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Updated: Oct 21, 2025

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    This study introduces LARCH, a novel conversational image search scheme. LARCH enhances image search by understanding complex user intents and integrating diverse knowledge for improved results.

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    Area of Science:

    • Computer Science
    • Artificial Intelligence
    • Information Retrieval

    Background:

    • Traditional image search struggles with complex user intents.
    • Existing conversational search focuses on dialogue, not image retrieval.
    • Conversational image search faces challenges in intent understanding and knowledge integration.

    Purpose of the Study:

    • To develop a novel scheme for conversational image search.
    • To address challenges in understanding multimodal conversational queries.
    • To effectively utilize diverse knowledge for enhanced image representation.

    Main Methods:

    • A multimodal hierarchical graph-based neural network for query embedding.
    • A multi-form knowledge embedding memory network for knowledge unification.
    • A gated neural network for knowledge-enhanced image representation.

    Main Results:

    • The proposed LARCH scheme significantly improves conversational image search performance.
    • Demonstrated effectiveness on an extended benchmark dataset.
    • Achieved better understanding of complex user intents from multimodal queries.

    Conclusions:

    • LARCH offers a robust solution for conversational image search.
    • The scheme effectively integrates multimodal information and external knowledge.
    • The developed methods advance the field of intelligent image retrieval.