  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 sketch-specific 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.
