2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) 2020
DOI: 10.1109/cvpr42600.2020.00947
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Action Segmentation With Joint Self-Supervised Temporal Domain Adaptation

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Cited by 104 publications
(89 citation statements)
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“…In addition, there exist several approaches to improve the performance of action segmentation models such as MS-TCN [3,26,9,10]. Chen et al [3] proposed to apply selfsupervised domain adaptation techniques when training a model such as MS-TCN, and it exploits unlabeled videos to boost the performance of action segmentation.…”
Section: Related Workmentioning
confidence: 99%
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“…In addition, there exist several approaches to improve the performance of action segmentation models such as MS-TCN [3,26,9,10]. Chen et al [3] proposed to apply selfsupervised domain adaptation techniques when training a model such as MS-TCN, and it exploits unlabeled videos to boost the performance of action segmentation.…”
Section: Related Workmentioning
confidence: 99%
“…Researches in temporal action segmentation have been improved to successfully segment thousands of video frames recorded with 15 fps [5,3,10]. However, we find out that existing state-of-the-art models sometimes generate segmentation results including action labels that are out of overall context.…”
Section: Introductionmentioning
confidence: 99%
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“…It works by designing an auxiliary task that labels can be self-annotated. For example, [5] proposed an auxiliary task that predicts temporal permutation for cross-domain videos to tackle the problem of Spatio-temporal variations for action segmentation. This self-supervised approach combined with MS-TCN has improved the MS-TCN stand-alone version accuracy on all three datasets 50Salads [33], GTEA [9] and Breakfast [21], and requires only 65% of the labeled training data for comparable performance.…”
Section: Action Recognitionmentioning
confidence: 99%