Abstract
This paper, for the very first time, introduces human sketches to the
landscape of XAI (Explainable Artificial Intelligence). We argue that sketch as
a ``human-centred'' data form, represents a natural interface to study
explainability. We focus on cultivating sketch-specific explainability designs.
This starts by identifying strokes as a unique building block that offers a
degree of flexibility in object construction and manipulation impossible in
photos. Following this, we design a simple explainability-friendly sketch
encoder that accommodates the intrinsic properties of strokes: shape, location,
and order. We then move on to define the first ever XAI task for sketch, that
of stroke location inversion SLI. Just as we have heat maps for photos, and
correlation matrices for text, SLI offers an explainability angle to sketch in
terms of asking a network how well it can recover stroke locations of an unseen
sketch. We offer qualitative results for readers to interpret as snapshots of
the SLI process in the paper, and as GIFs on the project page. A minor but
interesting note is that thanks to its sketch-specific design, our sketch
encoder also yields the best sketch recognition accuracy to date while having
the smallest number of parameters. The code is available at
\url{https://sketchxai.github.io}.