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Domain generalization person re-identification via style adaptation learning

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Abstract

Domain generalization person re-identification (DG Re-ID) aims to deploy a Re-ID model trained on multiple source domains to unseen domains without adaptation, which is a practical and challenging problem. Due to the significant drop in performance of DG Re-ID methods on unseen target domains, Mixture of Experts (MoE) is used to set up an expert branch for each domain to learn its unique features and thus improve the identification performance on unseen target domains. However, most of the MoE-based DG Re-ID methods only aggregate multiple branch features to simulate the unseen target domain and ignore the role of style in classification recognition among different domains. To address these problems, we propose Style-Adaptive Learning DG Re-ID (SALDG) method, which includes Global Branch, Expert Branches, Class-level Style-adaptive Learning (CSL) module, and Domain-level Style-adaptive Learning (DSL) module. The Global Branch and the Expert Branches extract the global feature and different domain style features respectively. The CSL module can learn the style information of each domain, and the DSL module adaptively mixes the style information of each domain learned by the CSL module to form more complex style information and enhance style diversity in the global feature space. Through extensive experiments, compared with the state-of-the-art methods, the mAP and R-1 accuracy of SALDG on Market1501, DukeMTMC, CUHK03, MSMT17 datasets are improved by approximately 4.1% and 5% respectively.

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Data availability

The data that support this research are available from the second author Xinsheng Dou, upon reasonable request.

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Acknowledgements

This work was supported in part by the Natural Science Foundation of China under Grant 61806071, and in part by the Natural Science Foundation of Hebei Province under Grant F2019202381 and Grant F2019202464.

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Authors and Affiliations

  1. School of Artificial Intelligence, Hebei University of Technology, No. 5340, Xiping Road, Beichen District, TianJin, 300401, China

    Yingchun Guo, Xinsheng Dou & Ye Zhu

  2. School of Electrical and Automation Engineering, Hebei University of Technology Finland Campus, No. 5340, Xiping Road, Beichen District, TianJin, 300401, China

    Xinyao Wang

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  1. Yingchun Guo
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Correspondence to Yingchun Guo.

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Guo, Y., Dou, X., Zhu, Y. et al. Domain generalization person re-identification via style adaptation learning. Int. J. Mach. Learn. & Cyber. (2024). https://doi.org/10.1007/s13042-024-02188-2

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