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Unsupervised Domain Adaptation for Person Re-Identification with Few and Unlabeled Target Data

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Advances in Visual Computing (ISVC 2020)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 12510))

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Abstract

Existing, fully supervised methods for person re-identification (ReID) require annotated data acquired in the target domain in which the method is expected to operate. This includes the IDs as well as images of persons in that domain. This is an obstacle in the deployment of ReID methods in novel settings. For solving this problem, semi-supervised or even unsupervised ReID methods have been proposed. Still, due to their assumptions and operational requirements, such methods are not easily deployable and/or prove less performant to novel domains/settings, especially those related to small person galleries. In this paper, we propose a novel approach for person ReID that alleviates these problems. This is achieved by proposing a completely unsupervised method for fine tuning the ReID performance of models learned in prior, auxiliary domains, to new, completely different ones. The proposed model adaptation is achieved based on only few and unlabeled target persons’ data. Extensive experiments investigate several aspects of the proposed method in an ablative study. Moreover, we show that the proposed method is able to improve considerably the performance of state-of-the-art ReID methods in state-of-the-art datasets.

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Acknowledgments

This work is partially funded by the H2020 project CONNEXIONs (GA 786731) and the FORTH-ICS internal RTD Programme “Ambient Intelligence and Smart Environments”.

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Authors and Affiliations

  1. Computer Science Department, University of Crete, Rethymno, Greece

    George Galanakis & Antonis A. Argyros

  2. Institute of Computer Science, FORTH, Heraklion, Greece

    George Galanakis, Xenophon Zabulis & Antonis A. Argyros

Authors
  1. George Galanakis
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  2. Xenophon Zabulis
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  3. Antonis A. Argyros
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Corresponding author

Correspondence to George Galanakis .

Editor information

Editors and Affiliations

  1. University of Nevada Reno, Reno, NV, USA

    George Bebis

  2. Stony Brook University, Stony Brook, NY, USA

    Zhaozheng Yin

  3. Drexel University, Philadelphia, PA, USA

    Edward Kim

  4. RWTH Aachen University, Aachen, Germany

    Jan Bender

  5. University of Edinburgh, Edinburgh, UK

    Kartic Subr

  6. IBM Research – Cambridge, Cambridge, MA, USA

    Bum Chul Kwon

  7. University of Waterloo, Waterloo, ON, Canada

    Jian Zhao

  8. Graz University of Technology, Graz, Austria

    Denis Kalkofen

  9. The Hong Kong Polytechnic University, Hong Kong, Hong Kong

    George Baciu

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Galanakis, G., Zabulis, X., Argyros, A.A. (2020). Unsupervised Domain Adaptation for Person Re-Identification with Few and Unlabeled Target Data. In: Bebis, G., et al. Advances in Visual Computing. ISVC 2020. Lecture Notes in Computer Science(), vol 12510. Springer, Cham. https://doi.org/10.1007/978-3-030-64559-5_28

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  • DOI: https://doi.org/10.1007/978-3-030-64559-5_28

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-64558-8

  • Online ISBN: 978-3-030-64559-5

  • eBook Packages: Computer ScienceComputer Science (R0)

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