Equipping Sketch Patches with Context-Aware Positional Encoding for
  Graphic Sketch Representation
Sicong Zang Zhijun Fang
The drawing order of a sketch records how it is created stroke-by-stroke by a
human being. For graphic sketch representation learning, recent studies have
injected sketch drawing orders into graph edge construction by linking each
patch to another in accordance to a temporal-based nearest neighboring
strategy. However, such constructed graph edges may be unreliable, since a
sketch could have variants of drawings. In this paper, we propose a
variant-drawing-protected method by equipping sketch patches with context-aware
positional encoding (PE) to make better use of drawing orders for learning
graphic sketch representation. Instead of injecting sketch drawings into graph
edges, we embed these sequential information into graph nodes only. More
specifically, each patch embedding is equipped with a sinusoidal absolute PE to
highlight the sequential position in the drawing order. And its neighboring
patches, ranked by the values of self-attention scores between patch
embeddings, are equipped with learnable relative PEs to restore the contextual
positions within a neighborhood. During message aggregation via graph
convolutional networks, a node receives both semantic contents from patch
embeddings and contextual patterns from PEs by its neighbors, arriving at
drawing-order-enhanced sketch representations. Experimental results indicate
that our method significantly improves sketch healing and controllable sketch
synthesis.