First-person vision is gaining interest as it offers a unique viewpoint on
people's interaction with objects, their attention, and even intention.
However, progress in this challenging domain has been relatively slow due to
the lack of sufficiently large datasets. In this paper, we introduce
EPIC-KITCHENS, a large-scale egocentric video benchmark recorded by 32
participants in their native kitchen environments. Our videos depict
nonscripted daily activities: we simply asked each participant to start
recording every time they entered their kitchen. Recording took place in 4
cities (in North America and Europe) by participants belonging to 10 different
nationalities, resulting in highly diverse cooking styles. Our dataset features
55 hours of video consisting of 11.5M frames, which we densely labeled for a
total of 39.6K action segments and 454.3K object bounding boxes. Our annotation
is unique in that we had the participants narrate their own videos (after
recording), thus reflecting true intention, and we crowd-sourced ground-truths
based on these. We describe our object, action and anticipation challenges, and
evaluate several baselines over two test splits, seen and unseen kitchens.
Dataset and Project page: http://epic-kitchens.github.ioComment: European Conference on Computer Vision (ECCV) 2018 Dataset and
Project page: http://epic-kitchens.github.i