In this work, we focus on improving the captions generated by image-caption generation systems. We propose a novel re-ranking approach that leverages visual-semantic measures to identify the ideal caption that maximally captures the visual information in the image. Our re-ranker utilizes the Belief Revision framework (Blok et al., 2003) to calibrate the original likelihood of the top-n captions by explicitly exploiting the semantic relatedness between the depicted caption and the visual context. Our experiments demonstrate the utility of our approach, where we observe that our re-ranker can enhance the performance of a typical image-captioning system without the necessity of any additional training or fine-tuning.
@article{arxiv.2209.08163,
title = {Belief Revision based Caption Re-ranker with Visual Semantic Information},
author = {Ahmed Sabir and Francesc Moreno-Noguer and Pranava Madhyastha and Lluís Padró},
journal= {arXiv preprint arXiv:2209.08163},
year = {2022}
}