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Related Experiment Videos

Comprehensive Instructional Video Analysis: The COIN Dataset and Performance Evaluation.

Yansong Tang, Jiwen Lu, Jie Zhou

    IEEE Transactions on Pattern Analysis and Machine Intelligence
    |March 17, 2020
    PubMed
    Summary

    A new large-scale dataset, COIN, addresses limitations in instructional video analysis. It features 11,827 videos across 180 tasks, enabling better research in diverse real-world applications.

    Related Experiment Videos

    Area of Science:

    • Computer Vision
    • Machine Learning
    • Human-Computer Interaction

    Background:

    • The proliferation of online instructional videos has increased the need for effective video analysis tools.
    • Existing datasets for instructional video analysis lack the scale and diversity required for real-world applications.

    Purpose of the Study:

    • To introduce COIN, a large-scale, comprehensive dataset for instructional video analysis.
    • To provide a benchmark for evaluating existing and novel methods in instructional video analysis.
    • To facilitate research into understanding task-consistency and ordering-dependency in instructional videos.

    Main Methods:

    • Developed a large-scale dataset (COIN) with 11,827 videos covering 180 tasks in 12 domains.
    • Utilized a novel toolbox for efficient annotation of step labels and temporal boundaries.
    • Evaluated multiple approaches on the COIN dataset across five different settings.
    • Exploited task-consistency and ordering-dependency to propose two new methods for localizing important steps.

    Main Results:

    • The COIN dataset provides a diverse and large-scale benchmark for instructional video analysis.
    • Proposed methods leveraging task-consistency and ordering-dependency show effectiveness in localizing key steps.
    • The developed annotation toolbox enables efficient video data processing.

    Conclusions:

    • The COIN dataset is expected to significantly advance research in comprehensive instructional video analysis.
    • The proposed methods offer simple yet effective solutions for action detection in instructional videos.
    • The dataset, toolbox, and code are publicly available to foster community research.