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.
@article{arxiv.1909.04402,
title = {Compositional Generalization in Image Captioning},
author = {Mitja Nikolaus and Mostafa Abdou and Matthew Lamm and Rahul Aralikatte and Desmond Elliott},
journal= {arXiv preprint arXiv:1909.04402},
year = {2019}
}