Compositional Generalization in Image Captioning

Mitja Nikolaus, Mostafa Abdou, Matthew Lamm, Rahul Aralikatte, Desmond Elliott


Abstract
Image captioning models are usually evaluated on their ability to describe a held-out set of images, not on their ability to generalize to unseen concepts. We study the problem of compositional generalization, which measures how well a model composes unseen combinations of concepts when describing images. State-of-the-art image captioning models show poor generalization performance on this task. We propose a multi-task model to address the poor performance, that combines caption generation and image–sentence ranking, and uses a decoding mechanism that re-ranks the captions according their similarity to the image. This model is substantially better at generalizing to unseen combinations of concepts compared to state-of-the-art captioning models.
Anthology ID:
K19-1009
Volume:
Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL)
Month:
November
Year:
2019
Address:
Hong Kong, China
Editors:
Mohit Bansal, Aline Villavicencio
Venue:
CoNLL
SIG:
SIGNLL
Publisher:
Association for Computational Linguistics
Note:
Pages:
87–98
Language:
URL:
https://aclanthology.org/K19-1009
DOI:
10.18653/v1/K19-1009
Bibkey:
Cite (ACL):
Mitja Nikolaus, Mostafa Abdou, Matthew Lamm, Rahul Aralikatte, and Desmond Elliott. 2019. Compositional Generalization in Image Captioning. In Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL), pages 87–98, Hong Kong, China. Association for Computational Linguistics.
Cite (Informal):
Compositional Generalization in Image Captioning (Nikolaus et al., CoNLL 2019)
Copy Citation:
PDF:
https://preview.aclanthology.org/nschneid-patch-1/K19-1009.pdf
Supplementary material:
 K19-1009.Supplementary_Material.pdf
Code
 mitjanikolaus/compositional-image-captioning
Data
MS COCO