NS-IL: Neuro-Symbolic Visual Question Answering Using Incrementally Learnt, Independent Probabilistic Models for Small Sample Sizes

Citation

Johnston P, Nogueira K & Swingler K (2023) NS-IL: Neuro-Symbolic Visual Question Answering Using Incrementally Learnt, Independent Probabilistic Models for Small Sample Sizes. IEEE Access, 11, pp. 141406-141420. https://doi.org/10.1109/access.2023.3341007

Abstract
This paper is motivated by the challenge of providing accurate and contextually relevant answers to natural language questions about visual scenes, particularly in support of individuals with visual impairments. We present a system that is capable of incrementally learning both visual concepts and symbolic facts to answer natural language questions about visual scenes via rich concepts. Deep neural networks are used to learn a feature space from which visual classes are learned as independent probability distributions, allowing new classes to be added arbitrarily with small sample sizes and without the risk of catastrophic forgetting associated with traditional neural networks. Visual classes are not limited to object labels, but also include visual attributes. A knowledge graph is used to represent facts about objects, such as their actions, locations and the relationships between different objects. This allows facts to be stored explicitly and added incrementally. A large language model is used to translate between natural language questions and knowledge graph traversal queries, providing a natural visual question answering process.

Keywords
General Engineering; General Materials Science; General Computer Science; Electrical and Electronic Engineering

Journal
IEEE Access: Volume 11

People

Mrs Penny Johnston

PhD Researcher, Computing Science and Mathematics - Division

Dr Keiller Nogueira

Lecturer, Computing Science

Professor Kevin Swingler

Professor, Computing Science