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    This study introduces a novel graph neural network (GNN) approach for sketch recognition. The multigraph transformer (MGT) effectively learns sketch representations from multiple graphs, outperforming existing methods.

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    Area of Science:

    • Computer Vision
    • Machine Learning
    • Artificial Intelligence

    Background:

    • Sketch representation is challenging due to signal sparsity and high abstraction.
    • Existing methods use Convolutional Neural Networks (CNNs) for static features or Recurrent Neural Networks (RNNs) for temporal sequences.

    Purpose of the Study:

    • To propose a novel method for learning meaningful representations of free-hand sketches.
    • To introduce a new graph neural network (GNN) model for sketch recognition.

    Main Methods:

    • Representing sketches as multiple sparsely connected graphs.
    • Designing a novel multigraph transformer (MGT) model to learn from these graph representations.
    • Capturing global and local geometric structures along with temporal information.

    Main Results:

    • The MGT achieved a small recognition gap compared to CNN upper bounds (72.80% vs. 74.22%) on 414k Google QuickDraw sketches.
    • MGT demonstrated faster inference speeds than CNN competitors.
    • The proposed approach significantly outperformed all RNN-based models.

    Conclusions:

    • Representing sketches as graphs and applying GNNs is a novel and effective approach for sketch recognition.
    • The multigraph transformer (MGT) offers a promising direction for improving sketch representation learning.