Belief Revision Based Caption Re-ranker with Visual Semantic Information

Ahmed Sabir, Francesc Moreno-Noguer, Pranava Madhyastha, Lluís Padró


Abstract
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 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 necessity of any additional training or fine-tuning.
Anthology ID:
2022.coling-1.487
Volume:
Proceedings of the 29th International Conference on Computational Linguistics
Month:
October
Year:
2022
Address:
Gyeongju, Republic of Korea
Venue:
COLING
SIG:
Publisher:
International Committee on Computational Linguistics
Note:
Pages:
5488–5506
Language:
URL:
https://aclanthology.org/2022.coling-1.487
DOI:
Bibkey:
Cite (ACL):
Ahmed Sabir, Francesc Moreno-Noguer, Pranava Madhyastha, and Lluís Padró. 2022. Belief Revision Based Caption Re-ranker with Visual Semantic Information. In Proceedings of the 29th International Conference on Computational Linguistics, pages 5488–5506, Gyeongju, Republic of Korea. International Committee on Computational Linguistics.
Cite (Informal):
Belief Revision Based Caption Re-ranker with Visual Semantic Information (Sabir et al., COLING 2022)
Copy Citation:
PDF:
https://aclanthology.org/2022.coling-1.487.pdf
Code
 ahmedssabir/belief-revision-score