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 …

Saliency detection is one of the basic challenges in computer vision. Recently, CNNs are the
most widely used and powerful techniques for saliency detection, in which feature maps …

Recent deep learning based salient object detection methods achieve gratifying
performance built upon Fully Convolutional Neural Networks (FCNs). However, most of them …

The large availability of depth sensors provides valuable complementary information for
salient object detection (SOD) in RGBD images. However, due to the inherent difference …

Fully convolutional neural networks (FCNs) have shown outstanding performance in many
dense labeling problems. One key pillar of these successes is mining relevant information …

Recent progress on salient object detection is beneficial from Fully Convolutional Neural
Network (FCN). The saliency cues contained in multi-level convolutional features are …

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

Biomedical image segmentation is one critical component in computer-aided system
diagnosis. However, various non-automatic segmentation methods are usually designed to …

Though deep learning techniques have made great progress in salient object detection
recently, the predicted saliency maps still suffer from incomplete predictions due to the …

Despite the remarkable progress made by the salient object detection of natural sensing
images (NSI-SOD), the complex background and scale diversity issues of remote sensing …