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

In this paper, we design a simple yet powerful deep network architecture, U 2-Net, for salient
object detection (SOD). The architecture of our U 2-Net is a two-level nested U-structure. The …

Abstract Deep Convolutional Neural Networks have been adopted for salient object
detection and achieved the state-of-the-art performance. Most of the previous works …

Fully convolutional neural networks (FCNs) have shown their advantages in the salient
object detection task. However, most existing FCNs-based methods still suffer from coarse …

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 …

Existing state-of-the-art salient object detection networks rely on aggregating multi-level
features of pre-trained convolutional neural networks (CNNs). However, compared to high …

Deep-learning based salient object detection methods achieve great progress. However, the
variable scale and unknown category of salient objects are great challenges all the time …

Most of existing salient object detection models have achieved great progress by
aggregating multi-level features extracted from convolutional neural networks. However …

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 …

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 …