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    End-to-end, single-stream temporal action detection in untrimmed videos

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    Type
    Conference Paper
    Authors
    Buch, Shyamal
    Escorcia, Victor cc
    Ghanem, Bernard cc
    Fei-Fei, Li
    Niebles, Juan Carlos
    KAUST Department
    Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
    Electrical Engineering Program
    VCC Analytics Research Group
    Date
    2019-05-01
    Permanent link to this record
    http://hdl.handle.net/10754/663479
    
    Metadata
    Show full item record
    Abstract
    In this work, we present a new intuitive, end-to-end approach for temporal action detection in untrimmed videos. We introduce our new architecture for Single-Stream Temporal Action Detection (SS-TAD), which effectively integrates joint action detection with its semantic sub-tasks in a single unifying end-to-end framework. We develop a method for training our deep recurrent architecture based on enforcing semantic constraints on intermediate modules that are gradually relaxed as learning progresses. We find that such a dynamic learning scheme enables SS-TAD to achieve higher overall detection performance, with fewer training epochs. By design, our single-pass network is very efficient and can operate at 701 frames per second, while simultaneously outperforming the state-of-the-art methods for temporal action detection on THUMOS’14.
    Citation
    Buch, S., Escorcia, V., Ghanem, B., & Niebles, J. C. (2017). End-to-End, Single-Stream Temporal Action Detection in Untrimmed Videos. Procedings of the British Machine Vision Conference 2017. doi:10.5244/c.31.93
    Publisher
    British Machine Vision Association and Society for Pattern Recognition
    Conference/Event name
    28th British Machine Vision Conference, BMVC 2017
    ISBN
    190172560X
    9781901725605
    DOI
    10.5244/c.31.93
    Additional Links
    http://www.bmva.org/bmvc/2017/papers/paper093/index.html
    ae974a485f413a2113503eed53cd6c53
    10.5244/c.31.93
    Scopus Count
    Collections
    Conference Papers; Electrical and Computer Engineering Program; Computer, Electrical and Mathematical Science and Engineering (CEMSE) Division

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