Chuhan Jiao

Institute for Visualisation and Interactive Systems, University of Stuttgart

  Pfaffenwaldring 5a, 70569 Stuttgart, Germany University of Stuttgart, SimTech Building, Room 01.035 Google Scholar

Biography

Chuhan Jiao is a PhD student in the Perceptual User Interfaces group since December 2022. He holds a Bachelor's degree in Computer Science and Technology from Donghua University, and a Master's degree (with honours) in Computer Science from Aalto University. His research interest lies at the intersection of computer vision and human-computer interaction. He now works on Unsupervised Learning of Deep Gaze Behavior Representations within MSCA Doctoral Network Eyes4ICU.

Open Thesis Projects

 

Multi-duration Saliency Prediction with Implicit Representation learningMaster

 

Gaze and Head Redirection with Implicit Neural RepresentationsMaster

Publications

1. 

   VisRecall: Quantifying Information Visualisation Recallability via Question Answering

   Yao Wang, Chuhan Jiao, Mihai Bâce, Andreas Bulling

   IEEE Transactions on Visualization and Computer Graphics (TVCG), 28(12), pp. 4995-5005, 2022.

   Abstract Links BibTeX Project Oral presentation

   Despite its importance for assessing the effectiveness of communicating information visually, fine-grained recallability of information visualisations has not been studied quantitatively so far. In this work, we propose a question-answering paradigm to study visualisation recallability and present VisRecall - a novel dataset consisting of 200 visualisations that are annotated with crowd-sourced human (N = 305) recallability scores obtained from 1,000 questions of five question types. Furthermore, we present the first computational method to predict recallability of different visualisation elements, such as the title or specific data values. We report detailed analyses of our method on VisRecall and demonstrate that it outperforms several baselines in overall recallability and FE-, F-, RV-, and U-question recallability. Our work makes fundamental contributions towards a new generation of methods to assist designers in optimising visualisations.

   doi: 10.1109/TVCG.2022.3198163

   Paper: wang22_tvcg.pdf

   Supplementary Material: wang22_tvcg_sup.pdf

   Dataset: https://darus.uni-stuttgart.de/dataset.xhtml?persistentId=doi:10.18419/darus-2826

   @article{wang22_tvcg, title = {VisRecall: Quantifying Information Visualisation Recallability via Question Answering}, author = {Wang, Yao and Jiao, Chuhan and Bâce, Mihai and Bulling, Andreas}, year = {2022}, pages = {4995-5005}, journal = {IEEE Transactions on Visualization and Computer Graphics (TVCG)}, volume = {28}, number = {12}, doi = {10.1109/TVCG.2022.3198163} }