scholarly journals Video Salient Object Detection via Fully Convolutional Networks

2018 ◽  
Vol 27 (1) ◽  
pp. 38-49 ◽  
Author(s):  
Wenguan Wang ◽  
Jianbing Shen ◽  
Ling Shao
Keyword(s):  
Object Detection ◽  
Salient Object Detection ◽  
Salient Object ◽  
Convolutional Networks ◽  
Fully Convolutional Networks

Salient Object Detection with Recurrent Fully Convolutional Networks

2019 ◽  
Vol 41 (7) ◽  
pp. 1734-1746 ◽  
Author(s):  
Linzhao Wang ◽  
Lijun Wang ◽  
Huchuan Lu ◽  
Pingping Zhang ◽  
Xiang Ruan
Keyword(s):  
Object Detection ◽  
Salient Object Detection ◽  
Salient Object ◽  
Convolutional Networks ◽  
Fully Convolutional Networks

scholarly journals Salient Object Detection Combining a Self-Attention Module and a Feature Pyramid Network

Electronics ◽  
2020 ◽  
Vol 9 (10) ◽  
pp. 1702
Author(s):  
Guangyu Ren ◽  
Tianhong Dai ◽  
Panagiotis Barmpoutis ◽  
Tania Stathaki
Keyword(s):  
Object Detection ◽  
Receptive Fields ◽  
Semantic Segmentation ◽  
Salient Object Detection ◽  
Object Localization ◽  
Salient Object ◽  
Convolutional Networks ◽  
Multi Scale ◽  
Fully Convolutional Networks ◽  
High Level

Salient object detection has achieved great improvements by using the Fully Convolutional Networks (FCNs). However, the FCN-based U-shape architecture may cause dilution problems in the high-level semantic information during the up-sample operations in the top-down pathway. Thus, it can weaken the ability of salient object localization and produce degraded boundaries. To this end, in order to overcome this limitation, we propose a novel pyramid self-attention module (PSAM) and the adoption of an independent feature-complementing strategy. In PSAM, self-attention layers are equipped after multi-scale pyramid features to capture richer high-level features and bring larger receptive fields to the model. In addition, a channel-wise attention module is also employed to reduce the redundant features of the FPN and provide refined results. Experimental analysis demonstrates that the proposed PSAM effectively contributes to the whole model so that it outperforms state-of-the-art results over five challenging datasets. Finally, quantitative results show that PSAM generates accurate predictions and integral salient maps, which can provide further help to other computer vision tasks, such as object detection and semantic segmentation.

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
Sign in / Sign up

Export Citation Format

Share Document