Abstract
Modeling visual saliency in graphical user interfaces (GUIs) allows to understand how people perceive GUI designs and what elements attract their attention. One aspect that is often overlooked is the fact that computational models depend on a series of design parameters that are not straightforward to decide. We systematically analyze how different design parameters affect scanpath evaluation metrics using a state-of-the-art computational model (DeepGaze++). We particularly focus on three design parameters: input image size, inhibition-of-return decay, and masking radius. We show that even small variations of these design parameters have a noticeable impact on standard evaluation metrics such as DTW or Eyenalysis. These effects also occur in other scanpath models, such as UMSS and ScanGAN, and in other datasets such as MASSVIS. Taken together, our results put forward the impact of design decisions for predicting users' viewing behavior on GUIs.
Abstract (translated)
在图形用户界面（GUIs）中建模视觉显著性可以帮助人们理解如何看待GUI设计以及哪些元素会吸引他们的注意。通常被忽视的一个方面是，计算模型依赖于一系列设计参数，而这些参数并不容易决定。我们使用最先进的计算模型（DeepGaze++）系统地分析不同设计参数如何影响扫描路径评估指标。我们特别关注三个设计参数：输入图像大小、抑制返回衰减和掩码半径。我们发现，即使是这些设计参数的小变化也会对标准评估指标，如DTW或Eyenalysis产生显著影响。这些影响也存在于其他扫描路径模型中，如UMSS和ScanGAN，以及其他数据集中。结合我们的结果，我们提出了设计决策对预测用户在GUIs上的观看行为具有影响的观点。
URL
https://arxiv.org/abs/2405.08981
