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 …

Co-saliency detection is a newly emerging and rapidly growing research area in the
computer vision community. As a novel branch of visual saliency, co-saliency detection …

We introduce a novel method using a new generative model that automatically learns
effective representations of the target and background appearance to detect, segment and …

This paper presents a new method for detecting salient objects in images using
convolutional neural networks (CNNs). The proposed network, named PAGE-Net, offers two …

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 …

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 …

This paper proposes a deep learning model to efficiently detect salient regions in videos. It
addresses two important issues: 1) deep video saliency model training with the absence of …

We study the problem of photo cropping, which aims to find a cropping window of an input
image to preserve as much as possible its important parts while being aesthetically …

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 …

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 …