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 …

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 …

Benefiting from the spatial cues embedded in depth images, recent progress on RGB-D
saliency detection shows impressive ability on some challenge scenarios. However, there …

Due to the recent progress in Deep Neural Networks, Reinforcement Learning (RL) has
become one of the most important and useful technology. It is a learning method where a …

Effective integration of contextual information is crucial for salient object detection. To
achieve this, most existing methods based on'skip'architecture mainly focus on how to …

The high cost of pixel-level annotations makes it appealing to train saliency detection
models with weak supervision. However, a single weak supervision source usually does not …

Salient object detection for RGB-D images aims to utilize color and depth information to
automatically localize objects of human interest in the scene and reduce the complexity of …

Light field data have been demonstrated in favor of many tasks in computer vision, but
existing works about light field saliency detection still rely on hand-crafted features. In this …

Contexts play an important role in salient object detection (SOD). High-level contexts
describe the relations between different parts/objects and thus are helpful for discovering the …

Ship detection is a crucial but challenging task in optical remote sensing images. Recently,
thanks to the emergence of deep neural networks (DNNs), significant progress has been …