# Flamingo: a Visual Language Model for Few-Shot Learning

**Authors:** Jean-Baptiste Alayrac, Jeff Donahue, Pauline Luc, Antoine Miech, Iain Barr, Yana Hasson, Karel Lenc, Arthur Mensch, Katie Millican, Malcolm Reynolds, Roman Ring, Eliza Rutherford, Serkan Cabi, Tengda Han, Zhitao Gong, Sina Samangooei, Marianne Monteiro, Jacob Menick, Sebastian Borgeaud, Andrew Brock  
**arXiv:** [2204.14198](https://arxiv.org/abs/2204.14198)  
**Date:** 2022-04-29  
**Summary page:** https://www.summarizepaper.com/en/paper/2204.14198v2/

## Key points

- Flamingo is a groundbreaking Visual Language Model (VLM) designed for rapidly adapting to novel tasks with minimal annotated examples in multimodal machine learning research.
- Developed by a team of researchers including Jean-Baptiste Alayrac, Jeff Donahue, Pauline Luc, Antoine Miech, and others.
- Flamingo introduces key architectural innovations enabling seamless integration of vision-only and language-only models, handling sequences of interleaved visual and textual data, and processing both images and videos as inputs.
- The flexibility of Flamingo models allows training on large-scale multimodal web corpora containing mixed text and images for in-context few-shot learning capabilities.
- Through comprehensive evaluation, the model demonstrated quick adaptation to various image and video tasks like visual question-answering and captioning tasks with state-of-the-art performance achieved through few-shot learning using task-specific examples.
- In benchmark tests, Flamingo consistently outperformed models fine-tuned on more task-specific data, showcasing its potential for advancing multimodal machine learning research.

## Summary

Flamingo is a groundbreaking Visual Language Model (VLM) that addresses the challenge of rapidly adapting to novel tasks with minimal annotated examples in multimodal machine learning research. Developed by a team of researchers including Jean-Baptiste Alayrac, Jeff Donahue, Pauline Luc, Antoine Miech, and others, Flamingo introduces key architectural innovations that enable it to seamlessly integrate vision-only and language-only models. It can also handle sequences of interleaved visual and textual data and process both images and videos as inputs. The flexibility of Flamingo models allows them to be trained on large-scale multimodal web corpora containing mixed text and images. This unique capability empowers Flamingo with in-context few-shot learning capabilities. Through a comprehensive evaluation process, the researchers demonstrated the model's ability to quickly adapt to various image and video tasks. These tasks range from open-ended challenges like visual question-answering and captioning tasks to close-ended multiple-choice visual question-answering. Remarkably, a single Flamingo model can achieve state-of-the-art performance in these diverse tasks through few-shot learning by providing task-specific examples. In extensive benchmark tests, Flamingo consistently outperformed models fine-tuned on significantly more task-specific data. The collaborative effort behind Flamingo showcases the potential for advancing multimodal machine learning research through innovative approaches like this Visual Language Model.