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Retrieval is the process of getting information out of memory storage and back into conscious awareness. This ability is essential for daily tasks like brushing hair and teeth, driving to work, and performing job duties. Retrieval occurs in three ways: recall, recognition, and relearning.
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Related Experiment Video

Updated: Jan 17, 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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PRVR: Partially Relevant Video Retrieval.

Xianke Chen, Daizong Liu, Xun Yang

    IEEE Transactions on Pattern Analysis and Machine Intelligence
    |September 24, 2025
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    Summary
    This summary is machine-generated.

    This study introduces Partially Relevant Video Retrieval (PRVR) for videos with multiple scenes. The new Multi-Scale Similarity Learning (MS-SL++) network effectively identifies query-relevant moments within videos.

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    Area of Science:

    • Computer Science
    • Artificial Intelligence
    • Machine Learning

    Background:

    • Current text-to-video retrieval (T2VR) assumes videos are trimmed for direct query relevance.
    • Real-world videos often contain multiple scenes, making retrieval challenging when only parts are relevant.
    • This necessitates a new approach for partially relevant video retrieval (PRVR).

    Purpose of the Study:

    • To address the challenge of retrieving videos where only a portion is relevant to a textual query.
    • To introduce and study the new setting of Partially Relevant Video Retrieval (PRVR).
    • To propose a novel network for accurately identifying query-relevant moments in videos.

    Main Methods:

    • Formulated the PRVR task as a multiple instance learning problem.
    • Proposed a Multi-Scale Similarity Learning (MS-SL++) network.
    • The network jointly learns clip-scale and frame-scale similarities for relevance determination.

    Main Results:

    • The MS-SL++ network demonstrated viability in the PRVR setting.
    • Experiments were conducted on three diverse video-text datasets: TVshow Retrieval, ActivityNet-Captions, and Charades-STA.
    • The proposed method successfully determines partial relevance between video-query pairs.

    Conclusions:

    • The proposed Multi-Scale Similarity Learning (MS-SL++) network is effective for Partially Relevant Video Retrieval (PRVR).
    • This work establishes a foundation for future research in retrieving relevant content from complex, multi-scene videos.
    • The findings highlight the importance of considering video structure for improved text-to-video retrieval.