论文标题
Sketchgnn：使用图神经网络的语义素描分割
SketchGNN: Semantic Sketch Segmentation with Graph Neural Networks
论文作者
论文摘要
我们介绍了Sketchgnn，这是一种用于语义分割和徒手矢量草图标签的卷积图神经网络。我们将基于输入的示意图视为图形，而节点代表沿输入笔划和编码中风结构信息的输入中的采样点。为了预测每个节点标签，我们的Sketchgnn使用图形卷积和静态动态分支网络体系结构在三个级别（即点级，中风级别和素描级别）中提取特征。 Sketchgnn显着提高了语义素描分割的最先进方法的准确性（基于像素的度量标准中，在大规模具有挑战性的SPG数据集中，基于像素的公制中提高了11.2％，基于组件的指标的准确性为18.2％），并且基于图像和基于序列的方法的幅度较少。
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.
