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HC-GCN: hierarchical contrastive graph convolutional network for unsupervised domain adaptation on person re-identification

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Abstract

The unsupervised domain adaptation (UDA) task on person re-identification (ReID) aims at spotting a person of interest under cross-camera by transferring the person knowledge learned from a labeled source domain to an unlabeled target domain. Recently, the contrastive loss provides an effective approach for UDA person ReID by comparing global features of the pedestrians. Generally, the fine-grained local features are favorable to distinguish the pedestrian appearance changes. However, the traditional contrastive loss-based UDA methods ignore the importance of local details and the relationship between the different granularities of features. To overcome this problem, we propose a hierarchical contrastive graph convolutional network, termed HC-GCN, for UDA person ReID. We first build an effective hierarchical graph model to learn the relationship between the global and local pedestrian features, where the local features are obtained by rough split and affine transformation. Moreover, we introduce the contrastive loss to suppress the pedestrian-irrelevant features, where the global and local contrastive losses are used. Experiments demonstrate that our method can achieve superior performance on the challenging Market-1501 and MSMT17 datasets.

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Data availability

The data that support the findings of this study are available from the corresponding author upon reasonable request.

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Acknowledgements

This work was supported in part by the National Natural Science Foundation of China under Grant 62071404; the Natural Science Foundation of Fujian Province under Grants 2021J011185, 2022J011234, and 2021J011191; the Youth Innovation Foundation of Xiamen City under Grant 3502Z20206068; and the Emerging Interdisciplinary Cultivation Project of Jiangxi Academy of Sciences under Grant 2022YXXJC0101.

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Authors and Affiliations

  1. Fujian Key Laboratory of Pattern Recognition and Image Understanding, School of Computer and Information Engineering, Xiamen University of Technology, Xiamen, 361024, China

    Si Chen, Bolun Xu, Xia Du, Weiwei Zhuang & Yun Wu

  2. Institute of Energy Research, Jiangxi Academy of Sciences, Nanchang, 330096, China

    Miaohui Zhang

  3. Fujian Key Laboratory of Sensing and Computing for Smart City, School of Informatics, Xiamen University, Xiamen, 361005, China

    Yan Yan

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  1. Si Chen
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  4. Yan Yan
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  5. Xia Du
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  7. Yun Wu
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Correspondence to Miaohui Zhang.

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Communicated by M. Buzzelli.

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Chen, S., Xu, B., Zhang, M. et al. HC-GCN: hierarchical contrastive graph convolutional network for unsupervised domain adaptation on person re-identification. Multimedia Systems 29, 2779–2790 (2023). https://doi.org/10.1007/s00530-023-01147-1

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