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Visual attention is a process that enables biological and machine vision systems to select the most relevant regions from a scene. Relevance is determined by two components: 1) top-down factors driven by task and 2) bottom-up factors that highlight image regions that are different from their surroundings. The latter are often referred to as "visual(More)
Significant recent progress has been made in developing high-quality saliency models. However, less effort has been undertaken on fair assessment of these models, over large standardized datasets and correctly addressing confounding factors. In this study, we pursue a critical and quantitative look at challenges (e.g., center-bias, map smoothing) in(More)
Einhäuser, Spain, and Perona (2008) explored an alternative hypothesis to saliency maps (i.e., spatial image outliers) and claimed that "objects predict fixations better than early saliency." To test their hypothesis, they measured eye movements of human observers while they inspected 93 photographs of common natural scenes (Uncommon Places dataset by(More)
—Several visual attention models have been proposed for describing eye movements over simple stimuli and tasks such as free viewing or visual search. Yet to date, there exists no computational framework that can reliably mimic human gaze behavior in more complex environments and tasks such as urban driving. Additionally, benchmark datasets, scoring(More)
Despite a considerable amount of previous work on bottom-up saliency modeling for predicting human fixations over static and dynamic stimuli, few studies have thus far attempted to model top-down and task-driven influences of visual attention. Here, taking advantage of the sequential nature of real-world tasks, we propose a unified Bayesian approach for(More)
Modeling how visual saliency guides the deployment of attention over visual scenes has attracted much interest recently — among both computer vision and experimental/computational researchers — since visual attention is a key function of both machine and biological vision systems. Research efforts in computer vision have mostly been focused on modeling(More)
Eye tracking has become the de facto standard measure of visual attention in tasks that range from free viewing to complex daily activities. In particular, saliency models are often evaluated by their ability to predict human gaze patterns. However, fixations are not only influenced by bottom-up saliency (computed by the models), but also by many top-down(More)
One challenge when tracking objects is to adapt the object representation depending on the scene context to account for changes in illumination, coloring, scaling, etc. Here, we present a solution that is based on our earlier approach for object tracking using particle filters and component-based descriptors. We extend the approach to deal with changing(More)
We introduce a new task-independent framework to model top-down overt visual attention based on graph-ical models for probabilistic inference and reasoning. We describe a Dynamic Bayesian Network (DBN) that infers probability distributions over attended objects and spatial locations directly from observed data. Probabilistic inference in our model is(More)
— A large number of studies have been reported on top-down influences of visual attention. However, less progress have been made in understanding and modeling its mechanisms in real-world tasks. In this paper, we propose an approach for learning spatial attention taking into account influences of physical actions on top-down attention. For this purpose, we(More)