  Augmenting pretrained language models (LMs) with a vision encoder (e.g.,
Flamingo) has obtained the state-of-the-art results in image-to-text
generation. However, these models store all the knowledge within their
parameters, thus often requiring enormous model parameters to model the
abundant visual concepts and very rich textual descriptions. Additionally, they
are inefficient in incorporating new data, requiring a computational-expensive
fine-tuning process. In this work, we introduce a Retrieval-augmented Visual
Language Model, Re-ViLM, built upon the Flamingo, that supports retrieving the
relevant knowledge from the external database for zero and in-context few-shot
image-to-text generations. By storing certain knowledge explicitly in the
external database, our approach reduces the number of model parameters and can
easily accommodate new data during evaluation by simply updating the database.
We also construct an interleaved image and text data that facilitates
in-context few-shot learning capabilities. We demonstrate that Re-ViLM
significantly boosts performance for image-to-text generation tasks, especially
for zero-shot and few-shot generation in out-of-domain settings with 4 times
less parameters compared with baseline methods.
