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Neural Multimodal Cooperative Learning Toward Micro-Video Understanding.

Yinwei Wei, Xiang Wang, Weili Guan

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    This study introduces a neural multimodal cooperative learning (NMCL) model to enhance micro-video analysis by separating consistent and complementary features. The model effectively improves representation expressiveness and outperforms existing methods.

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    Area of Science:

    • Artificial Intelligence
    • Computer Vision
    • Machine Learning

    Background:

    • Micro-videos possess limited descriptive power due to inherent characteristics.
    • Existing multimodal representations often overlook feature complementarity, focusing only on consistency.

    Purpose of the Study:

    • To explicitly separate consistent and complementary features from mixed multimodal information.
    • To improve the expressiveness of each modality in micro-video analysis by harnessing their combination.

    Main Methods:

    • A novel neural multimodal cooperative learning (NMCL) model was developed.
    • A relation-aware attention mechanism was employed to split consistent and complementary components.
    • An attention network was devised to capture category-related features and mitigate redundancy.

    Main Results:

    • The NMCL model effectively separates and integrates consistent and complementary features.
    • Experimental results on a real-world micro-video dataset demonstrate superior performance over state-of-the-art methods.
    • Further studies confirmed the effectiveness and cooperative benefits of the attention mechanism.

    Conclusions:

    • The proposed NMCL model offers a significant advancement in micro-video representation learning.
    • Explicitly addressing feature consistency and complementarity enhances multimodal understanding.
    • The attention-based approach effectively handles redundant information for improved prediction.