  Event cameras are novel bio-inspired sensors, which asynchronously capture
pixel-level intensity changes in the form of "events". Due to their sensing
mechanism, event cameras have little to no motion blur, a very high temporal
resolution and require significantly less power and memory than traditional
frame-based cameras. These characteristics make them a perfect fit to several
real-world applications such as egocentric action recognition on wearable
devices, where fast camera motion and limited power challenge traditional
vision sensors. However, the ever-growing field of event-based vision has, to
date, overlooked the potential of event cameras in such applications. In this
paper, we show that event data is a very valuable modality for egocentric
action recognition. To do so, we introduce N-EPIC-Kitchens, the first
event-based camera extension of the large-scale EPIC-Kitchens dataset. In this
context, we propose two strategies: (i) directly processing event-camera data
with traditional video-processing architectures (E$^2$(GO)) and (ii) using
event-data to distill optical flow information (E$^2$(GO)MO). On our proposed
benchmark, we show that event data provides a comparable performance to RGB and
optical flow, yet without any additional flow computation at deploy time, and
an improved performance of up to 4% with respect to RGB only information.
