As we navigate and behave in the world, we are constantly deciding, a few times per
second, where to look next. The outcomes of these decisions in response to visual input are …

Saliency in Context (SALICON) is an ongoing effort that aims at understanding and
predicting visual attention. Conventional saliency models typically rely on low-level image …

The prediction of salient areas in images has been traditionally addressed with hand-crafted
features based on neuroscience principles. This paper, however, addresses the problem …

Understanding where people look in images is an important problem in computer vision.
Despite significant research, it remains unclear to what extent human fixations can be …

This paper presents a novel deep architecture for saliency prediction. Current state of the art
models for saliency prediction employ Fully Convolutional networks that perform a non …

Eye-tracking-the process of measuring where people look in a visual field-has been widely
used to study how humans process visual information. In medical imaging, eye-tracking has …

Recent results suggest that state-of-the-art saliency models perform far from optimal in
predicting fixations. This lack in performance has been attributed to an inability to model the …

Background For two decades, EEG-based Brain-Computer Interface (BCI) systems have
been widely studied in research labs. Now, researchers want to consider out-of-the-lab …

Visual saliency has been an increasingly active research area in the last ten years with
dozens of saliency models recently published. Nowadays, one of the big challenges in the …

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 …