A Backpack Full of Skills: Egocentric Video Understanding with Diverse
  Task Perspectives
Simone Alberto Peirone Francesca Pistilli Antonio Alliegro Giuseppe Averta
Human comprehension of a video stream is naturally broad: in a few instants,
we are able to understand what is happening, the relevance and relationship of
objects, and forecast what will follow in the near future, everything all at
once. We believe that - to effectively transfer such an holistic perception to
intelligent machines - an important role is played by learning to correlate
concepts and to abstract knowledge coming from different tasks, to
synergistically exploit them when learning novel skills. To accomplish this, we
seek for a unified approach to video understanding which combines shared
temporal modelling of human actions with minimal overhead, to support multiple
downstream tasks and enable cooperation when learning novel skills. We then
propose EgoPack, a solution that creates a collection of task perspectives that
can be carried across downstream tasks and used as a potential source of
additional insights, as a backpack of skills that a robot can carry around and
use when needed. We demonstrate the effectiveness and efficiency of our
approach on four Ego4D benchmarks, outperforming current state-of-the-art
methods.