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Temporal Action Localization in the Deep Learning Era: A Survey.

Binglu Wang, Yongqiang Zhao, Le Yang

    IEEE Transactions on Pattern Analysis and Machine Intelligence
    |November 6, 2023
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    Summary
    This summary is machine-generated.

    This survey analyzes temporal action localization methods for intelligent video understanding. It categorizes supervised and weakly supervised approaches, offering new perspectives and highlighting confidence estimation challenges.

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    Area of Science:

    • Computer Vision
    • Artificial Intelligence
    • Machine Learning

    Background:

    • Temporal action localization is key for intelligent video understanding.
    • Deep learning backbone networks extract spatiotemporal features.
    • Supervised and weakly supervised learning drive progress in action localization.

    Purpose of the Study:

    • To provide a comprehensive survey of existing temporal action localization works.
    • To establish a well-organized taxonomy of current strategies.
    • To identify strengths, weaknesses, and future research directions.

    Main Methods:

    • Categorization of supervised learning methods, including anchor mechanisms and a novel classification approach.
    • Extension of weakly supervised learning mechanisms (pre-classification, post-classification) with enhancement strategies.
    • Analysis of confidence estimation as a critical bottleneck.

    Main Results:

    • A structured taxonomy highlighting the pros and cons of various action localization techniques.
    • Novel perspectives on supervised and weakly supervised learning strategies.
    • Identification of confidence estimation as an under-addressed challenge.

    Conclusions:

    • The survey offers a valuable resource for researchers in temporal action localization.
    • Provides guidance for newcomers and inspiration for experienced researchers.
    • Underscores the need for further research into confidence estimation for improved action localization models.