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

• URL: http://arxiv.org/abs/2010.08539v2
• Date: Sat, 6 Mar 2021 19:28:58 GMT
• Title: What Can You Learn from Your Muscles? Learning Visual Representation from Human Interactions
• Authors: Kiana Ehsani, Daniel Gordon, Thomas Nguyen, Roozbeh Mottaghi, Ali Farhadi
• Abstract summary: We use human interaction and attention cues to investigate whether we can learn better representations compared to visual-only representations. Our experiments show that our "muscly-supervised" representation outperforms a visual-only state-of-the-art method MoCo.
• Score: 50.435861435121915
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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