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Population-Based Evolutionary Gaming for Unsupervised Person Re-identification

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Abstract

Unsupervised person re-identification has achieved great success through the self-improvement of individual neural networks. However, limited by the lack of diversity of discriminant information, a single network has difficulty learning sufficient discrimination ability by itself under unsupervised conditions. To address this limit, we develop a population-based evolutionary gaming (PEG) framework in which a population of diverse neural networks are trained concurrently through selection, reproduction, mutation, and population mutual learning iteratively. Specifically, the selection of networks to preserve is modeled as a cooperative game and solved by the best-response dynamics, then the reproduction and mutation are implemented by cloning and fluctuating hyper-parameters of networks to learn more diversity, and population mutual learning improves the discrimination of networks by knowledge distillation from each other within the population. In addition, we propose a cross-reference scatter (CRS) to approximately evaluate re-ID models without labeled samples and adopt it as the criterion of network selection in PEG. CRS measures a model’s performance by indirectly estimating the accuracy of its predicted pseudo-labels according to the cohesion and separation of the feature space. Extensive experiments demonstrate that (1) CRS approximately measures the performance of models without labeled samples; (2) and PEG produces new state-of-the-art accuracy for person re-identification, indicating the great potential of population-based network cooperative training for unsupervised learning. The code is released on github.com/YunpengZhai/PEG.

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Data Availability

The datasets generated during and/or analysed during the current study are available from the corresponding author on reasonable request.

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Acknowledgements

This work is partially supported by grants from the Key-Area Research and Development Program of Guangdong Province under contact No. 2019B010153002, and grants from the National Natural Science Foundation of China under contract No. 61825101 and No. 62088102. The computing resources of Pengcheng Cloudbrain are used in this research.

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Authors and Affiliations

  1. National Engineering Research Center of Visual Technology, School of Computer Science, Peking University, Beijing, China

    Yunpeng Zhai, Peixi Peng, Mengxi Jia & Yonghong Tian

  2. School of Electronic and Computer Engineering, Peking University Shenzhen Graduate School, Shenzhen, China

    Yonghong Tian

  3. Peng Cheng Laboratory, Shenzhen, China

    Peixi Peng & Yonghong Tian

  4. AI Application Research Center, Huawei Technologies Co., Ltd, Shenzhen, China

    Shiyong Li

  5. State Key Laboratory of Digital Multimedia Technology, Hisense, Qingdao, China

    Weiqiang Chen & Xuesong Gao

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  1. Yunpeng Zhai
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Zhai, Y., Peng, P., Jia, M. et al. Population-Based Evolutionary Gaming for Unsupervised Person Re-identification. Int J Comput Vis 131, 1–25 (2023). https://doi.org/10.1007/s11263-022-01693-7

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