Taking a closer look at domain shift: Category-level adversaries for semantics consistent domain adaptation

Yawei Luo, Liang Zheng, Tao Guan, Junqing Yu*, Yi Yang

*Corresponding author for this work

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

    591 Citations (Scopus)

    Abstract

    We consider the problem of unsupervised domain adaptation in semantic segmentation. The key in this campaign consists in reducing the domain shift, i.e., enforcing the data distributions of the two domains to be similar. A popular strategy is to align the marginal distribution in the feature space through adversarial learning. However, this global alignment strategy does not consider the local category-level feature distribution. A possible consequence of the global movement is that some categories which are originally well aligned between the source and target may be incorrectly mapped. To address this problem, this paper introduces a category-level adversarial network, aiming to enforce local semantic consistency during the trend of global alignment. Our idea is to take a close look at the category-level data distribution and align each class with an adaptive adversarial loss. Specifically, we reduce the weight of the adversarial loss for category-level aligned features while increasing the adversarial force for those poorly aligned. In this process, we decide how well a feature is category-level aligned between source and target by a co-training approach. In two domain adaptation tasks, i.e., GTA5-> Cityscapes and SYNTHIA-> Cityscapes, we validate that the proposed method matches the state of the art in segmentation accuracy.

    Original languageEnglish
    Title of host publicationProceedings - 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2019
    PublisherIEEE Computer Society
    Pages2502-2511
    Number of pages10
    ISBN (Electronic)9781728132938
    DOIs
    Publication statusPublished - Jun 2019
    Event32nd IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2019 - Long Beach, United States
    Duration: 16 Jun 201920 Jun 2019

    Publication series

    NameProceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
    Volume2019-June
    ISSN (Print)1063-6919

    Conference

    Conference32nd IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2019
    Country/TerritoryUnited States
    CityLong Beach
    Period16/06/1920/06/19

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