VisRecall: Quantifying Information Visualisation Recallability via Question Answering

Yao Wang, Chuhan Jiao(Aalto University), Mihai Bâce and Andreas Bulling

IEEE Transactions on Visualization and Computer Graphics (TVCG)

This repository contains the dataset and models for predicting visualisation recallability.

$Root Directory
│
│─ README.md —— this file
│
|─ RecallNet —— Source code of the network to predict infovis recallability 
│  │
│  │─ environment.yaml —— conda environments
│  │
│  │─ notebooks 
│  │  │
│  │  │─ train_RecallNet.ipynb —— main notebook for training and validation
│  │  │
│  │  └─ massvis_recall.json —— saved recallability scores for MASSVIS dataset
│  │
│  └─ src
│     │
│     │─ singleduration_models.py —— RecallNet model
│     │
│     │─ sal_imp_utilities.py —— image processing utilities
│     │
│     │─ losses_keras2.py —— loss functions
│     │
│    ...
│
│
│─ WebInterface —— The Web interface for experiment, see WebInterface/README.md
│
│   
└─ VisRecall —— The dataset
   │
   │─ answer_raw —— raw answers from AMT workers
   │  
   │─ merged
   │  │
   │  │─ src —— original images
   │  │
   │  │─ qa —— question annotations
   │  │
   │  └─ image_annotation —— other metadata annotations
   │     
   └─ training_data
      │
      │─ all —— all averaged questions
      │
      └─ X-question —— a specific type of question (T-, FE-, F-, RV-, U-)

If you think this repository is useful to you, please consider citing our work as:

@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}
}

contact: yao.wang@vis.uni-stuttgart.de