Spatial pooling has been proven highly effective to capture long-range contextual
information for pixel-wise prediction tasks, such as scene parsing. In this paper, beyond …

The large availability of depth sensors provides valuable complementary information for
salient object detection (SOD) in RGBD images. However, due to the inherent difference …

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 …

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

In this paper, we propose an accurate edge detector using richer convolutional features
(RCF). Since objects in natural images possess various scales and aspect ratios, learning …

We provide a comprehensive evaluation of salient object detection (SOD) models. Our
analysis identifies a serious design bias of existing SOD datasets which assumes that each …

Salient object detection models often demand a considerable amount of computation cost to
make precise prediction for each pixel, making them hardly applicable on low-power …

Using attention mechanisms in saliency detection networks enables effective feature
extraction, and using linear methods can promote proper feature fusion, as verified in …

Recent years have witnessed a big leap in automatic visual saliency detection attributed to
advances in deep learning, especially Convolutional Neural Networks (CNNs). However …

Most existing RGB-D saliency detection methods adopt symmetric two-stream architectures
for learning discriminative RGB and depth representations. In fact, there is another level of …