21 citations · 41 across the 16 of their papers we have counts for
16 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…
Revisiting Channel Effectiveness: A Multi-Dimensional Evaluation with Primitive Visual Stimuli
Soohyun Lee, Seokhyeon Park, Minsuk Chang +1
Established channel effectiveness rankings primarily assess magnitude estimation accuracy in complete chart contexts, often neglecting other perceptual tasks such as discriminabili…
Disentangling Visual and Factual Correctness in LVLMs' Visualization Literacy
Soohyun Lee, Jaeyoung Kim, Seokhyeon Park +5
Large Vision-Language Models (LVLMs) show strong visualization interpretation, yet it is unclear whether their responses reflect genuine reasoning over visual evidence or factual p…
HookLens: Visual Analytics for Understanding React Hooks Structures
Suyeon Hwang, Minkyu Kweon, Jeongmin Rhee +5
Maintaining and refactoring React web applications is challenging, as React code often becomes complex due to its core API called Hooks. For example, Hooks often lead developers to…
Bridging Gulfs in UI Generation through Semantic Guidance
Seokhyeon Park, Soohyun Lee, Eugene Choi +4
While generative AI enables high-fidelity UI generation from text prompts, users struggle to articulate design intent and evaluate or refine results-creating gulfs of execution and…
GhostUI: Unveiling Hidden Interactions in Mobile UI
Minkyu Kweon, Seokhyeon Park, Soohyun Lee +3
Modern mobile applications rely on hidden interactions--gestures without visual cues like long presses and swipes--to provide functionality without cluttering interfaces. While exp…
InFerActive: Interactive Tree-Based Exploration of LLM Sampling for Safety Evaluation
Junhyeong Hwangbo, Soohyun Lee, Hyeon Jeon +4
Even LLMs that appear safe during evaluation can still produce harmful responses in deployment. Because stochastic sampling yields different responses to the same prompt, low-proba…
UMATO: Bridging Local and Global Structures for Reliable Visual Analytics with Dimensionality Reduction
Hyeon Jeon, Kwon Ko, Soohyun Lee +5
Due to the intrinsic complexity of high-dimensional (HD) data, dimensionality reduction (DR) techniques cannot preserve all the structural characteristics of the original data. The…
Dataset-Adaptive Dimensionality Reduction
Hyeon Jeon, Jeongin Park, Soohyun Lee +3
Selecting the appropriate dimensionality reduction (DR) technique and determining its optimal hyperparameter settings that maximize the accuracy of the output projections typically…
Leveraging Multimodal LLM for Inspirational User Interface Search
Seokhyeon Park, Yumin Song, Soohyun Lee +2
Inspirational search, the process of exploring designs to inform and inspire new creative work, is pivotal in mobile user interface (UI) design. However, exploring the vast space o…
Efficiently Crowdsourcing Visual Importance with Punch-Hole Annotation
Minsuk Chang, Soohyun Lee, Aeri Cho +4
We introduce a novel crowdsourcing method for identifying important areas in graphical images through punch-hole labeling. Traditional methods, such as gaze trackers and mouse-base…
Assessing Graphical Perception of Image Embedding Models using Channel Effectiveness
Soohyun Lee, Minsuk Chang, Seokhyeon Park +1
Recent advancements in vision models have greatly improved their ability to handle complex chart understanding tasks, like chart captioning and question answering. However, it rema…
Extracting Human Attention through Crowdsourced Patch Labeling
Minsuk Chang, Seokhyeon Park, Hyeon Jeon +3
In image classification, a significant problem arises from bias in the datasets. When it contains only specific types of images, the classifier begins to rely on shortcuts - simpli…
ZADU: A Python Library for Evaluating the Reliability of Dimensionality Reduction Embeddings
Hyeon Jeon, Aeri Cho, Jinhwa Jang +5
Dimensionality reduction (DR) techniques inherently distort the original structure of input high-dimensional data, producing imperfect low-dimensional embeddings. Diverse distortio…
Uniform Manifold Approximation with Two-phase Optimization
Hyeon Jeon, Hyung-Kwon Ko, Soohyun Lee +2
We introduce Uniform Manifold Approximation with Two-phase Optimization (UMATO), a dimensionality reduction (DR) technique that improves UMAP to capture the global structure of hig…
Distortion-Aware Brushing for Reliable Cluster Analysis in Multidimensional Projections
Hyeon Jeon, Michaël Aupetit, Soohyun Lee +4
Brushing is a common interaction technique in 2D scatterplots, allowing users to select clustered points within a continuous, enclosed region for further analysis or filtering. How…