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    This study introduces a Frequency-Spatial Complementation (FSC) model to improve Cross-Domain Few-Shot Learning (CD-FSL) by integrating frequency and spatial domains. The FSC model enhances generalization by leveraging frequency domain information for better style adaptation.

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    Area of Science:

    • Computer Science
    • Machine Learning
    • Artificial Intelligence

    Background:

    • Cross-Domain Few-Shot Learning (CD-FSL) faces challenges in recognizing targets with limited out-of-domain data.
    • Existing CD-FSL methods often overlook the frequency domain's contribution to domain generalization, focusing primarily on spatial domain enhancements.

    Purpose of the Study:

    • To propose a novel Frequency-Spatial Complementation (FSC) model that integrates both frequency and spatial domain information for improved CD-FSL.
    • To enhance the model's ability to capture style-related information and learn domain-invariant features.

    Main Methods:

    • Developed a Frequency-Spatial Complementation (FSC) model combining frequency and spatial domain information.
    • Introduced a Frequency and Spatial Fusion (FusionFS) module to improve style information capture.
    • Proposed Gradient-guided Unified Style Attack (GUSA) and Channel-specific Attack Intensity Calculation (CAIC) strategies for generating diverse training data.

    Main Results:

    • The FSC model demonstrated significant performance improvements across eight target domains.
    • The proposed attack strategies provided more diversified style data, particularly beneficial in single-source domain scenarios.
    • The FusionFS module effectively enhanced the model's capacity to learn from style variations.

    Conclusions:

    • Integrating frequency and spatial domains offers a promising direction for advancing CD-FSL.
    • The proposed FSC model and attack strategies effectively address limitations in current CD-FSL approaches.
    • The method shows robust performance in generalizing to various styles and domains with limited data.