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Temporal Action Segmentation: An Analysis of Modern Techniques.

Guodong Ding, Fadime Sener, Angela Yao

    IEEE Transactions on Pattern Analysis and Machine Intelligence
    |October 24, 2023
    PubMed
    Summary

    This survey provides a systematic review of temporal action segmentation (TAS) methods for long-range videos. It analyzes key techniques, benchmarks, and identifies research gaps in this rapidly growing field.

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    Area of Science:

    • Computer Vision
    • Artificial Intelligence
    • Machine Learning

    Background:

    • Temporal action segmentation (TAS) is crucial for understanding long videos with multiple actions.
    • Existing research has rapidly advanced TAS techniques, yet a comprehensive survey is lacking.

    Purpose of the Study:

    • To systematically analyze and summarize significant contributions and trends in temporal action segmentation.
    • To provide a structured overview of the TAS field for researchers.

    Main Methods:

    • Examination of task definitions, benchmarks, supervision types, and evaluation metrics.
    • Systematic investigation of frame representation and temporal modeling techniques.
    • Review of existing TAS works categorized by supervision levels.

    Main Results:

    • Identification of prevalent methodologies and their performance characteristics.
    • Analysis of the impact of different supervision strategies on TAS.
    • Highlighting of key advancements in frame representation and temporal modeling.

    Conclusions:

    • The survey consolidates current knowledge in temporal action segmentation.
    • Several research gaps are identified, guiding future research directions in video understanding.