In this paper, we present a unified statistical framework for modeling both saccadic eye
movements and visual saliency. By analyzing the statistical properties of human eye …

How people look at visual information reveals fundamental information about themselves,
their interests and their state of mind. While previous visual attention models output static 2D …

In this paper, we propose a new framework to predict visual scanpaths of observers while
they freely watch a visual scene. The visual fixations are inferred from bottom-up saliency …

To predict where subjects look under natural viewing conditions, biologically inspired
saliency models decompose visual input into a set of feature maps across spatial scales …

Visual attention is a field with a considerable history, with eye movement control and
prediction forming an important subfield. Fixation modeling in the past decades has been …

Eye movements are principally driven by two factors: bottom up salience and top-down
perceived interest that is based on our semantic interpretation of the scene. The two effects …

Saliency maps produced by different algorithms are often evaluated by comparing output to
fixated image locations appearing in human eye tracking data. There are challenges in …

Salient stimuli, such as color or motion contrasts, attract human attention, thus providing a
fast heuristic for focusing limited neural resources on behaviorally relevant sensory inputs …

We here study the predictability of eye movements when viewing high-resolution natural
videos. We use three recently published gaze data sets that contain a wide range of footage …

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 …