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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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Enhancing Text-Video Retrieval Performance with Low-Salient but Discriminative Objects.

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    This study introduces a new model for text-video retrieval that focuses on Low-Salient but Discriminative Objects (LSDOs). By emphasizing these overlooked elements, the model significantly improves retrieval accuracy.

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    Area of Science:

    • Computer Science
    • Artificial Intelligence
    • Machine Learning

    Background:

    • Text-video retrieval models often overlook crucial Low-Salient but Discriminative Objects (LSDOs).
    • Focusing on salient subjects like humans or animals limits comprehensive content understanding.

    Purpose of the Study:

    • To propose a novel model that enhances text-video retrieval by incorporating LSDOs.
    • To improve the accuracy and robustness of matching videos with their corresponding text descriptions.

    Main Methods:

    • Video modality: Feature selection for video-level LSDO features and cross-modal attention for frame-level LSDO features.
    • Text modality: Sparse aggregation to generate multiple object prototypes for text-level LSDO features.

    Main Results:

    • The proposed model achieves state-of-the-art results on benchmark datasets (MSR-VTT, MSVD, LSMDC, DiDeMo).
    • Demonstrated significant improvements across various evaluation metrics for text-video retrieval.

    Conclusions:

    • Emphasizing LSDOs is crucial for advancing text-video retrieval performance.
    • The novel model effectively captures and utilizes LSDO information across modalities for superior retrieval.