Adversarial Learning Based Saliency Detection

2017 ◽  
Author(s):  
Xuecai Hu ◽  
Xin Zhao ◽  
Kaiqi Huang ◽  
Tieniu Tan
Keyword(s):  
Saliency Detection ◽  
Adversarial Learning

scholarly journals Image Saliency Detection via Two-Stream Feature Fusion and Adversarial Learning

2021 ◽  
Vol 33 (3) ◽  
pp. 376-384
Author(s):  
Yihan Zhang ◽  
Zhaohui Zhang ◽  
Lina Huo ◽  
Bin Xie ◽  
Xiuqing Wang
Keyword(s):  
Feature Fusion ◽  
Saliency Detection ◽  
Adversarial Learning ◽  
Image Saliency

A Novel Saliency Detection Algorithm Based on Adversarial Learning Model

2020 ◽  
Vol 29 ◽  
pp. 4489-4504 ◽  
Author(s):  
Yingfeng Cai ◽  
Lei Dai ◽  
Hai Wang ◽  
Long Chen ◽  
Yicheng Li
Keyword(s):  
Saliency Detection ◽  
Learning Model ◽  
Detection Algorithm ◽  
Adversarial Learning

A Universal Descent Model for Saliency Detection

2019 ◽  
Author(s):  
Han Liu ◽  
Bo Li ◽  
Tao Zheng ◽  
Jiaxu Yao
Keyword(s):  
Saliency Detection

Saliency detection in deep learning era: trends of development

2019 ◽  
pp. 10-36 ◽  
Author(s):  
M. N. Favorskaya ◽  
L. C. Jain
Keyword(s):  
Deep Learning ◽  
Object Detection ◽  
Event Detection ◽  
Visual Analysis ◽  
Saliency Detection ◽  
Salient Object Detection ◽  
Public Image ◽  
Detection Methods ◽  
Salient Object ◽  
Salient Event

Introduction:Saliency detection is a fundamental task of computer vision. Its ultimate aim is to localize the objects of interest that grab human visual attention with respect to the rest of the image. A great variety of saliency models based on different approaches was developed since 1990s. In recent years, the saliency detection has become one of actively studied topic in the theory of Convolutional Neural Network (CNN). Many original decisions using CNNs were proposed for salient object detection and, even, event detection.Purpose:A detailed survey of saliency detection methods in deep learning era allows to understand the current possibilities of CNN approach for visual analysis conducted by the human eyes’ tracking and digital image processing.Results:A survey reflects the recent advances in saliency detection using CNNs. Different models available in literature, such as static and dynamic 2D CNNs for salient object detection and 3D CNNs for salient event detection are discussed in the chronological order. It is worth noting that automatic salient event detection in durable videos became possible using the recently appeared 3D CNN combining with 2D CNN for salient audio detection. Also in this article, we have presented a short description of public image and video datasets with annotated salient objects or events, as well as the often used metrics for the results’ evaluation.Practical relevance:This survey is considered as a contribution in the study of rapidly developed deep learning methods with respect to the saliency detection in the images and videos.

Saliency Detection Based on Improved Manifold Ranking via Convex Hull

2019 ◽  
Vol 31 (5) ◽  
pp. 761
Author(s):  
Xiao Lin ◽  
Zuxiang Liu ◽  
Xiaomei Zheng ◽  
Jifeng Huang ◽  
Lizhuang Ma
Keyword(s):  
Convex Hull ◽  
Saliency Detection ◽  
Manifold Ranking

scholarly journals Superpixel Segmentation Based on Anisotropic Edge Strength

2019 ◽  
Vol 5 (6) ◽  
pp. 57 ◽  
Author(s):  
Gang Wang ◽  
Bernard De Baets
Keyword(s):  
Distance Measure ◽  
Saliency Detection ◽  
Experimental Results ◽  
Segmentation Method ◽  
Superpixel Segmentation ◽  
Gaussian Kernels ◽  
First Derivative ◽  
Edge Strength

Superpixel segmentation can benefit from the use of an appropriate method to measure edge strength. In this paper, we present such a method based on the first derivative of anisotropic Gaussian kernels. The kernels can capture the position, direction, prominence, and scale of the edge to be detected. We incorporate the anisotropic edge strength into the distance measure between neighboring superpixels, thereby improving the performance of an existing graph-based superpixel segmentation method. Experimental results validate the superiority of our method in generating superpixels over the competing methods. It is also illustrated that the proposed superpixel segmentation method can facilitate subsequent saliency detection.

Two-stream Encoder GAN with Progressive Training for Co-Saliency Detection

2021 ◽  
pp. 1-1
Author(s):  
Xiaoliang Qian ◽  
Xi Cheng ◽  
Gong Cheng ◽  
Xiwen Yao ◽  
Liying Jiang
Keyword(s):  
Saliency Detection
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