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Updated: May 3, 2026

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Video DataFlywheel: Resolving the Impossible Data Trinity in Video-Language Understanding.

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    This study introduces Video DataFlywheel to improve video-language understanding datasets by iteratively refining annotations. The framework enhances data quality and scalability, boosting performance in tasks like video question answering.

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    Area of Science:

    • Artificial Intelligence
    • Computer Vision
    • Natural Language Processing

    Background:

    • Large-scale pre-training has advanced video-language understanding.
    • Data scarcity, quantity-diversity-quality trade-offs, and low-quality datasets hinder progress.
    • Existing synthetic annotation methods struggle with noise and scalability.

    Purpose of the Study:

    • To introduce an iterative framework, Video DataFlywheel, for refining video annotations.
    • To address noise in synthetic annotations and improve dataset scalability.
    • To enhance performance in video-language understanding tasks.

    Main Methods:

    • Video DataFlywheel framework for iterative annotation refinement.
    • Leveraging video-language models for synthetic annotation generation.
    • AdaTailr, a novel noise control method with weaker distribution assumptions.
    • Iterative pre-training and fine-tuning on human refinement examples.

    Main Results:

    • Video DataFlywheel achieved a 3% performance boost over existing baselines.
    • Improved dataset quality with minimal loss of diversity.
    • Significant performance gains in video question answering and text-video retrieval.

    Conclusions:

    • The Video DataFlywheel framework offers improved scalability and noise control for video-language datasets.
    • Iterative refinement combined with AdaTailr effectively addresses data challenges.
    • The refined dataset significantly enhances performance across various video-language understanding tasks.