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Multiple Expert Brainstorming for Domain Adaptive Person Re-Identification

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Computer Vision – ECCV 2020 (ECCV 2020)

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Abstract

Often the best performing deep neural models are ensembles of multiple base-level networks, nevertheless, ensemble learning with respect to domain adaptive person re-ID remains unexplored. In this paper, we propose a multiple expert brainstorming network (MEB-Net) for domain adaptive person re-ID, opening up a promising direction about model ensemble problem under unsupervised conditions. MEB-Net adopts a mutual learning strategy, where multiple networks with different architectures are pre-trained within a source domain as expert models equipped with specific features and knowledge, while the adaptation is then accomplished through brainstorming (mutual learning) among expert models. MEB-Net accommodates the heterogeneity of experts learned with different architectures and enhances discrimination capability of the adapted re-ID model, by introducing a regularization scheme about authority of experts. Extensive experiments on large-scale datasets (Market-1501 and DukeMTMC-reID) demonstrate the superior performance of MEB-Net over the state-of-the-arts. Code is available at https://github.com/YunpengZhai/MEB-Net.

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Acknowledgement

This work is partially supported by grants from the National Key R&D Program of China under grant 2017YFB1002400, the National Natural Science Foundation of China (NSFC) under contract No. 61825101, U1611461 and 61836012.

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Authors and Affiliations

  1. School of Electronic and Computer Engineering, Peking University, Beijing, China

    Yunpeng Zhai & Mengxi Jia

  2. Department of Computer Science and Technology, Peking University, Beijing, China

    Yunpeng Zhai & Yonghong Tian

  3. University of Chinese Academy of Sciences, Beijing, China

    Qixiang Ye

  4. Nanyang Technological University, Singapore, Singapore

    Shijian Lu

  5. Xiamen University, Xiamen, China

    Rongrong Ji

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  1. Yunpeng Zhai
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Correspondence to Yonghong Tian .

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Editors and Affiliations

  1. University of Oxford, Oxford, UK

    Andrea Vedaldi

  2. Graz University of Technology, Graz, Austria

    Horst Bischof

  3. University of Freiburg, Freiburg im Breisgau, Germany

    Thomas Brox

  4. University of North Carolina at Chapel Hill, Chapel Hill, NC, USA

    Jan-Michael Frahm

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Zhai, Y., Ye, Q., Lu, S., Jia, M., Ji, R., Tian, Y. (2020). Multiple Expert Brainstorming for Domain Adaptive Person Re-Identification. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, JM. (eds) Computer Vision – ECCV 2020. ECCV 2020. Lecture Notes in Computer Science(), vol 12352. Springer, Cham. https://doi.org/10.1007/978-3-030-58571-6_35

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  • DOI: https://doi.org/10.1007/978-3-030-58571-6_35

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