116 citations · 229 across the 10 of their papers we have counts for
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cs.AI2023
IRIS: Interpretable Rubric-Informed Segmentation for Action Quality Assessment
Hitoshi Matsuyama, Nobuo Kawaguchi, Brian Y. Lim
AI-driven Action Quality Assessment (AQA) of sports videos can mimic Olympic judges to help score performances as a second opinion or for training. However, these AI methods are un…
cs.AI2023★ 2 cited
Diagrammatization and Abduction to Improve AI Interpretability With Domain-Aligned Explanations for Medical Diagnosis
Brian Y. Lim, Joseph P. Cahaly, Chester Y. F. Sng +1
Many visualizations have been developed for explainable AI (XAI), but they often require further reasoning by users to interpret. Investigating XAI for high-stakes medical diagnosi…