## 3 Method
### SC Attention Mechanism
#### 3.2.2 Channel Attention Unit  
To extract the salient information from different channel feature maps while retaining global information, the CAU is composed of an up-conversion stage (yellow module in Figure 7) and a down-conversion stage (purple module in Figure 7). The up-conversion stage is used to extract salient information, while the down-conversion stage is used to extract global information.  
First, the channels of X are divided into \(X^{up}\) and \(X^{low}\). \(X^{up}\) is input into the up-conversion stage, where it is first processed by a 3 \(\times\) 3 convolution to extract feature  
Figure 7: Channel Attention Unit.  
information, followed by a max-pooling layer to aggregate the salient information of the feature map. On the other hand, \(X^{lox}\) is input into the down-conversion stage, where a \(1\times 1\) convolution is applied to produce a feature map that captures shallow hidden details, followed by an average-pooling layer to capture the global information of the feature map. This process generates two distinct spatial context descriptors, \(X^{max}\) and \(X^{avg}\), which are then passed to a shared network to generate two attention maps. To reduce parameter overhead, the first layer of the network uses Group Convolution (GWC) [32], followed by a ReLU activation layer. Although GWC reduces parameters and computational cost, it limits the flow of information between channel groups. To mitigate this, Pointwise Convolution (PWC) is employed to facilitate the flow of information across feature channels and restore the lost information. In summary, the channel attention calculation is represented by Equations (10) and (11):  
\[Y_{max}=\sigma(W_{1}(W_{0}(MaxPool(M_{0}(X^{up}))))=\sigma(W_{1}(W_{0}(X^{max}))) \tag{10}\]  
\[Y_{avg}=\sigma\Big{(}W_{1}\Big{(}W_{0}\Big{(}AvgPool\Big{(}M_{1}\Big{(}X^{low} \Big{)}\Big{)}\Big{)}\Big{)}\Big{)}=\sigma(W_{1}(W_{0}(X^{avg}))) \tag{11}\]  
where \(\sigma\) denotes the sigmoid function and \(M_{0}\in R^{\mathcal{C}\times 3\times 3\times\frac{\mathcal{C}}{2}}\) and \(M_{1}\in R^{\mathcal{C}\times 1\times 1\times\frac{\mathcal{C}}{2}}\) are used to extract feature information. \(W_{0}\in R^{\frac{\mathcal{C}}{2}\times 1\times 1\times\frac{\mathcal{C}}{2}}\) and \(W_{1}\in R^{\frac{\mathcal{C}}{8}\times 1\times 1\times\frac{\mathcal{C}}{2}}\) are shared for the inputs \(X^{max}\) and \(X^{avg}\), with \(W_{0}\) following the ReLU activation function.  
To further generate richer information features while saving space, this paper does not directly add the two parts, but instead, it adopts a cross-reconstruction operation to effectively combine and weight these two distinct attention maps. After the cross-reconstruction, the resulting attention maps are concatenated to obtain the final channel attention map. The cross-reconstruction operation is represented by Equation (12):  
\[\begin{split} Y_{1}^{max}\oplus Y_{1}^{avg}&=Y^{ \omega 1}\\ Y_{1}^{max}\oplus Y_{2}^{avg}&=Y^{\omega 2}\\ Y_{2}^{max}\oplus Y_{1}^{avg}&=Y^{\omega 3}\\ Y_{2}^{max}\oplus Y_{2}^{avg}&=Y^{\omega 4}\\ Y^{\omega 1}\cup Y^{\omega 2}\cup Y^{\omega 3}\cup Y^{ \omega 4}&=Y\end{split} \tag{12}\]  
where \(\cup\) denotes concatenation and \(\oplus\) denotes element-wise addition. Finally, the output is multiplied by X to obtain the final refined feature \(X_{c}\).