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