Research

We're advancing the state-of-the-art in artificial intelligence through fundamental and applied research in open collaboration with the community.

Notable Papers

RESEARCH

ML APPLICATIONS

GrokNet: Unified Computer Vision Model Trunk and Embeddings For Commerce

Sean Bell

Yiqun Liu

Sami Alsheikh

Yina Tang...

KDD

COMPUTER VISION

Live Face De-Identification in Video

Oran Gafni

Lior Wolf

Yaniv Taigman

International Conference on Computer Vision (ICCV)

RESEARCH

Single-Network Whole-Body Pose Estimation

Gines Hidalgo

Yaadhav Raaj

Haroon Idrees

Donglai Xiang...

International Conference on Computer Vision (ICCV)

SPEECH & AUDIO

A Universal Music Translation Network

Noam Mor

Lior Wolf

Adam Polyak

Yaniv Taigman

International Conference on Learning Representations (ICLR)

Latest Publications

May 03, 2021

NLP

Support-Set bottlenecks for video-text representation learning

The dominant paradigm for learning video-text representations -- noise contrastive learning -- increases the similarity of the representations of pairs of samples

Mandela Patrick, Po-Yao Huang, Florian Metze , Andrea Vedaldi, Alexander Hauptmann, Yuki M. Asano, João Henriques

May 03, 2021

May 03, 2021

HUMAN & MACHINE INTELLIGENCE

REINFORCEMENT LEARNING

Human-Level Performance in No-Press Diplomacy via Equilibrium Search

Prior AI breakthroughs in complex games have focused on either the purely adversarial or purely cooperative settings …

Jonathan Gray, Adam Lerer, Anton Bakhtin, Noam Brown

May 03, 2021

April 08, 2021

RESPONSIBLE AI

INTEGRITY

Towards measuring fairness in AI: the Casual Conversations dataset

This paper introduces a novel dataset to help researchers evaluate their computer vision and audio models for accuracy across a diverse set of age, genders, apparent skin tones and ambient lighting conditions

Caner Hazirbas, Joanna Bitton,Brian Dolhansky, Jacqueline Pan, Albert Gordo, Cristian Canton Ferrer

April 08, 2021

March 26, 2021

CORE MACHINE LEARNING

COMPUTER VISION

Learning with AMIGo: Adversarially Motivated Intrinsic Goals

A key challenge for reinforcement learning (RL) consists of learning in environments with sparse extrinsic rewards. In contrast to current RL methods, humans are able to learn new skills with little or no reward by using various forms of intrinsic motivation …

Andres Campero, Roberta Raileanu, Heinrich Kuttler, Joshua B. Tenenbaum, Tim Rocktaschel, Edward Grefenstette

March 26, 2021

Fundamental & Applied Research

At Facebook AI, we conduct both fundamental and applied research to advance our understanding and impact product experiences. We publish our discoveries in peer reviewed academic journals and conferences, and build AI technologies used by billions of people around the world.

Fundamental Research

FAIR seeks to further our fundamental understanding in both new and existing domains, covering the full spectrum of topics related to AI, with the mission of advancing the state-of-the-art of AI through open research for the benefit of all.

Along with the key principles of Facebook AI - openness, collaboration, excellence, and scale - we believe FAIR researchers also need to have the freedom and autonomy to design and follow their own research agendas so they can take on the most impactful work and develop the most disruptive projects, all while sharing their results with the community.

Applied Research

Facebook AI Applied Research engages in cutting-edge research that can improve and power new product experiences at huge scale for our community. Building on Facebook AI's key principles of openness, collaboration, excellence, and scale, we make big, bold research investments focused on building social value and bringing the world closer together.

Our Values

We align our fundamental and applied research efforts and applications around a few key principles:

Openness

We believe the latest advancements in AI should be published and open-sourced for the community to learn about and build upon.

Collaboration

We collaborate openly with both internal and external partners to share knowledge and cultivate diverse perspectives and needs.

Excellence

There is no shortage of new areas to explore in AI - our researchers focus on the projects that we believe will have the most positive impact on people and society.

Scale

To bring the benefits of AI to more people and improve accessibility, our research must account for both large scale data and computation needs.

Request for Proprosals

Facebook AI is pleased to invite university faculty to submit proposals that will help accelerate research on interpretable personalized recommendations using machine learning on graph data.

Help Us Pioneer the Future of AI

We share our open source frameworks, tools, libraries, and models for everything from research exploration to large-scale production deployment.