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Exert Diversity and Mitigate Bias: Domain Generalizable Person Re-identification with a Comprehensive Benchmark

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Abstract

Person re-identification (ReID), aiming at retrieving persons of the same identity across non-overlap** cameras, holds immense practical significance for security and surveillance applications. In pursuit of a more general and practical solution, recent research attention has gradually shifted from the traditional single-domain ReID to the domain generalizable person re-identification (DG-ReID). However, the DG-ReID landscape lacks a meticulously designed and all-encompassing benchmark to provide a common ground for competing approaches. To this end, in this paper, we first delve into the intricate challenges of DG-ReID and introduce a comprehensive and large-scale benchmark with enhanced distributional variety and shifts to facilitate the research progress. Furthermore, in response to the highlighted challenges, a novel DG-ReID framework based on diverse feature space learning with domain factorization is proposed to effectively learn rich domain-adaptive discriminative features through the two designed blocks with fairly limited additional cost in both memory and computation. Firstly, the feature diversification block promotes a diverse feature space capable of learning domain-specific characteristics under the rich distributional variety. Secondly, the domain-adaptive shielding block applies channel-wise shielding operations based on subspace-based domain factorization in order to prevent the model from prediction bias caused by distributional shifts. Our extensive experiments demonstrate the effectiveness of the proposed framework, surpassing the performance of current state-of-the-art methods under various evaluation protocols.

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Funding

This work was supported by National Natural Science Foundation of China (NSFC) under Grants 62225207 and 62106245.

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  1. University of Science and Technology of China, Hefei, China

    Bingyu Hu, Jiawei Liu, Yufei Zheng, Kecheng Zheng & Zheng-Jun Zha

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  1. Bingyu Hu
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  2. Jiawei Liu
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Correspondence to Jiawei Liu.

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Communicated by Zhun Zhong.

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Hu, B., Liu, J., Zheng, Y. et al. Exert Diversity and Mitigate Bias: Domain Generalizable Person Re-identification with a Comprehensive Benchmark. Int J Comput Vis (2024). https://doi.org/10.1007/s11263-024-02124-5

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