Personalized Dynamic Attention Tracking

该研究计划旨在通过开发、实施和验证视觉注意力的计算模型来进一步提高视觉注意力跟踪系统的能力，这些模型可以估计或预测视觉注意力随时间的分配（即人们在看哪里）。注意力跟踪有许多有用的应用，例如开发智能交互式显示，确定零售环境中购物者的偏好和偏见，或者为标志或网页创建有效的视觉布局。研究的主要重点将放在注意力模型的“个性化”上。视觉注意力的最佳计算模型是基于深度神经网络的。这些神经网络是在从大量观众那里获得的大量眼球运动数据库上进行训练的。大量的训练样本意味着可以在不过度拟合的情况下训练高容量网络，从而对新的、从未见过的图像的注意力进行良好的预测。然而，注意力模型的许多重要应用领域，如智能显示设备，都集中在个人用户身上。例如，手机通常由一个人使用，汽车由少数人驾驶。在这些应用中，一个注意力模型虽然能很好地适应大量人群的平均行为，但在预测个体的注意力分配时可能效果不佳。因此，我们的研究将侧重于开发技术，使大量人口的“普通人”注意力模型适应于在人口子集（例如男性/女性或老年人/年轻人或个人）上表现良好的模型。我们还将研究可能在个体群体中常见的注意偏差的检测。为了实现个性化注意力模型的目标，我们的PI研究团队，两名博士生，一名硕士研究生和本科生暑期实习生将采用最新的机器学习技术，如领域适应，利用稀疏的个性化数据来修改大型人口模型，使其适用于更小的个性化群体。个性化的注意力追踪技术可以让汽车仪表板或手机上的显示器受益。在你的车里，仪表盘会识别你，并调整仪表盘显示，提供重要的信息，以有效地吸引你的注意力，同时以不那么分散注意力的方式显示其他信息。另一个影响领域将是增强人们在日常生活中越来越多地使用的无处不在的技术。例如，在你最喜欢的零售店，根据你的注意力集中模式，商店将能够预测你的注意力偏差，同时保持你的隐私，允许它根据这些偏差提供服务（提供有用的信息显示，提供折扣和忠诚奖励）。个性化的注意力跟踪有望使人与机器之间的联系更加自然。

The proposed research program aims to further the capabilities of visual attention tracking systems by developing, implementing and validating computational models of visual attention that estimate or predict the allocation of visual attention over time (i.e. where people are looking). Attention tracking has many useful applications, such as in developing intelligent interactive displays, determining shoppers' preferences and biases in a retail setting, or creating effective visual layouts for signs or webpages. The primary focus of the research will be on the "personalization" of attention models. The best computational models of visual attention are based on deep neural networks. These neural networks are trained on extensive databases of eye movements obtained from large numbers of viewers. The large number of training examples means that high capacity networks can be trained without over-fitting, leading to good prediction of attention on new, never-seen-before, images. However, many of the important application areas of attention models, such as intelligent display devices, are focused on individual users. For example, cellphones are typically used by a single person and cars are driven by a small number of people. In these applications, an attention model that is tuned well to the average behaviour of large populations, may not work well in predicting attention allocations of individuals. Therefore, our research will focus on developing techniques that can adapt the large population "average person" attention models to ones that perform well on subsets of the population (e.g. male/female or old/young or individual persons). We will also investigate detection of attentional biases that may be common to groups of individuals. To achieve our objective of personalized attention models, our research team of the PI, two PhD students, a Masters student, and undergraduate summer interns will employ recent machine learning techniques such as domain adaptation to make use of sparse individualized data to modify large population models to work well for smaller, personalized, groups. Having personalized attention tracking can benefit displays such as those found in automobile dashboards or cellphones. In your car, the dashboard will recognize you and adjust the dashboard display to provide important information to effectively capture your attention, while presenting other information in less distracting fashion. Another area of impact will be in enhancing ubiquitous technology that people interact with more and more in every day life. For example, in your favorite retail outlet, based on your patterns of attentional focus, the store will be able to predict your attentional biases while maintaining your privacy, allowing it to provide services (presenting helpful information displays, offering discounts and loyalty rewards) based on these biases. Personalized attention tracking promises to make the connection between human and machine more natural.