Attention is the important ability to flexibly control limited computational resources. It has
been studied in conjunction with many other topics in neuroscience and psychology …

Abstract Models of gaze allocation in complex scenes are derived mainly from studies of
static picture viewing. The dominant framework to emerge has been image salience, where …

The use of RGB-D information for salient object detection (SOD) has been extensively
explored in recent years. However, relatively few efforts have been put toward modeling …

Salient object detection (SOD) in complex scenes and environments is a challenging
research topic. Most works focus on RGB-based SOD, which limits its performance of real …

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 …

How best to evaluate a saliency model's ability to predict where humans look in images is an
open research question. The choice of evaluation metric depends on how saliency is …

In this paper we provide an extensive evaluation of fixation prediction and salient object
segmentation algorithms as well as statistics of major datasets. Our analysis identifies …

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 …

Decreases in alpha synchronization are correlated with enhanced attention, whereas alpha
increases are correlated with inattention. However, correlation is not causality, and …

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 …