  Inspired by retrieval-augmented language generation and pretrained Vision and
Language (V&L) encoders, we present a new approach to image captioning that
generates sentences given the input image and a set of captions retrieved from
a datastore, as opposed to the image alone. The encoder in our model jointly
processes the image and retrieved captions using a pretrained V&L BERT, while
the decoder attends to the multimodal encoder representations, benefiting from
the extra textual evidence from the retrieved captions. Experimental results on
the COCO dataset show that image captioning can be effectively formulated from
this new perspective. Our model, named EXTRA, benefits from using captions
retrieved from the training dataset, and it can also benefit from using an
external dataset without the need for retraining. Ablation studies show that
retrieving a sufficient number of captions (e.g., k=5) can improve captioning
quality. Our work contributes towards using pretrained V&L encoders for
generative tasks, instead of standard classification tasks.
