## 3 Materials and Methods
### Datasets and Implementation Details  
For the datasets used, to train deep learning models for image segmentation, annotated datasets such as PASCAL VOC, COCO, and SBD are commonly used to pre-train models and evaluate segmentation performance [41; 42; 43]. However, considering that the specific application scenario of this paper is centered on the field of new media content post-production (e.g., TV shows, films, news, and variety programs), where frontal faces serve as the primary processing objects and high requirements for image clarity are imposed, it is therefore necessary to construct a custom dataset to meet specific needs. In this work, we constructed a dataset by capturing 864 images through manual screenshots from diverse video content, covering various scenes, including indoor, outdoor, meetings, single-person, and multi-person settings. The exact case numbers are listed in Table 1. The data of \"TV shows\" were used for modeling, where we adopted a static data-splitting strategy (hold-out method) to divide the \"TV shows\" data into training and test sets with an 8:2 ratio, where the training set was used for model learning and the test set for performance evaluation. The test set was manually labeled using Labelme [44]. The data from other scenarios were used for a generalization test.  
For the evaluation metrics, this study employs the Intersection over Union (IoU) and Dice coefficient as evaluation metrics for face segmentation. We firstly computed the score of each test sample and then calculated the average score and the standard error of the test set. The IoU metric is suitable for assessing the overall coverage of facial regions, particularly in scenarios requiring precise alignment of facial contours. When facial edges are blurred (e.g., in low-resolution images or profile contours) or occlusions exist, the Dice coefficient is more sensitive to boundary details and can accurately reflect the segmentation details of facial contours. The combination of the two ensures high precision and fineness in face segmentation. The dice coefficient is a set similarity measurement function, which is often used to calculate the similarity of two samples (range: [0, 1]):  
\[Dice=\frac{2|X\cap Y|}{|X|+|Y|},IoU=\frac{|X\cap Y|}{|X\cup Y|} \tag{1}\]  
where \(|X|\) and \(|Y|\) indicate the divided ground truth and predict-mask.  
To validate the statistical significance of Dice score improvements, we performed independent \(t\)-tests on the test results from each test scenario. A \(p\)-value < 0.05 was considered significant, indicating at least 95% confidence that the observed gains were not due to random chance.  
For the implementation details, we use SAM to generate a segmentation mask on the training image with sparse labels as prompts and the segmentation mask as a pseudo-label to train the model. In using SAM to generate the segmentation mask, we adopted the standard settings of the SAM model provided in the official library. We used the SAM model with ViT-B as the backbone. We implemented our method with PyTorch 1.13.1 with CUDA 11.6 acceleration and ran experiments on NVIDIA RTX 3060 (designed by NVIDIA, manufactured by Samsung Foundry in Hwaseong, South Korea) GPUs (20 GB VRAM each), an Intel i5-10400F CPU, and 16 GB RAM. All models were trained at 512 \(\times\) 512 resolution  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Scenario** & **TV Show** & **Film** & **Variety Show** & **Life Photo** \\ \hline Count & 333 & 200 & 120 & 211 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Case count number in each specific scenario.  
with a batch size of 12; the learning rate was initialized to 0.0001 and the iteration cycle was 50, requiring approximately 5 min for convergence.