## 3 Methodology
### Encoder
#### 3.1.2 Dual-Branch Feature Extraction Module  
CNN Feature Extraction Branch: We utilize a ResNet network as the backbone for our CNN feature extraction branch, comprising four blocks. The channel dimensions for these blocks are [64; 128; 256; 384], and the repetition stack counts are [2; 2; 2; 2]. Each residual block comprises one \(1\times 1\) convolutional kernel and one \(3\times 3\) convolutional kernel. We introduce the Convolutional Block Attention Module (CBAM) [54] after the ResNet network to obtain more effective features. CBAM applies attention to each channel and spatial position, allowing the network to select and focus on crucial features. This helps reduce sensitivity to irrelevant or redundant information, particularly when training data are limited, enabling better capture of key information. The CBAM consists of channel and spatial attention modules. The channel attention mechanism preserves the channel dimension while compressing the spatial dimension. By weighting the feature maps of each channel, the network can learn the importance of different channels, thereby enhancing its sensitivity to specific features. The mathematical expression for this mechanism is shown in Equation (1). \[ \begin{split}\text{A}_{c}(F_{x})&=\sigma(\text{MLP}( \text{AvgPool}(F_{x}))+\text{MLP}(\text{MaxPool}(F_{x})))\\ &=\sigma(W_{1}(W_{0}(F_{avg}^{c}))+W_{1}(W_{0}(F_{max}^{c}))) \end{split}  \tag{1}\]  
\(F_{x}\) represents the feature map output of the Filter module, represented as \(C\times H\times W\), where \(C\) is the number of channels, \(H\) is the height, and \(W\) is the width. AvgPool and MaxPool denote average pooling and max pooling operations conducted in the spatial dimension to obtain each channel's maximum and average values. \(F_{avg}^{c}\) represents the average value on channel \(c\), and \(F_{max}^{c}\) represents the maximum value on channel \(c\), where \(c\in C\). MLP is a multi-layer perceptron utilized for learning the weights and relationships between channels. \(\alpha\) represents the Sigmoid function, normalizing the attention weights of each channel to the range \((0,1)\) used in generating channel attention feature maps. \(W_{0}\) and \(W_{1}\) are the learned weight parameters. The spatial attention mechanism preserves spatial dimensions while compressing channel dimensions. By weighting the spatial dimensions of the feature map, the network can allocate different weights to different spatial locations, thus focusing more on crucial areas in the image. This enhances the network's ability to perceive local spatial information. The mathematical expression is shown in Equation (2). \[ \begin{split}\text{A}_{s}(\text{A}_{c}F_{x})&=\sigma (\tilde{f}^{\times 7}(\text{Cat}[\text{AvgPool}(\text{A}_{c}F_{x});\text{MaxPool}( \text{A}_{c}F_{x})]))\\ &=\sigma(\tilde{f}^{\times 7}(\text{Cat}[F_{avg}^{s};F_{max}^{s}]) \end{split}  \tag{2}\]  
\(\text{A}_{c}\) represents the output of the channel attention mechanism. \(F_{x}\) represents the feature map output of the Filter module. The AvgPool and MaxPool indicate the operations of average and max pooling. In MaxPool, the maximum value is extracted along the channels with the extraction times being the product of height and width. Similarly, AvgPool extracts the average value along the channels with extraction times also being the product of height  
Figure 2: The structure of the Filter module. and width. The Cat operation combines the feature maps extracted by MaxPool and AvgPool (with a single channel), resulting in a 2-channel feature map. \(\ell^{2\times 7}\) represents a convolution operation with a kernel size of \(7\times 7\), reducing the number of channels from 2 to 1. \(\alpha\) represents the Sigmoid function, normalizing the attention weights of the regions to the range \((0,1)\) used for generating spatial attention feature maps.