# Knowledge-rich Image Gist Understanding Beyond Literal Meaning

## TL;DR

This paper introduces a knowledge-rich approach to understanding the message conveyed by images and captions by capturing connotations beyond literal object meanings, using a large concept vocabulary and a new dataset.

## Contribution

It presents a novel methodology for gist understanding that combines image and text signals and introduces a new dataset for benchmarking this task.

## Key findings

- Combining image and text signals improves gist understanding accuracy.
- The proposed method achieves a MAP of 0.69 on the new dataset.
- Automated input sources like generated tags and captions are effective for end-to-end systems.

## Abstract

We investigate the problem of understanding the message (gist) conveyed by images and their captions as found, for instance, on websites or news articles. To this end, we propose a methodology to capture the meaning of image-caption pairs on the basis of large amounts of machine-readable knowledge that has previously been shown to be highly effective for text understanding. Our method identifies the connotation of objects beyond their denotation: where most approaches to image understanding focus on the denotation of objects, i.e., their literal meaning, our work addresses the identification of connotations, i.e., iconic meanings of objects, to understand the message of images. We view image understanding as the task of representing an image-caption pair on the basis of a wide-coverage vocabulary of concepts such as the one provided by Wikipedia, and cast gist detection as a concept-ranking problem with image-caption pairs as queries. To enable a thorough investigation of the problem of gist understanding, we produce a gold standard of over 300 image-caption pairs and over 8,000 gist annotations covering a wide variety of topics at different levels of abstraction. We use this dataset to experimentally benchmark the contribution of signals from heterogeneous sources, namely image and text. The best result with a Mean Average Precision (MAP) of 0.69 indicate that by combining both dimensions we are able to better understand the meaning of our image-caption pairs than when using language or vision information alone. We test the robustness of our gist detection approach when receiving automatically generated input, i.e., using automatically generated image tags or generated captions, and prove the feasibility of an end-to-end automated process.