  Building models that can be rapidly adapted to novel tasks using only a
handful of annotated examples is an open challenge for multimodal machine
learning research. We introduce Flamingo, a family of Visual Language Models
(VLM) with this ability. We propose key architectural innovations to: (i)
bridge powerful pretrained vision-only and language-only models, (ii) handle
sequences of arbitrarily interleaved visual and textual data, and (iii)
seamlessly ingest images or videos as inputs. Thanks to their flexibility,
Flamingo models can be trained on large-scale multimodal web corpora containing
arbitrarily interleaved text and images, which is key to endow them with
in-context few-shot learning capabilities. We perform a thorough evaluation of
our models, exploring and measuring their ability to rapidly adapt to a variety
of image and video tasks. These include open-ended tasks such as visual
question-answering, where the model is prompted with a question which it has to
answer; captioning tasks, which evaluate the ability to describe a scene or an
event; and close-ended tasks such as multiple-choice visual question-answering.
For tasks lying anywhere on this spectrum, a single Flamingo model can achieve
a new state of the art with few-shot learning, simply by prompting the model
with task-specific examples. On numerous benchmarks, Flamingo outperforms
models fine-tuned on thousands of times more task-specific data.
