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    Diagnosing Error in Temporal Action Detectors

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    detad.pdf
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    Format:
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    Description:
    Accepted Manuscript
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    Type
    Conference Paper
    Authors
    Alwassel, Humam cc
    Caba Heilbron, Fabian
    Escorcia, Victor cc
    Ghanem, Bernard cc
    KAUST Department
    Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
    Computer Science Program
    Electrical Engineering Program
    Visual Computing Center (VCC)
    KAUST Grant Number
    OSR-CRG2017-3405
    Date
    2018-10-07
    Online Publication Date
    2018-10-07
    Print Publication Date
    2018
    Permanent link to this record
    http://hdl.handle.net/10754/630247
    
    Metadata
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    Abstract
    Despite the recent progress in video understanding and the continuous rate of improvement in temporal action localization throughout the years, it is still unclear how far (or close?) we are to solving the problem. To this end, we introduce a new diagnostic tool to analyze the performance of temporal action detectors in videos and compare different methods beyond a single scalar metric. We exemplify the use of our tool by analyzing the performance of the top rewarded entries in the latest ActivityNet action localization challenge. Our analysis shows that the most impactful areas to work on are: strategies to better handle temporal context around the instances, improving the robustness w.r.t. the instance absolute and relative size, and strategies to reduce the localization errors. Moreover, our experimental analysis finds the lack of agreement among annotator is not a major roadblock to attain progress in the field. Our diagnostic tool is publicly available to keep fueling the minds of other researchers with additional insights about their algorithms.
    Citation
    Alwassel H, Caba Heilbron F, Escorcia V, Ghanem B (2018) Diagnosing Error in Temporal Action Detectors. Lecture Notes in Computer Science: 264–280. Available: http://dx.doi.org/10.1007/978-3-030-01219-9_16.
    Sponsors
    This publication is based upon work supported by the King Abdullah University of Science and Technology (KAUST) Office of Sponsored Research (OSR) under Award No. OSR-CRG2017-3405.
    Publisher
    Springer Nature
    Journal
    Lecture Notes in Computer Science
    Conference/Event name
    15th European Conference on Computer Vision, ECCV 2018
    DOI
    10.1007/978-3-030-01219-9_16
    arXiv
    1807.10706
    Additional Links
    https://link.springer.com/chapter/10.1007%2F978-3-030-01219-9_16
    ae974a485f413a2113503eed53cd6c53
    10.1007/978-3-030-01219-9_16
    Scopus Count
    Collections
    Conference Papers; Computer Science Program; Electrical Engineering Program; Visual Computing Center (VCC); Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division

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