research-article

Unsupervised Person Re-identification: Clustering and Fine-tuning

Authors:

Yi YangAuthors Info & Claims

ACM Transactions on Multimedia Computing, Communications, and Applications (TOMM), Volume 14, Issue 4

Article No.: 83, Pages 1 - 18

https://doi.org/10.1145/3243316

Published: 10 October 2018 Publication History

Abstract

The superiority of deeply learned pedestrian representations has been reported in very recent literature of person re-identification (re-ID). In this article, we consider the more pragmatic issue of learning a deep feature with no or only a few labels. We propose a progressive unsupervised learning (PUL) method to transfer pretrained deep representations to unseen domains. Our method is easy to implement and can be viewed as an effective baseline for unsupervised re-ID feature learning. Specifically, PUL iterates between (1) pedestrian clustering and (2) fine-tuning of the convolutional neural network (CNN) to improve the initialization model trained on the irrelevant labeled dataset. Since the clustering results can be very noisy, we add a selection operation between the clustering and fine-tuning. At the beginning, when the model is weak, CNN is fine-tuned on a small amount of reliable examples that locate near to cluster centroids in the feature space. As the model becomes stronger, in subsequent iterations, more images are being adaptively selected as CNN training samples. Progressively, pedestrian clustering and the CNN model are improved simultaneously until algorithm convergence. This process is naturally formulated as self-paced learning. We then point out promising directions that may lead to further improvement. Extensive experiments on three large-scale re-ID datasets demonstrate that PUL outputs discriminative features that improve the re-ID accuracy. Our code has been released at https://github.com/hehefan/Unsupervised-Person-Re-identification-Clustering-and-Fine-tuning.

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Cited By

Yang YWang GTiwari PPandey HLei Z(2025)Pixel and Feature Transfer Fusion for Unsupervised Cross-Dataset Person ReidentificationIEEE Transactions on Neural Networks and Learning Systems10.1109/TNNLS.2021.312826936:3(4220-4232)Online publication date: Mar-2025
https://doi.org/10.1109/TNNLS.2021.3128269
Dai YSun YLiu JTong ZDuan L(2025)Bridging the Source-to-Target Gap for Cross-Domain Person Re-identification with Intermediate DomainsInternational Journal of Computer Vision10.1007/s11263-024-02169-6133:1(410-434)Online publication date: 1-Jan-2025
https://dl.acm.org/doi/10.1007/s11263-024-02169-6
Liu JSun HLiu WGuo AZhang J(2024)UnA-Mix: Rethinking Image Mixtures for Unsupervised Person Re-IdentificationProcesses10.3390/pr1201016812:1(168)Online publication date: 10-Jan-2024
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Index Terms

Unsupervised Person Re-identification: Clustering and Fine-tuning
1. Computing methodologies
  1. Artificial intelligence
    1. Computer vision
      1. Computer vision representations
        Image representations
      2. Computer vision tasks
        Visual content-based indexing and retrieval

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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 14, Issue 4

Special Section on Deep Learning for Intelligent Multimedia Analytics

November 2018

221 pages

ISSN:1551-6857

EISSN:1551-6865

DOI:10.1145/3282485

Editor:
Alberto Del Bimbo
University of Firenze, Italy

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Copyright © 2018 ACM.

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Publication History

Published: 10 October 2018

Accepted: 01 July 2018

Revised: 01 June 2018

Received: 01 February 2018

Published in TOMM Volume 14, Issue 4

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Zhejiang Province Nature Science Foundation of China
111 Project
Cooperative Research Centres Programme
Data to Decisions CRC (D2D CRC)
National Nature Science Foundation of China
National Key Research and Development Program of China

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Cited By

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https://doi.org/10.1109/TNNLS.2021.3128269
Dai YSun YLiu JTong ZDuan L(2025)Bridging the Source-to-Target Gap for Cross-Domain Person Re-identification with Intermediate DomainsInternational Journal of Computer Vision10.1007/s11263-024-02169-6133:1(410-434)Online publication date: 1-Jan-2025
https://dl.acm.org/doi/10.1007/s11263-024-02169-6
Liu JSun HLiu WGuo AZhang J(2024)UnA-Mix: Rethinking Image Mixtures for Unsupervised Person Re-IdentificationProcesses10.3390/pr1201016812:1(168)Online publication date: 10-Jan-2024
https://doi.org/10.3390/pr12010168
Chen YWang KYe HTao LTie Z(2024)Person Re-Identification in Special Scenes Based on Deep Learning: A Comprehensive SurveyMathematics10.3390/math1216249512:16(2495)Online publication date: 13-Aug-2024
https://doi.org/10.3390/math12162495
Xu SXiang SMeng FWu Q(2024)Enhancing Unsupervised Domain Adaptation for Person Re-Identification with the Minimal Transfer Cost FrameworkComputers, Materials & Continua10.32604/cmc.2024.05515780:3(4197-4218)Online publication date: 2024
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徐锟(2024)Unsupervised Person Re-Identification Based on Discriminative Feature MiningModeling and Simulation10.12677/mos.2024.13318613:03(2011-2022)Online publication date: 2024
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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 Applications10.1145/370232821:1(1-23)Online publication date: 2-Nov-2024
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Liu JZhuang WLiu YWen YHuang JLin W(2024)Personalized Federated Mutual Learning for Unsupervised Camera-Aware Person Re-IdentificationACM Transactions on Multimedia Computing, Communications, and Applications10.1145/369645320:12(1-19)Online publication date: 23-Nov-2024
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Huang YHuang ZZhao JHu HChen D(2024)AMVFNet: Attentive Multi-View Fusion Network for 3D Object DetectionACM Transactions on Multimedia Computing, Communications, and Applications10.1145/368963921:1(1-18)Online publication date: 15-Oct-2024
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Lv KChen HZhao CTu KChen JLi YLi BLin Y(2024)Style Variable and Irrelevant Learning for Generalizable Person Re-identificationACM Transactions on Multimedia Computing, Communications, and Applications10.1145/367100320:9(1-22)Online publication date: 6-Jun-2024
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