  When trained at sufficient scale, auto-regressive language models exhibit the
notable ability to learn a new language task after being prompted with just a
few examples. Here, we present a simple, yet effective, approach for
transferring this few-shot learning ability to a multimodal setting (vision and
language). Using aligned image and caption data, we train a vision encoder to
represent each image as a sequence of continuous embeddings, such that a
pre-trained, frozen language model prompted with this prefix generates the
appropriate caption. The resulting system is a multimodal few-shot learner,
with the surprising ability to learn a variety of new tasks when conditioned on
examples, represented as a sequence of multiple interleaved image and text
embeddings. We demonstrate that it can rapidly learn words for new objects and
novel visual categories, do visual question-answering with only a handful of
examples, and make use of outside knowledge, by measuring a single model on a
variety of established and new benchmarks.

Computer Vision and Pattern Recognition;Computation and Language;Machine Learning