CycAs: Self-supervised Cycle Association for Learning Re-identifiable Descriptions

Zhongdao Wang, Jingwei Zhang, Liang Zheng, Yixuan Liu, Yifan Sun, Yali Li, Shengjin Wang*

*Corresponding author for this work

    Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

    49 Citations (Scopus)

    Abstract

    This paper proposes a self-supervised learning method for the person re-identification (re-ID) problem, where existing unsupervised methods usually rely on pseudo labels, such as those from video tracklets or clustering. A potential drawback of using pseudo labels is that errors may accumulate and it is challenging to estimate the number of pseudo IDs. We introduce a different unsupervised method that allows us to learn pedestrian embeddings from raw videos, without resorting to pseudo labels. The goal is to construct a self-supervised pretext task that matches the person re-ID objective. Inspired by the data association concept in multi-object tracking, we propose the Cycle Association (CycAs) task: after performing data association between a pair of video frames forward and then backward, a pedestrian instance is supposed to be associated to itself. To fulfill this goal, the model must learn a meaningful representation that can well describe correspondences between instances in frame pairs. We adapt the discrete association process to a differentiable form, such that end-to-end training becomes feasible. Experiments are conducted in two aspects: We first compare our method with existing unsupervised re-ID methods on seven benchmarks and demonstrate CycAs’ superiority. Then, to further validate the practical value of CycAs in real-world applications, we perform training on self-collected videos and report promising performance on standard test sets.

    Original languageEnglish
    Title of host publicationComputer Vision – ECCV 2020 - 16th European Conference, 2020, Proceedings
    EditorsAndrea Vedaldi, Horst Bischof, Thomas Brox, Jan-Michael Frahm
    PublisherSpringer Science and Business Media Deutschland GmbH
    Pages72-88
    Number of pages17
    ISBN (Print)9783030586201
    DOIs
    Publication statusPublished - 2020
    Event16th European Conference on Computer Vision, ECCV 2020 - Glasgow, United Kingdom
    Duration: 23 Aug 202028 Aug 2020

    Publication series

    NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
    Volume12356 LNCS
    ISSN (Print)0302-9743
    ISSN (Electronic)1611-3349

    Conference

    Conference16th European Conference on Computer Vision, ECCV 2020
    Country/TerritoryUnited Kingdom
    CityGlasgow
    Period23/08/2028/08/20

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