SuperCNN: A Superpixelwise Convolutional Neural Network for Salient Object Detection

2015 ◽  
Vol 115 (3) ◽  
pp. 330-344 ◽  
Author(s):  
Shengfeng He ◽  
Rynson W. H. Lau ◽  
Wenxi Liu ◽  
Zhe Huang ◽  
Qingxiong Yang
Keyword(s):  
Neural Network ◽  
Object Detection ◽  
Convolutional Neural Network ◽  
Salient Object Detection ◽  
Salient Object

scholarly journals Model-Assisted Labeling and Self-Training for Label Noise Reduction in the Detection of Stains on Images of Laundry

Mathematics ◽  
2021 ◽  
Vol 9 (19) ◽  
pp. 2498
Author(s):  
Tamino Huxohl ◽  
Franz Kummert
Keyword(s):  
Neural Network ◽  
Object Detection ◽  
Convolutional Neural Network ◽  
Salient Object Detection ◽  
The Self ◽  
Salient Object ◽  
Label Noise ◽  
Average Improvement ◽  
The Creation ◽  
Noisy Labels

In this work, the creation of a dataset labeled in a pixel-wise manner for the uncommon domain of stain detection on patterned laundry is described. The unique properties of images in this dataset—stains are small and sometimes occur in large amounts—led to the creation of noisy labels. Indeed, the training of a fully convolutional neural network for salient object detection with this dataset revealed that the model predicts stains missed by human labelers. Thus, the reduction in label noise by adding overlooked regions with the help of the model’s predictions is examined in two different experiments. In the model-assisted labeling experiment, a simulation is ran where a human selects correct regions from the predictions. In the self-training experiment, regions of high certainty are automatically selected from the predictions. Re-training the model with the revised labels shows that model-assisted labeling leads to an average improvement in performance by 8.52%. In contrast, with self-training, the performance increase is generally lower (2.58% on average) and a decrease is even possible since regions of high certainty are often false positives.

Edge-Aware Convolution Neural Network Based Salient Object Detection

2019 ◽  
Vol 26 (1) ◽  
pp. 114-118 ◽  
Author(s):  
Wenlong Guan ◽  
Tiantian Wang ◽  
Jinqing Qi ◽  
Lihe Zhang ◽  
Huchuan Lu
Keyword(s):  
Neural Network ◽  
Object Detection ◽  
Salient Object Detection ◽  
Convolution Neural Network ◽  
Salient Object

MFC-Net : Multi-feature fusion cross neural network for salient object detection

2021 ◽  
pp. 104243
Author(s):  
Zhenyu Wang ◽  
Yunzhou Zhang ◽  
Yan Liu ◽  
Shichang Liu ◽  
Sonya Coleman ◽  
...  
Keyword(s):  
Neural Network ◽  
Object Detection ◽  
Feature Fusion ◽  
Salient Object Detection ◽  
Salient Object

Salient object detection for RGB-D image by single stream recurrent convolution neural network

2019 ◽  
Vol 363 ◽  
pp. 46-57 ◽  
Author(s):  
Zhengyi Liu ◽  
Song Shi ◽  
Quntao Duan ◽  
Wei Zhang ◽  
Peng Zhao
Keyword(s):  
Neural Network ◽  
Object Detection ◽  
Salient Object Detection ◽  
Convolution Neural Network ◽  
Salient Object ◽  
Single Stream

Improved salient object detection using hybrid Convolution Recurrent Neural Network

2021 ◽  
Vol 166 ◽  
pp. 114064
Author(s):  
NalliyannaV. Kousik ◽  
Yuvaraj Natarajan ◽  
R. Arshath Raja ◽  
Suresh Kallam ◽  
Rizwan Patan ◽  
...  
Keyword(s):  
Neural Network ◽  
Object Detection ◽  
Recurrent Neural Network ◽  
Salient Object Detection ◽  
Salient Object

scholarly journals DeepSaliency: Multi-Task Deep Neural Network Model for Salient Object Detection

2016 ◽  
Vol 25 (8) ◽  
pp. 3919-3930 ◽  
Author(s):  
Xi Li ◽  
Liming Zhao ◽  
Lina Wei ◽  
Ming-Hsuan Yang ◽  
Fei Wu ◽  
...  
Keyword(s):  
Neural Network ◽  
Object Detection ◽  
Network Model ◽  
Neural Network Model ◽  
Deep Neural Network ◽  
Salient Object Detection ◽  
Salient Object
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