Weakly supervised multi-scale recurrent convolutional neural network for co-saliency detection and co-segmentation

2019 ◽  
Vol 32 (21) ◽  
pp. 16571-16588 ◽  
Author(s):  
Aditya Kompella ◽  
Raghavendra V. Kulkarni
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Saliency Detection ◽  
Multi Scale ◽  
Weakly Supervised

A novel multi-scale convolutional neural network for motor imagery classification

2021 ◽  
Vol 68 ◽  
pp. 102747
Author(s):  
Mouad Riyad ◽  
Mohammed Khalil ◽  
Abdellah Adib
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Motor Imagery ◽  
Multi Scale

scholarly journals Chicken Image Segmentation via Multi-Scale Attention-Based Deep Convolutional Neural Network

IEEE Access ◽  
2021 ◽  
pp. 1-1
Author(s):  
Wei Li ◽  
Yang Xiao ◽  
Xibin Song ◽  
Na Lv ◽  
Xinbo Jiang ◽  
...  
Keyword(s):  
Neural Network ◽  
Image Segmentation ◽  
Convolutional Neural Network ◽  
Deep Convolutional Neural Network ◽  
Multi Scale

scholarly journals Attention-Based Multi-Scale Convolutional Neural Network (A+MCNN) for Multi-Class Classification in Road Images

Sensors ◽  
2021 ◽  
Vol 21 (15) ◽  
pp. 5137
Author(s):  
Elham Eslami ◽  
Hae-Bum Yun
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Contextual Information ◽  
Automated Classification ◽  
Automated Recognition ◽  
Pavement Distress ◽  
Multi Scale ◽  
Pavement Distresses ◽  
Multi Class Classification ◽  
Transportation Applications

Automated pavement distress recognition is a key step in smart infrastructure assessment. Advances in deep learning and computer vision have improved the automated recognition of pavement distresses in road surface images. This task remains challenging due to the high variation of defects in shapes and sizes, demanding a better incorporation of contextual information into deep networks. In this paper, we show that an attention-based multi-scale convolutional neural network (A+MCNN) improves the automated classification of common distress and non-distress objects in pavement images by (i) encoding contextual information through multi-scale input tiles and (ii) employing a mid-fusion approach with an attention module for heterogeneous image contexts from different input scales. A+MCNN is trained and tested with four distress classes (crack, crack seal, patch, pothole), five non-distress classes (joint, marker, manhole cover, curbing, shoulder), and two pavement classes (asphalt, concrete). A+MCNN is compared with four deep classifiers that are widely used in transportation applications and a generic CNN classifier (as the control model). The results show that A+MCNN consistently outperforms the baselines by 1∼26% on average in terms of the F-score. A comprehensive discussion is also presented regarding how these classifiers perform differently on different road objects, which has been rarely addressed in the existing literature.

scholarly journals Hyperspectral Image Classification Based on Multi-Scale Residual Network with Attention Mechanism

2021 ◽  
Vol 13 (3) ◽  
pp. 335
Author(s):  
Yuhao Qing ◽  
Wenyi Liu
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
Image Classification ◽  
Classification Accuracy ◽  
Hyperspectral Image ◽  
Principal Component ◽  
Hyperspectral Image Classification ◽  
Deep Network ◽  
Multi Scale

In recent years, image classification on hyperspectral imagery utilizing deep learning algorithms has attained good results. Thus, spurred by that finding and to further improve the deep learning classification accuracy, we propose a multi-scale residual convolutional neural network model fused with an efficient channel attention network (MRA-NET) that is appropriate for hyperspectral image classification. The suggested technique comprises a multi-staged architecture, where initially the spectral information of the hyperspectral image is reduced into a two-dimensional tensor, utilizing a principal component analysis (PCA) scheme. Then, the constructed low-dimensional image is input to our proposed ECA-NET deep network, which exploits the advantages of its core components, i.e., multi-scale residual structure and attention mechanisms. We evaluate the performance of the proposed MRA-NET on three public available hyperspectral datasets and demonstrate that, overall, the classification accuracy of our method is 99.82 %, 99.81%, and 99.37, respectively, which is higher compared to the corresponding accuracy of current networks such as 3D convolutional neural network (CNN), three-dimensional residual convolution structure (RES-3D-CNN), and space–spectrum joint deep network (SSRN).

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