RESEARCH
COMPUTER VISION
Audio-Visual Instance Discrimination
November 04, 2020
Abstract
We present a self-supervised approach to learn audio-visual representations from video. Our method uses contrastive learning for cross-modal discrimination of video from audio and vice versa. We show that optimizing for cross-modal discrimination, rather than within-modal discrimination, is important to learn good representations from video and audio. With this simple but powerful insight, our method achieves state-of-the-art results when finetuned on action recognition tasks.
AUTHORS
Publisher
ECCV Workshop - MVA
Research Topics
Related Publications
June 05, 2026
CONVERSATIONAL AI
RANKING AND RECOMMENDATIONS
Superintelligent Retrieval Agent: The Next Frontier of Agentic Retrieval
Zeyu Yang, Qi Ma, Jason Chen, Anshumali Shrivastava
June 05, 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 20, 2026
HUMAN & MACHINE INTELLIGENCE
RESEARCH
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 20, 2026
May 18, 2026
CONVERSATIONAL AI
RESEARCH
GIM: Evaluating models via tasks that integrate multiple cognitive domains
Rohit Patel, Alexandre Rezende, Steven McClain
May 18, 2026
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.
Our approach
Latest news
Foundational models
Meta © 2026