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Hybrid Modality Metric Learning for Visible-Infrared Person Re-Identification

Authors:

Jinqiao WangAuthors Info & Claims

ACM Transactions on Multimedia Computing, Communications, and Applications (TOMM), Volume 18, Issue 1s

Article No.: 25, Pages 1 - 15

https://doi.org/10.1145/3473341

Published: 25 January 2022 Publication History

Abstract

Visible-infrared person re-identification (Re-ID) has received increasing research attention for its great practical value in night-time surveillance scenarios. Due to the large variations in person pose, viewpoint, and occlusion in the same modality, as well as the domain gap brought by heterogeneous modality, this hybrid modality person matching task is quite challenging. Different from the metric learning methods for visible person re-ID, which only pose similarity constraints on class level, an efficient metric learning approach for visible-infrared person Re-ID should take both the class-level and modality-level similarity constraints into full consideration to learn sufficiently discriminative and robust features. In this article, the hybrid modality is divided into two types, within modality and cross modality. We first fully explore the variations that hinder the ranking results of visible-infrared person re-ID and roughly summarize them into three types: within-modality variation, cross-modality modality-related variation, and cross-modality modality-unrelated variation. Then, we propose a comprehensive metric learning framework based on four kinds of paired-based similarity constraints to address all the variations within and cross modality. This framework focuses on both class-level and modality-level similarity relationships between person images. Furthermore, we demonstrate the compatibility of our framework with any paired-based loss functions by giving detailed implementation of combing it with triplet loss and contrastive loss separately. Finally, extensive experiments of our approach on SYSU-MM01 and RegDB demonstrate the effectiveness and superiority of our proposed metric learning framework for visible-infrared person Re-ID.

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Cited By

Zhu YZheng YLiu JLi YZha Z(2024)Noise-Resistance Learning via Multi-Granularity Consistency for Unsupervised Domain Adaptive Person Re-IdentificationACM Transactions on Multimedia Computing, Communications, and Applications10.1145/3702328Online publication date: 2-Nov-2024
https://doi.org/10.1145/3702328
Yuan BLu JYou SBao B(2024)Unbiased Feature Learning with Causal Intervention for Visible-Infrared Person Re-IdentificationACM Transactions on Multimedia Computing, Communications, and Applications10.1145/367473720:10(1-20)Online publication date: 27-Jun-2024
https://dl.acm.org/doi/10.1145/3674737
Liu ZYang YWu KLiu QXu XMa XTang J(2024)ASIFusion: An Adaptive Saliency Injection-Based Infrared and Visible Image Fusion NetworkACM Transactions on Multimedia Computing, Communications, and Applications10.1145/366589320:9(1-23)Online publication date: 23-May-2024
https://dl.acm.org/doi/10.1145/3665893
Show More Cited By

Index Terms

Hybrid Modality Metric Learning for Visible-Infrared Person Re-Identification
1. Computing methodologies
  1. Artificial intelligence
    1. Computer vision
      1. Computer vision tasks
        Biometrics

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Published In

cover image ACM Transactions on Multimedia Computing, Communications, and Applications

ACM Transactions on Multimedia Computing, Communications, and Applications Volume 18, Issue 1s

February 2022

352 pages

ISSN:1551-6857

EISSN:1551-6865

DOI:10.1145/3505206

Editor:
Alberto Del Bimbo
University of Firenze, Italy

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 25 January 2022

Accepted: 01 June 2021

Revised: 01 May 2021

Received: 01 January 2020

Published in TOMM Volume 18, Issue 1s

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Key-Area Research and Development Program of Guangdong Province
National Natural Science Foundation of China
Open Project of Key Laboratory of Ministry of Public Security for Road Traffic Safety

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Cited By

Zhu YZheng YLiu JLi YZha Z(2024)Noise-Resistance Learning via Multi-Granularity Consistency for Unsupervised Domain Adaptive Person Re-IdentificationACM Transactions on Multimedia Computing, Communications, and Applications10.1145/3702328Online publication date: 2-Nov-2024
https://doi.org/10.1145/3702328
Yuan BLu JYou SBao B(2024)Unbiased Feature Learning with Causal Intervention for Visible-Infrared Person Re-IdentificationACM Transactions on Multimedia Computing, Communications, and Applications10.1145/367473720:10(1-20)Online publication date: 27-Jun-2024
https://dl.acm.org/doi/10.1145/3674737
Liu ZYang YWu KLiu QXu XMa XTang J(2024)ASIFusion: An Adaptive Saliency Injection-Based Infrared and Visible Image Fusion NetworkACM Transactions on Multimedia Computing, Communications, and Applications10.1145/366589320:9(1-23)Online publication date: 23-May-2024
https://dl.acm.org/doi/10.1145/3665893
Zeng XWang XXie Y(2024)Multiple Pseudo-Siamese Network with Supervised Contrast Learning for Medical Multi-modal RetrievalACM Transactions on Multimedia Computing, Communications, and Applications10.1145/363744120:5(1-23)Online publication date: 11-Jan-2024
https://dl.acm.org/doi/10.1145/3637441
Liu HWu JLi FJiang JHong R(2024)SYRER: Synergistic Relational Reasoning for RGB-D Cross-Modal Re-IdentificationIEEE Transactions on Multimedia10.1109/TMM.2023.333805826(5600-5614)Online publication date: 2024
https://doi.org/10.1109/TMM.2023.3338058
Li ZLiu HPeng XJiang W(2024)Inter-Intra Modality Knowledge Learning and Clustering Noise Alleviation for Unsupervised Visible-Infrared Person Re-IdentificationIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2024.336730436:8(3934-3947)Online publication date: Aug-2024
https://doi.org/10.1109/TKDE.2024.3367304
Zhang GZhang YZhang HChen YZheng Y(2024)Learning dual attention enhancement feature for visible–infrared person re-identificationJournal of Visual Communication and Image Representation10.1016/j.jvcir.2024.10407699:COnline publication date: 2-Jul-2024
https://dl.acm.org/doi/10.1016/j.jvcir.2024.104076
Ning EWang CZhang HNing XTiwari P(2024)Occluded person re-identification with deep learningExpert Systems with Applications: An International Journal10.1016/j.eswa.2023.122419239:COnline publication date: 17-Apr-2024
https://dl.acm.org/doi/10.1016/j.eswa.2023.122419
Zhang LZhao XDu HSun JWang J(2024)Learning enhancing modality-invariant features for visible-infrared person re-identificationInternational Journal of Machine Learning and Cybernetics10.1007/s13042-024-02168-6Online publication date: 22-Apr-2024
https://doi.org/10.1007/s13042-024-02168-6
Si THe FLi P(2024)UnifiedSC: a unified framework via collaborative optimization for multi-task person re-identificationApplied Intelligence10.1007/s10489-024-05333-054:4(2962-2975)Online publication date: 22-Feb-2024
https://dl.acm.org/doi/10.1007/s10489-024-05333-0
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