Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/162036
Title: Rescaling egocentric vision: collection, pipeline and challenges for EPIC-KITCHENS-100
Authors: Damen, Dima
Doughty, Hazel
Farinella, Giovanni Maria
Furnari, Antonino
Kazakos, Evangelos
Ma, Jian
Moltisanti, Davide
Munro, Jonathan
Perrett, Toby
Price, Will
Wray, Michael
Keywords: Engineering::Computer science and engineering
Issue Date: 2022
Source: Damen, D., Doughty, H., Farinella, G. M., Furnari, A., Kazakos, E., Ma, J., Moltisanti, D., Munro, J., Perrett, T., Price, W. & Wray, M. (2022). Rescaling egocentric vision: collection, pipeline and challenges for EPIC-KITCHENS-100. International Journal of Computer Vision, 130(1), 33-55. https://dx.doi.org/10.1007/s11263-021-01531-2
Journal: International Journal of Computer Vision 
Abstract: 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 (Damen in Scaling egocentric vision: ECCV, 2018), 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.
URI: https://hdl.handle.net/10356/162036
ISSN: 0920-5691
DOI: 10.1007/s11263-021-01531-2
Rights: © The Author(s) 2021. This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecomm ons.org/licenses/by/4.0/.
Fulltext Permission: open
Fulltext Availability: With Fulltext
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