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

 

Gaze and Head Redirection with Implicit Neural RepresentationsMaster

Publications

1. 

   SUPREYES: SUPer Resolution for EYES Using Implicit Neural Representation Learning

   Chuhan Jiao, Zhiming Hu, Mihai Bâce, Andreas Bulling

   Proc. ACM Symposium on User Interface Software and Technology (UIST), pp. 1–13, 2023.

   Abstract Links BibTeX Project

   We introduce SUPREYES – a novel self-supervised method to increase the spatio-temporal resolution of gaze data recorded using low(er)-resolution eye trackers. Despite continuing advances in eye tracking technology, the vast majority of current eye trackers – particularly mobile ones and those integrated into mobile devices – suffer from low-resolution gaze data, thus fundamentally limiting their practical usefulness. SUPREYES learns a continuous implicit neural representation from low-resolution gaze data to up-sample the gaze data to arbitrary resolutions. We compare our method with commonly used interpolation methods on arbitrary scale super-resolution and demonstrate that SUPREYES outperforms these baselines by a significant margin. We also test on the sample downstream task of gaze-based user identification and show that our method improves the performance of original low-resolution gaze data and outperforms other baselines. These results are promising as they open up a new direction for increasing eye tracking fidelity as well as enabling new gaze-based applications without the need for new eye tracking equipment.

   doi: 10.1145/3586183.3606780

   Paper: jiao23_uist.pdf

   @inproceedings{jiao23_uist, author = {Jiao, Chuhan and Hu, Zhiming and B{\^a}ce, Mihai and Bulling, Andreas}, title = {SUPREYES: SUPer Resolution for EYES Using Implicit Neural Representation Learning}, booktitle = {Proc. ACM Symposium on User Interface Software and Technology (UIST)}, year = {2023}, pages = {1--13}, doi = {10.1145/3586183.3606780} }

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}, journal = {IEEE Transactions on Visualization and Computer Graphics (TVCG)}, volume = {28}, number = {12}, pages = {4995-5005}, doi = {10.1109/TVCG.2022.3198163} }