  Fine-grained action recognition datasets exhibit environmental bias, where
multiple video sequences are captured from a limited number of environments.
Training a model in one environment and deploying in another results in a drop
in performance due to an unavoidable domain shift. Unsupervised Domain
Adaptation (UDA) approaches have frequently utilised adversarial training
between the source and target domains. However, these approaches have not
explored the multi-modal nature of video within each domain. In this work we
exploit the correspondence of modalities as a self-supervised alignment
approach for UDA in addition to adversarial alignment.
  We test our approach on three kitchens from our large-scale dataset,
EPIC-Kitchens, using two modalities commonly employed for action recognition:
RGB and Optical Flow. We show that multi-modal self-supervision alone improves
the performance over source-only training by 2.4% on average. We then combine
adversarial training with multi-modal self-supervision, showing that our
approach outperforms other UDA methods by 3%.
