Salient object detection, which simulates human visual perception in locating the most
significant object (s) in a scene, has been widely applied to various computer vision tasks …

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

In this work, we propose a novel depth-induced multi-scale recurrent attention network for
saliency detection. It achieves dramatic performance especially in complex scenarios. There …

Effective convolutional features play an important role in saliency estimation but how to learn
powerful features for saliency is still a challenging task. FCN-based methods directly apply …

Saliency detection is a fundamental yet challenging task in computer vision, aiming at
highlighting the most visually distinctive objects in an image. We propose a novel recurrent …

Abstract Deep Neural Networks (DNNs) have substantially improved the state-of-the-art in
salient object detection. However, training DNNs requires costly pixel-level annotations. In …

The Nonlinear autoregressive exogenous (NARX) model, which predicts the current value of
a time series based upon its previous values as well as the current and past values of …

Fully convolutional neural networks (FCNs) have shown outstanding performance in many
dense labeling problems. One key pillar of these successes is mining relevant information …

Recent progress on salient object detection is beneficial from Fully Convolutional Neural
Network (FCN). The saliency cues contained in multi-level convolutional features are …

Saliency detection aims to highlight the most relevant objects in an image. Methods using
conventional models struggle whenever salient objects are pictured on top of a cluttered …