116 citations · 229 across the 10 of their papers we have counts for
6 papers · 1 filter
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…
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…
Interpretable Directed Diversity: Leveraging Model Explanations for Iterative Crowd Ideation
Yunlong Wang, Priyadarshini Venkatesh, Brian Y. Lim
Feedback in creativity support tools can help crowdworkers to improve their ideations. However, current feedback methods require human assessment from facilitators or peers. This i…
Directed Diversity: Leveraging Language Embedding Distances for Collective Creativity in Crowd Ideation
Samuel Rhys Cox, Yunlong Wang, Ashraf Abdul +2
Crowdsourcing can collect many diverse ideas by prompting ideators individually, but this can generate redundant ideas. Prior methods reduce redundancy by presenting peers' ideas o…