In order to obtain richer local contextual features, the encoder first utilizes a multi-scale convolutional attention module (MCA) to learn the spatial information of the hippocampus. Considering the importance
a novel fault diagnosis model called adaptive multiscale convolutional neural network(AMCNN)is developed in this paper.A new multiscale convolutional learning structure is designed to automatically mine multiple-scale features from time-series data,embedding the adaptive attention module to adjust the ...
In this paper, CAFM and MSFM are introduced into the backbone network for improvement, and the step size of the downsampling convolutional layer in Stage4 is changed from 2 to 1.The following is a detailed description of CAFM and MSFM. 2.1.1. Combined Attention Fusion Module (CAFM) The ...
DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution--阅读笔记 特征响应的简单双线性插值。 挑战2的解决方案: 一个标准的处理方法就是将图像转换成图像,然后聚集特征或分数图。作者提出一个由空间金字塔池(spatialpyramid pooling)衍生的方案:在...越来越多的数据表达。不变性意味着...
The proposed ArchNetv2 includes a convolutional block attention module to improve feature learning. It works at multiple detection scales and can efficiently recognize large objects (e.g., stairs) and small objects (e.g., windows) simultaneously. Experimental results show that ArchNetv2 can ...
First, methods involving the use of traditional convolutional neural networks mainly focus on local perception domains; they lack global perception and do not assign different weights to different parts of the input. Although an attention mechanism places more emphasis on the global perception domain, ...
Melanoma segmentation based on a convolutional neural network (CNN) has recently attracted extensive attention. However, the features captured by CNN are always local that result in discontinuous fea...
Comparison of the increasing RF size in convolutional layers in the proposed CMSFL module (left) and traditional methods (right). This is a sample to represent the number of convolution operations to increase the RF from (7, 5, 3) to (9, 7, 5, 3). ...
A spatial-temporal attention-based method and a new dataset for remote sensing image change detection Remote Sens. (2020) Chen, H., Wu, C., Du, B., Zhang, L., 2019a. Deep Siamese multi-scale convolutional network for change detection in... ChenH. et al. Change detection in multisour...
(2020) proposed an IAN automatic segmentation method based on 3D fully convolutional network (FCN), demonstrating superior performance over SSM methods, but the Dice score on CBCT images only reached 57%. Kwak et al. (2020) explored UNet-based IAN segmentation on both 2D and 3D images, ...