  In urban or crowded environments, humans rely on eye contact for fast and
efficient communication with nearby people. Autonomous agents also need to
detect eye contact to interact with pedestrians and safely navigate around
them. In this paper, we focus on eye contact detection in the wild, i.e.,
real-world scenarios for autonomous vehicles with no control over the
environment or the distance of pedestrians. We introduce a model that leverages
semantic keypoints to detect eye contact and show that this high-level
representation (i) achieves state-of-the-art results on the publicly-available
dataset JAAD, and (ii) conveys better generalization properties than leveraging
raw images in an end-to-end network. To study domain adaptation, we create
LOOK: a large-scale dataset for eye contact detection in the wild, which
focuses on diverse and unconstrained scenarios for real-world generalization.
The source code and the LOOK dataset are publicly shared towards an open
science mission.
