With the rapid development of deep learning techniques, deep convolutional neural
networks (DCNNs) have become more important for object detection. Compared with …

Object detection, including objectness detection (OD), salient object detection (SOD), and
category-specific object detection (COD), is one of the most fundamental yet challenging …

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 …

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 …

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 …

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 …

Deep convolutional neural networks have achieved competitive performance in salient
object detection, in which how to learn effective and comprehensive features plays a critical …

Recently, contour information largely improves the performance of saliency detection.
However, the discussion on the correlation between saliency and contour remains scarce. In …

Saliency detection is one of the basic challenges in computer vision. Recently, CNNs are the
most widely used and powerful techniques for saliency detection, in which feature maps …

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 …