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    This study introduces a Variational Multiple Instance Graph (VMIG) method to enhance cross-modal image retrieval. VMIG improves both accuracy and semantic diversity for user-oriented image search services.

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    Area of Science:

    • Computer Science
    • Artificial Intelligence
    • Information Retrieval

    Background:

    • Cross-modal image retrieval is crucial for user-oriented services.
    • Current methods struggle with semantic diversity and retrieval accuracy.
    • Short, broad keyword queries pose challenges for retrieval systems.

    Purpose of the Study:

    • To develop an end-to-end solution for improved cross-modal image retrieval.
    • To enhance both retrieval accuracy and semantic diversity.
    • To address limitations of single-point embedding and lack of cross-modal understanding.

    Main Methods:

    • Introduced Variational Multiple Instance Graph (VMIG) framework.
    • Employed a query-guided variational autoencoder for continuous semantic space learning.
    • Utilized multiple instance learning with multi-head attention and instance graph construction for cross-modal alignment.

    Main Results:

    • VMIG effectively captures diverse query semantics.
    • The method successfully connects diverse features across modalities.
    • Experimental results demonstrate significant improvements in both accuracy and semantic diversity.

    Conclusions:

    • The proposed VMIG solution offers a robust approach to cross-modal image retrieval.
    • VMIG enhances user experience by providing semantically diverse and accurate results.
    • This method advances the field by effectively fusing heterogeneous modalities.