  This paper introduces the pipeline to extend the largest dataset in
egocentric vision, EPIC-KITCHENS. The effort culminates in EPIC-KITCHENS-100, a
collection of 100 hours, 20M frames, 90K actions in 700 variable-length videos,
capturing long-term unscripted activities in 45 environments, using
head-mounted cameras. Compared to its previous version, EPIC-KITCHENS-100 has
been annotated using a novel pipeline that allows denser (54% more actions per
minute) and more complete annotations of fine-grained actions (+128% more
action segments). This collection enables new challenges such as action
detection and evaluating the "test of time" - i.e. whether models trained on
data collected in 2018 can generalise to new footage collected two years later.
The dataset is aligned with 6 challenges: action recognition (full and weak
supervision), action detection, action anticipation, cross-modal retrieval
(from captions), as well as unsupervised domain adaptation for action
recognition. For each challenge, we define the task, provide baselines and
evaluation metrics
