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 …

Depth cues are known to be useful for visual perception. However, direct measurement of
depth is often impracticable. Fortunately, though, modern learning-based methods offer …

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 …

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) …

Salient object detection (SOD) and camouflaged object detection (COD) are related yet
distinct binary mapping tasks. These tasks involve multiple modalities sharing …

Cross-modality salient object detection (SOD) mainly includes RGB-D salient object
detection and RGB-T salient object detection. Depth or thermal infrared information is used …

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

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 …