Data for: "Scanpath Prediction on Information Visualizations"

We propose Unified Model of Saliency and Scanpaths (UMSS) - a model that learns to predict multi-duration saliency and scanpaths (i.e. sequences of eye fixations) on information visualisations. Although scanpaths provide rich information about the importance of different visualisation elements during the visual exploration process, prior work has been limited to predicting aggregated attention statistics, such as visual saliency. We present in-depth analyses of gaze behaviour for different information visualisation elements (e.g. Title, Label, Data) on the popular MASSVIS dataset. We show that while, overall, gaze patterns are surprisingly consistent across visualisations and viewers, there are also structural differences in gaze dynamics for different elements. Informed by our analyses, UMSS first predicts multi-duration element-level saliency maps, then probabilistically samples scanpaths from them. Extensive experiments on MASSVIS show that our method consistently outperforms state-of-the-art methods with respect to several, widely used scanpath and saliency evaluation metrics. Our method achieves a relative improvement in sequence score of 11.5 % for scanpath prediction, and a relative improvement in Pearson correlation coefficient of up to 23.6 % for saliency prediction. These results are auspicious and point towards richer user models and simulations of visual attention on visualisations without the need for any eye tracking equipment. This dataset contains saliency maps and scanpaths for UMSS and baseline methods. The structure of the dataset is described in the README-File.

我们提出了一种统一的显著性与扫描路径模型（UMSS），该模型旨在预测信息可视化中的多持续时间显著性和扫描路径（即眼动注视序列）。尽管扫描路径能够提供关于视觉探索过程中不同可视化元素重要性的丰富信息，但先前的研究工作却局限于预测汇总的注意力统计数据，例如视觉显著度。我们在流行的MASSVIS数据集上对不同信息可视化元素（如标题、标签、数据）的注视行为进行了深入分析。我们发现，尽管总体而言，注视模式在不同可视化作品和观众之间表现出惊人的一致性，但不同元素的眼动动态结构也存在差异。基于我们的分析，UMSS首先预测元素级别的多持续时间显著度图，然后从这些图中概率性地采样扫描路径。在MASSVIS数据集上的大量实验表明，我们的方法在多个广泛使用的扫描路径和显著度评估指标上，均优于现有最佳方法。我们的方法在扫描路径预测的序列得分上实现了11.5%的相对提升，在显著度预测的皮尔逊相关系数上实现了高达23.6%的相对提升。这些结果颇具预兆，指向了更丰富的用户模型和可视化作品上视觉注意力的模拟，而无需任何眼动追踪设备。本数据集包含了UMSS和基线方法的显著度图和扫描路径。数据集的结构在README文件中进行了描述。