Non-monotonic Logical Reasoning Guiding Deep Learning for Explainable Visual Question Answering

TL;DR

This paper introduces a hybrid architecture combining deep learning with non-monotonic logical reasoning to improve explainability and accuracy in visual question answering, especially with limited data and unknown constraints.

Contribution

It proposes a novel integration of logical reasoning and deep learning for explainable VQA, enabling incremental learning and better handling of incomplete domain knowledge.

Findings

Better accuracy with small datasets compared to end-to-end deep networks

Comparable accuracy with larger datasets

Enhanced reasoning and planning capabilities for robots

Abstract

State of the art algorithms for many pattern recognition problems rely on deep network models. Training these models requires a large labeled dataset and considerable computational resources. Also, it is difficult to understand the working of these learned models, limiting their use in some critical applications. Towards addressing these limitations, our architecture draws inspiration from research in cognitive systems, and integrates the principles of commonsense logical reasoning, inductive learning, and deep learning. In the context of answering explanatory questions about scenes and the underlying classification problems, the architecture uses deep networks for extracting features from images and for generating answers to queries. Between these deep networks, it embeds components for non-monotonic logical reasoning with incomplete commonsense domain knowledge, and for decision tree induction. It also incrementally learns and reasons with previously unknown constraints governing the domain's states. We evaluated the architecture in the context of datasets of simulated and real-world images, and a simulated robot computing, executing, and providing explanatory descriptions of plans. Experimental results indicate that in comparison with an ``end to end'' architecture of deep networks, our architecture provides better accuracy on classification problems when the training dataset is small, comparable accuracy with larger datasets, and more accurate answers to explanatory questions. Furthermore, incremental acquisition of previously unknown constraints improves the ability to answer explanatory questions, and extending non-monotonic logical reasoning to support planning and diagnostics improves the reliability and efficiency of computing and executing plans on a simulated robot.