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    This study introduces Relationship-Incremental Scene Graph Generation (RISGG) to incrementally learn object relationships in images. The proposed DaCFA-Net significantly improves performance by addressing old class and background shifts in incremental learning.

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    Area of Science:

    • Computer Vision
    • Machine Learning
    • Artificial Intelligence

    Background:

    • Scene Graph Generation (SGG) identifies semantic relationships between objects in images.
    • Incremental learning presents challenges like old class shift and background shift in SGG.

    Purpose of the Study:

    • To address the challenges in Relationship-Incremental Scene Graph Generation (RISGG).
    • To propose a novel network, DaCFA-Net, for effective RISGG.

    Main Methods:

    • A Divide-and-Conquer (DaC) pipeline decouples relationship class recognition to mitigate old class shift.
    • A Feature Adapter (FA) bridges feature space gaps and mines old relationship information.
    • The combined DaC and FA form the DaCFA-Net architecture.

    Main Results:

    • DaCFA-Net demonstrates significant performance gains on benchmark datasets.
    • The proposed method achieves approximately 20% improvement over existing SGG baselines on the VG dataset.

    Conclusions:

    • DaCFA-Net effectively handles old class and background shifts in RISGG.
    • The approach offers a robust solution for incremental learning in scene graph generation.