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

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...

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Vydané v:	International journal of computer vision Ročník 130; číslo 1; s. 33 - 55
Hlavní autori:	Damen, Dima, Doughty, Hazel, Farinella, Giovanni Maria, Furnari, Antonino, Kazakos, Evangelos, Ma, Jian, Moltisanti, Davide, Munro, Jonathan, Perrett, Toby, Price, Will, Wray, Michael
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	New York Springer US 01.01.2022 Springer Springer Nature B.V
Predmet:	Activity recognition Annotations Artificial Intelligence Computer Imaging Computer Science Datasets Image Processing and Computer Vision Kitchens Pattern Recognition Pattern Recognition and Graphics Pipelines Rescaling Vision Annotation quality Egocentric vision Multi-benchmark large-scale dataset First-person vision Video dataset Action understanding
ISSN:	0920-5691, 1573-1405
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