# Visual Semantic Relatedness Dataset for Image Captioning

Source: [Visual Semantic Relatedness Dataset for Image Captioning](<https://www.sota2.com/research/paper/68fa6f341e209131cea446ae>)

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

Data updated: 2025-10-26T01:15:37.285Z

Authors: Ahmed Sabir, Francesc Moreno-Noguer, Llu\\'is Padr\\'o

Published: 2023-01-20T20:04:35.000Z

## Abstract

Modern image captioning system relies heavily on extracting knowledge from images to capture the concept of a static story. In this paper, we propose a textual visual context dataset for captioning, in which the publicly available dataset COCO Captions (Lin et al., 2014) has been extended with information about the scene (such as objects in the image). Since this information has a textual form, it can be used to leverage any NLP task, such as text similarity or semantic relation methods, into captioning systems, either as an end-to-end training strategy or a post-processing based approach.

## 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/2301.08784>)

[PDF](<https://arxiv.org/pdf/2301.08784.pdf>)