  Interactive object understanding, or what we can do to objects and how is a
long-standing goal of computer vision. In this paper, we tackle this problem
through observation of human hands in in-the-wild egocentric videos. We
demonstrate that observation of what human hands interact with and how can
provide both the relevant data and the necessary supervision. Attending to
hands, readily localizes and stabilizes active objects for learning and reveals
places where interactions with objects occur. Analyzing the hands shows what we
can do to objects and how. We apply these basic principles on the EPIC-KITCHENS
dataset, and successfully learn state-sensitive features, and object
affordances (regions of interaction and afforded grasps), purely by observing
hands in egocentric videos.
