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Multimedia and DTV Technologies

S3D: Stacking Segmental P3D for Action Quality Assessment


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.

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Authors:
Ye Tian, Austin Reiter, Gregory D. Hager, Trac D. Tran
Submitted On:
5 October 2018 - 2:08am
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AI Referee: Score Olympic Games

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[1] Ye Tian, Austin Reiter, Gregory D. Hager, Trac D. Tran, "S3D: Stacking Segmental P3D for Action Quality Assessment", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3501. Accessed: Nov. 16, 2018.
@article{3501-18,
url = {http://sigport.org/3501},
author = {Ye Tian; Austin Reiter; Gregory D. Hager; Trac D. Tran },
publisher = {IEEE SigPort},
title = {S3D: Stacking Segmental P3D for Action Quality Assessment},
year = {2018} }
TY - EJOUR
T1 - S3D: Stacking Segmental P3D for Action Quality Assessment
AU - Ye Tian; Austin Reiter; Gregory D. Hager; Trac D. Tran
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3501
ER -
Ye Tian, Austin Reiter, Gregory D. Hager, Trac D. Tran. (2018). S3D: Stacking Segmental P3D for Action Quality Assessment. IEEE SigPort. http://sigport.org/3501
Ye Tian, Austin Reiter, Gregory D. Hager, Trac D. Tran, 2018. S3D: Stacking Segmental P3D for Action Quality Assessment. Available at: http://sigport.org/3501.
Ye Tian, Austin Reiter, Gregory D. Hager, Trac D. Tran. (2018). "S3D: Stacking Segmental P3D for Action Quality Assessment." Web.
1. Ye Tian, Austin Reiter, Gregory D. Hager, Trac D. Tran. S3D: Stacking Segmental P3D for Action Quality Assessment [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3501

VIDEO ENHANCEMENT WITH CONVEX OPTIMIZATION METHODS


Video enhancement methods enable to optimize the viewing of video content at the end-user side. Most approaches do not consider the compressed nature of the available content. In the present work, we build upon a recently proposed video enhancement approach that explicitly models a compression stage. To apply the enhancement framework on compressed representations requires to extract specific syntax elements during their decoding. This additional information embeds the enhanced result in a domain that closely fits the observation.

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Authors:
Benoit Boyadjis, Andrei Purica, Beatrice Pesquet-Popescu, Frederic Dufaux
Submitted On:
12 April 2018 - 12:23pm
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poster presentation for video enhancement with convex optimization methods

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[1] Benoit Boyadjis, Andrei Purica, Beatrice Pesquet-Popescu, Frederic Dufaux, "VIDEO ENHANCEMENT WITH CONVEX OPTIMIZATION METHODS", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/2430. Accessed: Nov. 16, 2018.
@article{2430-18,
url = {http://sigport.org/2430},
author = {Benoit Boyadjis; Andrei Purica; Beatrice Pesquet-Popescu; Frederic Dufaux },
publisher = {IEEE SigPort},
title = {VIDEO ENHANCEMENT WITH CONVEX OPTIMIZATION METHODS},
year = {2018} }
TY - EJOUR
T1 - VIDEO ENHANCEMENT WITH CONVEX OPTIMIZATION METHODS
AU - Benoit Boyadjis; Andrei Purica; Beatrice Pesquet-Popescu; Frederic Dufaux
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/2430
ER -
Benoit Boyadjis, Andrei Purica, Beatrice Pesquet-Popescu, Frederic Dufaux. (2018). VIDEO ENHANCEMENT WITH CONVEX OPTIMIZATION METHODS. IEEE SigPort. http://sigport.org/2430
Benoit Boyadjis, Andrei Purica, Beatrice Pesquet-Popescu, Frederic Dufaux, 2018. VIDEO ENHANCEMENT WITH CONVEX OPTIMIZATION METHODS. Available at: http://sigport.org/2430.
Benoit Boyadjis, Andrei Purica, Beatrice Pesquet-Popescu, Frederic Dufaux. (2018). "VIDEO ENHANCEMENT WITH CONVEX OPTIMIZATION METHODS." Web.
1. Benoit Boyadjis, Andrei Purica, Beatrice Pesquet-Popescu, Frederic Dufaux. VIDEO ENHANCEMENT WITH CONVEX OPTIMIZATION METHODS [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/2430