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Deep Learning for Free-Hand Sketch: A Survey.

Peng Xu, Timothy M Hospedales, Qiyue Yin

    IEEE Transactions on Pattern Analysis and Machine Intelligence
    |February 7, 2022
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    Summary
    This summary is machine-generated.

    This survey explores deep learning for free-hand sketches, covering unique data challenges and current research. It highlights advancements and future directions in sketch-based applications.

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    Area of Science:

    • Computer Vision
    • Machine Learning
    • Artificial Intelligence

    Background:

    • Free-hand sketches are a fundamental form of human visual communication.
    • Touchscreen devices have increased the accessibility and popularity of digital sketch creation.
    • Deep learning has significantly advanced the capabilities in processing and understanding sketch data.

    Purpose of the Study:

    • To provide a comprehensive survey of deep learning techniques applied to free-hand sketch data.
    • To analyze the unique characteristics and challenges of sketch data compared to natural images.
    • To review the evolution of free-hand sketch research in the context of deep learning.

    Main Methods:

    • Systematic review of existing literature and datasets in deep learning for sketches.
    • Detailed taxonomy and experimental evaluation of state-of-the-art methods.
    • Analysis of intrinsic traits and challenges specific to free-hand sketch data.

    Main Results:

    • Identification of key deep learning approaches for sketch analysis and generation.
    • Evaluation of current datasets, research topics, and leading methodologies.
    • Discussion of the differences between sketch data and other visual modalities.

    Conclusions:

    • Deep learning has revolutionized free-hand sketch research, enabling new applications.
    • Addressing the unique challenges of sketch data is crucial for further progress.
    • Identifying bottlenecks and future research directions is essential for the community.