## 3 Materials and Methods
### Methods
#### 3.4.2 EEMA-VGG16 Sub-Network  
Given the outstanding performance of the VGG16 network in transfer learning and classification tasks, we selected its first 13 convolutional layers and 4 max-pooling layers as the basis of the feature extractor. These convolutional layers are divided into five convolutional blocks with 2, 2, 3, 3, and 3 layers, respectively. Each convolutional block (except the first body block) is followed by a max-pooling layer to gradually reduce the size of the feature map. As the feature extraction process deepens, the feature map gradually decreases in resolution, and the information captured also shifts from shallow spatial details to deep semantic features. Shallow feature maps are rich in spatial information such  
Figure 4: The architecture of MS-WFFNet.  
as edges, shapes, and textures, while deep feature maps focus on expressing high-level semantic information about the object.  
Since spatial information plays a key role in distinguishing objects from backgrounds, we introduce EEMA into the truncated VGG16 structure to form the EEMA-VGG16 sub-network. EEMA is designed based on EMA [39] and its structure is shown in Figure 5. EMA reshapes some channels into batch dimensions and groups channels into multiple sub-feature groups to ensure that spatial semantic features are evenly distributed within each group. Specifically, the input features are divided into g groups, each of which is processed by a 1 \(\times\) 1 branch and a 3 \(\times\) 3 branch. In the 1 \(\times\) 1 branch, the feature map is globally pooled in one dimension along the horizontal and vertical directions to capture the long-range dependencies in the current direction while retaining the position information in other directions. Subsequently, this branch skips the traditional channel dimensionality reduction operation and fuses the merged feature maps along the spatial dimension with a 1 \(\times\) 1 convolutional kernel. The fused feature map is re-split into feature maps in two directions, the weights are generated by the Sigmoid function, and the original input is re-weighted. In the 3 \(\times\) 3 branch, a 3 \(\times\) 3 convolutional kernel is used to extract local features. Finally, the channel weights of each branch are recalibrated through global average pooling and the SoftMax function and further aggregated in cross-dimensional interactions to capture pair-wise relationships at the pixel level.  
We make two improvements to EMA: First, in the 1 \(\times\) 1 branch, although one-dimensional global pooling can capture global dependencies, this form of linear weighting of dependencies only aggregates features in the direction and lacks detailed modeling of the relationship between different spatial positions. To this end, we introduce a multi-head attention mechanism after one-dimensional global pooling; generate query, key, and value through a linear layer; and learn the relative importance of different positions in the same direction in a nonlinear way to capture more complex dependencies. Second, in the 3 \(\times\) 3 branch, to enhance the interaction with the 1 \(\times\) 1 branch, inspired by ACNet [40], we introduce one-dimensional asymmetric convolutions (1 \(\times\) 3 and 3 \(\times\) 1) to improve the spatial structure understanding ability of this branch. In addition, we add EEMA after each convolutional block to enhance the model's attention to the object area and label the feature maps of the five EEMA outputs as _f_\({}_{i}\) (_i_ = 1,2,3,4,5) in sequence.  
Figure 5: The structure of the EEMA.