  Sketch semantic segmentation is a well-explored and pivotal problem in
computer vision involving the assignment of pre-defined part labels to
individual strokes. This paper presents ContextSeg - a simple yet highly
effective approach to tackling this problem with two stages. In the first
stage, to better encode the shape and positional information of strokes, we
propose to predict an extra dense distance field in an autoencoder network to
reinforce structural information learning. In the second stage, we treat an
entire stroke as a single entity and label a group of strokes within the same
semantic part using an auto-regressive Transformer with the default attention
mechanism. By group-based labeling, our method can fully leverage the context
information when making decisions for the remaining groups of strokes. Our
method achieves the best segmentation accuracy compared with state-of-the-art
approaches on two representative datasets and has been extensively evaluated
demonstrating its superior performance. Additionally, we offer insights into
solving part imbalance in training data and the preliminary experiment on
cross-category training, which can inspire future research in this field.
