Personalized Dynamic Attention Tracking

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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.