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Cited by 966Domain Adaptation and Few-Shot LearningMultimodal Machine Learning ApplicationsAdvanced Neural Network Applications

The DOI matches its Crossref record; 154 of the 154 references it cites were found in a registry or an index.

Flamingo: A Visual Language Model for Few-Shot Learning

Alayrac, Jean-Baptiste, Donahue, Jeff, Luc, Pauline, Miech, Antoine, Barr, Iain, Hasson, Yana, Lenc, Karel, Mensch, Arthur, Millican, Katherine, Reynolds, Malcolm, Ring, Roman, Rutherford, Eliza, Cabi, Serkan, Han, Tengda, Gong, Zhitao, Samangooei, Sina, Monteiro, Marianne, Menick, Jacob L, Borgeaud, Sebastian, Brock, Andy, Nematzadeh, Aida, Sharifzadeh, Sahand, Binkowski, Mikolaj, Barreira, Ricardo, Vinyals, Oriol, Zisserman, Andrew, Simonyan, Karén·2022·Advances in Neural Information Processing Systems 35
DOI 10.52202/068431-1723
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Abstract

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.

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Crossref · 10.52202/068431-1723

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Metadata

Authors
Alayrac, J.-B., Donahue, J., Luc, P., Miech, A., Barr, I., Hasson, Y., Lenc, K., Mensch, A., Millican, K., Reynolds, M., Ring, R., Rutherford, E., Cabi, S., Han, T., Gong, Z., Samangooei, S., Monteiro, M., Menick, J. L., Borgeaud, S., Brock, A., Nematzadeh, A., Sharifzadeh, S., Binkowski, M., Barreira, R., Vinyals, O., Zisserman, A., & Simonyan, K.
ORCID
Venue
Advances in Neural Information Processing Systems 35
Publisher
Neural Information Processing Systems Foundation, Inc. (NeurIPS)
Published
2022
Pages
23716-23736
ISSN
1049-5258 (ISSN-L)
DOI
10.52202/068431-1723
OpenAlex
W7133196460
Cited by
966
References
154
Citations by year
2026: 847 · 2025: 107 · 2024: 3 · 2023: 7 · 2022: 2
FWCI
15.48
Type
conference paper
Language
en
Open access
closed
Locations
  • neural information processing systems (publishedVersion)
  • Oxford University Research Archive (ORA) (University of Oxford) (publishedVersion)
Topics
  • Physical Sciences › Computer Science › Artificial Intelligence › Domain Adaptation and Few-Shot Learning
  • Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Multimodal Machine Learning Applications
  • Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
Keywords
vision-language models, few-shot learning, image-text models

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