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 main components: (1) converting symbolic knowledge of an ontology into continuous space by learning n-ball embeddings that capture properties of subsumption and disjointness, (2) guiding the training and inference of a vision model using the learnt embeddings. We propose techniques to measure the quality of n-ball embeddings and evaluate ViOCE using the task of few-shot image classification, where it demonstrates superior performance in two standard benchmarks. We further introduce a metric to use background knowledge to measure the degree of incorrect predictions.
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Towards Knowledge-aware Few-shot Learning with Ontology-based n-ball Concept Embeddings
OpenAlex · Domain Adaptation and Few-Shot Learning · 2021
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 main components: (1) converting symbolic knowledge of an ontology into continuous space by learning n-ball embeddings that capture properties of subsumption and disjointness, (2) guiding the training and inference of a vision model using the learnt embeddings. We propose techniques to measure the quality of n-ball embeddings and evaluate ViOCE using the task of few-shot image classification, where it demonstrates superior performance in two standard benchmarks. We further introduce a metric to use background knowledge to measure the degree of incorrect predictions.