116 citations · 229 across the 10 of their papers we have counts for
11 papers
RePrompt: Automatic Prompt Editing to Refine AI-Generative Art Towards Precise Expressions
Yunlong Wang, Shuyuan Shen, Brian Y. Lim
Generative AI models have shown impressive ability to produce images with text prompts, which could benefit creativity in visual art creation and self-expression. However, it is un…
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…
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…
Debiased-CAM to mitigate systematic error with faithful visual explanations of machine learning
Wencan Zhang, Mariella Dimiccoli, Brian Y. Lim
Model explanations such as saliency maps can improve user trust in AI by highlighting important features for a prediction. However, these become distorted and misleading when expla…
Towards Relatable Explainable AI with the Perceptual Process
Wencan Zhang, Brian Y. Lim
Machine learning models need to provide contrastive explanations, since people often seek to understand why a puzzling prediction occurred instead of some expected outcome. Current…
SalienTrack: providing salient information for semi-automated self-tracking feedback with model explanations
Yunlong Wang, Jiaying Liu, Homin Park +4
Self-tracking can improve people's awareness of their unhealthy behaviors and support reflection to inform behavior change. Increasingly, new technologies make tracking easier, lea…