  In this report, we present the technical details of our approach to the
EPIC-KITCHENS-100 Unsupervised Domain Adaptation (UDA) Challenge for Action
Recognition. The EPIC-KITCHENS-100 dataset consists of daily kitchen activities
focusing on the interaction between human hands and their surrounding objects.
It is very challenging to accurately recognize these fine-grained activities,
due to the presence of distracting objects and visually similar action classes,
especially in the unlabelled target domain. Based on an existing method for
video domain adaptation, i.e., TA3N, we propose to learn hand-centric features
by leveraging the hand bounding box information for UDA on fine-grained action
recognition. This helps reduce the distraction from background as well as
facilitate the learning of domain-invariant features. To achieve high quality
hand localization, we adopt an uncertainty-aware domain adaptation network,
i.e., MEAA, to train a domain-adaptive hand detector, which only uses very
limited hand bounding box annotations in the source domain but can generalize
well to the unlabelled target domain. Our submission achieved the 1st place in
terms of top-1 action recognition accuracy, using only RGB and optical flow
modalities as input.
