Rescaling Egocentric Vision: Collection, Pipeline and Challenges for EPIC-KITCHENS-100

نویسندگان

چکیده

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

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ژورنال

عنوان ژورنال: International Journal of Computer Vision

سال: 2021

ISSN: ['0920-5691', '1573-1405']

DOI: https://doi.org/10.1007/s11263-021-01531-2