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Reinforced domain adaptation with attention and adversarial learning for unsupervised person Re-ID

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Abstract

Most existing methods of unsupervised person re-identification (re-ID) still suffer from two aspects of challenges: inter-domain inconsistency and pseudo-label inaccuracy. To alleviate the two problems, we propose an reinforced domain adaptation (RDA) re-ID method by innovatively employing adversarial learning and spatial-channel attention. Specifically, to handle the inter-domain inconsistency problem, we specially design an adversarial learning module to reduce the feature discrepancy between target domain image and translated source domain image, and take Wasserstein distance as the discriminative function because that it can provide an effective gradient for model optimization regardless of the distribution difference between the source domain and the target domain. To handle the pseudo-label inaccuracy problem, we design an attention module to highlight the person region of the image so as to improve accuracy of person clustering and matching. An improved re-ID model can therefore be obtained by jointly training the translated source-domain images with ground-truth identities and target-domain images with pseudo identities. In addition, in order to maintain the semantic consistency of source domain images before and after style translation, we design a closed-loop training mechanism to refine the style translation based on the feedback from person re-ID result, finally making the style translation and person re-ID collaboratively converge to their best state. In the experiments, our proposed framework is shown to outperform state-of-the-art methods on multiple tasks of unsupervised person re-ID.

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Acknowledgments

This work is supported by the National Natural Science Foundation of China (Nos. 61866004, 61966004, 61962007), the Guangxi Natural Science Foundation (Nos. 2018GXNSFDA281009, 2019GXNSFDA245018, 2018GXNSFDA294001), Research Fund of Guangxi Key Lab of Multi-source Information Mining & Security (No.20-A-03-01), Guangxi “Bagui Scholar” Teams for Innovation and Research Project, and Innovation Project of Guangxi Graduate Education(JXXYYJSCXXM-2021-007).

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Authors and Affiliations

  1. Guangxi Key Lab of Multi-source Information Mining & Security, Guangxi Normal University, Guilin, China

    Peiyi Wei, Canlong Zhang & Zhixin Li

  2. School of Computer Science and Information Security, Guilin University of Electronic Technology, Guilin, China

    Yanping Tang

  3. College of Computer Science and Communication Engineering, Guangxi University of Science and Technology, Liuzhou, Guangxi, 545006, China

    Zhiwen Wang

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  1. Peiyi Wei
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Correspondence to Canlong Zhang.

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Wei, P., Zhang, C., Tang, Y. et al. Reinforced domain adaptation with attention and adversarial learning for unsupervised person Re-ID. Appl Intell 53, 4109–4123 (2023). https://doi.org/10.1007/s10489-022-03640-y

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