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Joint attribute soft-sharing and contextual local: a multi-level features learning network for person re-identification

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Abstract

In person re-identification (re-id), the key to retrieving the correct person image is to extract discriminative features. The features at different levels are considered complementary. In this work, we design a person re-id learning network that can extract mutually multi-level features called ASCLNet. ASCLNet contains three feature branches, and each branch can extract mutually different levels of features. Furthermore, we propose two novel modules and apply them to learning local and attribute features in ASCLNet. One is the contextual local module, which can learn the local feature with context information from the local body part; the other is the attribute soft-sharing module, which enables shared feature representation among attributes. With the support of these two modules, ASCLNet can extract multi-level features that are more discriminative. Moreover, experimental results show that ASCLNet achieves excellent performances on Market-1501 and DukeMTMC-reID datasets with mAP of 88.85% and 80.18%, respectively.

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Data availability

The datasets used in this paper are public datasets.

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Authors and Affiliations

  1. School of Computer Science and Technology, Zhejiang Sci-Tech University, Hangzhou, 310018, Zhejiang, China

    Wangmeng Wang, Yanbing Chen, Dengwen Wang, Zhixin Tie & Linbing Tao

  2. KeYi College, Zhejiang Sci-Tech University, Shaoxing, 312369, Zhejiang, China

    Zhixin Tie

  3. Faculty of Applied Sciences, Macao Polytechnic University, Macao, 999078, China

    Wei Ke

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  1. Wangmeng Wang
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  2. Yanbing Chen
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Correspondence to Yanbing Chen.

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Wang, W., Chen, Y., Wang, D. et al. Joint attribute soft-sharing and contextual local: a multi-level features learning network for person re-identification. Vis Comput 40, 2251–2264 (2024). https://doi.org/10.1007/s00371-023-02914-x

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