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Joint Disentangling and Adaptation for Cross-Domain Person Re-Identification

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Computer Vision – ECCV 2020 (ECCV 2020)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 12347))

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Abstract

Although a significant progress has been witnessed in supervised person re-identification (re-id), it remains challenging to generalize re-id models to new domains due to the huge domain gaps. Recently, there has been a growing interest in using unsupervised domain adaptation to address this scalability issue. Existing methods typically conduct adaptation on the representation space that contains both id-related and id-unrelated factors, thus inevitably undermining the adaptation efficacy of id-related features. In this paper, we seek to improve adaptation by purifying the representation space to be adapted. To this end, we propose a joint learning framework that disentangles id-related/unrelated features and enforces adaptation to work on the id-related feature space exclusively. Our model involves a disentangling module that encodes cross-domain images into a shared appearance space and two separate structure spaces, and an adaptation module that performs adversarial alignment and self-training on the shared appearance space. The two modules are co-designed to be mutually beneficial. Extensive experiments demonstrate that the proposed joint learning framework outperforms the state-of-the-art methods by clear margins.

Y. Zou—Work done during an internship at NVIDIA Research.

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Authors and Affiliations

  1. Carnegie Mellon University, Pittsburgh, USA

    Yang Zou & B. V. K. Vijaya Kumar

  2. NVIDIA, Santa Clara, USA

    Xiaodong Yang, Zhiding Yu & Jan Kautz

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  1. Yang Zou
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  2. Xiaodong Yang
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  3. Zhiding Yu
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  4. B. V. K. Vijaya Kumar
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  5. Jan Kautz
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Correspondence to Yang Zou .

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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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Zou, Y., Yang, X., Yu, Z., Kumar, B.V.K.V., Kautz, J. (2020). Joint Disentangling and Adaptation for Cross-Domain 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 12347. Springer, Cham. https://doi.org/10.1007/978-3-030-58536-5_6

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  • DOI: https://doi.org/10.1007/978-3-030-58536-5_6

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