Regularizing Oversmoothing of Temporal Convolutional Networks for Action Segmentation into Human Assembly Operations
Author:
Affiliation:
1. Graduate School of Integrated Science and Technology, Shizuoka University
2. Manufacturing Technology Division, Yamaha Motor Co., Ltd.
Publisher
Research Institute of Signal Processing, Japan
Subject
General Energy
Link
https://www.jstage.jst.go.jp/article/jsp/27/4/27_75/_pdf
Reference19 articles.
1. [1] M. Al-Amin et al.: Action recognition in manufacturing assembly using multimodal sensor fusion, Procedia Manufacturing, Vol. 29, pp. 158–167, 2019.
2. [2] J. F. Arinez et al.: Artificial intelligence in advance manufacturing: Current status and future outlook, Manufacturing Science and Engineering, Vol. 142, pp. 1–16, 2020.
3. [3] Y. A. Farha and J. Gall: MS-TCN: Multi-stage temporal convolutional network for action segmentation, CVPR, pp. 3575–3584, 2019.
4. [4] C. Lea et al.: Temporal convolutional networks for action segmentation and detection, CVPR, pp. 156–165, 2016.
5. [5] P. Lei et al.: Temporal deformable residual networks for action segmentation in videos, CVPR, pp. 6742–6751, 2018.
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1. Understanding Human Work Behavior by Action Segmentation Model with Feature Selection;2023 IEEE Asia-Pacific Conference on Computer Science and Data Engineering (CSDE);2023-12-04
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