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 …

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 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 …

By integrating complementary information from RGB image and depth map, the ability of
salient object detection (SOD) for complex and challenging scenes can be improved. In …

We present DFormer, a novel RGB-D pretraining framework to learn transferable
representations for RGB-D segmentation tasks. DFormer has two new key innovations: 1) …

Mirror segmentation, an emerging discipline in the field of computer vision, involves the
identification and marking of mirrors in an image. Current mirror segmentation methods rely …

Deep learning techniques have largely solved the problem of rail surface defect detection
(SDD), however, two aspects have yet to be addressed. In most existing approaches, two …

This article discusses the limitations of single-and two-modal salient object detection (SOD)
methods and the emergence of multi-modal SOD techniques that integrate Visible, Depth, or …

RGB-D salient object detection (SOD) has gained tremendous attention in recent years. In
particular, transformer has been employed and shown great potential. However, existing …

Salient object detection methods based on two-modal images have achieved remarkable
success with the aid of image acquisition equipment. However, environmental factors often …