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Complex Relation Embedding for Scene Graph Generation.

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    This study introduces Complex-valued Relation Embedding (CoRE) for scene graph generation. CoRE uses complex space and Hermitian inner products to better capture object interactions, improving relationship prediction accuracy.

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    Area of Science:

    • Computer Vision
    • Artificial Intelligence
    • Machine Learning

    Background:

    • Scene graph generation (SGG) aims to represent visual relationships between objects in images.
    • Existing methods struggle with the long-tail issue due to imbalanced class distribution.
    • Current SGG models often use simple feature concatenation, neglecting complex object interactions.

    Purpose of the Study:

    • To improve scene graph generation by addressing limitations in current relation representation.
    • To develop a novel approach for learning richer object interactions in complex space.
    • To enhance the accuracy and diversity of visual relationships predicted in scene graphs.

    Main Methods:

    • Introduced complex-valued representations for entities in scene graphs.
    • Formulated relation triplets using Hermitian inner product in complex space (CoRE).
    • Investigated the impact of real and complex components of the Hermitian inner product.

    Main Results:

    • CoRE significantly improves scene graph generation performance on benchmark datasets.
    • The proposed method demonstrates effectiveness and superiority over existing approaches.
    • Experiments show enhanced generalization capabilities for both biased and unbiased inference.

    Conclusions:

    • Complex-valued representations and Hermitian inner products offer a more powerful way to model object interactions.
    • CoRE effectively addresses the limitations of real-space feature concatenation in SGG.
    • The approach shows promise for more accurate and diverse scene graph generation.