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

Crowd counting is a fundamental and challenging task that requires rich information to
generate a pixel-level crowd density map. Additionally, the development of thermal sensing …

Salient object detection (SOD) is an important preprocessing operation for various computer
vision tasks. Most of existing RGB-D SOD models employ additive or connected strategies to …

The semantic segmentation of road scenes is an important task in autonomous driving.
Deep learning has enabled the development of a variety of semantic segmentation networks …

The ability of capturing the complementary information of multi-modality data is critical to the
development of multi-modality salient object detection (SOD). Most of existing studies …

Compared with the traditional object segmentation/detection, camouflaged object detection
is much more difficult due to the indefinable boundaries and high intrinsic similarities …

RGB-D salient object detection (SOD) enjoys significant advantages in understanding 3D
geometry of the scene. However, the geometry information conveyed by depth maps are …

The 3-D object detection with light detection and ranging (LiDAR) point clouds is a
challenging problem, which requires 3-D scene understanding, yet this task is critical to …

Salient object detection (SOD) aims to identify standout elements in a scene, with recent
advancements primarily focused on integrating depth data (RGB-D) or temporal data from …

Scene parsing is a fundamental task in computer vision. Various RGB-D (color and depth)
scene parsing methods based on fully convolutional networks have achieved excellent …