## 4 Experiments
### Comparison to SOTA on Different Partition Protocols  
To verify the background and foreground discrimination performance of pseudo-labels on unlabeled data, we temporarily used real labels for unlabeled data from PASCAL VOC 2012. Table 1 shows a comparison between our proposed model and the results of converting the predictions of SOTA models into binary predictions. Table 1 shows a comparison of the proposed method with the results of converting the predictions of SOTA models into binary predictions on the unlabeled data of PASCAL VOC 2012. Table 1 shows the accuracy (Acc), macro-F1 score (\(F_{1}\)), background IoU (BG IoU), foreground IoU (FG IoU), and mean IoU (mIoU), showing that our method is experimentally superior in most indicators. In particular, in 1/8, a small labeled ratio experiment, some BG IoU increased and FG IoU increased significantly at the same time. This can be interpreted as a result of using more accurate foreground pseudo-labels in a small real information environment. In conclusion, under the 1/8, 1/4, and 1/2 partition protocols, our method outperforms the highest previous studies' mIoU scores for background and foreground classification by +1.84%, +0.36%, and +0.003%, respectively. Table 2 shows the mIoU score on the validation set of PASCAL VOC 2012, comparing our re-implementation results of existing SOTA models with the case trained using the object areas proposed by our model \begin{table}
\begin{tabular}{c c c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multirow{2}{*}{**Acc**} & \multicolumn{3}{c}{**1/8**} & \multicolumn{3}{c}{**1/4**} & \multicolumn{3}{c}{**1/2**} \\  & & \multicolumn{1}{c}{**BG**} & \multicolumn{1}{c}{**FG**} & \multicolumn{1}{c}{**IoU**} & \multicolumn{1}{c}{**IoU**} & \multicolumn{1}{c}{**Acc**} & \multicolumn{1}{c}{**F\({}_{1}\)**} & \multicolumn{1}{c}{**BG**} & \multicolumn{1}{c}{**FG**} & \multicolumn{1}{c}{**mIoU**} & \multicolumn{1}{c}{**Acc**} & \multicolumn{1}{c}{**F\({}_{1}\)**} & \multicolumn{1}{c}{**BG IoU**} & \multicolumn{1}{c}{**FG**} & \multicolumn{1}{c}{**IoU**} & \multicolumn{1}{c}{**IoU**} & \multicolumn{1}{c}{**mIoU**} \\ \hline CPS * & 0.917 & 0.902 & 88.822 & 75.810 & 82.316 & 0.916 & 0.900 & 88.593 & 75.422 & 82.007 & 0.917 & 0.900 & 88.839 & 75.196 & 82.018 \\ PS-MT * & 0.919 & 0.906 & 88.943 & 76.452 & 82.697 & **0.930** & 0.917 & 90.326 & 79.429 & 84.877 & **0.931** & 0.919 & **90.465** & 79.751 & 85.108 \\ U\({}^{2}\)PL* & 0.901 & 0.884 & 86.617 & 72.268 & 79.443 & 0.917 & 0.904 & 88.668 & 76.631 & 82.649 & 0.925 & 0.912 & 89.746 & 78.259 & 84.002 \\ \hline Ours & **0.927** & **0.915** & **89.924** & **79.160** & **84.542** & **0.930** & **0.919** & **90.337** & **80.136** & **85.237** & 0.930 & **0.920** & 90.273 & **79.949** & **85.111** \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Binary performance comparison with SOTA for unlabeled data of PASCAL VOC 2012 under different partition protocols. Predictions from state-of-the-art models are converted to binary predictions, and all methods are based on DeepLabv3+. * represents our re-implementation. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Method** & **1/8** & **1/4** & **1/2** \\ \hline CPS * & 74.537 & 75.557 & 75.786 \\ PS-MT * & 74.072 & 74.391 & 75.803 \\ U\({}^{2}\)PL * & 73.004 & 74.808 & 76.004 \\ \hline CPS (w/Ours) * & **74.849** & **76.112** & **76.153** \\ PS-MT (w/Ours) * & **74.158** & **75.702** & **75.839** \\ U\({}^{2}\)PL (w/Ours) * & **74.945** & **76.742** & **76.908** \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Comparison of SOTA and models trained with the help of our model on the PASCAL VOC 2012 val set under different partitioning protocols. All methods are based on DeepLabv3+, and * represents our re-implementation. in the generation of pseudo-labels for each SOTA model. In all the experimental results in Table 2, SOTA models combined with our model show better performance than existing models.