  In this report, we describe the technical details of our submission to the
EPIC-Kitchens-100 Unsupervised Domain Adaptation (UDA) Challenge in Action
Recognition. To tackle the domain-shift which exists under the UDA setting, we
first exploited a recent Domain Generalization (DG) technique, called Relative
Norm Alignment (RNA). Secondly, we extended this approach to work on unlabelled
target data, enabling a simpler adaptation of the model to the target
distribution in an unsupervised fashion. To this purpose, we included in our
framework UDA algorithms, such as multi-level adversarial alignment and
attentive entropy. By analyzing the challenge setting, we notice the presence
of a secondary concurrence shift in the data, which is usually called
environmental bias. It is caused by the existence of different environments,
i.e., kitchens. To deal with these two shifts (environmental and temporal), we
extended our system to perform Multi-Source Multi-Target Domain Adaptation.
Finally, we employed distinct models in our final proposal to leverage the
potential of popular video architectures, and we introduced two more losses for
the ensemble adaptation. Our submission (entry 'plnet') is visible on the
leaderboard and ranked in 2nd position for 'verb', and in 3rd position for both
'noun' and 'action'.
