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Unsupervised vehicle re-identification based on mixed sample contrastive learning

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Abstract

This paper proposes a mixed sample contrastive learning framework that constructs memory dictionary with discrete samples and reliable clusters for unsupervised vehicle re-identification. Firstly, we introduce a discrete sample separation (DSS) module including a discrete sample criterion and a discrete sample separation operation. Specifically, for a specific feature cluster, the discrete sample criterion drives the DSS to mine the discrete sample. Such that the original cluster can be separated into a more reliable cluster and discrete samples. Furthermore, a mixed sample contrastive learning (MSCL) strategy is designed to construct a mixed sample memory dictionary for training the model with more superior learning target. Moreover, a discrete sample loss (DSL) is proposed to calculate the contrastive loss of the model and dynamically update the memory dictionary during training. Extensive experiments show our method performs favorably against state of the arts. The code will be published on github after the paper is accepted.

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Acknowledgements

This work is supported by National Nature Science Foundation of China (Grant No. 61871106 and No. 61370152), Key R& D projects of Liaoning Province, China (Grant No. 2020JH2/10100029), and the Open Project Program Foundation of the Key Laboratory of Opto-Electronics Information Processing, Chinese Academy of Sciences (OEIP-O-202002).

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Authors and Affiliations

  1. College of Information Science and Engineering, Northeastern University, Shenyang, 110004, China

    Yuefeng Wang, Ying Wei, Ruipeng Ma, Lin Wang & Cuyuan Wang

  2. Information Technology R&D Innovation Center of Peking University, Shaoxing, China

    Yuefeng Wang & Ying Wei

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  1. Yuefeng Wang
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  2. Ying Wei
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Correspondence to Ying Wei.

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Wang, Y., Wei, Y., Ma, R. et al. Unsupervised vehicle re-identification based on mixed sample contrastive learning. SIViP 16, 2083–2091 (2022). https://doi.org/10.1007/s11760-022-02170-x

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