Abstract
This paper, for the first time, marries large foundation models with human sketch understanding. We demonstrate what this brings
– a paradigm shift in terms of generalised sketch representation learning (e.g., classification). This generalisation happens on two fronts: (i)
generalisation across unknown categories (i.e., open-set), and (ii) generalisation traversing abstraction levels (i.e., good and bad sketches),
both being timely challenges that remain unsolved in the sketch literature. Our design is intuitive and centred around transferring the already
stellar generalisation ability of CLIP to benefit generalised learning for
sketches. We first “condition” the vanilla CLIP model by learning sketchspecific prompts using a novel auxiliary head of raster to vector sketch
conversion. This importantly makes CLIP “sketch-aware”. We then make
CLIP acute to the inherently different sketch abstraction levels. This
is achieved by learning a codebook of abstraction-specific prompt biases, a weighted combination of which facilitates the representation of
sketches across abstraction levels – low abstract edge-maps, medium abstract sketches in TU-Berlin, and highly abstract doodles in QuickDraw.
Our framework surpasses popular sketch representation learning algorithms in both zero-shot and few-shot setups and in novel settings across
different abstraction boundaries.