Learning to Segment Actions from Observation and Narration

Daniel Fried, Jean-Baptiste Alayrac, Phil Blunsom, Chris Dyer, Stephen Clark, Aida Nematzadeh


Abstract
We apply a generative segmental model of task structure, guided by narration, to action segmentation in video. We focus on unsupervised and weakly-supervised settings where no action labels are known during training. Despite its simplicity, our model performs competitively with previous work on a dataset of naturalistic instructional videos. Our model allows us to vary the sources of supervision used in training, and we find that both task structure and narrative language provide large benefits in segmentation quality.
Anthology ID:
2020.acl-main.231
Volume:
Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics
Month:
July
Year:
2020
Address:
Online
Venue:
ACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
2569–2588
Language:
URL:
https://www.aclweb.org/anthology/2020.acl-main.231
DOI:
10.18653/v1/2020.acl-main.231
Bib Export formats:
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PDF:
http://aclanthology.lst.uni-saarland.de/2020.acl-main.231.pdf
Video:
 http://slideslive.com/38929315