Salient object detection, which simulates human visual perception in locating the most
significant object (s) in a scene, has been widely applied to various computer vision tasks …

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 …

Complex backgrounds and similar appearances between objects and their surroundings are
generally recognized as challenging scenarios in Salient Object Detection (SOD). This …

Multi-level feature fusion is a fundamental topic in computer vision for detecting, segmenting
and classifying objects at various scales. When multi-level features meet multi-modal cues …

Existing RGB-D Salient Object Detection (SOD) methods take advantage of depth cues to
improve the detection accuracy, while pay insufficient attention to the quality of depth …

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 …

RGB-D salient object detection (SOD) is usually formulated as a problem of classification or
regression over two modalities, ie, RGB and depth. Hence, effective RGB-D feature …

This paper proposes a novel joint learning and densely-cooperative fusion (JL-DCF)
architecture for RGB-D salient object detection. Existing models usually treat RGB and depth …

Existing RGB-D salient object detection (SOD) models usually treat RGB and depth as
independent information and design separate networks for feature extraction from each …

We explore the potential of pooling techniques on the task of salient object detection by
expanding its role in convolutional neural networks. In general, two pooling-based modules …