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
October 14, 2021
GRAPHICS
COMPUTER VISION
Ego4D: Around the World in 3,000 Hours of Egocentric Video
We introduce Ego4D, a massive-scale egocentric video dataset and benchmark suite. It offers 3,025 hours of dailylife activity video spanning hundreds of scenarios (household, outdoor, workplace, leisure, etc.) captured by 855 unique camera wearers from 74 worldwide locations and 9 different countries. …
Kristen Grauman, Andrew Westbury, Eugene Byrne, Zachary Chavis, Antonino Furnari, Rohit Girdhar, Jackson Hamburger, Hao Jiang, Miao Liu, Xingyu Liu, Miguel Martin, Tushar Nagarajan, Ilija Radosavovic, Santhosh Ramakrishnan, Fiona Ryan,Jayant Sharma, Michael Wray, Mengmeng Xu, Eric Zhongcong Xu, Chen Zhao, …
October 14, 2021
October 01, 2021
COMPUTER VISION
CONVERSATIONAL AI
DVD: A Diagnostic Dataset for Multi-step Reasoning in Video Grounded Dialogue
A video-grounded dialogue system is required to understand both dialogue, which contains semantic dependencies from turn to turn …
Hung Le, Chinnadhurai Sankar, Seungwhan Moon, Ahmad Beiram, Alborz Geramifard, Satwik Kottur
October 01, 2021
September 27, 2021
RANKING & RECOMMENDATIONS
Transformers4Rec: Bridging the Gap between NLP and Sequential / Session-Based Recommendation
Much of the recent progress in sequential and session-based recommendation has been driven by improvements in model architecture and pretraining techniques originating in the field of Natural Language Processing. …
Gabriel de Souza Pereira Moreira, Sara Rabhi, Jeong Min Lee, Ronay Ak, Even Oldridge
September 27, 2021
August 29, 2021
NLP
Many-Speakers Single Channel Speech Separation with Optimal Permutation Training
Single channel speech separation has experienced great progress in the last few years …
Shaked Dovrat,Eliya Nachmani, Lior Wolf
August 29, 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.
