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Heterogeneous dual network with feature consistency for domain adaptation person re-identification

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Abstract

To reduce the noisy pseudo-labels generated by clustering for unsupervised domain adaptation (UDA) person re-identification (re-ID), the method of collaborative training between dual networks has been proposed and proved to be effective. However, most of these methods ignore the coupling problem between dual networks with the same architecture, which makes them inevitably share a high similarity and lack heterogeneity and complementarity. In this paper, we propose a heterogeneous dual network (HDNet) framework with two asymmetric networks, one of which applies convolution with limited receptive fields to obtain local information and the other uses Transformer to capture long-range dependency. Additionally, we propose feature consistency loss (FCL) that does not rely on pseudo-labels. FCL focuses more on the consistency of the sample in the feature space rather than the class prediction space, driving the feature learning of UDA re-ID from the task level to the feature level. Furthermore, we propose an adaptive channel mutual-aware (ACMA) module which contains two branches to focus on the global and local information between channels. We evaluate our proposed method on three popular datasets: DukeMTMC-reID, Market-1501 and MSMT17. Extensive experimental results demonstrate that our method achieves a competitive performance.

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Availability of data and materials

The datasets analysed during the current study are available in the Ref [63,64,65] and [29].

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Acknowledgements

This work was partially supported by the Fundamental Research Funds for the Central Universities (JUSRP41908), the National Natural Science Foundation of China (61362030, 61201429), China Postdoctoral Science Foundation (2015M581720, 2016M600360), 111 Projects under Grant B12018.

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Authors and Affiliations

  1. Jiangsu Provincial Engineering Laboratory of Pattern Recognition and Computational Intelligence, Jiangnan University, Wuxi, 214122, China

    Hua Zhou & Min Jiang

  2. Key Laboratory of Advanced Process Control for Light Industry (Ministry of Education), Jiangnan University, Wuxi, 214122, China

    Jun Kong

  3. Department of Electronic and Information Engineering, The Hong Kong Polytechnic University, Hong Kong, 999077, China

    Tianshan Liu

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Zhou, H., Kong, J., Jiang, M. et al. Heterogeneous dual network with feature consistency for domain adaptation person re-identification. Int. J. Mach. Learn. & Cyber. 14, 1951–1965 (2023). https://doi.org/10.1007/s13042-022-01739-9

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