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# VisRecall: Quantifying Information Visualisation Recallability via Question Answering
*Yao Wang, Chuhan Jiao(Aalto University), Mihai Bâce and Andreas Bulling*
submitted to The IEEE Transactions on Visualization and Computer Graphics (TVCG2022)
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
│ │
│ ...
│
└─ 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-)
```
contact: yao.wang@vis.uni-stuttgart.de