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    This study introduces a novel approach to Compositional Zero-Shot Learning (CZSL) by utilizing complex spaces to better model human-like attribute understanding. The method enhances recognition of novel object compositions.

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    Area of Science:

    • Computer Science
    • Artificial Intelligence
    • Machine Learning

    Background:

    • Existing Compositional Zero-Shot Learning (CZSL) methods struggle to model human-like composition comprehension.
    • Real-space limitations hinder dynamic connections between attributes, objects, and compositions.

    Purpose of the Study:

    • To develop a CZSL approach that mimics human cognitive abilities in understanding compositions.
    • To overcome the limitations of real-space models in capturing dynamic attribute-object dependencies.

    Main Methods:

    • Expanding the CZSL distance metric to complex spaces for unified attribute, object, and composition measures.
    • Establishing an imaginary-connected embedding in complex space to model attribute understanding.
    • Introducing a visual bias-based attribute extraction module for prototype-driven attribute selection.

    Main Results:

    • Successfully incorporated phase information in training and inference to represent attribute-object dependencies.
    • Preserved independent acquisition of primitives while modeling their interdependencies.
    • Demonstrated superior performance over baseline methods on three benchmark datasets.

    Conclusions:

    • The proposed complex-space CZSL approach effectively models human-like composition understanding.
    • The method offers a more dynamic and connected way to represent and recognize novel compositions.
    • This work advances CZSL by bridging the gap between computational models and human cognition.