Methods:This study proposes a multi-scale dilated convolution module, which is composed of three parallel dilated convolutions with different expansion coefficients. The original FCN network usually adopts bilinear interpolation or deconvolution methods when upsampling, both of which lead to information ...
Competitive multi-scale convolution. arXiv:1511.05635, 2015.Liao, Z.; Carneiro, G. Competitive multi-scale convolution. arXiv 2015, arXiv:1511.05635.Zhibin Liao and Gustavo Carneiro. Competitive multi-scale convolution. arXiv preprint arXiv:1511.05635, 2015....
Inception module Inception is a multi-branch multi-scale convolution module [21]. It can extract the multi-scale features from the input image by different-scale kernels. Its structure is shown in Fig. 3, including 1 × 1, 3 × 3 and 5 × 5 convolutional kernels, where 1 × 1 convolut...
The presented module uses dilated convolutions to systematically aggregate multi-scale contextual information without losing resolution. The architecture is based on the fact that dilated convolutions support exponential expansion of the receptive field without loss of resolution or coverage. We show that ...
The output of 1 × 1 × 1 convolution is used as the weight of the MCA input. We call the entire convolution module FMCA, which is described as follows: (1)F1=Dw_Conv(F)(2)Weight=Conv(F1+∑i=01Scalei(Dw_Conv(F1)))(3)Output=Weight⊗F1 where F represents the input feature of...
Figure 2. Structure diagram of multi-scale convolution. The multi-scale convolution module has four branches in total. The first branch uses 1×11×1 convolution. The 1×11×1 convolution can increase the dimension and reduce the number of feature channels and uses a small amount of computati...
A context module is constructed based on the dilated convolution as below: The Basic Context Module and The Large Context Module 这个上下文模块一共有8层,其中7层运用了具有不同膨胀因子的3×3卷积(膨胀因子分别为:1, 1, 2, 4, 8, 16, 1) 最后一层是 1×1 卷积,用来将输出通道数转变为输入通道...
Multi-scale Residual Block (MSRB) was designed. MSRB combined a multi-scale convolution module and residual connection to improve the feature extraction capability of the network. Multi-scale Attention Module (MSAM) was proposed, which could effectively strengthen useful features and suppress useless fe...
研究背景与意义:金属工件是现代工业生产中不可或缺的重要组成部分。金属工件的质量和性能直接影响到产品的品质和效率,因此对金属工件的研究和改进具有重要的意义。随着科技的不断进步和工业的发展,对金属工件的要求也越来越高,传统的金属加工方法已经无法满足现代工业的需求。因此,寻找原创创新点来改进金属工件的制造和加...
The music emotion selection module adopts a GAI framework of Variational Autoencoder (VAE) and integrates multi-scale parallel convolution and attention mechanism modules. Experimental results demonstrate that this approach is competitive compared to existing deep learning architectures on PMEmo, RAVDESS, ...