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

arXiv: https://arxiv.org/abs/1703.09913 · HTML: https://mlsanity.com/paper/1703.09913/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 Mar 2017.

## Built on by (most cited first, 4 of 4)

- [Group-aware Contrastive Regression for Action Quality Assessment](https://mlsanity.com/paper/2108.07797/) (2021)
- [Deep Neural Networks for the Assessment of Surgical Skills: A Systematic Review](https://mlsanity.com/paper/2103.05113/) (2021)
- [Video-based Formative and Summative Assessment of Surgical Tasks using Deep Learning](https://mlsanity.com/paper/2203.09589/) (2022)
- [Cognitive-Motor Integration in Assessing Bimanual Motor Skills](https://mlsanity.com/paper/2404.10889/) (2024)

## Abstract (arXiv)

We present 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.