text: 86,98,1215192815900037122,713389493076758528,Xavier Bresson,"New paper on graph/sparse Transformer applied to sketch recognition.  Interestingly, a standard Transformer applied to sketch points does not work well. But a Transformer on graphs of sketch points perform quite well.  paper <LINK> code <LINK> <LINK> <LINK> A next step would be to generate sketches using graph Transformers instead of RNNs. See the nice blog post of @hardmaru about the sketch generative task. <LINK>",https://arxiv.org/abs/1912.11258,"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 this https URL ",Multi-Graph Transformer for Free-Hand Sketch Recognition,2,"['New paper on graph/sparse Transformer applied to sketch recognition. \n\nInterestingly, a standard Transformer applied to sketch points does not work well. But a Transformer on graphs of sketch points perform quite well. \n\npaper <LINK>\ncode <LINK> <LINK> <LINK>', 'A next step would be to generate sketches using graph Transformers instead of RNNs. See the nice blog post of @hardmaru about the sketch generative task.\nhttps://t.co/9ORZaMgsWi']",19,12,418