Salient object detection, which simulates human visual perception in locating the most
significant object (s) in a scene, has been widely applied to various computer vision tasks …

Light field imaging has emerged as a technology allowing to capture richer visual
information from our world. As opposed to traditional photography, which captures a 2D …

Most recent methods for RGB (red–green–blue)-thermal salient object detection (SOD)
involve several floating-point operations and have numerous parameters, resulting in slow …

Existing state-of-the-art saliency detection methods heavily rely on CNN-based
architectures. Alternatively, we rethink this task from a convolution-free sequence-to …

Complex backgrounds and similar appearances between objects and their surroundings are
generally recognized as challenging scenarios in Salient Object Detection (SOD). This …

Existing RGB-D Salient Object Detection (SOD) methods take advantage of depth cues to
improve the detection accuracy, while pay insufficient attention to the quality of depth …

Fully convolutional neural networks (FCNs) have shown their advantages in the salient
object detection task. However, most existing FCNs-based methods still suffer from coarse …

Multi-level feature fusion is a fundamental topic in computer vision for detecting, segmenting
and classifying objects at various scales. When multi-level features meet multi-modal cues …

In this paper, we propose the first framework (UCNet) to employ uncertainty for RGB-D
saliency detection by learning from the data labeling process. Existing RGB-D saliency …

The use of RGB-D information for salient object detection (SOD) has been extensively
explored in recent years. However, relatively few efforts have been put toward modeling …