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arXiv · 2305.01855

Multimodal Data Augmentation for Image Captioning using Diffusion Models

Changrong Xiao·Sean Xin Xu·Kunpeng Zhang
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Abstract

Image captioning, an important vision-language task, often requires a tremendous number of finely labeled image-caption pairs for learning the underlying alignment between images and texts. In this paper, we proposed a multimodal data augmentation method, leveraging a recent text-to-image model called Stable Diffusion, to expand the training set via high-quality generation of image-caption pairs. Extensive experiments on the MS COCO dataset demonstrate the advantages of our approach over several benchmark methods, and particularly a significant boost when having fewer training instances. In addition, models trained on our augmented datasets also outperform prior unpaired image captioning methods by a large margin. Finally, further improvement regarding the training efficiency and effectiveness can be obtained after intentionally filtering the generated data based on quality assessment.

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Changrong Xiao, Sean Xin Xu, Kunpeng Zhang. 2023-05-03. Multimodal Data Augmentation for Image Captioning using Diffusion Models. https://doi.org/10.1145/3607827.3616839

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