Labeling + UI & UX for Data Annotation Data is the key for AI but data collection is labor-intensive and time-consuming.
We explore and design various effective data annotation tools.
Representative Paper
Spatial Labeling: Leveraging Spatial Layout for Improving Label Quality in Non-Expert Image Annotation Chia-Ming Chang, Chia-Hsien Lee, and Takeo Igarashi
CHI 2021 *28 cited Google Scholar
[Project Page]
Related Publications
Dynamic Labeling: A Control System for Labeling Styles in Image Annotation Tasks Chia-Ming Chang, Yi He, Xusheng Du, Xi Yang, and Haoran Xie
HCII 2024
SyncLabeling: A Synchronized Audio Segmentation Interface for Mobile Devices Yi Tang, Chia-Ming Chang, Xi Yang, and Takeo Igarashi
MobileHCI 2023 *1 cited Google Scholar
RelRoll: A Relative Elicitation Mechanism for Scoring Annotation with A Case Study on Speech Emotion Yijun Zhou, JinHong Lu, Xiang 'Anthony’ Chen, Chia-Ming Chang, and Takeo Igarashi
GI 2023 *1 cited Google Scholar
An Empirical Study on the Effect of Quick and Careful Labeling Styles in Image Annotation Chia-Ming Chang, Xi Yang, and Takeo Igarashi
GI 2022 *3 cited Google Scholar
DualLabel: Secondary Labels for Challenging Image Annotation Chia-Ming Chang, Yi He, Xi Yang, Haoran Xie, and Takeo Igarashi
GI 2022 *3 cited Google Scholar
[Project Page]
ConfLabeling: Assisting Image Labeling with User and System Confidence Yi Lu, Chia-Ming Chang, and Takeo Igarashi
HCII 2022 *1 cited Google Scholar
[Project Page]
Trafne: A Training Framework for Non-Expert Annotators with Auto Validation and Expert Feedback Shugo Miyata, Chia-Ming Chang, and Takeo Igarashi
HCII 2022 *1 cited Google Scholar
[Project Page]
A Hierarchical Task Assignment for Manual Image Labeling Chia-Ming Chang,Siddharth Deepak Mishra, Takeo Igarashi
VL/HCC 2019*7 cited Google Scholar
