## 5 Discussion  
The proposed weakly supervised face segmentation framework introduces a novel paradigm that overcomes critical limitations of existing weakly supervised methods. By harnessing the Segment Anything Model (SAM) for pseudo-label generation through \begin{table}
\begin{tabular}{c c c c} \hline \hline \(\alpha\) & **Dice** & **IoU** & \(p\)**-Value** \\ \hline \(\alpha=0.9\) & 0.82 \(\pm\) 0.02 & 0.69 \(\pm\) 0.03 & \textless{}0.001 \\ \(\alpha=0.99\) & 0.84 \(\pm\) 0.01 & 0.71 \(\pm\) 0.02 & \textless{}0.05 \\ \(\alpha=0.999\) (Ours) & 0.85 \(\pm\) 0.01 & 0.73 \(\pm\) 0.02 & - \\ \(\alpha=0.999\) & 0.83 \(\pm\) 0.01 & 0.70 \(\pm\) 0.02 & \textless{}0.05 \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Analysis of the hyperparameter \(\alpha\), which controls the dual-model learning process. \(p\)-Values are computed against \(\alpha=0.999\) (ours). \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Method** & **Dice** & **IoU** & \(p\)**-Value** \\ \hline CPS & 0.81 \(\pm\) 0.02 & 0.68 \(\pm\) 0.03 & \textless{}0.001 \\ \hline Ours & 0.85 \(\pm\) 0.01 & 0.73 \(\pm\) 0.02 & - \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Comparison with alternative learning design, Cross Pseudo Supervision (CPS). \(p\)-Values (Dice) are computed using independent \(t\)-tests. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Run** & **Dice Mean** & **Dice Std** & **IoU Mean** & **IoU Std** \\ \hline
1 & 0.8490 & 0.0095 & 0.7280 & 0.0190 \\
2 & 0.8510 & 0.0105 & 0.7320 & 0.0210 \\
3 & 0.8485 & 0.0098 & 0.7275 & 0.0195 \\
4 & 0.8515 & 0.0102 & 0.7325 & 0.0208 \\
5 & 0.8495 & 0.0099 & 0.7290 & 0.0197 \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Five repeated experiments with amplified variance on TV show test set. intuitive point-based supervision, it eliminates laborious pixel-level annotation while achieving accuracy comparable to fully supervised models. The core innovation lies in its EMA-driven teacher-student architecture, where a stabilized teacher model (updated via exponential moving average of student weights) generates reliable pseudo-labels to guide student model training. This unidirectional mutual learning mechanism ensures robust performance in challenging scenarios such as separating faces from dynamic camera movements in variety shows or intricate period costumes, where conventional approaches falter due to annotation scarcity or noise propagation. Our experimental results demonstrate that the proposed framework achieves notable improvements in accuracy compared to fully supervised baselines while reducing annotation costs, as validated by the optimization of twin models through iterative training. This dual-model architecture ensures robustness and adaptability, enabling precise segmentation even in complex scenarios (e.g., hair, glasses, or shadows). Unlike existing weakly supervised methods that often compromise on boundary precision, our approach combines SAM's contextual understanding with UNet++'s fine-grained feature extraction, addressing a key limitation of prior work where traditional weak supervision fails in facial details. This work contributes to the broader goal of democratizing AI-driven media production by reducing reliance on manual annotation. By enabling cost-effective, scalable face segmentation, it opens avenues for small-scale studios or indie filmmakers to adopt advanced post-production tools. Furthermore, the framework's modular design (e.g., decoupling pseudo-label generation from network training) could inspire similar approaches in other weakly supervised vision tasks, such as object segmentation in surveillance or medical imaging. In this study, the model was trained exclusively on an image dataset confined to TV show scenes. To systematically evaluate its generalization capability, we constructed a test set encompassing diverse real-world scenarios (e.g., TV shows, films, news, variety shows, and life photos) during the evaluation phase.