VIFIDEL: Evaluating the Visual Fidelity of Image Descriptions

Pranava Madhyastha, Josiah Wang, Lucia Specia


Abstract
We address the task of evaluating image description generation systems. We propose a novel image-aware metric for this task: VIFIDEL. It estimates the faithfulness of a generated caption with respect to the content of the actual image, based on the semantic similarity between labels of objects depicted in images and words in the description. The metric is also able to take into account the relative importance of objects mentioned in human reference descriptions during evaluation. Even if these human reference descriptions are not available, VIFIDEL can still reliably evaluate system descriptions. The metric achieves high correlation with human judgments on two well-known datasets and is competitive with metrics that depend on and rely exclusively on human references.
Anthology ID:
P19-1654
Volume:
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics
Month:
July
Year:
2019
Address:
Florence, Italy
Editors:
Anna Korhonen, David Traum, Lluís Màrquez
Venue:
ACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
6539–6550
Language:
URL:
https://aclanthology.org/P19-1654
DOI:
10.18653/v1/P19-1654
Bibkey:
Cite (ACL):
Pranava Madhyastha, Josiah Wang, and Lucia Specia. 2019. VIFIDEL: Evaluating the Visual Fidelity of Image Descriptions. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pages 6539–6550, Florence, Italy. Association for Computational Linguistics.
Cite (Informal):
VIFIDEL: Evaluating the Visual Fidelity of Image Descriptions (Madhyastha et al., ACL 2019)
Copy Citation:
PDF:
https://aclanthology.org/P19-1654.pdf
Data
MS COCO