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Learning meaningful representations of free-hand sketches remains achallenging task given the signal sparsity and the high-level abstraction ofsketches. Existing techniques have focused on exploiting either the staticnature of sketches with Convolutional Neural Networks (CNNs) or the temporalsequential property with Recurrent Neural Networks (RNNs). In this work, wepropose a new representation of sketches as multiple sparsely connected graphs.We design a novel Graph Neural Network (GNN), the Multi-Graph Transformer(MGT), for learning representations of sketches from multiple graphs whichsimultaneously capture global and local geometric stroke structures, as well astemporal information. We report extensive numerical experiments on a sketchrecognition task to demonstrate the performance of the proposed approach.Particularly, MGT applied on 414k sketches from Google QuickDraw: (i) achievessmall recognition gap to the CNN-based performance upper bound (72.80% vs.74.22%), and (ii) outperforms all RNN-based models by a significant margin. Tothe best of our knowledge, this is the first work proposing to representsketches as graphs and apply GNNs for sketch recognition. Code and trainedmodels are available athttps://github.com/PengBoXiangShang/multigraph_transformer.

Multi-Graph Transformer for Free-Hand Sketch Recognition