  We benchmark contemporary action recognition models (TSN, TRN, and TSM) on
the recently introduced EPIC-Kitchens dataset and release pretrained models on
GitHub (https://github.com/epic-kitchens/action-models) for others to build
upon. In contrast to popular action recognition datasets like Kinetics,
Something-Something, UCF101, and HMDB51, EPIC-Kitchens is shot from an
egocentric perspective and captures daily actions in-situ. In this report, we
aim to understand how well these models can tackle the challenges present in
this dataset, such as its long tail class distribution, unseen environment test
set, and multiple tasks (verb, noun and, action classification). We discuss the
models' shortcomings and avenues for future research.
