## 4 Results  
### Comparison Results  
We compare our method with two weakly supervised segmentation approaches that utilize point-based annotations [45; 46]. The results are shown in Tables 2 and 3. Our method consistently achieves the highest Dice and IoU scores across all scenarios, demonstrating superior segmentation accuracy. For instance, in the TV show scenario, our Dice score (\(0.85\pm 0.01\)) outperforms TMI 2021 (+10%) and CVPR 2023 (+5%). Notably, the proposed method exhibits significant performance gains in complex scenes such as variety show (Dice: \(0.72\pm 0.02\) vs. TMI 2021: \(0.65\pm 0.04\)), which can be attributed to its enhanced robustness to dynamic lighting and occlusions.  
Furthermore, our results demonstrate smaller standard deviations compared to baseline methods, indicating higher consistency and generalization stability. In the challenging scenarios, existing methods suffer from severe performance degradation--for example, TMI 2021's IoU drops to \(0.45\pm 0.05\) in the variety show category, while our approach maintains strong performance.  
These findings collectively validate that our design effectively addresses challenges inherent to diverse visual contexts, particularly under uncontrolled environments. The marginally improved metrics over CVPR 2023 also suggest that our technical innovations better exploit point annotations for precise boundary localization.  
As illustrated in Figure 4, our method demonstrates superior boundary precision and reduced false negatives compared to other methods. In complex scenarios such as period films (e.g., film row) and variety shows (e.g., variety show row), the proposed SAM-based pseudo-labeling strategy effectively captures subtle facial contours despite elaborate costumes or dynamic poses, which often fail due to fixed thresholding or lack of global context modeling. Notably, the EMA-driven teacher-student architecture mitigates noise propagation, as evidenced by the clean boundaries in our results versus the fragmented outputs of TMI 2021. This stability is critical for reducing false negatives in challenging cases like partially occluded faces (TV show column), where traditional methods struggle with ambiguous edges.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multicolumn{3}{c}{**TV Show**} & \multicolumn{3}{c}{**Film**} \\ \cline{2-7}  & **Dice** & **IoU** & _p_**-Value** & **Dice** & **IoU** & _p_**-Value** \\ \hline TMI 2021 [45] & \(0.75\pm 0.03\) & \(0.60\pm 0.04\) & \(<\)0.001 & \(0.80\pm 0.02\) & \(0.65\pm 0.03\) & \(<\)0.001 \\ CVPR 2023 [46] & \(0.80\pm 0.02\) & \(0.68\pm 0.03\) & \(<\)0.05 & \(0.85\pm 0.02\) & \(0.70\pm 0.02\) & \(<\)0.05 \\ \hline Ours & \(0.85\pm 0.01\) & \(0.73\pm 0.02\) & — & \(0.89\pm 0.01\) & \(0.82\pm 0.01\) & — \\ \hline \hline \end{tabular}
\end{table}
Table 2: Test results of the compared methods and our method on TV show and film scenarios. _p_-Values (Dice) are computed using independent _t_-tests.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multicolumn{3}{c}{**Variety Show**} & \multicolumn{3}{c}{**Life Photo**} \\ \cline{2-7}  & **Dice** & **IoU** & _p_**-Value** & **Dice** & **IoU** & _p_**-Value** \\ \hline TMI 2021 [45] & \(0.65\pm 0.04\) & \(0.45\pm 0.05\) & \(<\)0.001 & \(0.80\pm 0.03\) & \(0.65\pm 0.04\) & \(<\)0.001 \\ CVPR 2023 [46] & \(0.68\pm 0.03\) & \(0.50\pm 0.04\) & \(<\)0.05 & \(0.83\pm 0.02\) & \(0.70\pm 0.03\) & \(<\)0.05 \\ \hline Ours & \(0.72\pm 0.02\) & \(0.52\pm 0.03\) & — & \(0.88\pm 0.01\) & \(0.79\pm 0.02\) & — \\ \hline \hline \end{tabular}
\end{table}
Table 3: Test results of the compared methods and our method on variety show and life photo scenarios. _p_-Values (Dice) are computed using independent _t_-tests.