  Understanding users' activities from head-mounted cameras is a fundamental
task for Augmented and Virtual Reality (AR/VR) applications. A typical approach
is to train a classifier in a supervised manner using data labeled by humans.
This approach has limitations due to the expensive annotation cost and the
closed coverage of activity labels. A potential way to address these
limitations is to use self-supervised learning (SSL). Instead of relying on
human annotations, SSL leverages intrinsic properties of data to learn
representations. We are particularly interested in learning egocentric video
representations benefiting from the head-motion generated by users' daily
activities, which can be easily obtained from IMU sensors embedded in AR/VR
devices. Towards this goal, we propose a simple but effective approach to learn
video representation by learning to tell the corresponding pairs of video clip
and head-motion. We demonstrate the effectiveness of our learned representation
for recognizing egocentric activities of people and dogs.
