Weakly Supervised Segmentation of Small Buildings with Point Labels

Jae Hun Lee, Chan Young Kim, Sanghoon Sull

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

    17 Citations (Scopus)

    Abstract

    Most supervised image segmentation methods require delicate and time-consuming pixel-level labeling of building or objects, especially for small objects. In this paper, we present a weakly supervised segmentation network for aerial/satellite images, separately considering small and large objects. First, we propose a simple point labeling method for small objects, while large objects are fully labeled. Then, we present a segmentation network trained with a small object mask to separate small and large objects in the loss function. During training, we employ a memory bank to cope with the limited number of point labels. Experiments results with three public datasets demonstrate the feasibility of our approach.

    Original languageEnglish
    Title of host publicationProceedings - 2021 IEEE/CVF International Conference on Computer Vision, ICCV 2021
    PublisherInstitute of Electrical and Electronics Engineers Inc.
    Pages7386-7395
    Number of pages10
    ISBN (Electronic)9781665428125
    DOIs
    Publication statusPublished - 2021
    Event18th IEEE/CVF International Conference on Computer Vision, ICCV 2021 - Virtual, Online, Canada
    Duration: 2021 Oct 112021 Oct 17

    Publication series

    NameProceedings of the IEEE International Conference on Computer Vision
    ISSN (Print)1550-5499

    Conference

    Conference18th IEEE/CVF International Conference on Computer Vision, ICCV 2021
    Country/TerritoryCanada
    CityVirtual, Online
    Period21/10/1121/10/17

    Bibliographical note

    Funding Information:
    Acknowledgements. This work was conducted by Center for Applied Research in Artificial Intelligence (CARAI) grant funded by Defense Acquisition Program Administration (DAPA) and Agency for Defense Development (ADD) (UD190031RD).

    Publisher Copyright:
    © 2021 IEEE

    ASJC Scopus subject areas

    • Software
    • Computer Vision and Pattern Recognition

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