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    This study introduces a new method for Partially Relevant Video Retrieval (PRVR) that bridges the gap between text and video. The approach enhances video understanding by using a semantic-visual library for better cross-modal alignment.

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    Area of Science:

    • Computer Science
    • Artificial Intelligence
    • Multimedia Retrieval

    Background:

    • Partially Relevant Video Retrieval (PRVR) faces challenges due to the modality gap between text and video.
    • Partial semantic correspondence between text and visuals further complicates PRVR.

    Purpose of the Study:

    • To propose a bidirectional cross-modal alignment mechanism for improved PRVR.
    • To address the lack of direct visual cues for textual semantics in unsupervised learning.

    Main Methods:

    • Developed a semantic-visual association library to store paired visual and textual features with annotations.
    • Implemented dynamic retrieval of semantically similar visual samples to guide visual representation learning.
    • Enhanced textual representations by integrating aligned visual features to narrow the modality gap.

    Main Results:

    • The proposed method achieves state-of-the-art performance on benchmark datasets.
    • Demonstrated improved cross-modal consistency through refined visual and textual representations.
    • Successfully addressed challenges in unsupervised learning for PRVR.

    Conclusions:

    • The bidirectional cross-modal alignment mechanism effectively enhances PRVR.
    • The semantic-visual association library is crucial for unsupervised cross-modal learning.
    • The method shows significant improvements in scenarios with partial semantic correspondence.