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 …

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

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 …

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 High-Resolution Transformer (HRFormer) can maintain high-resolution representation
and share global receptive fields. It is friendly towards salient object detection (SOD) in …

RGB-D salient object detection (SOD) recently has attracted increasing research interest by
benefiting conventional RGB SOD with extra depth information. However, existing RGB-D …

Spatiotemporal data mining (STDM) discovers useful patterns from the dynamic interplay
between space and time. Several available surveys capture STDM advances and report a …

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 …

Deep neural network based methods have made a significant breakthrough in salient object
detection. However, they are typically limited to input images with low resolutions (400x400 …

Recently, change detection (CD) of remote sensing images have achieved great progress
with the advances of deep learning. However, current methods generally deliver incomplete …