使用分析和深度学习方法在真实网页上建模人类视觉搜索性能
Modeling Human Visual Search Performance on Realistic Webpages Using Analytical and Deep Learning Methods
摘要：
可视化搜索建模不仅提供了一个在真实用户上实际测试之前预测界面可用性的机会，而且还促进了对人类行为的科学理解。在这项工作中，我们首先进行了一组分析的大规模数据集的视觉搜索任务的现实网页。然后，我们提出了一个深度神经网络，它学习预测网页内容的可扫描性，即，用户找到特定目标有多容易我们的模型利用了基于几何的特征（如目标大小）和非结构化特征（如原始图像像素）。这种方法使我们能够模拟复杂的相互作用，可能涉及到一个现实的视觉搜索任务，这是不容易实现的传统的分析模型。我们分析模型行为，以提供我们对模型学习的显着性图如何与人类直觉保持一致以及学习的每种目标类型的语义表示如何与其视觉搜索性能相关的见解。
英文摘要：
Modeling visual search not only offers an opportunity to predict the usability of an interface before actually testing it on real users, but also advances scientific understanding about human behavior. In this work, we first conduct a set of analyses on a large-scale dataset of visual search tasks on realistic webpages. We then present a deep neural network that learns to predict the scannability of webpage content, i.e., how easy it is for a user to find a specific target. Our model leverages both heuristic-based features such as target size and unstructured features such as raw image pixels. This approach allows us to model complex interactions that might be involved in a realistic visual search task, which can not be easily achieved by traditional analytical models. We analyze the model behavior to offer our insights into how the salience map learned by the model aligns with human intuition and how the learned semantic representation of each target type relates to its visual search performance.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
