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 …

Co-saliency detection is a newly emerging and rapidly growing research area in the
computer vision community. As a novel branch of visual saliency, co-saliency detection …

The use of RGB-D information for salient object detection (SOD) has been extensively
explored in recent years. However, relatively few efforts have been put toward modeling …

The existing binary foreground map (FM) measures to address various types of errors in
either pixel-wise or structural ways. These measures consider pixel-level match or image …

Benefit from the quick development of deep learning techniques, salient object detection has
achieved remarkable progresses recently. However, there still exists following two major …

Foreground map evaluation is crucial for gauging the progress of object segmentation
algorithms, in particular in the filed of salient object detection where the purpose is to …

Abstract Deep Neural Networks (DNNs) have substantially improved the state-of-the-art in
salient object detection. However, training DNNs requires costly pixel-level annotations. In …

Recognizing multiple labels of an image is a practical yet challenging task, and remarkable
progress has been achieved by searching for semantic regions and exploiting label …

The visual saliency detection model simulates the human visual system to perceive the
scene and has been widely used in many vision tasks. With the development of acquisition …

Salient object detection has recently witnessed substantial progress due to powerful
features extracted using deep convolutional neural networks (CNNs). However, existing …