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
- Editors:
- Nicoletta Calzolari, Chu-Ren Huang, Hansaem Kim, James Pustejovsky, Leo Wanner, Key-Sun Choi, Pum-Mo Ryu, Hsin-Hsi Chen, Lucia Donatelli, Heng Ji, Sadao Kurohashi, Patrizia Paggio, Nianwen Xue, Seokhwan Kim, Younggyun Hahm, Zhong He, Tony Kyungil Lee, Enrico Santus, Francis Bond, Seung-Hoon Na
- 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
Export citation
@inproceedings{sabir-etal-2022-belief,
title = "Belief Revision Based Caption Re-ranker with Visual Semantic Information",
author = "Sabir, Ahmed and
Moreno-Noguer, Francesc and
Madhyastha, Pranava and
Padr{\'o}, Llu{\'\i}s",
editor = "Calzolari, Nicoletta and
Huang, Chu-Ren and
Kim, Hansaem and
Pustejovsky, James and
Wanner, Leo and
Choi, Key-Sun and
Ryu, Pum-Mo and
Chen, Hsin-Hsi and
Donatelli, Lucia and
Ji, Heng and
Kurohashi, Sadao and
Paggio, Patrizia and
Xue, Nianwen and
Kim, Seokhwan and
Hahm, Younggyun and
He, Zhong and
Lee, Tony Kyungil and
Santus, Enrico and
Bond, Francis and
Na, Seung-Hoon",
booktitle = "Proceedings of the 29th International Conference on Computational Linguistics",
month = oct,
year = "2022",
address = "Gyeongju, Republic of Korea",
publisher = "International Committee on Computational Linguistics",
url = "https://aclanthology.org/2022.coling-1.487",
pages = "5488--5506",
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.",
}
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%0 Conference Proceedings %T Belief Revision Based Caption Re-ranker with Visual Semantic Information %A Sabir, Ahmed %A Moreno-Noguer, Francesc %A Madhyastha, Pranava %A Padró, Lluís %Y Calzolari, Nicoletta %Y Huang, Chu-Ren %Y Kim, Hansaem %Y Pustejovsky, James %Y Wanner, Leo %Y Choi, Key-Sun %Y Ryu, Pum-Mo %Y Chen, Hsin-Hsi %Y Donatelli, Lucia %Y Ji, Heng %Y Kurohashi, Sadao %Y Paggio, Patrizia %Y Xue, Nianwen %Y Kim, Seokhwan %Y Hahm, Younggyun %Y He, Zhong %Y Lee, Tony Kyungil %Y Santus, Enrico %Y Bond, Francis %Y Na, Seung-Hoon %S Proceedings of the 29th International Conference on Computational Linguistics %D 2022 %8 October %I International Committee on Computational Linguistics %C Gyeongju, Republic of Korea %F sabir-etal-2022-belief %X 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. %U https://aclanthology.org/2022.coling-1.487 %P 5488-5506
Markdown (Informal)
[Belief Revision Based Caption Re-ranker with Visual Semantic Information](https://aclanthology.org/2022.coling-1.487) (Sabir et al., COLING 2022)
- Belief Revision Based Caption Re-ranker with Visual Semantic Information (Sabir et al., COLING 2022)
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.