Guided residual network for RGB-D salient object detection with efficient depth feature learning

2021 ◽  
Author(s):  
Jian Wang ◽  
Shuhan Chen ◽  
Xiao Lv ◽  
Xiuqi Xu ◽  
Xuelong Hu
Keyword(s):  
Object Detection ◽  
Feature Learning ◽  
Salient Object Detection ◽  
Salient Object ◽  
Residual Network ◽  
Depth Feature

Embedding Attention and Residual Network for Accurate Salient Object Detection

2020 ◽  
Vol 50 (5) ◽  
pp. 2050-2062 ◽  
Author(s):  
Shuhan Chen ◽  
Ben Wang ◽  
Xiuli Tan ◽  
Xuelong Hu
Keyword(s):  
Object Detection ◽  
Salient Object Detection ◽  
Salient Object ◽  
Residual Network

scholarly journals Salient Object Detection by Lossless Feature Reflection

2018 ◽  
Author(s):  
Pingping Zhang ◽  
Wei Liu ◽  
Huchuan Lu ◽  
Chunhua Shen
Keyword(s):  
Object Detection ◽  
Large Scale ◽  
Structural Information ◽  
Saliency Detection ◽  
Feature Learning ◽  
Salient Object Detection ◽  
Superior Performance ◽  
Salient Object ◽  
Convolutional Network ◽  
Performance Improvements

Salient object detection, which aims to identify and locate the most salient pixels or regions in images, has been attracting more and more interest due to its various real-world applications. However, this vision task is quite challenging, especially under complex image scenes. Inspired by the intrinsic reflection of natural images, in this paper we propose a novel feature learning framework for large-scale salient object detection. Specifically, we design a symmetrical fully convolutional network (SFCN) to learn complementary saliency features under the guidance of lossless feature reflection. The location information, together with contextual and semantic information, of salient objects are jointly utilized to supervise the proposed network for more accurate saliency predictions. In addition, to overcome the blurry boundary problem, we propose a new structural loss function to learn clear object boundaries and spatially consistent saliency. The coarse prediction results are effectively refined by these structural information for performance improvements. Extensive experiments on seven saliency detection datasets demonstrate that our approach achieves consistently superior performance and outperforms the very recent state-of-the-art methods.

Salient object detection via a local and global method based on deep residual network

2018 ◽  
Vol 54 ◽  
pp. 1-9 ◽  
Author(s):  
Dandan Zhu ◽  
Ye Luo ◽  
Lei Dai ◽  
Xuan Shao ◽  
Qiangqiang Zhou ◽  
...  
Keyword(s):  
Object Detection ◽  
Salient Object Detection ◽  
Salient Object ◽  
Residual Network ◽  
Global Method

Reverse Attention-Based Residual Network for Salient Object Detection

2020 ◽  
Vol 29 ◽  
pp. 3763-3776 ◽  
Author(s):  
Shuhan Chen ◽  
Xiuli Tan ◽  
Ben Wang ◽  
Huchuan Lu ◽  
Xuelong Hu ◽  
...  
Keyword(s):  
Object Detection ◽  
Salient Object Detection ◽  
Salient Object ◽  
Residual Network

scholarly journals WGI-Net: A weighted group integration network for RGB-D salient object detection

2021 ◽  
Author(s):  
Yanliang Ge ◽  
Cong Zhang ◽  
Kang Wang ◽  
Ziqi Liu ◽  
Hongbo Bi
Keyword(s):  
Object Detection ◽  
Information Aggregation ◽  
Salient Object Detection ◽  
Supplementary Information ◽  
Depth Information ◽  
Salient Object ◽  
Detection Depth ◽  
Group Integration ◽  
Feature Information ◽  
Depth Feature

AbstractSalient object detection is used as a pre-process in many computer vision tasks (such as salient object segmentation, video salient object detection, etc.). When performing salient object detection, depth information can provide clues to the location of target objects, so effective fusion of RGB and depth feature information is important. In this paper, we propose a new feature information aggregation approach, weighted group integration (WGI), to effectively integrate RGB and depth feature information. We use a dual-branch structure to slice the input RGB image and depth map separately and then merge the results separately by concatenation. As grouped features may lose global information about the target object, we also make use of the idea of residual learning, taking the features captured by the original fusion method as supplementary information to ensure both accuracy and completeness of the fused information. Experiments on five datasets show that our model performs better than typical existing approaches for four evaluation metrics.

A deep multimodal feature learning network for RGB-D salient object detection

2021 ◽  
Vol 92 ◽  
pp. 107006
Author(s):  
Fangfang Liang ◽  
Lijuan Duan ◽  
Wei Ma ◽  
Yuanhua Qiao ◽  
Jun Miao
Keyword(s):  
Object Detection ◽  
Feature Learning ◽  
Salient Object Detection ◽  
Salient Object ◽  
Learning Network

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.

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