Flamingo: a Visual Language Model for Few-Shot Learning

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. 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.

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• 1 We introduce Flamingo, a family of Visual Language Models (VLM) with this ability.
• 2 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.
• 3 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.

This research advances how AI systems learn, reason, and solve problems — with direct implications for automation and scientific discovery.

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