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European Conference on Computer Vision

ECCV 2020: Computer Vision – ECCV 2020 pp 526–544Cite as

Unsupervised Domain Adaptation with Noise Resistible Mutual-Training for Person Re-identification

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Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 12356))

Abstract

Unsupervised domain adaptation (UDA) in the task of person re-identification (re-ID) is highly challenging due to large domain divergence and no class overlap between domains. Pseudo-label based self-training is one of the representative techniques to address UDA. However, label noise caused by unsupervised clustering is always a trouble to self-training methods. To depress noises in pseudo-labels, this paper proposes a Noise Resistible Mutual-Training (NRMT) method, which maintains two networks during training to perform collaborative clustering and mutual instance selection. On one hand, collaborative clustering eases the fitting to noisy instances by allowing the two networks to use pseudo-labels provided by each other as an additional supervision. On the other hand, mutual instance selection further selects reliable and informative instances for training according to the peer-confidence and relationship disagreement of the networks. Extensive experiments demonstrate that the proposed method outperforms the state-of-the-art UDA methods for person re-ID.

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Acknowledgments

This work was supported by the National Natural Science Foundation of China under Grant 61702163.

Author information

Authors and Affiliations

  1. Inception Institute of Artificial Intelligence, Abu Dhabi, UAE

    Fang Zhao, Shengcai Liao, Guo-Sen Xie & Ling Shao

  2. Institute of North Electronic Equipment, Beijing, China

    Jian Zhao

  3. Tencent AI Lab, Shenzhen, China

    Kaihao Zhang

  4. Mohamed bin Zayed University of Artificial Intelligence, Abu Dhabi, UAE

    Ling Shao

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  1. Fang Zhao
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  2. Shengcai Liao
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  3. Guo-Sen Xie
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  4. Jian Zhao
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  5. Kaihao Zhang
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  6. Ling Shao
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Corresponding author

Correspondence to Shengcai Liao .

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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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Zhao, F., Liao, S., Xie, GS., Zhao, J., Zhang, K., Shao, L. (2020). Unsupervised Domain Adaptation with Noise Resistible Mutual-Training for 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 12356. Springer, Cham. https://doi.org/10.1007/978-3-030-58621-8_31

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  • DOI: https://doi.org/10.1007/978-3-030-58621-8_31

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