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Graph correlation-refined centroids for unsupervised person re-identification

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Abstract

This paper aims at studying unsupervised person re-identification (re-ID) which does not require any annotations. Recently, many approaches tackle this problem through contrastive learning due to its effective feature representation for unsupervised tasks. Especially, a uni-centroid representation is always obtained by averaging all the instance features within a cluster having the same pseudolabel. However, due to the unsatisfied clustering results, a cluster often contains some noisy samples, making the generated centroids imperfect. To address this issue, we propose a new graph correlation module (GCM) that can adaptively mine the relationship between each sample within the cluster and a high-quality relation-aware centroid is formed for momentum updating. Moreover, to increase the complexity of the task and prevent the model from falling into a local optimum, the original features extracted from the model are directly used to update the corresponding centroid. Extensive experiments demonstrate the superiority of the proposed method over state-of-the-art approaches on fully unsupervised re-ID tasks.

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Data availability

The datasets generated during the current study are available in the Market-1501 and DukeMTMC-re-ID.

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Acknowledgements

This research is partly supported by NSFC (62176169), and SCU-Luzhou Municipal Peoples Government Strategic Cooperation Project (2020CDLZ-10).

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Authors and Affiliations

  1. National Key Laboratory of Fundamental Science on Synthetic Vision, Sichuan University, Chengdu, China

    Xin Zhang, Keren Fu & Yanci Zhang

  2. College of Computer Science, Sichuan University, Chengdu, China

    Xin Zhang, Keren Fu & Yanci Zhang

Authors
  1. Xin Zhang
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  2. Keren Fu
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  3. Yanci Zhang
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Contributions

Xin Zhang contributed to the conception of the study and performed the experiment; Xin Zhang performed the data analyses and wrote the manuscript; Keren Fu contributed significantly to analysis and manuscript revision; Yanci Zhang helped perform the analysis with constructive discussions. All authors reviewed the manuscript.

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Correspondence to Yanci Zhang.

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Zhang, X., Fu, K. & Zhang, Y. Graph correlation-refined centroids for unsupervised person re-identification. SIViP 17, 1457–1464 (2023). https://doi.org/10.1007/s11760-022-02354-5

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  • DOI: https://doi.org/10.1007/s11760-022-02354-5

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