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arXiv · 1909.04402

Compositional Generalization in Image Captioning

Mitja Nikolaus·Mostafa Abdou·Matthew Lamm·Rahul Aralikatte·Desmond Elliott
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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.

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Mitja Nikolaus, Mostafa Abdou, Matthew Lamm, Rahul Aralikatte, Desmond Elliott. 2019-09-10. Compositional Generalization in Image Captioning. https://doi.org/10.18653/v1%2Fk19-1009

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