Salient object detection (SOD) in optical remote sensing images (RSIs) is a valuable and
challenging task. Although many SOD methods for RSIs have been proposed, there are still …

The visual saliency detection model simulates the human visual system to perceive the
scene and has been widely used in many vision tasks. With the development of acquisition …

Advanced data augmentation strategies have widely been studied to improve the
generalization ability of deep learning models. Regional dropout is one of the popular …

Computational saliency models for still images have gained significant popularity in recent
years. Saliency prediction from videos, on the other hand, has received relatively little …

Co-saliency detection aims at extracting the common salient regions from an image group
containing two or more relevant images. It is a newly emerging topic in computer vision …

Multi-modal feature fusion and saliency reasoning are two core sub-tasks of RGB-D salient
object detection. However, most existing models employ linear fusion strategies (eg …

Deep learning techniques have largely solved the problem of rail surface defect detection
(SDD), however, two aspects have yet to be addressed. In most existing approaches, two …

Video saliency detection aims to continuously discover the motion-related salient objects
from the video sequences. Since it needs to consider the spatial and temporal constraints …

As a newly emerging and significant topic in computer vision community, co-saliency
detection aims at discovering the common salient objects in multiple related images. The …

By recording the whole scene around the capturer, virtual reality (VR) techniques can
provide viewers the sense of presence. To provide a satisfactory quality of experience, there …