# Improving Image Captioning by Leveraging Knowledge Graphs

Source: [Improving Image Captioning by Leveraging Knowledge Graphs](<https://www.sota2.com/research/paper/68fa654b1e209131cea095ad>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-25T21:22:52.893Z

Authors: Yimin Zhou, Yiwei Sun, Vasant Honavar

Published: 2019-01-25T16:00:57.000Z

## Abstract

We explore the use of a knowledge graphs, that capture general or commonsense knowledge, to augment the information extracted from images by the state-of-the-art methods for image captioning. The results of our experiments, on several benchmark data sets such as MS COCO, as measured by CIDEr-D, a performance metric for image captioning, show that the variants of the state-of-the-art methods for image captioning that make use of the information extracted from knowledge graphs can substantially outperform those that rely solely on the information extracted from images.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/1901.08942>)

[PDF](<https://arxiv.org/pdf/1901.08942.pdf>)