Who's Better? Who's Best? Pairwise Deep Ranking for Skill Determination

Research output: Chapter in Book/Report/Conference proceedingConference Contribution (Conference Proceeding)

79 Citations (Scopus)
280 Downloads (Pure)


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.
Original languageEnglish
Title of host publication2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR 2018)
Subtitle of host publicationProceedings of a meeting held 18-22 June 2018, Salt Lake City, Utah, USA
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Number of pages10
ISBN (Electronic)9781538664209
ISBN (Print)9781538664216
Publication statusPublished - Feb 2019
EventComputer Vision and Pattern Recognition - Salt Lake City, United States
Duration: 18 Jun 201822 Jun 2018

Publication series

ISSN (Print)1063-6919
ISSN (Electronic)2575-7075


ConferenceComputer Vision and Pattern Recognition
Abbreviated titleCVPR
Country/TerritoryUnited States
CitySalt Lake City


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