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 …

Although we are continuously bombarded with visual input, only a fraction of incoming visual
events is perceived, remembered or acted on. The neural underpinnings of various forms of …

Existing video understanding approaches, such as 3D convolutional neural networks and
Transformer-Based methods, usually process the videos in a clip-wise manner. Hence huge …

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 …

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 …

In this paper we provide an extensive evaluation of fixation prediction and salient object
segmentation algorithms as well as statistics of major datasets. Our analysis identifies …

We extensively compare, qualitatively and quantitatively, 41 state-of-the-art models (29
salient object detection, 10 fixation prediction, 1 objectness, and 1 baseline) over seven …

Previous research in visual saliency has been focused on two major types of models namely
fixation prediction and salient object detection. The relationship between the two, however …

Automatic estimation of salient object regions across images, without any prior assumption
or knowledge of the contents of the corresponding scenes, enhances many computer vision …

Research in visual saliency has been focused on two major types of models namely fixation
prediction and salient object detection. The relationship between the two, however, has …