Detecting and segmenting salient objects from natural scenes, often referred to as salient
object detection, has attracted great interest in computer vision. While many models have …

We extensively compare, qualitatively and quantitatively, 41 state-of-the-art models (29
salient object detection, 10 fixation prediction, 1 objectness, and 1 baseline) over seven …

Modeling visual attention-particularly stimulus-driven, saliency-based attention-has been a
very active research area over the past 25 years. Many different models of attention are now …

Salient object detection models mimic the behavior of human beings and capture the most
salient region/object from the images or scenes, this field contains many important …

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 …

Computational models of visual attention have become popular over the past decade, we
believe primarily for two reasons: First, models make testable predictions that can be …

Saliency detection has been widely studied to predict human fixations, with various
applications in computer vision and image processing. For saliency detection, we argue in …

A large number of saliency models, each based on a different hypothesis, have been
proposed over the past 20 years. In practice, while subscribing to one hypothesis or …

Scores of visual attention models have been developed over the past several decades of
research. Differences in implementation, assumptions, and evaluations have made …

We propose a biologically-motivated computational model for learning task-driven and
object-based visual attention control in interactive environments. In this model, top-down …