论文标题
使用分析和深度学习方法在现实网页上对人类的视觉搜索性能进行建模
Modeling Human Visual Search Performance on Realistic Webpages Using Analytical and Deep Learning Methods
论文作者
论文摘要
建模视觉搜索不仅提供了一个机会，可以在实际对真实用户进行测试之前预测界面的可用性，而且还提高了对人类行为的科学理解。在这项工作中，我们首先在现实网页上的大型视觉搜索任务数据集上进行了一系列分析。然后，我们提出了一个深层的神经网络，该网络学会了预测网页内容的扫描性，即用户找到特定目标的容易性。我们的模型利用了基于启发式的功能，例如目标大小和非结构化特征，例如原始图像像素。这种方法使我们能够建模可能与现实的视觉搜索任务有关的复杂交互作用，而传统分析模型无法轻易实现。我们分析了模型行为，以提供有关模型的显着性图与人类直觉相一致的洞察力以及每个目标类型的学习语义表示与其视觉搜索性能如何相关的。
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.
