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A Comprehensive Survey of Scene Graphs: Generation and Application.

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    Scene graphs offer a structured way to represent visual scenes, enabling advanced computer vision tasks like relationship detection and image captioning. This survey provides a comprehensive overview of scene graph generation and applications.

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    Area of Science:

    • Computer Vision
    • Artificial Intelligence
    • Data Representation

    Background:

    • Current computer vision focuses on object recognition, but higher-level scene understanding is needed.
    • Scene graphs provide a structured representation of objects, attributes, and relationships within an image.
    • Advanced tasks like visual relationship detection, image captioning, and visual question answering require deeper scene comprehension.

    Purpose of the Study:

    • To provide a systematic survey of current scene graph research.
    • To define scene graphs and discuss their generation methods.
    • To explore scene graph applications and common datasets.

    Main Methods:

    • Summarizing the definition of scene graphs.
    • Discussing scene graph generation (SGG) methods, including those using prior knowledge.
    • Investigating major applications and datasets for scene graphs.

    Main Results:

    • Scene graphs are powerful tools for scene understanding, bridging object recognition and higher-level reasoning.
    • Research in scene graphs is cross-modal, complex, and rapidly evolving.
    • A comprehensive review of SGG methods and applications is presented.

    Conclusions:

    • Scene graphs are crucial for advancing visual scene understanding beyond object detection.
    • This survey offers a structured overview and insights into future directions for scene graph research.
    • The field of scene graphs is dynamic, with significant potential for future development.