  Most successful computer vision models transform low-level features, such as
Gabor filter responses, into richer representations of intermediate or
mid-level complexity for downstream visual tasks. These mid-level
representations have not been explored for event cameras, although it is
especially relevant to the visually sparse and often disjoint spatial
information in the event stream. By making use of locally consistent
intermediate representations, termed as superevents, numerous visual tasks
ranging from semantic segmentation, visual tracking, depth estimation shall
benefit. In essence, superevents are perceptually consistent local units that
delineate parts of an object in a scene. Inspired by recent deep learning
architectures, we present a novel method that employs lifetime augmentation for
obtaining an event stream representation that is fed to a fully convolutional
network to extract superevents. Our qualitative and quantitative experimental
results on several sequences of a benchmark dataset highlights the significant
potential for event-based downstream applications.
