  This technical report extends our work presented in [9] with more
experiments. In [9], we tackle long-term video understanding, which requires
reasoning from current and past or future observations and raises several
fundamental questions. How should temporal or sequential relationships be
modelled? What temporal extent of information and context needs to be
processed? At what temporal scale should they be derived? [9] addresses these
questions with a flexible multi-granular temporal aggregation framework. In
this report, we conduct further experiments with this framework on different
tasks and a new dataset, EPIC-KITCHENS-100.
