Focusing on the issue of how to effectively capture and utilize cross-modality information in
RGB-D salient object detection (SOD) task, we present a convolutional neural network …

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 …

In this article, we deal with the problem of object detection on remote sensing images.
Previous researchers have developed numerous deep convolutional neural network (CNN) …

Current RGB-D salient object detection (SOD) methods utilize the depth stream as
complementary information to the RGB stream. However, the depth maps are usually of low …

Recently, relying on convolutional neural networks (CNNs), many methods for salient object
detection in optical remote-sensing images (ORSI-SOD) are proposed. However, most …

Salient object detection (SOD) in optical remote sensing images (RSIs), or RSI-SOD, is an
emerging topic in understanding optical RSIs. However, due to the difference between …

RGB-T salient object detection (SOD) aims at utilizing the complementary cues of RGB and
Thermal (T) modalities to detect and segment the common objects. However, on one hand …

RGB-D Salient Object Detection (RGB-D SOD) aims at detecting remarkable objects by
complementary information from RGB images and depth cues. Although many outstanding …

Existing methods for Salient Object Detection in Optical Remote Sensing Images (ORSI-
SOD) mainly adopt Convolutional Neural Networks (CNNs) as the backbone, such as VGG …

In the computer vision community, great progresses have been achieved in salient object
detection from natural scene images (NSI-SOD); by contrast, salient object detection in …