research-article

Rank-in-Rank Loss for Person Re-identification

Authors:

Ruimin HuAuthors Info & Claims

ACM Transactions on Multimedia Computing, Communications, and Applications (TOMM), Volume 18, Issue 2s

Article No.: 130, Pages 1 - 21

https://doi.org/10.1145/3532866

Published: 06 October 2022 Publication History

Abstract

Person re-identification (re-ID) is commonly investigated as a ranking problem. However, the performance of existing re-ID models drops dramatically, when they encounter extreme positive-negative class imbalance (e.g., very small ratio of positive and negative samples) during training. To alleviate this problem, this article designs a rank-in-rank loss to optimize the distribution of feature embeddings. Specifically, we propose a Differentiable Retrieval-Sort Loss (DRSL) to optimize the re-ID model by ranking each positive sample ahead of the negative samples according to the distance and sorting the positive samples according to the angle (e.g., similarity score). The key idea of the proposed DRSL lies in minimizing the distance between samples of the same category along with the angle between them. Considering that the ranking and sorting operations are non-differentiable and non-convex, the DRSL also performs the optimization of automatic derivation and backpropagation. In addition, the analysis of the proposed DRSL is provided to illustrate that the DRSL not only maintains the inter-class distance distribution but also preserves the intra-class similarity structure in terms of angle constraints. Extensive experimental results indicate that the proposed DRSL can improve the performance of the state-of-the-art re-ID models, thus demonstrating its effectiveness and superiority in the re-ID task.

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Index Terms

Rank-in-Rank Loss for Person Re-identification
1. Information systems
  1. Information retrieval

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

ACM Transactions on Multimedia Computing, Communications, and Applications Volume 18, Issue 2s

June 2022

383 pages

ISSN:1551-6857

EISSN:1551-6865

DOI:10.1145/3561949

Editor:
Abdulmotaleb El Saddik
Mohamed Bin Zayed University of Artificial Intelligence, UAE and University of Ottawa, Canada

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Publication History

Published: 06 October 2022

Online AM: 30 April 2022

Accepted: 19 April 2022

Revised: 23 February 2022

Received: 29 October 2021

Published in TOMM Volume 18, Issue 2s

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https://dl.acm.org/doi/10.1145/3649900
Feng ZXu JMa LZhang S(2024)Efficient Video Transformers via Spatial-temporal Token Merging for Action RecognitionACM Transactions on Multimedia Computing, Communications, and Applications10.1145/363378120:4(1-21)Online publication date: 11-Jan-2024
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Liu ZLi DZhang XZhang ZZhang PShan CHan J(2024)Pedestrian Attribute Recognition via Spatio-temporal Relationship Learning for Visual SurveillanceACM Transactions on Multimedia Computing, Communications, and Applications10.1145/363262420:6(1-15)Online publication date: 8-Mar-2024
https://dl.acm.org/doi/10.1145/3632624
Cui ZZhou JPeng Y(2024)DMA: Dual Modality-Aware Alignment for Visible-Infrared Person Re-IdentificationIEEE Transactions on Information Forensics and Security10.1109/TIFS.2024.335240819(2696-2708)Online publication date: 10-Jan-2024
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Jahan MHassan MHossin SHossain MHasan M(2024)Unsupervised person Re-identificationNeurocomputing10.1016/j.neucom.2023.127193572:COnline publication date: 12-Apr-2024
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Liu FYe MDu B(2024)Learning a generalizable re-identification model from unlabelled data with domain-agnostic expertVisual Intelligence10.1007/s44267-024-00062-x2:1Online publication date: 1-Oct-2024
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Xie HYang ZZhu HWang ZEl Saddik AMei TCucchiara RBertini MTobon Vallejo DAtrey PHossain M(2023)Striking a Balance: Unsupervised Cross-Domain Crowd Counting via Knowledge DiffusionProceedings of the 31st ACM International Conference on Multimedia10.1145/3581783.3611797(6520-6529)Online publication date: 27-Oct-2023
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