SketchGNN: Semantic Sketch Segmentation with Graph Neural Networks

• URL: http://arxiv.org/abs/2003.00678v2
• Date: Thu, 25 Mar 2021 09:05:02 GMT
• Title: SketchGNN: Semantic Sketch Segmentation with Graph Neural Networks
• Authors: Lumin Yang, Jiajie Zhuang, Hongbo Fu, Xiangzhi Wei, Kun Zhou and Youyi Zheng
• Abstract summary: We introduce SketchGNN, a convolutional graph neural network for semantic segmentation and labeling of freehand vector sketches. To predict the per-node labels, our SketchGNN uses graph convolution and a static-dynamic branching network architecture.
• Score: 40.32629073485205
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We introduce SketchGNN, a convolutional graph neural network for semantic segmentation and labeling of freehand vector sketches. We treat an input stroke-based sketch as a graph, with nodes representing the sampled points along input strokes and edges encoding the stroke structure information. To predict the per-node labels, our SketchGNN uses graph convolution and a static-dynamic branching network architecture to extract the features at three levels, i.e., point-level, stroke-level, and sketch-level. SketchGNN significantly improves the accuracy of the state-of-the-art methods for semantic sketch segmentation (by 11.2% in the pixel-based metric 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.

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