@inproceedings{39ed3095dbc442d38126303c0dd62803,
title = "S3D: Stacking Segmental P3D for Action Quality Assessment",
abstract = "Action quality assessment is crucial in areas of sports, surgery and assembly line where action skills can be evaluated. In this paper, we propose the Segment-based P3D-fused network S3D built-upon ED-TCN and push the performance on the UNLV-Dive dataset by a significant margin. We verify that segment-aware training performs better than full-video training which turns out to focus on the water spray. We show that temporal segmentation can be embedded with few efforts.",
keywords = "3D CNN, Action quality, Regression, Spatiotemporal, Temporal convolution, Temporal segmentation",
author = "Xiang Xiang and Ye Tian and Austin Reiter and Hager, {Gregory D.} and Tran, {Trac D.}",
note = "Publisher Copyright: {\textcopyright} 2018 IEEE.; 25th IEEE International Conference on Image Processing, ICIP 2018 ; Conference date: 07-10-2018 Through 10-10-2018",
year = "2018",
month = aug,
day = "29",
doi = "10.1109/ICIP.2018.8451364",
language = "English (US)",
series = "Proceedings - International Conference on Image Processing, ICIP",
publisher = "IEEE Computer Society",
pages = "928--932",
booktitle = "2018 IEEE International Conference on Image Processing, ICIP 2018 - Proceedings",
}