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    This study introduces generalized zero-shot cross-modal retrieval protocols and a semantic-aware ranking algorithm to enhance performance on seen and unseen classes. A modified evaluation metric is also proposed for more intuitive cross-modal retrieval assessment.

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    Area of Science:

    • Computer Science
    • Artificial Intelligence
    • Machine Learning

    Background:

    • Cross-modal retrieval is crucial for diverse applications.
    • Existing algorithms struggle with generalizing to new object classes.
    • There is a need for robust generalized zero-shot cross-modal retrieval methods.

    Purpose of the Study:

    • Establish protocols for generalized zero-shot cross-modal retrieval.
    • Analyze the generalization capabilities of current cross-modal algorithms.
    • Propose a semantic-aware ranking algorithm to improve retrieval performance.

    Main Methods:

    • Developed protocols for generalized zero-shot cross-modal retrieval.
    • Introduced a semantic-aware ranking algorithm as an add-on.
    • Modified standard evaluation metrics (MAP, NDCG) for improved intuition.

    Main Results:

    • Demonstrated the effectiveness of the proposed protocols and algorithm.
    • Showed improved performance on both seen and unseen classes.
    • Validated the enhanced evaluation metric's intuitiveness.

    Conclusions:

    • The proposed approach significantly enhances generalized zero-shot cross-modal retrieval.
    • The semantic-aware ranking algorithm offers a flexible improvement for existing methods.
    • The modified evaluation metric provides a more meaningful assessment of cross-modal retrieval performance.