Detecting and segmenting salient objects from natural scenes, often referred to as salient
object detection, has attracted great interest in computer vision. While many models have …

Detection and localization of regions of images that attract immediate human visual attention
is currently an intensive area of research in computer vision. The capability of automatic …

Most salient object detection approaches use U-Net or feature pyramid networks (FPN) as
their basic structures. These methods ignore two key problems when the encoder …

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

While deep neural networks achieve great performance on fitting the training distribution, the
learned networks are prone to overfitting and are susceptible to adversarial attacks. In this …

As an essential problem in computer vision, salient object detection (SOD) has attracted an
increasing amount of research attention over the years. Recent advances in SOD are …

In this work, we propose a novel depth-induced multi-scale recurrent attention network for
saliency detection. It achieves dramatic performance especially in complex scenarios. There …

The main purpose of RGB-D salient object detection (SOD) is how to better integrate and
utilize cross-modal fusion information. In this paper, we explore these issues from a new …

Existing RGB-D salient object detection (SOD) approaches concentrate on the cross-modal
fusion between the RGB stream and the depth stream. They do not deeply explore the effect …

Recent progress on salient object detection (SOD) mostly benefits from the explosive
development of Convolutional Neural Networks (CNNs). However, much of the improvement …