%0 Generic %1 wang2024salchartqa %A Wang, Yao %D 2024 %K darus mult ubs_10005 ubs_10018 ubs_20008 ubs_20024 ubs_30086 ubs_30200 ubs_40336 unibibliografie %R 10.18419/darus-3884 %T SalChartQA: Question-driven Saliency on Information Visualisations (Dataset and Reproduction Data) %X Understanding the link between visual attention and user’s needs when visually exploring information visualisations is under-explored due to a lack of large and diverse datasets to facilitate these analyses. To fill this gap, we introduce SalChartQA - a novel crowd-sourced dataset that uses the BubbleView interface as a proxy for human gaze and a question-answering (QA) paradigm to induce different information needs in users. SalChartQA contains 74,340 answers to 6,000 questions on 3,000 visualisations. Informed by our analyses demonstrating the tight correlation between the question and visual saliency, we propose the first computational method to predict question-driven saliency on information visualisations. Our method outperforms state-of-the-art saliency models, improving several metrics, such as the correlation coefficient and the Kullback-Leibler divergence. These results show the importance of information needs for shaping attention behaviour and paving the way for new applications, such as task-driven optimisation of visualisations or explainable AI in chart question-answering. The files of this dataset are documented in README.md.

@misc{wang2024salchartqa, abstract = {Understanding the link between visual attention and user’s needs when visually exploring information visualisations is under-explored due to a lack of large and diverse datasets to facilitate these analyses. To fill this gap, we introduce SalChartQA - a novel crowd-sourced dataset that uses the BubbleView interface as a proxy for human gaze and a question-answering (QA) paradigm to induce different information needs in users. SalChartQA contains 74,340 answers to 6,000 questions on 3,000 visualisations. Informed by our analyses demonstrating the tight correlation between the question and visual saliency, we propose the first computational method to predict question-driven saliency on information visualisations. Our method outperforms state-of-the-art saliency models, improving several metrics, such as the correlation coefficient and the Kullback-Leibler divergence. These results show the importance of information needs for shaping attention behaviour and paving the way for new applications, such as task-driven optimisation of visualisations or explainable AI in chart question-answering. The files of this dataset are documented in README.md. }, added-at = {2024-01-29T09:24:24.000+0100}, affiliation = {Wang, Yao/Universität Stuttgart}, author = {Wang, Yao}, biburl = {https://puma.ub.uni-stuttgart.de/bibtex/2ae8c71d9c85dbd491fd21810b53d68b6/unibiblio}, doi = {10.18419/darus-3884}, howpublished = {Software}, interhash = {2d25eac5f85f0cc2308087c1cef14966}, intrahash = {ae8c71d9c85dbd491fd21810b53d68b6}, keywords = {darus mult ubs_10005 ubs_10018 ubs_20008 ubs_20024 ubs_30086 ubs_30200 ubs_40336 unibibliografie}, note = {Related to: Y. Wang, W. Wang, A. Abdelhafez, M. Elfares, Z. Hu, M. Bâce, A. Bulling. "SalChartQA: Question-driven Saliency on Information Visualisations", in Proceedings of the 2024 CHI Conference on Human Factors in Computing Systems. doi: 10.1145/3613904.3642942}, orcid-numbers = {Wang, Yao/0000-0002-3633-8623}, timestamp = {2024-01-29T09:24:24.000+0100}, title = {SalChartQA: Question-driven Saliency on Information Visualisations (Dataset and Reproduction Data)}, year = 2024 }