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Self-attention mechanism in person re-identification models

  • 1193: Intelligent Processing of Multimedia Signals
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Abstract

In recent years, person re-identification based on video has become a hot topic in the field of person re-identification. The self-attention mechanism can improve the ability of deep neural networks in computer vision tasks such as image classification, image segmentation and natural language processing tasks. In order to verify whether the self-attention can improve the performance or not in person re-identification tasks, this paper applies two self-attention mechanisms, non-local attention and recurrent criss-cross attention to person re-identification model, and experiments are conducted on Market-1501, DukeMTMC-reID and MSMT17 person re-identification datasets. The results show that the self-attention mechanism can improve the accuracy of the person re-identification model. The accuracy is higher when the self-attention module is inserted into the convolutional layers of the re-identification network.

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Acknowledgments

This work was partially supported by the National Key R&D Program of China (2018YFB1308300), China Postdoctoral Science Foundation (2018M631620), and partially by Cross-Training Plan of High Level Talents and Training Project of Beijing, and Beijing Natural Science Foundation(Grant No.4202026).

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Authors and Affiliations

  1. School of Automation, Beijing Information Science & Technology University, Beijing, 100192, China

    Wenbai Chen, Yue Lu, Hang Ma, Qili Chen & Xibao Wu

  2. School of Information and Engineering, Yanshan University, Qinhuangdao, 066004, China

    Peiliang Wu

  3. State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China

    Peiliang Wu

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  1. Wenbai Chen
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  2. Yue Lu
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Correspondence to Wenbai Chen.

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Chen, W., Lu, Y., Ma, H. et al. Self-attention mechanism in person re-identification models. Multimed Tools Appl 81, 4649–4667 (2022). https://doi.org/10.1007/s11042-020-10494-4

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