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Rescaling Egocentric Vision by Damen, Dima; Doughty, Hazel; Farinella, Giovanni Maria; Furnari, Antonino; Kazakos, Evangelos; Ma, Jian; Moltisanti, Davide; Munro, Jonathan; Perrett, Toby; Price, Will; Wray, Michael is a scholarly article available to read on EtoBox.

What is Rescaling Egocentric Vision about?

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

Author

Damen, Dima; Doughty, Hazel; Farinella, Giovanni Maria; Furnari, Antonino; Kazakos, Evangelos; Ma, Jian; Moltisanti, Davide; Munro, Jonathan; Perrett, Toby; Price, Will; Wray, Michael

Published

2020

Language

EN