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Updated: Nov 29, 2025

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    This study introduces a new Semantic Conditioned Dynamic Modulation (SCDM) mechanism to improve temporal sentence grounding in videos. The method enhances semantic alignment between text and video for more accurate localization of video segments.

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    Area of Science:

    • Computer Vision
    • Artificial Intelligence
    • Machine Learning

    Background:

    • Temporal sentence grounding aims to identify video segments matching a given sentence.
    • Existing methods primarily focus on semantic matching between sentences and video segments.
    • A need exists for improved methods that dynamically align sentence semantics with temporal video content.

    Purpose of the Study:

    • To propose a novel Semantic Conditioned Dynamic Modulation (SCDM) mechanism for temporal sentence grounding.
    • To leverage sentence semantics to dynamically modulate temporal convolution operations.
    • To enhance the precision and accuracy of localizing video segments corresponding to sentences.

    Main Methods:

    • Developed a Semantic Conditioned Dynamic Modulation (SCDM) mechanism.
    • Integrated SCDM with a hierarchical temporal convolutional architecture.
    • Utilized fine-grained clip-level actionness scores to refine temporal boundaries.

    Main Results:

    • The proposed SCDM mechanism significantly improves temporal grounding accuracy on benchmark datasets.
    • SCDM demonstrates advantages in stabilizing model training.
    • The method effectively associates relevant video content for precise grounding.

    Conclusions:

    • The SCDM mechanism offers a robust approach to temporal sentence grounding.
    • Dynamic modulation based on sentence semantics enhances video-text alignment.
    • The proposed method advances the state-of-the-art in video understanding and retrieval.