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 …

RGB-D salient object detection (SOD) has been in the spotlight recently because it is an
important preprocessing operation for various vision tasks. However, despite advances in …

Salient object detection for RGB-D images aims to utilize color and depth information to
automatically localize objects of human interest in the scene and reduce the complexity of …

Current RGB-D salient object detection (RGB-D SOD) methods mainly develop a
generalizable model trained by binary cross-entropy (BCE) loss based on convolutional or …

In this paper, a novel joint loss Generative Adversarial Networks (GAN) framework is
proposed for thermography nondestructive testing named Defect-Detection Network …

The suitable employment of RGB and depth data shows great significance in promoting the
development of computer vision tasks and robot-environment interactions. However, there …

Salient object detection for RGB-D image aims to automatically detect the objects of human
interest by color and depth information. In the paper generative adversarial network is …

Detecting salient objects in RGB-D images attracts more and more attention in recent years.
It benefits from the widespread use of depth sensors and can be applied in the …

The multi-modal salient object detection model based on RGB-D information has better
robustness in the real world. However, it remains nontrivial to better adaptively balance multi …