What Can You Learn From Your Muscles? Learning Visual Representation from Human Interactions

Kiana Ehsani · Daniel Gordon · Thomas H Nguyen · Roozbeh Mottaghi · Ali Farhadi


Keywords: [ computer vision ] [ representation learning ]

[ Abstract ]
[ Slides
[ Paper ]
Mon 3 May 9 a.m. PDT — 11 a.m. PDT


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:

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