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.

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AUTHORS

Written by

Ishan Misra

Nuno Vasconcelos

Pedro Morgado

Publisher

ECCV Workshop - MVA

Research Topics

Computer Vision

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