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  11. Markov Progressive Framework, A Universal Paradigm For Modeling Long Videos

Markov Progressive Framework, a Universal Paradigm for Modeling Long Videos

Bo Pang, Gao Peng, Yizhuo Li

    IEEE Transactions on Pattern Analysis and Machine Intelligence
    |July 12, 2024

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    View abstract on PubMed

    Summary
    This summary is machine-generated.

    Training video models for long-term temporal semantics is challenging due to computational complexity. The Markov Progressive (MaPro) framework enables end-to-end training of long videos with limited resources, improving performance on various models and datasets.

    Area of Science:

    • Computer Vision
    • Machine Learning
    • Deep Learning

    Background:

    • Video models face computational complexity challenges, limiting end-to-end training for long-term temporal semantics.
    • Current methods of splitting videos into clips result in fragmented information and hinder long-term semantic understanding.

    Purpose of the Study:

    • Introduce the Markov Progressive (MaPro) framework for efficient end-to-end video model training.
    • Enable modeling of long videos with limited computational resources while preserving temporal information.

    Main Methods:

    • Design a theoretical framework (MaPro) with a paradigm model using Markov operators.
    • Implement MaPro on CNN- and Transformer-based models, enabling multi-step sequential training.
    • Ensure multi-step progressive modeling is equivalent to conventional end-to-end modeling.

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    Main Results:

    • MaPro significantly improves performance across different backbones and datasets.
    • The SlowOnly network improved by 4.1 mAP on Charades and 2.5 top-1 accuracy on Kinetics.
    • TimeSformer performance on Kinetics improved by 2.0 top-1 accuracy with MaPro.

    Conclusions:

    • MaPro is a general and robust training method for video models.
    • Achieves significant performance gains with minimal parameter and computation overhead.
    • Effectively addresses the challenge of modeling long-term temporal semantics in videos.