Aleatoric uncertainty captures noise within the observations. For camouflaged object
detection, due to similar appearance of the camouflaged foreground and the background, it's …

Recently, fully convolutional networks (FCNs) have made great progress in the task of
salient object detection and existing state-of-the-arts methods mainly focus on how to …

Salient object detection (SOD) in optical remote sensing images (RSIs) plays a significant
role in many areas such as agriculture, environmental protection, and the military. However …

Due to the rapid development of deep learning, the performance of salient object detection
has been constantly refreshed. Nevertheless, it is still challenging for existing methods to …

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 …

Camouflaged objects share very similar colors but have different semantics with the
surroundings. Cognitive scientists observe that both the global contour (ie, boundary) and …

Deep learning models excel at image recognition of macroscopic objects, but their
applications to nanoscale particles are limited. Here, we explored their potential for source …

Neural networks have been proved to perform well in network intrusion detection. In order to
acquire better features of network traffic, more learning layers are necessarily required …

Capturing sufficient global context and rich spatial structure information is critical for dense
prediction tasks. Convolutional Neural Network (CNN) is particularly adept at modeling fine …

Compared with the conventional hand-crafted approaches, the deep learning based ISOD
(image salient object detection) models have achieved tremendous performance …