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This paper presents a method for assessing skill from video, applicable to a variety of tasks, ranging from surgery to drawing and rolling pizza dough. We formulate the problem as pairwise (who’s better?) and overall (who’s best?) ranking of video collections, using supervised deep ranking. We propose a novel loss function that learns discriminative features when a pair of videos exhibit variance in skill, and learns shared features when a pair of videos exhibit comparable skill levels. Results demonstrate our method is applicable across tasks, with the percentage of correctly ordered pairs of videos ranging from 70% to 83% for four datasets. We demonstrate the robustness of our approach via sensitivity analysis of its parameters. We see this work as effort toward the automated organization of how-to video collections and overall, generic skill determination in video.
|Title of host publication||2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR 2018)|
|Subtitle of host publication||Proceedings of a meeting held 18-22 June 2018, Salt Lake City, Utah, USA|
|Publisher||Institute of Electrical and Electronics Engineers (IEEE)|
|Number of pages||10|
|Publication status||Published - Feb 2019|
|Event||Computer Vision and Pattern Recognition - Salt Lake City, United States|
Duration: 18 Jun 2018 → 22 Jun 2018
|Conference||Computer Vision and Pattern Recognition|
|City||Salt Lake City|
|Period||18/06/18 → 22/06/18|
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4/04/16 → 2/02/22