# Dual Attention Equivariant Network for Weakly Supervised Semantic Segmentation
## 4 Experiment
### Evaluation of Experimental Results
CE: cross-equivariant loss. Figure 6: Pseudo-labels comparison was generated on PASCAL VOC 2012. (**a**) Original images. (**b**) Ground truth labels. (**c**) Pseudo-labels generated by the SEAM model and AffinityNet. (**d**) Pseudo-labels generated by the DAEN model and AffinityNet. Table 5 shows the performance comparison of the proposed DAEN model with other weakly supervised semantic segmentation models. For fair comparison, all models employ the same Resnet38 network as the backbone. It can be seen that the proposed DAEN model achieves the highest mIoU score over all models. The reason is that the proposed DAEN model can better capture features of important but easily ignored regions in target objects and lead to better class activation maps for the segmentation task. This also can be explained by Figure 7 that shows the segmentation results of the proposed DAEN and SEAM models. It can be seen from Figure 7 that the proposed DAEN model can produce better semantic segmentation labels than SEAM model. Furthermore, we conducted extensive experiments on the LUAD-HistoSeg dataset. LUAD-HistoSeg is a tissue section image segmentation dataset of lung adenocarcinoma, which is usually used for medical image analysis and computer-aided diagnosis research. The categories contained in the LUAD-HistoSeg dataset include tumor epithelial cells (TE), necrosis (NEC), lymphocytes (LYM), and tumor-associated stroma (TAS). The experimental results are shown in Table 6. It is seen that, compared with the HistoSegNet and SEAM models, our model has a higher mIoU score, which further verifies the effectiveness of our model. \begin{table}
\begin{tabular}{c c c c c c} \hline
**Category** & **SEAM** & **DAEN** & **Category** & **SEAM** & **DAEN** \\ \hline background & 87.3 & **87.7** & diningtable & 51.6 & **54.6** \\ aeroplant & 67.6 & 63.8 & dog & 68.9 & **76.4** \\ bicycle & 40.2 & 39.1 & horse & 75.7 & **77.4** \\ bird & 81.8 & **83.3** & motorbike & 79.1 & 77.5 \\ boat & 42.5 & 36.3 & person & 52.1 & **60.8** \\ bottle & 56.2 & **65.6** & pottedplant & 44.0 & **48.1** \\ bus & 72.9 & **75.3** & sheep & 89.9 & **90.5** \\ car & 75.9 & **77.6** & sofa & 51.9 & **57.3** \\ cat & 60.5 & **71.4** & train & 67.0 & 64.0 \\ chair & 33.5 & **33.7** & tv & 55.2 & 55.2 \\ cow & 78.8 & **79.5** & mIoU & 63.5 & **65.5** \\ \hline \end{tabular}
\end{table} 
Table 4: IoU(%) of each category for DAEN and SEAM models on PASCAL VOC 2012. \begin{table}
\begin{tabular}{c c c c} \hline
**Methods** & **Backbone** & **val** & **Test** \\ \hline AffinityNet [33] & Resnet38 & 61.7 & 63.7 \\ SSDD [26] & Resnet38 & 64.9 & 65.5 \\ Araslanov [25] & Resnet38 & 62.7 & 64.3 \\ PSA [40] & Resnet38 & 62.8 & 63.8 \\ RRM [41] & Resnet38 & 62.6 & 62.9 \\ SEAM [22] & Resnet38 & 64.5 & 65.7 \\ WS-FCN [32] & Resnet38 & 65.0 & 64.2 \\ \hline SEAM *[22] & Resnet38 & 63.5 & 64.7 \\
**Our DAEN *** & Resnet38 & **65.5** & **66.8** \\ \hline \end{tabular}
\end{table} 
Table 5: The performance comparison in terms of mIoU (%) of the proposed DAEN model with other weakly supervised semantic segmentation models on PASCAL VOC 2012. Note: * indicates the results obtained from running with the hyperparameters provided by SEAM on our server.