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READ: Reciprocal Attention Discriminator for Image-to-Video Re-identification

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Computer Vision – ECCV 2020 (ECCV 2020)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 12359))

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Abstract

Person re-identification (re-ID) is the problem of visually identifying a person given a database of identities. In this work, we focus on image-to-video re-ID which compares a single query image to videos in the gallery. The main challenge is the asymmetry association of an image and a video, and overcoming the difference caused by the additional temporal dimension. To this end, we propose an attention-aware discriminator architecture. The attention occurs across different modalities, and even different identities to aggregate useful spatio-temporal information for comparison. The information is effectively fused into a united feature, followed by the final prediction of a similarity score. The performance of the method is shown with image-to-video person re-identification benchmarks (DukeMTMC-VideoReID, and MARS).

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Author information

Authors and Affiliations

  1. Seoul, South Korea

    Minho Shim

  2. Department of Computer Science, ETH Zürich, Zürich, Switzerland

    Hsuan-I Ho

  3. School of Electrical Engineering, KAIST, Daejeon, South Korea

    Jinhyung Kim

  4. Clova AI, NAVER Corporation, Seongnam, South Korea

    Dongyoon Wee

Authors
  1. Minho Shim
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  2. Hsuan-I Ho
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  3. Jinhyung Kim
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  4. Dongyoon Wee
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Editor information

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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Shim, M., Ho, HI., Kim, J., Wee, D. (2020). READ: Reciprocal Attention Discriminator for Image-to-Video Re-identification. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, JM. (eds) Computer Vision – ECCV 2020. ECCV 2020. Lecture Notes in Computer Science(), vol 12359. Springer, Cham. https://doi.org/10.1007/978-3-030-58568-6_20

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  • DOI: https://doi.org/10.1007/978-3-030-58568-6_20

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-58567-9

  • Online ISBN: 978-3-030-58568-6

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