  We propose a novel framework named ViOCE that integrates ontology-based
background knowledge in the form of $n$-ball concept embeddings into a neural
network based vision architecture. The approach consists of two components -
converting symbolic knowledge of an ontology into continuous space by learning
n-ball embeddings that capture properties of subsumption and disjointness, and
guiding the training and inference of a vision model using the learnt
embeddings. We evaluate ViOCE using the task of few-shot image classification,
where it demonstrates superior performance on two standard benchmarks.
