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Cascaded attention-guided multi-granularity feature learning for person re-identification

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Abstract

Attention mechanism has been extensively employed in the task of person re-identification, as it helps to extract much more discriminative feature representations. However, most of existing works either incorporate a single-scale attention module, or the embedded attentions work independently. Though promising results are achieved, they may fail to mine different subtle visual clues. To mitigate this issue, a novel framework called cascaded attention network (CANet) is proposed, which allows to mine diverse clues and integrate them into final multi-granularity features by a cascaded manner. Specifically, we design a novel hybrid pooling attention module (HPAM) and plug it into backbone network at different stages. To make them work collaboratively, an inter-attention regularization is applied, such that they can localize complementary salient features. Then, CANet extracts global and local features from a part-based pyramidal architecture. For better feature robustness, supervision is applied to not only the pyramidal branches, but also those intermediate attention modules. Furthermore, within each supervision branch, hybrid pooling with two different strides is executed to enhance feature representation capabilities. Extensive experiments with ablation analysis demonstrate the effectiveness of the proposed method, and state-of-the-art results are achieved on three public benchmark datasets, including Market-1501, CUHK03, and DukeMTMC-ReID.

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Acknowledgements

This work was supported by the Research Funds of Suzhou Vocational University (SVU2021YY03), Science and Technology Program of Suzhou (SS202151, SNG2021037), the Innovation Project of Engineering Research Center of Integration and Application of Digital Learning Technology, Ministry of Education (1221046) and in part by the Program to Cultivate Middle-aged and Young Cadre Teacher of Suzhou Vocational University.

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  1. Yuanfeng Yang, Xun Sun, Liang Zhang and Ligang Fang authors contributed equally to this study.

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  1. School of Computer Engineering, Suzhou Vocational University, Suzhou, 215104, China

    Husheng Dong, Yuanfeng Yang, Xun Sun, Liang Zhang & Ligang Fang

  2. Jiangsu Province Support Software Engineering R &D Center for Modern Information Technology Application in Enterprise, Suzhou, 215104, China

    Husheng Dong

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  1. Husheng Dong
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Dong, H., Yang, Y., Sun, X. et al. Cascaded attention-guided multi-granularity feature learning for person re-identification. Machine Vision and Applications 34, 4 (2023). https://doi.org/10.1007/s00138-022-01353-3

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