Learning effective representations of visual data that generalize to a variety of downstream tasks has been a long quest for computer vision. Most representation learning approaches rely solely on visual data such as images or videos. In this paper, we explore a novel approach, where we use human interaction and attention cues to investigate whether we can learn better representations compared to visual-only representations. For this study, we collect a dataset of human interactions capturing body part movements and gaze in their daily lives. Our experiments show that our "muscly-supervised" representation that encodes interaction and attention cues outperforms a visual-only state-of-the-art method MoCo (He et al.,2020), on a variety of target tasks: scene classification (semantic), action recognition (temporal), depth estimation (geometric), dynamics prediction (physics) and walkable surface estimation (affordance). Our code and dataset are available at: https://github.com/ehsanik/muscleTorch.
@article{arxiv.2010.08539,
title = {What Can You Learn from Your Muscles? Learning Visual Representation from Human Interactions},
author = {Kiana Ehsani and Daniel Gordon and Thomas Nguyen and Roozbeh Mottaghi and Ali Farhadi},
journal= {arXiv preprint arXiv:2010.08539},
year = {2021}
}