## 2 Data Processing and Network Description
### Relation Network
#### 2.3.2 Feature Embedding Module  
The feature embedding module, as previously mentioned, plays a crucial role in the transformation of input images. In the context of few-shot learning tasks, our objective is to transform the images from the support set and query set into vector representations for similarity computation and pattern recognition.  
In this paper, we adopt a CNN that incorporates the Squeeze-and-Excitation Network (SENet) [29] as the feature extractor to capture key features in the images. CNNs possess the characteristics of local perception and weight sharing, enabling effective extraction of both local and global information from the images. The model aims to enhance the expressive power of the network by adaptively learning the importance of feature channels. Its core idea lies in the introduction of a mechanism called \"Squeeze-and-Excitation\", which automatically learns the weights for each channel to amplify more important feature channels and attenuate relatively less important ones. The SENet structure consists of two main parts: the Squeeze part and the Excitation part.  
* The Squeeze part reduces the dimensionality of the input feature maps through global average pooling, transforming them into a fixed-size vector. This vector can be regarded as the global statistical information of the entire feature map, encompassing the overall characteristics of each channel. Specifically, for an input feature map with a size of H \(\times\) W \(\times\) C (height \(\times\) width \(\times\) number of channels), the Squeeze operation produces a feature map of size \(1\times 1\times\) C.
* The Excitation part is the core component of SENet, which processes the output of the Squeeze operation through a fully connected layer and an activation function. The output size of the fully connected layer is C \(\times\) r (where r is a tunable scaling factor typically chosen to be small), followed by a ReLU activation function for non-linear mapping. Finally, another fully connected layer restores the size of the feature map to C. This process can be seen as a re-calibration of the feature channels, allowing for the learning of weights for each channel.  
In SENet, the output of the Excitation part is normalized using a sigmoid function to ensure that the weights for each channel fall within the range of 0 to 1. Ultimately, the normalized weights are applied to each channel of the input feature map, achieving channel-wise adaptive weighting. By introducing the SENet module, the network can adaptively learn the importance of each channel and its importance in different spatial locations. This enables SENet to effectively leverage the relationships among channels and the diversity of features, thereby improving the performance of image classification tasks.  
In the feature extraction process, multiple layers of convolutional layers and activation functions are used to enhance the expressive power and non-linear fitting capability of the model. A global pooling layer and a fully connected layer are introduced at the output end of the CNN to map the extracted high-dimensional features to a low-dimensional space. Max pooling effectively reduces the feature dimensionality, reducing computational complexity while preserving global information. The fully connected layer is responsible for linearly transforming the pooled features, generating the final low-dimensional vector representation. The network structure of the feature embedding module used in this paper is shown in Table 1.