# Integrating Knowledge and Reasoning in Image Understanding

## TL;DR

This paper surveys recent methods integrating knowledge and reasoning into deep learning for image understanding, highlighting challenges and future pathways for enhancing reasoning capabilities.

## Contribution

It provides a comprehensive overview of reasoning mechanisms and knowledge integration techniques in image understanding, emphasizing recent advancements and future directions.

## Key findings

- Knowledge integration improves image understanding tasks.
- Reasoning mechanisms enhance interpretability of models.
- External knowledge sources are increasingly used in neural networks.

## Abstract

Deep learning based data-driven approaches have been successfully applied in various image understanding applications ranging from object recognition, semantic segmentation to visual question answering. However, the lack of knowledge integration as well as higher-level reasoning capabilities with the methods still pose a hindrance. In this work, we present a brief survey of a few representative reasoning mechanisms, knowledge integration methods and their corresponding image understanding applications developed by various groups of researchers, approaching the problem from a variety of angles. Furthermore, we discuss upon key efforts on integrating external knowledge with neural networks. Taking cues from these efforts, we conclude by discussing potential pathways to improve reasoning capabilities.