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.

scholarly journals Hybrid-Attention Network for RGB-D Salient Object Detection

2020 ◽  
Vol 10 (17) ◽  
pp. 5806 ◽  
Author(s):  
Yuzhen Chen ◽  
Wujie Zhou
Keyword(s):  
Object Detection ◽  
Salient Object Detection ◽  
Depth Information ◽  
Salient Object ◽  
Depth Sensor ◽  
Attention Network ◽  
Depth Maps ◽  
Saliency Prediction ◽  
Ablation Study ◽  
High Level

Depth information has been widely used to improve RGB-D salient object detection by extracting attention maps to determine the position information of objects in an image. However, non-salient objects may be close to the depth sensor and present high pixel intensities in the depth maps. This situation in depth maps inevitably leads to erroneously emphasize non-salient areas and may have a negative impact on the saliency results. To mitigate this problem, we propose a hybrid attention neural network that fuses middle- and high-level RGB features with depth features to generate a hybrid attention map to remove background information. The proposed network extracts multilevel features from RGB images using the Res2Net architecture and then integrates high-level features from depth maps using the Inception-v4-ResNet2 architecture. The mixed high-level RGB features and depth features generate the hybrid attention map, which is then multiplied to the low-level RGB features. After decoding by several convolutions and upsampling, we obtain the final saliency prediction, achieving state-of-the-art performance on the NJUD and NLPR datasets. Moreover, the proposed network has good generalization ability compared with other methods. An ablation study demonstrates that the proposed network effectively performs saliency prediction even when non-salient objects interfere detection. In fact, after removing the branch with high-level RGB features, the RGB attention map that guides the network for saliency prediction is lost, and all the performance measures decline. The resulting prediction map from the ablation study shows the effect of non-salient objects close to the depth sensor. This effect is not present when using the complete hybrid attention network. Therefore, RGB information can correct and supplement depth information, and the corresponding hybrid attention map is more robust than using a conventional attention map constructed only with depth information.

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.

scholarly journals Multi-Interactive Dual-Decoder for RGB-Thermal Salient Object Detection

2021 ◽  
pp. 1-1
Author(s):  
Zhengzheng Tu ◽  
Zhun Li ◽  
Chenglong Li ◽  
Yang Lang ◽  
Jin Tang
Keyword(s):  
Object Detection ◽  
Salient Object Detection ◽  
Salient Object

CDNet: Complementary Depth Network for RGB-D Salient Object Detection

2021 ◽  
pp. 1-1
Author(s):  
Wen-Da Jin ◽  
Jun Xu ◽  
Qi Han ◽  
Yi Zhang ◽  
Ming-Ming Cheng
Keyword(s):  
Object Detection ◽  
Salient Object Detection ◽  
Salient Object
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