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Global relational attention with a maximum suppression constraint for vehicle re-identification

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Abstract

The goal of vehicle re-identification is to identify the same vehicle from multiple cameras, which is a challenging task. There are many solutions to this problem, among which the self-attention mechanism is very popular. It can capture the long-range dependence in an image, thereby suppressing the irrelevant features. Most of the existing designs are based on isolated pairwise query-key interactions to refine a node. They implicitly mine attention patterns without explicitly modeling node weights. In this paper, we propose a global relational attention mechanism, which makes full use of the global dependence of a node to learn and infer its weight value. Global dependence can measure the importance of nodes more robustly and efficiently. To capture more discriminative features, we propose a maximum suppression constraint to adaptively adjust weight values to expand the range of attention. In addition, we design a pair of effective attention modules based on the proposed attention mechanism, that focus on mining the discriminative features related to vehicle identities from the spatial and channel dimensions. We conduct a large number of experiments on the VeRi-776 and VehicleID datasets, and the experimental results demonstrate the effectiveness of our method.

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All data included in this study are available upon request by contact with the corresponding author.

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Acknowledgements

This work is jointly supported by the National Natural Science Foundation of China (62176139 and 61876098), and by the Major Basic Research Project of the Natural Science Foundation of Shandong Province (ZR2021ZD15).

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Authors and Affiliations

  1. School of Software, Shandong University, No. 1500, Shunhua Road, High-tech Industrial Development Zone, Jinan, 250101, Shandong, China

    Xiyu Pang & Yilong Yin

  2. School of Information Science and Electrical Engineering, Shandong Jiaotong University, No. 5001, Haitang Road, Changqing District, Jinan, 250357, Shandong, China

    Xiyu Pang & Xin Tian

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  1. Xiyu Pang
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Correspondence to Yilong Yin.

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Pang, X., Yin, Y. & Tian, X. Global relational attention with a maximum suppression constraint for vehicle re-identification. Int. J. Mach. Learn. & Cyber. 15, 1729–1742 (2024). https://doi.org/10.1007/s13042-023-01993-5

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