University of Maryland LibrariesDigital Repository at the University of Maryland
    • Login
    View Item 
    •   DRUM
    • Theses and Dissertations from UMD
    • UMD Theses and Dissertations
    • View Item
    •   DRUM
    • Theses and Dissertations from UMD
    • UMD Theses and Dissertations
    • View Item
    JavaScript is disabled for your browser. Some features of this site may not work without it.

    Modeling Deep Context in Spatial and Temporal Domain

    Thumbnail
    View/Open
    Dai_umd_0117E_19542.pdf (16.95Mb)
    No. of downloads: 93

    Date
    2018
    Author
    Dai, Xiyang
    Advisor
    Davis, Larry S.
    DRUM DOI
    https://doi.org/10.13016/ifop-it5w
    Metadata
    Show full item record
    Abstract
    Context has been one of the most important aspects in computer vision researches because it provides useful guidance to solve variant tasks in both spatial and temporal domain. As the recent rise of deep learning methods, deep networks have shown impressive performances on many computer vision tasks. Model deep context explicitly and implicitly in deep networks can further boost the effectiveness and efficiency of deep models. In spatial domain, implicitly model context can be useful to learn discriminative texture representations. We present an effective deep fusion architecture to capture both the second order and first older statistics of texture features; Meanwhile, explicitly model context can also be important to challenging task such as fine-grained classification. We then present a deep multi-task network that explicitly captures geometry constraints by simultaneously conducting fine-grained classification and key-point localization. In temporal domain, explicitly model context can be crucial to activity recognition and localization. We present a temporal context network to explicitly capture relative context around a proposal, which samples two temporal scales pair-wisely for precise temporal localization of human activities; Meanwhile, implicitly model context can lead to better network architecture for video applications. We then present a temporal aggregation network that learns a deep hierarchical representation for capturing temporal consistency. Finally, we conduct research on jointly modeling context in both spatial and temporal domain for human action understanding, which requires to predict where, when and what a human action happens in a crowd scene. We present a decoupled framework that has dedicated branches for spatial localization and temporal recognition. Contexts in spatial and temporal branches are modeled explicitly and fused together later to generate final predictions.
    URI
    http://hdl.handle.net/1903/21735
    Collections
    • Computer Science Theses and Dissertations
    • UMD Theses and Dissertations

    DRUM is brought to you by the University of Maryland Libraries
    University of Maryland, College Park, MD 20742-7011 (301)314-1328.
    Please send us your comments.
    Web Accessibility
     

     

    Browse

    All of DRUMCommunities & CollectionsBy Issue DateAuthorsTitlesSubjectsThis CollectionBy Issue DateAuthorsTitlesSubjects

    My Account

    LoginRegister
    Pages
    About DRUMAbout Download Statistics

    DRUM is brought to you by the University of Maryland Libraries
    University of Maryland, College Park, MD 20742-7011 (301)314-1328.
    Please send us your comments.
    Web Accessibility