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231225s2022 xx |||||o 00| ||eng c |
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|a 10.1109/TPAMI.2021.3089127
|2 doi
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|a pubmed24n1089.xml
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|a eng
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| 100 |
1 |
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|a Souri, Yaser
|e verfasserin
|4 aut
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| 245 |
1 |
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|a Fast Weakly Supervised Action Segmentation Using Mutual Consistency
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|c 2022
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|a Text
|b txt
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|a ƒaComputermedien
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|2 rdamedia
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|a ƒa Online-Ressource
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|2 rdacarrier
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| 500 |
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|a Date Completed 16.09.2022
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| 500 |
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|a Date Revised 19.11.2022
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|a published: Print-Electronic
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|a Citation Status MEDLINE
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|a Action segmentation is the task of predicting the actions for each frame of a video. As obtaining the full annotation of videos for action segmentation is expensive, weakly supervised approaches that can learn only from transcripts are appealing. In this paper, we propose a novel end-to-end approach for weakly supervised action segmentation based on a two-branch neural network. The two branches of our network predict two redundant but different representations for action segmentation and we propose a novel mutual consistency (MuCon) loss that enforces the consistency of the two redundant representations. Using the MuCon loss together with a loss for transcript prediction, our proposed approach achieves the accuracy of state-of-the-art approaches while being 14 times faster to train and 20 times faster during inference. The MuCon loss proves beneficial even in the fully supervised setting
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|a Journal Article
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| 650 |
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|a Research Support, Non-U.S. Gov't
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| 700 |
1 |
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|a Fayyaz, Mohsen
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Minciullo, Luca
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Francesca, Gianpiero
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Gall, Juergen
|e verfasserin
|4 aut
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| 773 |
0 |
8 |
|i Enthalten in
|t IEEE transactions on pattern analysis and machine intelligence
|d 1979
|g 44(2022), 10 vom: 14. Okt., Seite 6196-6208
|w (DE-627)NLM098212257
|x 1939-3539
|7 nnns
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1 |
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|g volume:44
|g year:2022
|g number:10
|g day:14
|g month:10
|g pages:6196-6208
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| 856 |
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|u http://dx.doi.org/10.1109/TPAMI.2021.3089127
|3 Volltext
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