# Dual Attention Equivariant Network for Weakly Supervised Semantic Segmentation
## 4 Experiment
### Evaluation of Experimental Results  
We selected the SEAM model as the baseline to validate the effectiveness of our proposed model and conducted experiments first on the PASCAL VOC 2012 dataset. For our CSM module, we conducted a series of ablation experiments. We replace the CSM module in the DAEN model with a channel attention module (called CM) and with a spatial attention module (called SM), respectively. Table 1 shows the mIoU scores of DAEN, CM, SM, and SEAM in five rounds of experiments, respectively. It can be seen that the mIoU score of the generated CAMs of SM, CM, and DAEN outperform SEAM by 0.41%, 0.56%, and 0.72%, respectively, indicating that both the channel attention module and spatial attention module can effectively activate some important regions of target objects that are easily neglected by the SEAM model. Furthermore, the fact that the proposed DAEN model has the highest mIoU validates the effectiveness of the combination of the channel attention module and spatial attention module. We also investigated the role of each loss function on the DAEN model, as presented in Table 2. The mIoU for the classification loss is 50.44%, indicating its pivotal role in enhancing the model's recognition performance on target objects. The mIoU for the equivariant regularization loss in the model is 13.04%. Despite its relatively lower mIoU compared to the classification loss, the equivariant regularization loss serves the purpose of maintaining the model's spatial consistency for target objects to enhances the model's learning capabilities for target shape and structure. The mIoU for the cross-equivariant regularization loss is 50.58%, demonstrating its positive impact on the model by performing accurate target classification and preserving consistent deformations in target shapes. Combining the above three loss functions allows for the comprehensive utilization of their respective advantages, thereby enhancing the model performance. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & **1** & **2** & **3** & **4** & **5** & **Average Result** \\ \hline SEAM & 54.362 & 54.726 & 54.563 & 54.607 & 54.598 & 54.57 \\
**SM** & 55.086 & 54.933 & 54.949 & 55.018 & 54.963 & 54.98 \\
**CM** & 55.129 & 55.236 & 55.100 & 55.160 & 55.040 & 55.13 \\
**DAEN** & 55.360 & 55.302 & 55.351 & 55.244 & 55.211 & 55.29 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: The mIoU(%) scores of DAEN, CM, SM, and SEAM in five rounds of experiments with different random seeds. Table 3 shows the average mIoU of feeding, respectively, the CAMs generated by the SEAM and DAEN models into AffinityNet [33] for further refinement. It can be seen from Table 3 that the average mIoU of DAEN is 64.36%, which is 1.7% higher than that of SEAM model. Figure 6c,d also show the pseudo-labels generated by the combination of SEAM and DAEN models with AffinityNet, respectively. It is seen that our DAEN has a significant improvement over SEAM. This is because, in the initial CAM generation stage, the proposed DAEN model can accurately activate some important but easily neglected regions that SEAM can not, thus providing the AffinityNet with more enriched semantic structures. We then use these pseudo-labels as full supervision labels to train the segmentation model DeepLab [39] that utilizes Resnet38 as backbone to obtain final segmentation results. Table 4 shows, respectively, the average segmentation results of each category for DAEN and SEAM models. It can be seen that, among the 21 categories, 15 categories of the proposed DAEN achieve a higher mIoU score than the SEAM model, which validates the effectiveness of the proposed DAEN model. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**CLA** & **EQ** & **CE** & **mIoU** \\ \hline ✓ & & & & 50.44\% \\  & ✓ & & 13.04\% \\  & & ✓ & 50.58\% \\ ✓ & ✓ & & 51.43\% \\  & ✓ & ✓ & 18.35\% \\ ✓ & & ✓ & 54.38\% \\ ✓ & ✓ & ✓ & 55.36\% \\ \hline \hline \end{tabular}
\end{table} 
Table 2: The ablation experiments on each loss function of the DAEN. CLA: classification loss. EQ: equivariant loss.