  We introduce a novel privacy-preserving methodology for performing Visual
Question Answering on the edge. Our method constructs a symbolic representation
of the visual scene, using a low-complexity computer vision model that jointly
predicts classes, attributes and predicates. This symbolic representation is
non-differentiable, which means it cannot be used to recover the original
image, thereby keeping the original image private. Our proposed hybrid solution
uses a vision model which is more than 25 times smaller than the current
state-of-the-art (SOTA) vision models, and 100 times smaller than end-to-end
SOTA VQA models. We report detailed error analysis and discuss the trade-offs
of using a distilled vision model and a symbolic representation of the visual
scene.
