  We introduce an approach for pre-training egocentric video models using
large-scale third-person video datasets. Learning from purely egocentric data
is limited by low dataset scale and diversity, while using purely exocentric
(third-person) data introduces a large domain mismatch. Our idea is to discover
latent signals in third-person video that are predictive of key
egocentric-specific properties. Incorporating these signals as knowledge
distillation losses during pre-training results in models that benefit from
both the scale and diversity of third-person video data, as well as
representations that capture salient egocentric properties. Our experiments
show that our Ego-Exo framework can be seamlessly integrated into standard
video models; it outperforms all baselines when fine-tuned for egocentric
activity recognition, achieving state-of-the-art results on Charades-Ego and
EPIC-Kitchens-100.
