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.

为了将对视频流的整体感知有效地传递给智能机器，我们通过学习将概念关联和来自不同任务的抽象知识协同利用来学习新技能，提出了一种统一的视频理解方法，它结合了人类行为的共享时间建模和最小开销，支持多个下游任务及在学习新技能时的合作，并通过四个 Ego4D 基准测试表明了我们方法的有效性和高效性。