5 papers · 1 filter
PromptMap: Supporting Exploratory Text-to-Image Generation
Yuhan Guo, Xingyou Liu, Xiaoru Yuan +1
Text-to-image generative models can be tremendously valuable in supporting creative tasks by providing inspirations and enabling quick exploration of different design ideas. Howeve…
Datasets of Visualization for Machine Learning
Can Liu, Ruike Jiang, Shaocong Tan +4
Datasets of visualization play a crucial role in automating data-driven visualization pipelines, serving as the foundation for supervised model training and algorithm benchmarking.…
AutoLegend: A User Feedback-Driven Adaptive Legend Generator for Visualizations
Can Liu, Xiyao Mei, Zhibang Jiang +2
We propose AutoLegend to generate interactive visualization legends using online learning with user feedback. AutoLegend accurately extracts symbols and channels from visualization…
Breathing New Life into Existing Visualizations: A Natural Language-Driven Manipulation Framework
Can Liu, Jiacheng Yu, Yuhan Guo +3
We propose an approach to manipulate existing interactive visualizations to answer users' natural language queries. We analyze the natural language tasks and propose a design space…
PrompTHis: Visualizing the Process and Influence of Prompt Editing during Text-to-Image Creation
Yuhan Guo, Hanning Shao, Can Liu +2
Generative text-to-image models, which allow users to create appealing images through a text prompt, have seen a dramatic increase in popularity in recent years. However, most user…