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 …

Recent progress on saliency detection is substantial, benefiting mostly from the explosive
development of Convolutional Neural Networks (CNNs). Semantic segmentation and …

We provide a comprehensive evaluation of salient object detection (SOD) models. Our
analysis identifies a serious design bias of existing SOD datasets which assumes that each …

Training a generic objectness measure to produce a small set of candidate object windows,
has been shown to speed up the classical sliding window object detection paradigm. We …

Visual saliency analysis detects salient regions/objects that attract human attention in
natural scenes. It has attracted intensive research in different fields such as computer vision …

Automatic estimation of salient object regions across images, without any prior assumption
or knowledge of the contents of the corresponding scenes, enhances many computer vision …

Recent advent of graph signal processing (GSP) has spurred intensive studies of signals
that live naturally on irregular data kernels described by graphs (eg, social networks …

Detecting visually salient regions in images is one of the fundamental problems in computer
vision. We propose a novel method to decompose an image into large scale perceptually …

CNN-based salient object detection (SOD) methods achieve impressive performance.
However, the way semantic information is encoded in them and whether they are category …

The success of current deep saliency detection methods heavily depends on the availability
of large-scale supervision in the form of per-pixel labeling. Such supervision, while labor …