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Caption Assisted Multimodal Large Language Model for Video Moment Retrieval.

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    This study introduces CALCE, a novel framework for precise video moment retrieval using Multimodal Large Language Models (MLLMs). CALCE enhances MLLMs

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    Area of Science:

    • Artificial Intelligence
    • Computer Vision
    • Natural Language Processing

    Background:

    • Multimodal Large Language Models (MLLMs) show promise in various tasks but struggle with precise video moment retrieval.
    • Fine-grained spatial and temporal understanding is crucial for accurate video moment identification.

    Purpose of the Study:

    • To develop a novel framework, CALCE, for enhanced moment retrieval in videos.
    • To improve the precision and accuracy of MLLMs in identifying specific video segments.

    Main Methods:

    • A two-stage framework, Caption Assisted MLLM from Coarse to finE (CALCE), is proposed.
    • The first stage uses audio captions to assist MLLMs and a clustering algorithm on sparsely sampled frames (key/non-key).
    • The second stage refines retrieval with a higher sampling rate, using predictions from the first stage to filter frames.

    Main Results:

    • CALCE demonstrates superior performance in video moment retrieval tasks.
    • The framework effectively retrieves video moments progressively from coarse to precise.
    • Experiments on QVHighlights and Charades-STA datasets confirm CALCE's effectiveness.

    Conclusions:

    • CALCE significantly enhances the capability of MLLMs for precise video moment retrieval.
    • The proposed two-stage approach overcomes limitations in fine-grained spatial and temporal understanding.