Due to the light absorption and scattering induced by the water medium, underwater images
usually suffer from some degradation problems, such as low contrast, color distortion, and …

Focusing on the issue of how to effectively capture and utilize cross-modality information in
RGB-D salient object detection (SOD) task, we present a convolutional neural network …

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

Under ideal environmental conditions, RGB-based deep convolutional neural networks can
achieve high performance for salient object detection (SOD). In scenes with cluttered …

Camouflaged object detection (COD) aims to accurately detect objects hidden in the
surrounding environment. However, the existing COD methods mainly locate camouflaged …

In recent years, RGB-T salient object detection (SOD) has attracted continuous attention,
which makes it possible to identify salient objects in environments such as low light by …

Despite the remarkable advances in visual saliency analysis for natural scene images
(NSIs), salient object detection (SOD) for optical remote sensing images (RSIs) still remains …

Salient object detection (SOD) for optical remote sensing images (RSIs) aims at locating and
extracting visually distinctive objects/regions from the optical RSIs. Since some saliency …

There are two main issues in RGB-D salient object detection:(1) how to effectively integrate
the complementarity from the cross-modal RGB-D data;(2) how to prevent the contamination …

Despite the remarkable progress made by the salient object detection of natural sensing
images (NSI-SOD), the complex background and scale diversity issues of remote sensing …