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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| Vydáno v: | International journal of computer vision Ročník 130; číslo 1; s. 33 - 55 |
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| Hlavní autoři: | , , , , , , , , , , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
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Springer US
01.01.2022
Springer Springer Nature B.V |
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| ISSN: | 0920-5691, 1573-1405 |
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| 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. |
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| AbstractList | 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. 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. |
| Audience | Academic |
| Author | Doughty, Hazel Furnari, Antonino Damen, Dima Ma, Jian Price, Will Farinella, Giovanni Maria Munro, Jonathan Kazakos, Evangelos Perrett, Toby Moltisanti, Davide Wray, Michael |
| Author_xml | – sequence: 1 givenname: Dima surname: Damen fullname: Damen, Dima email: Dima.Damen@bristol.ac.uk organization: University of Bristol – sequence: 2 givenname: Hazel surname: Doughty fullname: Doughty, Hazel organization: University of Bristol, Present Address: University of Amsterdam – sequence: 3 givenname: Giovanni Maria surname: Farinella fullname: Farinella, Giovanni Maria organization: University of Catania – sequence: 4 givenname: Antonino surname: Furnari fullname: Furnari, Antonino organization: University of Catania – sequence: 5 givenname: Evangelos surname: Kazakos fullname: Kazakos, Evangelos organization: University of Bristol – sequence: 6 givenname: Jian surname: Ma fullname: Ma, Jian organization: University of Bristol – sequence: 7 givenname: Davide surname: Moltisanti fullname: Moltisanti, Davide organization: University of Bristol, NTU – sequence: 8 givenname: Jonathan surname: Munro fullname: Munro, Jonathan organization: University of Bristol – sequence: 9 givenname: Toby surname: Perrett fullname: Perrett, Toby organization: University of Bristol – sequence: 10 givenname: Will surname: Price fullname: Price, Will organization: University of Bristol – sequence: 11 givenname: Michael surname: Wray fullname: Wray, Michael organization: University of Bristol |
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| SubjectTerms | 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 |
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| Title | Rescaling Egocentric Vision: Collection, Pipeline and Challenges for EPIC-KITCHENS-100 |
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