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 …

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 …

A thought-provoking argument that consciousness—more widespread than previously
assumed—is the feeling of being alive, not a type of computation or a clever hack In The …

Scalable and effective exploration remains a key challenge in reinforcement learning (RL).
While there are methods with optimality guarantees in the setting of discrete state and action …

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 existing superpixel-wise saliency detection algorithms, superpixel generation often is an
isolated preprocessing step. The performance of saliency maps is determined by the …

Saliency estimation has become a valuable tool in image processing. Yet, existing
approaches exhibit considerable variation in methodology, and it is often difficult to attribute …

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 …

A novel Boolean Map based Saliency (BMS) model is proposed. An image is characterized
by a set of binary images, which are generated by randomly thresholding the image's color …

The mutual information is a core statistical quantity that has applications in all areas of
machine learning, whether this is in training of density models over multiple data modalities …