  First person action recognition is becoming an increasingly researched area
thanks to the rising popularity of wearable cameras. This is bringing to light
cross-domain issues that are yet to be addressed in this context. Indeed, the
information extracted from learned representations suffers from an intrinsic
"environmental bias". This strongly affects the ability to generalize to unseen
scenarios, limiting the application of current methods to real settings where
labeled data are not available during training. In this work, we introduce the
first domain generalization approach for egocentric activity recognition, by
proposing a new audio-visual loss, called Relative Norm Alignment loss. It
re-balances the contributions from the two modalities during training, over
different domains, by aligning their feature norm representations. Our approach
leads to strong results in domain generalization on both EPIC-Kitchens-55 and
EPIC-Kitchens-100, as demonstrated by extensive experiments, and can be
extended to work also on domain adaptation settings with competitive results.
