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Related Experiment Video

Updated: Jan 5, 2026

Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment
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Fine-Grained Video Captioning via Graph-based Multi-Granularity Interaction Learning.

Yichao Yan, Ning Zhuang, Bingbing Ni

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    This summary is machine-generated.

    This study introduces a Graph-based Learning for Multi-Granularity Interaction Representation (GLMGIR) framework for detailed team sports auto-narrative generation. The novel approach effectively models complex interactions and improves fine-grained video description accuracy.

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    Area of Science:

    • Computer Vision
    • Natural Language Processing
    • Artificial Intelligence

    Background:

    • Generating detailed linguistic descriptions for multi-subject interactive videos, particularly in team sports, presents significant challenges.
    • Existing video captioning methods struggle with modeling fine-grained individual actions and complex spatio-temporal group interactions.

    Purpose of the Study:

    • To propose a novel framework, Graph-based Learning for Multi-Granularity Interaction Representation (GLMGIR), for fine-grained team sports auto-narrative generation.
    • To develop a new dataset, the Sports Video Narrative (SVN) dataset, to facilitate reproducible research in this area.
    • To introduce a new evaluation metric, Fine-grained Captioning Evaluation (FCE), suitable for assessing fine-grained sports narrative generation.

    Main Methods:

    • The GLMGIR framework incorporates a multi-granular interaction modeling module to progressively extract and encode intra- and inter-team interactions.
    • A multi-granular attention module is utilized to process action/event descriptions across multiple spatio-temporal resolutions.
    • The framework integrates these modules collaboratively to generate detailed narratives.

    Main Results:

    • The proposed GLMGIR framework demonstrates effectiveness in generating fine-grained narratives for team sports videos.
    • Experiments on the newly collected SVN dataset validate the framework's performance.
    • The novel FCE metric proves more suitable than traditional metrics like METEOR for evaluating fine-grained sports narratives.

    Conclusions:

    • The GLMGIR framework offers a robust solution for the challenging task of detailed team sports auto-narrative generation.
    • The SVN dataset and FCE metric provide valuable resources for future research in this domain.
    • This work advances the state-of-the-art in automatic video description for complex interactive sports scenarios.