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The Devil Is in the Details: Self-supervised Attention for Vehicle 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

In recent years, the research community has approached the problem of vehicle re-identification (re-id) with attention-based models, specifically focusing on regions of a vehicle containing discriminative information. These re-id methods rely on expensive key-point labels, part annotations, and additional attributes including vehicle make, model, and color. Given the large number of vehicle re-id datasets with various levels of annotations, strongly-supervised methods are unable to scale across different domains. In this paper, we present Self-supervised Attention for Vehicle Re-identification (SAVER), a novel approach to effectively learn vehicle-specific discriminative features. Through extensive experimentation, we show that SAVER improves upon the state-of-the-art on challenging VeRi, VehicleID, Vehicle-1M and VERI-Wild datasets.

P. Khorramshahi and N. Peri—Equally contributed to this work.

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Acknowledgement

This research is supported in part by the Northrop Grumman Mission Systems Research in Applications for Learning Machines (REALM) initiative, and by the Office of the Director of National Intelligence (ODNI), Intelligence Advanced Research Projects Activity (IARPA), via IARPA R&D Contract No. D17PC00345. The views and conclusions contained herein are those of the authors and should not be interpreted as necessarily representing the official policies or endorsements, either expressed or implied, of ODNI, IARPA, or the U.S. Government. The U.S. Government is authorized to reproduce and distribute reprints for Governmental purposes notwithstanding any copyright annotation thereon.

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Authors and Affiliations

  1. Center for Automation Research, UMIACS, and the Department of Electrical and Computer Engineering, University of Maryland, College Park, USA

    Pirazh Khorramshahi, Neehar Peri & Rama Chellappa

  2. Research Center for Information Technology Innovation, Academia Sinica, Taipei, Taiwan

    Jun-cheng Chen

Authors
  1. Pirazh Khorramshahi
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  2. Neehar Peri
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  3. Jun-cheng Chen
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  4. Rama Chellappa
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Corresponding author

Correspondence to Pirazh Khorramshahi .

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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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Khorramshahi, P., Peri, N., Chen, Jc., Chellappa, R. (2020). The Devil Is in the Details: Self-supervised Attention for Vehicle 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_22

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  • DOI: https://doi.org/10.1007/978-3-030-58568-6_22

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