74 lines
2.2 KiB
Markdown
74 lines
2.2 KiB
Markdown
# VisRecall: Quantifying Information Visualisation Recallability via Question Answering
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[](https://doi.org/10.18419/darus-2826)
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*Yao Wang, Chuhan Jiao(Aalto University), Mihai Bâce and Andreas Bulling*
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IEEE Transactions on Visualization and Computer Graphics (TVCG)
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This repository contains the dataset and models for predicting visualisation recallability.
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```
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$Root Directory
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│
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│─ README.md —— this file
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│
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|─ RecallNet —— Source code of the network to predict infovis recallability
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│ │
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│ │─ environment.yaml —— conda environments
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│ │
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│ │─ notebooks
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│ │ │
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│ │ │─ train_RecallNet.ipynb —— main notebook for training and validation
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│ │ │
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│ │ └─ massvis_recall.json —— saved recallability scores for MASSVIS dataset
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│ │
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│ └─ src
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│ │
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│ │─ singleduration_models.py —— RecallNet model
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│ │
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│ │─ sal_imp_utilities.py —— image processing utilities
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│ │
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│ │─ losses_keras2.py —— loss functions
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│ │
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│ ...
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│
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│
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│─ WebInterface —— The Web interface for experiment, see WebInterface/README.md
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│
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│
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└─ VisRecall —— The dataset
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│
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│─ answer_raw —— raw answers from AMT workers
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│
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│─ merged
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│ │
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│ │─ src —— original images
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│ │
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│ │─ qa —— question annotations
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│ │
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│ └─ image_annotation —— other metadata annotations
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│
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└─ training_data
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│
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│─ all —— all averaged questions
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│
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└─ X-question —— a specific type of question (T-, FE-, F-, RV-, U-)
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```
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If you think this repository is useful to you, please consider citing our work as:
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```
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@article{wang22_tvcg,
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title = {VisRecall: Quantifying Information Visualisation Recallability via Question Answering},
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author = {Wang, Yao and Jiao, Chuhan and Bâce, Mihai and Bulling, Andreas},
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year = {2022},
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pages = {4995-5005},
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journal = {IEEE Transactions on Visualization and Computer Graphics (TVCG)},
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volume = {28},
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number = {12},
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doi = {10.1109/TVCG.2022.3198163}
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}
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```
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contact: yao.wang@vis.uni-stuttgart.de
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