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

Screen content, which is often computer-generated, has many characteristics distinctly
different from conventional camera-captured natural scene content. Such characteristic …

With the rise of deep convolutional neural networks, object detection has achieved
prominent advances in past years. However, such prosperity could not camouflage the …

Traditional target detection methods cannot effectively screen key features, which leads to
overfitting and produces a model with a weak generalization ability. In this paper, an …

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 …

Despite the remarkable advances in visual saliency analysis for natural scene images
(NSIs), salient object detection (SOD) for optical remote sensing images (RSIs) still remains …

In this article, we propose a Dual Relation-aware Attention Network (DRANet) to handle the
task of scene segmentation. How to efficiently exploit context is essential for pixel-level …

Foreground map evaluation is crucial for gauging the progress of object segmentation
algorithms, in particular in the filed of salient object detection where the purpose is to …

Attention is the important ability to flexibly control limited computational resources. It has
been studied in conjunction with many other topics in neuroscience and psychology …

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