This paper proposes a novel joint learning and densely-cooperative fusion (JL-DCF)
architecture for RGB-D salient object detection. Existing models usually treat RGB and depth …

Existing RGB-D salient object detection (SOD) models usually treat RGB and depth as
independent information and design separate networks for feature extraction from each …

In this paper, we aim to predict human eye fixation with view-free scenes based on an end-to-
end deep learning architecture. Although convolutional neural networks (CNNs) have made …

Previous RGB-D fusion systems based on convolutional neural networks typically employ a
two-stream architecture, in which RGB and depth inputs are learned independently. The …

RGB-based salient object detection (SOD) algorithms have shown good ability to segment
salient objects from images, but the performance is still unsatisfactory when dealing with …

In this work, we propose a novel depth-induced multi-scale recurrent attention network for
RGB-D saliency detection, named as DMRA. It achieves dramatic performance especially in …

Recently, with the advent of deep convolutional neural networks (DCNN), the improvements
in visual saliency prediction research are impressive. One possible direction to approach the …

Emergency prediction and driver attention prediction are fundamental tasks within the realm
of self-driving vehicles and assistant driving systems. The utilization of visual saliency …

Deep learning-based salient object detection techniques have shown impressive results
compared to conventional saliency detection by handcrafted features. Integrating …

Co-saliency detection aims to discover common and salient objects in an image group
containing more than two relevant images. Moreover, depth information has been …