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 …

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

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 …

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

Abstract Existing CNNs-Based RGB-D salient object detection (SOD) networks are all
required to be pretrained on the ImageNet to learn the hierarchy features which helps …

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 …

Aerial scene classification remains challenging as: 1) the size of key objects in determining
the scene scheme varies greatly and 2) many objects irrelevant to the scene scheme are …

In this paper, we present a weakly-supervised RGB-D salient object detection model via
scribble supervision. Specifically, as a multimodal learning task, we focus on effective …