Research
Computer Vision
Leveraging the Present to Anticipate the Future in Videos
June 16, 2019
Abstract
Anticipating actions before they are executed is crucial for a wide range of practical applications including autonomous driving and robotics. While most prior work in this area requires partial observation of executed actions, in the paper we focus on anticipating actions seconds before they start. Our proposed approach is the fusion of a purely anticipatory model with a complementary model constrained to reason about the present. In particular, the latter predicts present action and scene attributes, and reasons about how they evolve over time. By doing so, we aim at modeling action anticipation at a more conceptual level than directly predicting future actions. Our model outperforms previously reported methods on the EPIC-KITCHENS and Breakfast datasets.
AUTHORS
Publisher
Research Topics
Related Publications
July 13, 2026
S-EMBER: A Large-Scale Benchmark for Streaming Egocentric Memory Retrieval
Xiaodong Wang, Xuanyi Zhao, Pedro Rodriguez, Devendra Singh Sachan, Barlas Oguz, Seungwhan Moon, Shang-Wen Li, Gargi Ghosh, Xin Dong, Wen-Tau Yih
July 13, 2026
July 03, 2026
Human & Machine Intelligence
Robotics
Interpreting Physics in Video World Models
Sonia Joseph, Quentin Garrido, Randall Balestriero, Matthew Kowal, Thomas Fel, Shahab Bakhtiari, Blake Richards, Mike Rabbat
July 03, 2026
May 26, 2026
Human & Machine Intelligence
Theory
Misalignment Between Backpropagation and the Hierarchy of Brain Responses to Images
Josephine Raugel, Max Seitzer, Marc Szafraniec, Huy V. Vo, Jérémy Rapin, Patrick Labatut, Piotr Bojanowski, Valentin Wyart, Jean Remi King
May 26, 2026
May 19, 2026
Human & Machine Intelligence
EgoBabyVLM: Benchmarking Cross-Modal Learning from Naturalistic Egocentric Video Data
Dongyan Lin, Phillip Rust, Angel Villar Corrales, Alvin W. M. Tan, Mahi Luthra, Charles-Eric Saint-James, Rashel Moritz, Sheila Krogh-Jespersen, Vanessa Stark, Surya Parimi, Jiayi Shen, Youssef Benchekroun, Yosuke Higuchi, Martin Gleize, Tom Fizycki, Nicolas Hamilakis, Manel Khentout, Sho Tsuji, Balázs Kégl, Juan Pino, Michael C. Frank, Emmanuel Dupoux
May 19, 2026
June 11, 2019
Computer Vision
ELF OpenGo: An Analysis and Open Reimplementation of AlphaZero | Facebook AI Research
Yuandong Tian, Jerry Ma, Qucheng Gong, Shubho Sengupta, Zhuoyuan Chen, James Pinkerton, Larry Zitnick
June 11, 2019
April 30, 2018
NLP
Computer Vision
Mastering the Dungeon: Grounded Language Learning by Mechanical Turker Descent | Facebook AI Research
Zhilin Yang, Saizheng Zhang, Jack Urbanek, Will Feng, Alexander H. Miller, Arthur Szlam, Douwe Kiela, Jason Weston
April 30, 2018
October 10, 2016
Speech & Audio
Computer Vision
Polysemous Codes | Facebook AI Research
Matthijs Douze, Hervé Jégou, Florent Perronnin
October 10, 2016
June 18, 2018
Speech & Audio
Computer Vision
Low-shot learning with large-scale diffusion | Facebook AI Research
Matthijs Douze, Arthur Szlam, Bharath Hariharan, Hervé Jégou
June 18, 2018
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.
Meta © 2026
