Free-hand sketches are appealing for humans as a universal tool to depict the
visual world. Humans can recognize varied sketches of a category easily by
identifying the concurrence and layout of the intrinsic semantic components of
the category, since humans draw free-hand sketches based a common consensus
that which types of semantic components constitute each sketch category. For
example, an airplane should at least have a fuselage and wings. Based on this
analysis, a semantic component-level memory module is constructed and embedded
in the proposed structured sketch recognition network in this paper. The memory
keys representing semantic components of each sketch category can be
self-learned and enhance the recognition network's explainability. Our proposed
networks can deal with different situations of sketch recognition, i.e., with
or without semantic components labels of strokes. Experiments on the SPG and
SketchIME datasets demonstrate the memory module's flexibility and the
recognition network's explainability. The code and data are available at
this https URL

通过构建一个语义组件级别的记忆模块，本文提出的结构化素描识别网络可以增强识别网络的解释能力，并可以处理带有或不带有语义组件标签的素描识别情况。在 SPG 和 SketchIME 数据集上的实验证明了记忆模块的灵活性和识别网络的解释能力。