# Ontology-based n-ball Concept Embeddings Informing Few-shot Image Classification Source: [Ontology-based n-ball Concept Embeddings Informing Few-shot Image Classification]() Collection index: [llms.txt]() Data updated: 2025-10-25T23:33:16.767Z Authors: Mirantha Jayathilaka, Tingting Mu, Uli Sattler Published: 2021-09-19T05:35:43.000Z ## Abstract 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. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. ## Original sources and implementation [Original paper]() [PDF]()