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 …

Existing state-of-the-art saliency detection methods heavily rely on CNN-based
architectures. Alternatively, we rethink this task from a convolution-free sequence-to …

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 …

To get more accurate saliency maps, recent methods mainly focus on aggregating multi-
level features from fully convolutional network (FCN) and introducing edge information as …

Recent progress on saliency detection is substantial, benefiting mostly from the explosive
development of Convolutional Neural Networks (CNNs). Semantic segmentation and …

Deep convolutional neural networks (CNNs) have been successfully applied to a wide
variety of problems in computer vision, including salient object detection. To detect and …

Saliency in Context (SALICON) is an ongoing effort that aims at understanding and
predicting visual attention. Conventional saliency models typically rely on low-level image …

The human attention mechanism can be understood and simulated by closely associating
the saliency prediction task to neuroscience and psychology. Furthermore, saliency …

The output of many algorithms in computer-vision is either non-binary maps or binary maps
(eg, salient object detection and object segmentation). Several measures have been …

Although depth information plays an important role in the human vision system, it is not yet
well-explored in existing visual saliency computational models. In this work, we first …