Convolutional neural network (CNN)-based encoder-decoder models have profoundly
inspired recent works in the field of salient object detection (SOD). With the rapid …

Salient object detection models mimic the behavior of human beings and capture the most
salient region/object from the images or scenes, this field contains many important …

Object detection is one of the most important and challenging branches of computer vision,
which has been widely applied in people's life, such as monitoring security, autonomous …

The last decade has witnessed a growing interest in video salient object detection (VSOD).
However, the research community long-term lacked a well-established VSOD dataset …

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 …

The ability to capture inter-frame dynamics has been critical to the development of video
salient object detection (VSOD). While many works have achieved great success in this field …

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 …

The visual saliency detection model simulates the human visual system to perceive the
scene and has been widely used in many vision tasks. With the development of acquisition …

Salient object detection from RGB-D images is an important yet challenging vision task,
which aims at detecting the most distinctive objects in a scene by combining color …

This paper conducts a systematic study on the role of visual attention in Unsupervised Video
Object Segmentation (UVOS) tasks. By elaborately annotating three popular video …