Convolutional neural networks (CNNs) are good at extracting contexture features within
certain receptive fields, while transformers can model the global long-range dependency …

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 …

RGB-D saliency detection has attracted increasing attention, due to its effectiveness and the
fact that depth cues can now be conveniently captured. Existing works often focus on …

Most recent methods for RGB (red–green–blue)-thermal salient object detection (SOD)
involve several floating-point operations and have numerous parameters, resulting in slow …

Salient object detection is the pixel-level dense prediction task which can highlight the
prominent object in the scene. Recently U-Net framework is widely used, and continuous …

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) …

In recent years, various neural network architectures for computer vision have been devised,
such as the visual transformer and multilayer perceptron (MLP). A transformer based on an …

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

The ability to deal with intra and inter-modality features has been critical to the development
of RGB-D salient object detection. While many works have advanced in leaps and bounds in …

RGB-D saliency detection aims to fuse multi-modal cues to accurately localize salient
regions. Existing works often adopt attention modules for feature modeling, with few …