## 3 Proposed Method
### FFCon Module  
Using only the latent semantic vectors of the last layer of the backbone often ignores part of the coarse-grained word-level semantic information, causing the model's effect on text understanding to be affected. However, relying solely on hierarchical attention features may lead to certain problems. The lower-layer network only retains better-position information, but the text's semantic information has a coarser granularity. Therefore, conveying semantic information from higher to lower layers or completing the fusion of locational information and semantic information will become a crucial aspect.  
The feature pyramid network [41] is a typical feature fusion network, which better fuses the high- and low-resolution information of the image to achieve good recognition results. We propose the FFCon module to effectively reduce the problem of coarse-grained semantic analysis in the lower layers of the backbone network during the semantic analysis process and to effectively integrate location information.  
Assuming that we have obtained the text representation vector \(Q^{n}\in R^{Seq^{\prime}\times E}\) calculated by the hierarchical deeper attention module, the process of inputting the FFCon module is shown in Figure 4.  
First, after the connection block receives two layers of text representation input, the calculation process can be expressed as follows:  
\[A^{n}=Q^{n}, \tag{9}\]  
\[A^{t-1}=\text{LN}\Big{(}Q^{t-1}+\text{MLP}\big{(}A^{t}\big{)}\Big{)},\ t\in 0,1,2, \cdots,n, \tag{10}\]  
where \(Q^{t-1}\) represents the low-level semantic vector, \(A^{t}\) represents the high-level semantic vector after passing through the connection block, and \(A^{t-1}\in R^{Soft}\times E\) denotes the result of the \(t-1\)th layer after passing through the connection block, which is also the semantic vector passed down to the lower layer. \(A^{n}=Q^{n}\) means that the highest-level semantic vector will not be modified and will continue to be retained and transmit information to the lower levels. \(LN\) indicates layer normalization. MLP refers to multiple fully connected feed-forward networks, consisting of two linear transformations, including the Mish activation function [42] in the middle, which can be expressed as follows:  
\[\text{MLP}(x)=\text{Mish}(x\cdot W_{1}+b_{1})W_{2}+b_{2}, \tag{11}\]  
\[\text{Mis}h\ (z)=x\times tanh(\ \text{ln}(1+e^{x})), \tag{12}\]  
where \(W_{1}\) and \(W_{2}\) are linear projections, and \(b_{1}\) and \(b_{2}\) represent the deviations in the fully connected neural network. Different parameters are used between layers. The inner layer dimension is larger than the input and output dimensions. The semantic information is mapped to a high-dimensional space and then to a low-dimensional space, extracting deeper layer features. This processing method, similar to the computing mechanism in the Transformer [9], makes the core information of the statement in the high layers' fusion features more explicit after shrinking and Mish activation, and enlarges the important influence factor, which is conducive to subsequent feature fusion and classification. In this network, the input and output dimensions are as follows: \(d_{model}=768\), and the dimension of the inner layer is \(d_{ff}=768\times 3\). Therefore, our model can learn fine-grained and comprehensive representations in the text by further shrinking and integrating high-level semantic information into lower-level features.