Abstract Recently, Meta AI Research approaches a general, promptable segment anything
model (SAM) pre-trained on an unprecedentedly large segmentation dataset (SA-1B) …

The recently proposed camouflaged object detection (COD) attempts to segment objects that
are visually blended into their surroundings, which is extremely complex and difficult in real …

Robust and reliable semantic segmentation in complex scenes is crucial for many real-life
applications such as autonomous safe driving and nighttime rescue. In most approaches, it …

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 …

Abstract Deep Learning-based Unsupervised Salient Object Detection (USOD) mainly relies
on the noisy saliency pseudo labels that have been generated from traditional handcraft …

Domain-generalized urban-scene semantic segmentation (USSS) aims to learn generalized
semantic predictions across diverse urban-scene styles. Unlike generic domain gap …

Recent years have witnessed growing interests in RGB-D Salient Object Detection (SOD),
benefiting from the ample spatial layout cues embedded in depth maps to help SOD models …

Abstract Image Salient Object Detection (SOD) is a fundamental research topic in the area of
computer vision. Recently, the multimodal information in RGB, Depth (D), and Thermal (T) …

Rails are critical to the safe transportation of railway system, and their surface quality is a
vital aspect to consider. Existing defect detection methods struggle to identify irregular defect …

Current RGB-D salient object detection (RGB-D SOD) methods mainly develop a
generalizable model trained by binary cross-entropy (BCE) loss based on convolutional or …