Recently First Person Action Recognition (FPAR) has gained great interest of the researchers' community, mainly due to the increasing spread of wearable devices, the release of large and well-annotated datasets and the huge investments in novel technologies e.g., autonomous drones and robots, self-driving systems. Although egocentric vision rapidly attracted the interest of the research community, this setup presents some important challenges, most notably the ego-motion and the domain shift in feature space. Approaches in the literature often exploit multiple modalities to help mitigating these problems. However, domain shifts affect each modality in a different way, so it is important to develop algorithms that can better leverage the complementarity among modalities to achieve model resilience across domains, allowing the model to better recognize actions under various domain shifts.