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Sketch semantic segmentation is a well-explored and pivotal problem incomputer vision involving the assignment of pre-defined part labels toindividual strokes. This paper presents ContextSeg - a simple yet highlyeffective approach to tackling this problem with two stages. In the firststage, to better encode the shape and positional information of strokes, wepropose to predict an extra dense distance field in an autoencoder network toreinforce structural information learning. In the second stage, we treat anentire stroke as a single entity and label a group of strokes within the samesemantic part using an auto-regressive Transformer with the default attentionmechanism. By group-based labeling, our method can fully leverage the contextinformation when making decisions for the remaining groups of strokes. Ourmethod achieves the best segmentation accuracy compared with state-of-the-artapproaches on two representative datasets and has been extensively evaluateddemonstrating its superior performance. Additionally, we offer insights intosolving part imbalance in training data and the preliminary experiment oncross-category training, which can inspire future research in this field.

ContextSeg: Sketch Semantic Segmentation by Querying the Context with Attention