Generalizing a person retrieval model hetero- and homogeneously

Zhun Zhong, Liang Zheng, Shaozi Li*, Yi Yang

*Corresponding author for this work

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

    74 Citations (Scopus)

    Abstract

    Person re-identification (re-ID) poses unique challenges for unsupervised domain adaptation (UDA) in that classes in the source and target sets (domains) are entirely different and that image variations are largely caused by cameras. Given a labeled source training set and an unlabeled target training set, we aim to improve the generalization ability of re-ID models on the target testing set. To this end, we introduce a Hetero-Homogeneous Learning (HHL) method. Our method enforces two properties simultaneously: (1) camera invariance, learned via positive pairs formed by unlabeled target images and their camera style transferred counterparts; (2) domain connectedness, by regarding source/target images as negative matching pairs to the target/source images. The first property is implemented by homogeneous learning because training pairs are collected from the same domain. The second property is achieved by heterogeneous learning because we sample training pairs from both the source and target domains. On Market-1501, DukeMTMC-reID and CUHK03, we show that the two properties contribute indispensably and that very competitive re-ID UDA accuracy is achieved. Code is available at: https://github.com/zhunzhong07/HHL.

    Original languageEnglish
    Title of host publicationComputer Vision – ECCV 2018 - 15th European Conference, 2018, Proceedings
    EditorsVittorio Ferrari, Cristian Sminchisescu, Yair Weiss, Martial Hebert
    PublisherSpringer Verlag
    Pages176-192
    Number of pages17
    ISBN (Print)9783030012601
    DOIs
    Publication statusPublished - 2018
    Event15th European Conference on Computer Vision, ECCV 2018 - Munich, Germany
    Duration: 8 Sept 201814 Sept 2018

    Publication series

    NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
    Volume11217 LNCS
    ISSN (Print)0302-9743
    ISSN (Electronic)1611-3349

    Conference

    Conference15th European Conference on Computer Vision, ECCV 2018
    Country/TerritoryGermany
    CityMunich
    Period8/09/1814/09/18

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