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

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 …

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 …

Contexts play an important role in the saliency detection task. However, given a context
region, not all contextual information is helpful for the final task. In this paper, we propose a …

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 …

Traditional1 salient object detection models often use hand-crafted features to formulate
contrast and various prior knowledge, and then combine them artificially. In this work, we …

Salient object detection has recently witnessed substantial progress due to powerful
features extracted using deep convolutional neural networks (CNNs). However, existing …

Visual saliency is a fundamental problem in both cognitive and computational sciences,
including computer vision. In this paper, we discover that a high-quality visual saliency …

This paper presents a saliency detection algorithm by integrating both local estimation and
global search. In the local estimation stage, we detect local saliency by using a deep neural …

A key problem in salient object detection is how to effectively model the semantic properties
of salient objects in a data-driven manner. In this paper, we propose a multi-task deep …