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We present a method for assessing skill from video, applicable to a varietyof tasks, ranging from surgery to drawing and rolling pizza dough. We formulatethe problem as pairwise (who's better?) and overall (who's best?) ranking ofvideo collections, using supervised deep ranking. We propose a novel lossfunction that learns discriminative features when a pair of videos exhibitvariance in skill, and learns shared features when a pair of videos exhibitcomparable skill levels. Results demonstrate our method is applicable acrosstasks, with the percentage of correctly ordered pairs of videos ranging from70% to 83% for four datasets. We demonstrate the robustness of our approach viasensitivity analysis of its parameters. We see this work as effort toward theautomated organization of how-to video collections and overall, generic skilldetermination in video.

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