SalChartQA/README.md

SalChartQA: Question-driven Saliency on Information Visualisations

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