SalChartQA/README.md

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# SalChartQA: Question-driven Saliency on Information Visualisations
[![Identifier](https://img.shields.io/badge/doi-10.18419%2Fdarus--3884-d45815.svg)](https://doi.org/10.18419/darus-3884)
*Yao Wang, Weitian Wang, Abdullah Abdelhafez, Mayar Elfares, Zhiming Hu, Mihai Bâce, and Andreas Bulling*
Proceedings of the ACM SIGCHI Conference on Human Factors in Computing Systems (CHI 2024)
```
$Root Directory
│─ README.md —— this file
|─ Code —— Source code of the VisSalFormer model to predict question-driven saliency
│ │
│ |─ environment.yml —— conda environment
│ │
│ |─ env.py —— python envorinment $TORCH_HOME and $TRANSFORMERS_CACHE
│ │
│ │─ dataset_new.py —— dataloader for SalChartQA
│ │
│ │─ evaluation.py —— evaluation script to load VisSalFormer weights and make predictions
│ │
│ │─ evaluation.sh —— bash script to run evaluation.py
│ │
│ │─ model_swin.py —— definition of the VisSalFormer model
│ │
│ │─ tokenizer_bert.py —— tokenizer of Bert
│ │
│ └─ VisSalFormer_weights.tar —— weights of VisSalFormer, download at [darus-3884](https://doi.org/10.18419/darus-3884)
└─ SalChartQA.zip —— The SalChartQA dataset, download at [darus-3884](https://doi.org/10.18419/darus-3884)
│─ fixationByVis —— BubbleView data (mouse clicks) of AMT workers
│─ image_questions.json —— visualisation-question pairs
│─ raw_img —— original visualisations from the ChartQA dataset
│─ saliency_all —— saliency maps from all AMT workers
│─ saliency_ans —— saliency maps aggretated by all AMT workers who either answered a question correctly or wrongly
└─ unified_approved.csv —— responses from AMT workers
```
If you think our work is useful to you, please consider citing our paper as:
```
@inproceedings{wang24_chi,
title = {SalChartQA: Question-driven Saliency on Information Visualisations},
author = {Wang, Yao and Wang, Weitian and Abdelhafez, Abdullah and Elfares, Mayar and Hu, Zhiming and B{\^a}ce, Mihai and Bulling, Andreas},
year = {2024},
pages = {1--14},
booktitle = {Proc. ACM SIGCHI Conference on Human Factors in Computing Systems (CHI)},
doi = {10.1145/3613904.3642942}
}
```
contact: yao.wang@vis.uni-stuttgart.de