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 …

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 …

Despite the recent success in face recognition and object classification, in the field of human
gaze prediction, computer models are still struggling to accurately mimic human attention …

Traditional saliency detection can effectively detect possible objects using an attentional
mechanism instead of automatic object detection, and thus is widely used in natural scene …

Visual saliency computation is about detecting and understanding pertinent regions and
elements in a visual scene. Given limited computational resources, the human visual system …

Recent advances in saliency detection have used deep learning to obtain high-level
features to detect salient regions. These advances have demonstrated superior results over …

Studies in neuroscience are suggesting that human visual attention is enhanced through a
process of competing interactions among neurons representing all of the stimuli present in …

Most of the saliency methods are evaluated on their ability to generate saliency maps, and
not on their functionality in a complete vision pipeline, like for instance, image classification …

Most of the saliency methods are evaluated for their ability to generate saliency maps, and
not for their functionality in a complete vision pipeline, for instance, image classification or …

Human attention is more likely to be interested in different objects or striking in image
processing called salient object. Existing approaches worked well in finding the salient …