Writing📚 BooksResumePublicationsContact
MB
By Michael Brenndoerfer
Flamingo: Few-Shot Vision-Language Learning with Gated Cross-Attention
Back to Writing

Flamingo: Few-Shot Vision-Language Learning with Gated Cross-Attention

Michael Brenndoerfer•November 2, 2025•12 min read•2,762 words•Interactive
Data, Analytics & AISoftware EngineeringMachine LearningHistory of Language AI

A comprehensive guide to DeepMind's Flamingo, the breakthrough few-shot vision-language model that achieved state-of-the-art performance across image-text tasks without task-specific fine-tuning. Learn about gated cross-attention mechanisms, few-shot learning in multimodal settings, and Flamingo's influence on modern AI systems.

History of Language AI Cover
Part of History of Language AI

This article is part of the free-to-read History of Language AI book

View full handbook
Reading Level

Choose your expertise level to adjust how many terms are explained. Beginners see more tooltips, experts see fewer to maintain reading flow. Hover over underlined terms for instant definitions.

Loading component...
Loading component...

Reference

BIBTEXAcademic
@misc{flamingofewshotvisionlanguagelearningwithgatedcrossattention, author = {Michael Brenndoerfer}, title = {Flamingo: Few-Shot Vision-Language Learning with Gated Cross-Attention}, year = {2025}, url = {https://mbrenndoerfer.com/writing/flamingo-few-shot-vision-language-learning-gated-cross-attention}, organization = {mbrenndoerfer.com}, note = {Accessed: 2025-11-02} }
APAAcademic
Michael Brenndoerfer (2025). Flamingo: Few-Shot Vision-Language Learning with Gated Cross-Attention. Retrieved from https://mbrenndoerfer.com/writing/flamingo-few-shot-vision-language-learning-gated-cross-attention
MLAAcademic
Michael Brenndoerfer. "Flamingo: Few-Shot Vision-Language Learning with Gated Cross-Attention." 2025. Web. 11/2/2025. <https://mbrenndoerfer.com/writing/flamingo-few-shot-vision-language-learning-gated-cross-attention>.
CHICAGOAcademic
Michael Brenndoerfer. "Flamingo: Few-Shot Vision-Language Learning with Gated Cross-Attention." Accessed 11/2/2025. https://mbrenndoerfer.com/writing/flamingo-few-shot-vision-language-learning-gated-cross-attention.
HARVARDAcademic
Michael Brenndoerfer (2025) 'Flamingo: Few-Shot Vision-Language Learning with Gated Cross-Attention'. Available at: https://mbrenndoerfer.com/writing/flamingo-few-shot-vision-language-learning-gated-cross-attention (Accessed: 11/2/2025).
SimpleBasic
Michael Brenndoerfer (2025). Flamingo: Few-Shot Vision-Language Learning with Gated Cross-Attention. https://mbrenndoerfer.com/writing/flamingo-few-shot-vision-language-learning-gated-cross-attention
Direct link:
https://mbrenndoerfer.com/writing/flamingo-few-shot-vision-language-learning-gated-cross-attention
History of Language AI Cover
Part of History of Language AI

This article is part of the free-to-read History of Language AI book

View full handbook
Michael Brenndoerfer

About the author: Michael Brenndoerfer

All opinions expressed here are my own and do not reflect the views of my employer.

Michael currently works as an Associate Director of Data Science at EQT Partners in Singapore, where he drives AI and data initiatives across private capital investments.

With over a decade of experience spanning private equity, management consulting, and software engineering, he specializes in building and scaling analytics capabilities from the ground up. He has published research in leading AI conferences and holds expertise in machine learning, natural language processing, and value creation through data.

View Full ResumePublications

Related Content

t-SNE: Complete Guide to Dimensionality Reduction & High-Dimensional Data Visualization
Notebook
Data, Analytics & AIMachine Learning

t-SNE: Complete Guide to Dimensionality Reduction & High-Dimensional Data Visualization

Nov 2, 2025•23 min read

A comprehensive guide covering t-SNE (t-Distributed Stochastic Neighbor Embedding), including mathematical foundations, probability distributions, KL divergence optimization, and practical implementation. Learn how to visualize complex high-dimensional datasets effectively.

LIME Explainability: Complete Guide to Local Interpretable Model-Agnostic Explanations
Notebook
Data, Analytics & AIMachine Learning

LIME Explainability: Complete Guide to Local Interpretable Model-Agnostic Explanations

Nov 2, 2025•25 min read

A comprehensive guide covering LIME (Local Interpretable Model-Agnostic Explanations), including mathematical foundations, implementation strategies, and practical applications. Learn how to explain any machine learning model's predictions with interpretable local approximations.

UMAP: Complete Guide to Uniform Manifold Approximation and Projection for Dimensionality Reduction
Notebook
Data, Analytics & AIMachine Learning

UMAP: Complete Guide to Uniform Manifold Approximation and Projection for Dimensionality Reduction

Nov 2, 2025•26 min read

A comprehensive guide covering UMAP dimensionality reduction, including mathematical foundations, fuzzy simplicial sets, manifold learning, and practical implementation. Learn how to preserve both local and global structure in high-dimensional data visualization.

Show more articles

Stay updated

Get notified when I publish new articles on data and AI, private equity, technology, and more.