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SketchGCN: Semantic Sketch Segmentation with Graph Convolutional Networks

2020-03-02 05:48:55
Lumin Yang, Jiajie Zhuang, Hongbo Fu, Kun Zhou, Youyi Zheng

Abstract

We introduce SketchGCN, a graph convolutional neural network for semantic segmentation and labeling of free-hand sketches. We treat an input sketch as a 2D pointset, and encode the stroke structure information into graph node/edge representations. To predict the per-point labels, our SketchGCN uses graph convolution and a global-local branching network architecture to extract both intra-stroke and inter-stroke features. SketchGCN significantly improves the accuracy of the state-of-the-art methods for semantic sketch segmentation (by 11.4% in the pixel-basedmetric and 18.2% in the component-based metric over a large-scale challenging SPG dataset) and has magnitudes fewer parameters than both image-based and sequence-based methods.

Abstract (translated)

URL

https://arxiv.org/abs/2003.00678

PDF

https://arxiv.org/pdf/2003.00678.pdf


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