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) …

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 …

Image modality is not perfect as it often fails in certain conditions, eg, night and fast motion.
This significantly limits the robustness and versatility of existing multi-modal (ie, Image+ X) …

Fusing an arbitrary number of modalities is vital for achieving robust multi-modal fusion of
semantic segmentation yet remains less explored to date. Recent endeavors regard RGB …

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

Most existing RGB-D salient object detection (SOD) methods rely on high-quality depth
images. However, their performance is limited when processing low-quality depth maps …

At present, adding Transformer to CNN has promoted the rapid development of colorectal
polyp image processing. However, from the perspective of multi-scale feature interaction …

Most existing RGB-D salient object detection (SOD) methods tend to achieve higher
performance by integrating additional modules, such as feature enhancement and edge …

Existing salient object detection methods are capable of predicting binary maps that
highlight visually salient regions. However, these methods are limited in their ability to …

In many binary segmentation tasks, most CNNs-based methods use a U-shape encoder-
decoder network as their basic structure. They ignore two key problems when the encoder …