  Learning meaningful representations of free-hand sketches remains a
challenging task given the signal sparsity and the high-level abstraction of
sketches. Existing techniques have focused on exploiting either the static
nature of sketches with Convolutional Neural Networks (CNNs) or the temporal
sequential property with Recurrent Neural Networks (RNNs). In this work, we
propose 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 which
simultaneously capture global and local geometric stroke structures, as well as
temporal information. We report extensive numerical experiments on a sketch
recognition task to demonstrate the performance of the proposed approach.
Particularly, MGT applied on 414k sketches from Google QuickDraw: (i) achieves
small 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. To
the best of our knowledge, this is the first work proposing to represent
sketches as graphs and apply GNNs for sketch recognition. Code and trained
models are available at
https://github.com/PengBoXiangShang/multigraph_transformer.
