## 3 Methods
### The Architecture of the Fusion Network
#### 3.1.2 Cross-Modal Differential Features Attention Module (CDAM)  
To integrate the inherent features of different modal images, we propose a cross-modal differential features attention module. The architecture of the CDAM module is shown in the Figure 3.  
Figure 1: Architecture of the proposed DAGANFuse. Here, MSCB represents the multi-scale convolution block. CSAD represents the cross-scale attention decoder. D_IR and D_VIS are two discriminators. Figure 2: Cross-scale attention decoder (CSAD). Different from CBAM [34], we use two convolutional layers to replace the multi-layer perceptron (MLP) in CBAM. Based on this, we propose an attention framework using differential features path module (DFPM) and fusion features path module (FFPM) in parallel. On the two parallel paths, the initial fusion feature \(\Phi_{f}^{l}\in R^{H\times W\times C}\)--where H and W represent the height and width of the source image, and C represents the number of channels--is used to compute its attention maps from the spatial and channel dimensions, respectively. Combine the results from the two paths and allocate the resulting attention weights to measure the activity levels of different modalities of images at the same scale. In DFPM, the differential feature \(\Phi_{d}^{l}\) is the concatenation of \(\Phi_{d1}^{l}\) and \(\Phi_{d2}^{l}\), which are defined as follows:  
\[\Phi_{d1}^{l}=\Phi_{ir}^{l}-\[PERSON]_{vis}^{l} \tag{1}\]  
\[\Phi_{d2}^{l}=\Phi_{vis}^{l}-\Phi_{ir}^{l} \tag{2}\]  
The generated intermediate fused features, termed \(\Phi_{f}^{l-1}\), will be seen as the initial fused features of the next CDAM. Particularly, at the fourth scale layer, the initial fusion feature is a concatenation of \(\Phi_{ir}^{l}\) and \(\Phi_{vis}^{l}\). In the spatial-independent path, we utilize the maximum and average pooling operations to obtain their initial spatial attentional matrices. Then, we concatenate them  
Figure 3: Cross-modal differential features attention module (CDAM). Here, \(\Phi_{f}^{l}\) is the initial fusion feature. \(\Phi_{h_{1}}^{l}(\Phi_{d2}^{l})\) represents the difference between \(\Phi_{ir}^{l}(\Phi_{vis}^{l})\) and \(\Phi_{vis}^{l}(\Phi_{ir}^{l})\). ��⃝\) denotes the concatenation operation. \(\otimes\) denotes the multiplication operation. \(\oplus\) denotes the addition operation. The upper part is the fusion features path module (FFPM), and the lower part is the differential features path module (DFPM). into a convolution layer to generate the spatial attentional matrix \(\Phi_{f}^{SA,l}\in R^{1\times H\times W}\) and \(\Phi_{d}^{SA,l}\in R^{1\times H\times W}\), which are formulated as follows:  
\[\Phi_{f}^{SA,l}(i,j) = \text{Conv}([\text{IMP}(\Phi_{f}^{l}(c,i,j)),\text{AP}(\Phi_{f}^{l }(c,i,j))]) \tag{3}\] \[\Phi_{d}^{SA,l}(i,j) = \text{Conv}([\text{IMP}(\Phi_{d}^{l}(c,i,j)),\text{AP}(\Phi_{d}^{ l}(c,i,j))]) \tag{4}\]  
where \(MP(\cdot)\) and \(AP(\cdot)\) denote the global maximum and average pooling operations. Conv represents the convolution. Meanwhile, in the channel-independent path, we first use the maximum pooling and average pooling operations. Then, the obtained initial channel attention vectors pass through two convolution layers and a PReLU activation layer, and are concatenated and fed into a convolutional layer to obtain the channel attention vector \(\Phi_{f}^{CA,l}(c)\in R^{C\times 1\times 1}\) and \(\Phi_{d}^{CA,l}(c)\in R^{C\times 1\times 1}\), which are formulated as follows:  
\[\Phi_{f}^{CA,l}(c) = \text{Conv}([\text{Conv}(\text{PReLU}(\text{Conv}(\text{MP}( \Phi_{f}^{l}(c,i,j))))),\text{Conv}(\text{PReLU}(\text{Conv}(\text{AP}(\Phi_{f }^{l}(c,i,j)))))]) \tag{5}\] \[\Phi_{d}^{CA,l}(c) = \text{Conv}(\text{Conv}(\text{PReLU}(\text{Conv}(\text{MP}(\Phi_ {d}^{l}(c,i,j))))),\text{Conv}(\text{PReLU}(\text{Conv}(\text{AP}(\Phi_{d}^{ l}(c,i,j))))))\] (6) Figure 1:0  
Then, we element-wise multiply the obtained two pairs of spatial attention matrices and channel attention vectors.