Road networks play a fundamental role in our daily life. It is of importance to extract the road
structure in a timely and precise manner with the rapid evolution of urban road structure …

In this paper, we identify and address a serious design bias of existing salient object
detection (SOD) datasets, which unrealistically assume that each image should contain at …

The use of deep learning for water extraction requires precise pixel-level labels. However, it
is very difficult to label high-resolution remote-sensing images at the pixel level. Therefore …

RGB-D salient object detection (SOD) has attracted increasingly more attention as it shows
more robust results in complex scenes compared with RGB SOD. However, state-of-the-art …

Compared with current RGB or RGB-D saliency detection datasets, those for light field
saliency detection often suffer from many defects, eg, insufficient data amount and diversity …

Accurate multi-organ segmentation in 3D CT images is imperative for enhancing computer-
aided diagnosis and radiotherapy planning. However, current deep learning-based methods …

We explore the impact of transformers on accurate and reliable salient object detection. For
accuracy, we integrate the transformer with a deterministic model and delineate its …

The presence of Deformable Linear Objects (DLOs) such as wires, cables or ropes in our
everyday life is massive. However, the applicability of robotic solutions to DLOs is still …

Most existing RGB-D salient object detection (SOD) models adopt a two-stream structure to
extract the information from the input RGB and depth images. Since they use two …

Salient object ranking (SOR) aims to segment salient objects in an image and
simultaneously predict their saliency rankings, according to the shifted human attention over …