# Multiplexing Multi-Scale Features Network for Salient Target Detection
## 2 MMF-Network
### Multi-Scale Aggregation Module  
In conventional FPNs [20], multi-scale features are aggregated in a top-down manner, preventing underlying features from acquiring semantic information about high-level fea  
Figure 2: MMF-Network’s network model diagram.  
tures. However, this structure often lacks the ability to effectively integrate the underlying features and the high-level features when processing. The underlying features usually contain rich spatial detail information, while the high-level features contain more semantic information. Because the FCN mainly relies on high-level features for upsampling and prediction and lacks a full utilization of underlying features and their effective combination with high-level features, its performance is often limited when processing tasks require both spatial detail and semantic information. To address this shortcoming, the MINet [21] aggregation interaction module is designed to complete the feature transmission of the top to the bottom by integrating the features of adjacent layers. However, the ability of the bottom features to partake in the transfer process is limited. [PERSON] et al. [12] proposed a two-way dense feature aggregation module to address this issue. This module facilitates multi-scale feature extraction by connecting paths across scales to avoid the dilution of underlying features.  
The multi-scale aggregation module (MAM) is inspired by the structure of this model and employs a novel approach to feature aggregation. Unlike traditional cascade operations, the MAM does not rely on a simple top-down or bottom-up approach. Instead, it continuously stacks and expands the feature aggregation module, thereby enabling the extraction of more significant information. This method not only avoids increasing the number of parameters, but also minimizes the loss of underlying characteristics. The objective is to achieve a more accurate and significant detection effect. The fundamental principle of the MAM is to generate the original feature \(E^{i}\) (\(i=0\), 1, 2, 3, 4) through the effective aggregation of the operational backbone network \(B^{i}=c(E^{i})\) and then to obtain the new feature sequence \(f^{i}_{MAM}\) through iterative transmission, where \(E^{i}\) represents the layer \(i\) feature and \(B^{i}\) represents the aggregated feature of each layer. A diagram of its structure is shown in Figure 3.  
From the above figure, the input of the module is \(E^{i}(i=0,1,2,3,4)\), where \(E^{0}\) is the lowest layer feature map and \(E^{4}\) is the highest layer feature map; the convolution layer, the batch normalization layer, and the Relu activation layer can obtain the same feature map with this input feature scale. In this process, the model can enhance the nonlinear features of an image and reduce the number of channels to generate a complete branch. \(f^{i}_{MAM}\) is obtained by the stacking of \(B^{i}\) branches. This stacking process is as follows:  
\[f^{i}_{MAM}=\begin{cases}L(B^{i}),i=0\\ L(\sum_{0}^{j}D(B^{i}))+L(B^{i}),j=i-1,i=1,2,3,4\end{cases} \tag{1}\]  
Figure 3: \(B^{i}\) model structure.  
\(f^{i}_{MAM}\) features representing the input feature \(B^{i}\) are processed by the MAM module, in which \(L\) represents the loss layer and \(D\) represents the downsampling operation.