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Research Article | Open Access

Joint training with local soft attention and dual cross-neighbor label smoothing for unsupervised person re-identification

School of Mathematics and Computer Sciences, Nanchang University, Nanchang 330031, China
Institute of Metaverse, Nanchang University, Nanchang 330031, China.
Jiangxi Key Laboratory of Smart City, Nanchang 330031, China.
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Graphical Abstract

Abstract

Existing unsupervised person re-identificationapproaches fail to fully capture the fine-grained features of local regions, which can result in people with similar appearances and different identities being assigned the same label after clustering. The identity-independent information contained in different local regions leads to different levels of local noise. To address these challenges, joint training with local soft attention and dual cross-neighbor label smoothing (DCLS) is proposed in this study. First, the joint training is divided into global and local parts, whereby a soft attention mechanism is proposed for the local branch to accurately capture the subtle differences in local regions, which improves the ability of the re-identification model in identifying a person’s local significant features. Second, DCLS is designed to progressively mitigate label noise in different local regions. The DCLS uses global and local similarity metrics to semantically align the global and local regions of the person and further determines the proximity association between local regions through the cross information of neighboring regions, thereby achieving label smoothing of the global and local regions throughout the training process. In extensive experiments, the proposed method outperformed existing methods under unsupervised settings on several standard person re-identification datasets.

Keywords

person re-identification (Re-ID)unsupervised learning (USL)local soft attentionjoint trainingdual cross-neighbor label smoothing (DCLS)

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Computational Visual Media
Volume 10 Issue 3,
June 2024
Pages 543-558
DOI: 10.1007/s41095-023-0354-4
Cite this article:
Han Q, Li L, Min W, et al. Joint training with local soft attention and dual cross-neighbor label smoothing for unsupervised person re-identification. Computational Visual Media, 2024, 10(3): 543-558. https://doi.org/10.1007/s41095-023-0354-4

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Received: 28 February 2023
Accepted: 26 April 2023
Published: 27 April 2024
© The Author(s) 2024.

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