CVPR 2023 – SketchXAI: A First Look at Explainability for Human Sketches

In this episode we discuss SketchXAI: A First Look at Explainability for Human Sketches
by Zhiyu Qu, Yulia Gryaditskaya, Ke Li, Kaiyue Pang, Tao Xiang, Yi-Zhe Song. The paper introduces human sketches to the landscape of Explainable Artificial Intelligence (XAI). Sketch is argued to be a “human-centered” data form that represents a natural interface to study explainability. The authors design a simple explainability-friendly sketch encoder that accommodates the intrinsic properties of strokes and define the first ever XAI task for sketch, stroke location inversion (SLI). The authors offer qualitative results and snapshots of the SLI process, as well as providing code available at https://sketchxai.github.io.