  First person action recognition is an increasingly researched topic because
of the growing 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 in real settings where
trimmed labeled data are not available during training. In this work, we
propose to leverage over the intrinsic complementary nature of audio-visual
signals to learn a representation that works well on data seen during training,
while being able to generalize across different domains. To this end, we
introduce an audio-visual loss that aligns the contributions from the two
modalities by acting on the magnitude of their feature norm representations.
This new loss, plugged into a minimal multi-modal action recognition
architecture, leads to strong results in cross-domain first person action
recognition, as demonstrated by extensive experiments on the popular
EPIC-Kitchens dataset.
