## 3 Experiments
### Ablation Study  
Due to the large differences in the output features of CNN and Transformer, directly merging these two features will lead to poor model performance. Existing methods, such as Conformer [47] and Mobile-Former [48], essentially interact with CNN and Transformer branches from the shallow to the deep layers of the network. This method destroys the independence of CNN and Transformer, and makes their respective advantages gradually disappear. Therefore, we designed the Dual Lightweight Aggregation Module (DLAM)and the Dual Attention Aggregation Module (DAAM) to fuse the features of CNN and Transformer without affecting their respective training. In the ablation experiment, we verified the model performance of DLAM and DAAM at different locations, and also explored the different internal structure settings of DLAM and DAAM. The first two rows of Table 4 show the segmentation accuracy of the ResNet-50 and CSWin-T benchmark models on Cloud and Cloud Shadow datasets, respectively. From the data in the third row, it can be seen that the evaluation index MIoU of the model without the two-branch feature aggregation module is lower than that of the model using only CSWin-T. This shows that simply fusing the Transformer branch and the CNN branch does not improve the detection effect, but reduces the performance of the model. The next five rows of data permutate and combine DLAM and DAAM in four fusion stages to explore their impact on model performance at different locations. In the DLAM and DAAM columns, we use \"/\" to indicate the module without markers, and use numbers 1-4 to indicate that the modules using markers fuse the output features from the CNN and the Transformer in the fusion stage of the corresponding numbers. As can be seen from the data in the fourth row, the MIoU increased by 1.16% after using DLAM instead of the coarse fusion method at each stage. This proves the effectiveness of DLAM. Rows 5 and 6 replace the feature aggregation modules of stages 4 and 3 with DAAM, further improving the performance of the model. When DAAM was used in both stages 3 and 4, the optimal MIoU of 94.13% was obtained. These data all confirm that the use of DAAM for two-branch fusion is more effective than the use of DRAM alone. It is important to note that the effect of DAAM is related to its position in the fusion phase. The fifth and last rows of Table 4 show that the models using DAAM in the second and fourth fusion phases are not as good as the models using DAAM in the fourth fusion phase. This is because DAAM is essentially an attention module and is generally more effective for features with rich semantic information (such as output features from CNNs and Transformer stages 3 and 4), while increasing DAAM too much will lead to increased complexity of the model and is prone to overfitting. At the same time, when DAAM is used in shallow networks (stages 1 and 2 of the model in this study), the computational burden of the module increases dramatically due to the high resolution of the output feature map of CNN and Transformer. Therefore, as a lightweight fusion module, DLAM is suitable for fusing shallow feature information in stages 1 and 2. Although DAAM has better performance, it is computationally intensive and suitable for integrating rich semantic features in stages 3 and 4. In order to explore the optimal module structure of DLAM and DAAM, we verified the influence of their different internal structures on the model performance, and the \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model** & **DLAM** & **DAAM** & **MIoU (\%)** \\ \hline ResNet-50 & / & / & 92.18 \\ \hline CSWin-T & / & / & 92.55 \\ \hline  & / & / & 92.34 \\ \cline{2-4}  & 1,2,3,4 & / & 93.50 \\ \cline{2-4} ResNet-50 + CSWin-T & 1,2,3 & 4 & 93.76 \\ \cline{2-4}  & 1,2,4 & 3 & 93.68 \\ \cline{2-4}  & 1,2 & 3,4 & 94.13 \\ \cline{2-4}  & 1,3 & 2,4 & 93.75 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Ablation results of DLAM and DAAM at different positions.