93 citations · 216 across the 8 of their papers we have counts for
8 papers
TailVis: Expressive Chart Refinement Preserving Data-Binding Integrity
Yumin Song, Seokhyeon Park, Soohyun Lee +4
Creating static visualizations for presentations and publications requires granular refinements of visual details, even for simple charts. Existing data-driven visualization tools…
One vs. Many: Comprehending Accurate Information from Multiple Erroneous and Inconsistent AI Generations
Yoonjoo Lee, Kihoon Son, Tae Soo Kim +4
As Large Language Models (LLMs) are nondeterministic, the same input can generate different outputs, some of which may be incorrect or hallucinated. If run again, the LLM may corre…
Authors' Values and Attitudes Towards AI-bridged Scalable Personalization of Creative Language Arts
Taewook Kim, Hyomin Han, Eytan Adar +2
Generative AI has the potential to create a new form of interactive media: AI-bridged creative language arts (CLA), which bridge the author and audience by personalizing the author…
CreativeConnect: Supporting Reference Recombination for Graphic Design Ideation with Generative AI
DaEun Choi, Sumin Hong, Jeongeon Park +2
Graphic designers often get inspiration through the recombination of references. Our formative study (N=6) reveals that graphic designers focus on conceptual keywords during this p…
PromptPaint: Steering Text-to-Image Generation Through Paint Medium-like Interactions
John Joon Young Chung, Eytan Adar
While diffusion-based text-to-image (T2I) models provide a simple and powerful way to generate images, guiding this generation remains a challenge. For concepts that are difficult…
Increasing Diversity While Maintaining Accuracy: Text Data Generation with Large Language Models and Human Interventions
John Joon Young Chung, Ece Kamar, Saleema Amershi
Large language models (LLMs) can be used to generate text data for training and evaluating other models. However, creating high-quality datasets with LLMs can be challenging. In th…
viz2viz: Prompt-driven stylized visualization generation using a diffusion model
Jiaqi Wu, John Joon Young Chung, Eytan Adar
Creating stylized visualization requires going beyond the limited, abstract, geometric marks produced by most tools. Rather, the designer builds stylized idioms where the marks are…
Neglected Free Lunch -- Learning Image Classifiers Using Annotation Byproducts
Dongyoon Han, Junsuk Choe, Seonghyeok Chun +5
Supervised learning of image classifiers distills human knowledge into a parametric model through pairs of images and corresponding labels (X,Y). We argue that this simple and wide…