This paper presents a comprehensive survey on vision-based robotic gras**. We
conclude three key tasks during vision-based robotic gras**, which are object localization …

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 …

The recently proposed camouflaged object detection (COD) attempts to segment objects that
are visually blended into their surroundings, which is extremely complex and difficult in real …

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 …

RGB-D saliency detection has attracted increasing attention, due to its effectiveness and the
fact that depth cues can now be conveniently captured. Existing works often focus on …

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 …

RGB-D saliency detection aims to fuse multi-modal cues to accurately localize salient
regions. Existing works often adopt attention modules for feature modeling, with few …

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

RGB-D salient object detection (SOD) is usually formulated as a problem of classification or
regression over two modalities, ie, RGB and depth. Hence, effective RGB-D feature …