2024)Occlusion-Aware Transformer With Second-Order Attention for Person Re-IdentificationIEEE Transactions on Image Processing10.1109/TIP.2024.339336033(3200-3211)Online publication date: 2024
https://doi.org/10.1109/TIP.2024.3393360
He SChen WWang KLuo HWang FJiang WDing H(2024)Region Generation and Assessment Network for Occluded Person Re-IdentificationIEEE Transactions on Information Forensics and Security10.1109/TIFS.2023.331895619(120-132)Online publication date: 1-Jan-2024
https://dl.acm.org/doi/10.1109/TIFS.2023.3318956
Yu ZLi LXie JWang CLi WNing X(2024)Pedestrian 3D Shape Understanding for Person Re-Identification via Multi-View LearningIEEE Transactions on Circuits and Systems for Video Technology10.1109/TCSVT.2024.335885034:7(5589-5602)Online publication date: Jul-2024
https://doi.org/10.1109/TCSVT.2024.3358850
Lin GBao ZHuang ZLi ZZheng WChen Y(2024)A Multi-Level Relation-Aware Transformer model for occluded person re-identificationNeural Networks10.1016/j.neunet.2024.106382177(106382)Online publication date: Sep-2024
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Hou Sliu XWu CYin GWang XWang Z(2024)Self-selective receptive field network for person re-identificationComplex & Intelligent Systems10.1007/s40747-024-01565-210:6(7777-7797)Online publication date: 5-Aug-2024
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