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
- 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)
- PDF:
- https://preview.aclanthology.org/ingest-acl-2023-videos/P19-1654.pdf
- Data
- MS COCO