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 …

Being able to learn dense semantic representations of images without supervision is an
important problem in computer vision. However, despite its significance, this problem …

We solve the problem of salient object detection by investigating how to expand the role of
pooling in convolutional neural networks. Based on the U-shape architecture, we first build a …

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 …

Despite deep end-to-end learning methods have shown their superiority in removing non-
uniform motion blur, there still exist major challenges with the current multi-scale and scale …

The existing binary foreground map (FM) measures to address various types of errors in
either pixel-wise or structural ways. These measures consider pixel-level match or image …

We explore the potential of pooling techniques on the task of salient object detection by
expanding its role in convolutional neural networks. In general, two pooling-based modules …

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