## 3 Methods
### SimFA Module
The operations are defined as  
\[L_{p} =\text{Conv}_{1\times 1}(\text{DWConv}_{3\times 3}(L)), \tag{11}\] \[L_{d} =\text{Conv}_{1\times 1}\big{(}\text{GELU}(L_{p})\big{)}. \tag{12}\]  
The global feature \(E_{n}\) and the local detail feature \(L_{d}\) are then fused via element-wise addition to generate the integrated feature map \(H_{s}\):  
\[H_{s}=E_{m}+L_{d}. \tag{13}\]  
To better integrate with low-level features, SimFA performs unified channel alignment after feature concatenation and adopts a structured dual-branch mechanism to semantically distinguish between global modeling and local enhancement, thereby avoiding the information conflict often caused by simple concatenation. Meanwhile, the module plays a fine-grained guidance role during the high-resolution feature reconstruction stage, enablinga more effective combination of shallow features with upsampled semantic information and improving the preservation of boundary details. To ensure semantic consistency during feature integration, the similarity between the upsampled decoder features and the encoder features is implicitly modeled through both spatial alignment and adaptive response modulation. This is achieved by capturing contextual correlation in the global branch and enhancing boundary-localized similarity in the local branch, which jointly determines the semantic similarity distribution across spatial positions. The resulting fused representation \(H_{s}\) thus encodes both semantic consistency and discriminative detail, forming a basis for effective low-high feature merging. Although the above mechanism achieves efficient feature aggregation through nonlocal interaction and local detail estimation, it still lacks dynamic modeling along the channel dimension, which may result in insufficient activation of fine-grained features and affect segmentation quality. To address this issue, SimFA further incorporates a channel-adaptive modulation mechanism inspired by SimAM [30]. Leveraging the ability of SimAM to model channel-wise attention, this mechanism computes intra-channel variance to adaptively modulate feature responses, assigning more appropriate activation strengths to different pixels. This allows the model to emphasize critical facial regions while highlighting important regions, such as boundaries and lips, and simultaneously suppress redundant background information, such as surrounding hair or earrings, thereby reducing interference and improving both segmentation accuracy and boundary clarity. Additionally, the module performs fine-grained modulation during the upsampling phase, where the fused channel attention mask is used to reweight pixel responses, enabling adaptive feature recalibration. This effectively mitigates information redundancy and semantic conflict during semantic feature fusion, enhancing high-resolution feature recovery and yielding sharper edges and more precise contours of facial regions. The modulation process is calculated as  
\[X_{\text{simam}}=\frac{(H_{S}-\mu)^{2}}{4\Big{(}\frac{e^{2}}{N}+\epsilon\Big{)} }+0.5, \tag{14}\]  
Figure 3: The architecture of the proposed SimFA module. The input features are first split into two parallel branches: **E** (global branch), which captures semantic structures via nonlocal interactions, and **L** (local branch), which focuses on boundary and texture refinement. The outputs of the two branches are subsequently fused and adaptively modulated through a channel-wise attention mechanism to enhance fine-grained segmentation accuracy. where \(\mu\) is the channel-wise mean, and \(\sigma^{2}\) is the channel variance. Finally, the modulated feature map is obtained by element-wise multiplication with the Sigmoid-activated attention map [31]:  
\[H_{\text{SimFA}}=H_{s}\cdot\text{Sigmoid}(x_{\text{simam}}), \tag{15}\]  
Overall, SimFA is not a simple fusion module but a comprehensive feature refinement framework that balances global structure and fine-grained details.