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While deep-learning models have been shown to perform well on image-to-textdatasets, it is difficult to use them in practice for captioning images. Thisis because captions traditionally tend to be context-dependent and offercomplementary information about an image, while models tend to producedescriptions that describe the visual features of the image. Prior research incaption generation has explored the use of models that generate captions whenprovided with the images alongside their respective descriptions or contexts.We propose and evaluate a new approach, which leverages existing large languagemodels to generate captions from textual descriptions and context alone,without ever processing the image directly. We demonstrate that afterfine-tuning, our approach outperforms current state-of-the-art image-textalignment models like OSCAR-VinVL on this task on the CIDEr metric.

CapText: Large Language Model-based Caption Generation From Image Context and Description