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 …

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 …

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 …

How to effectively exchange and aggregate the information of multiple modalities (eg RGB
image and depth map) is a big challenge in the RGB-D salient object detection community …

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 …

Urban scene parsing is the core of the intelligent transportation system, and RGB–thermal
urban scene parsing has recently attracted increasing research interest in the field of …

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 …

The existing fusion-based RGB-D salient object detection methods usually adopt the
bistream structure to strike a balance in the fusion trade-off between RGB and depth (D) …