# FDCNet: Frontend-Backend Fusion Dilated Network Through Channel-Attention Mechanism
## 3 Our Proposed Method--FDCNet
### The Channel-Attention Block  
Attention Model has now become an important concept in neural networks that has been researched in diverse application domains [29]. Reference [10] introduced the SE building block. It models the interdependencies between the channels of its convolutional features so as to improve the representational power of a network. Since most of the previous works directly combine the feature maps from different convolutions, they do not consider their respective weights when fusing. On the other hand, the channels of convolutional layers are always ignored, causing the deficiency of spatial information. The SE blocks can conduct the feature recalibration, selectively emphasizing informative features and suppressing less useful ones. Owing to that, the network can learn to use global information. Furthermore, it is also helpful for capturing spatial correlations without requiring additional supervision. One final point, it is computationally lightweight. With so many benefits, it only imposes a slight increase in model complexity and computational burden. According to Reference [10], the SE block has been demonstrated to be accumulated through the entire network to improve the performance. Therefore, we transform the SE block to our framework as the channel-attention block. The specific architecture is shown in Figure 2a. The channel-attention block includes three processes: squeezing \(S\), excitation \(E\), and rescaling \(R\). The output feature map of the concatenation \(N\) is firstly sent to a squeezing operation \(S\). The squeezing operation aggregates the feature maps across the spatial dimension and generates channel-wise statistics through the global average pooling. The spatial dimension is \(h\times w\times c\), which  
Figure 2: (**a**) The architecture of the channel-attention block. (**b**) The architecture of the dilated block. The parameters in the first line are the sizes of the convolutional kernels and the channels. becomes \(1\times 1\times c\) after shrinking. The corresponding channel descriptor \(D_{x}(x=1,2,\cdots,c)\) of the feature map in \(N_{x}(x=1,2,\cdots,c)\) each channel is calculated by:  
\[D_{x}=S(N_{x})=\frac{1}{h\times w}\sum\
olimits_{i=1}^{h}\sum\
olimits_{j=1}^ {w}n_{x}(i,j) \tag{1}\]  
where \(n_{x}(i,j)\) is the element value of the \(i\)th row and the \(j\)th column on the feature map \(N_{x}\). Through this we get the channel descriptor \(D=[D_{x},x=1,2,\cdots,c]\). The channel descriptor embeds the global distribution of channel-wise feature responses, so that its lower layers are able to utilize the information of the global receptive fields. Then, \(D\) is fed into an excitation operation \(E\), generating the extractor \(T\). \(E\) composes with two fully connected layers around the non-linearity, a Relu function and a sigmoid function. It is represented as:  
\[T=E(D;FC)=\sigma(g(D;FC))=\sigma(FC_{2}\delta(FC_{1}D) \tag{2}\]  
where \(FC_{1}\) is a dimensionality-reduction layer with reduction ratio \(k\), \(k\) is a crucial hyperparameter which could vary the capacity and computational cost of the blocks in the model, and \(FC_{2}\) is a dimensionality-increasing layer. Following [10], we set \(k=16\) to achieve a good balance between accuracy and complexity. \(\delta\) is the Relu function, \(\sigma\) is the sigmoid function. The two fully connected layers can limit the model complexity by reducing the dimension and learning a nonlinear interaction between channels to fit the complex dependency of channels and aid generalization. Furthermore, the sigmoid activation emphasis multiple channels opposed to one-hot activation. The whole excitation operation fully captures channel-wise dependencies and governs the excitation of each channel, obtaining the normalized weights between 0 and 1. Finally, the input of the channel-attention block \(N\) is reweighted by the extractor \(T\):  
\[F=R(N;T)=T\cdot N \tag{3}\]  
where \(R\) represents the channel-wise multiplication between the input feature map \(N\) and the extractor \(T\).