## 5 Discussion  
To gain a deeper understanding of the limitations of FP-Deeplab in fine-grained facial semantic segmentation, we performed a detailed error analysis based on the model's predictions on the CelebAMask-HQ dataset. As shown in the confusion matrix in Figure 7, several representative error patterns emerge.  
One prominent issue is the confusion between semantically and visually similar facial parts, especially those with symmetrical spatial distributions. Misclassifications frequently occur between left and right brows, left and right eyes, and upper and lower lips. These categories share highly similar visual features and are often difficult to distinguish, particularly in low-resolution or occluded regions. Additionally, confusion between ears and earrings is observed, likely due to their spatial adjacency and overlapping textures. These inter-class ambiguities indicate that the current feature representations, though effective globally, are occasionally insufficient for capturing subtle local distinctions.  
\begin{table}
\begin{tabular}{l l l l l} \hline \hline
**Backbone** & **Mean F1** & **Mean IoU** & **Params (M)/10\({}^{6}\)** & **GFLOPs** \\ \hline MobileNetV2 & 84.8 & 75.64 & 7.2 & 51.8 \\ Xception & 85.0 & 75.89 & 56.7 & 165.8 \\ \hline \hline \end{tabular}
\end{table}
Table 4: The impact of using Xception and MobileNetV2 as backbones on the segmentation performance of FP-Deeplab.  
Boundary-level errors are another source of segmentation inaccuracy. In particular, the transitions between hair and background or between neck and clothing tend to be imprecise. These regions often exhibit weak texture contrast and complex edge patterns, which pose challenges for the network in maintaining spatial consistency. Such boundary imprecision suggests the need for explicit edge modeling or auxiliary supervision that enhances the network's sensitivity to fine structural transitions.  
Moreover, FP-Deeplab demonstrates reduced accuracy in detecting small or infrequent facial components such as hats, earrings, and necklaces. Due to their limited spatial extent and class imbalance in the dataset, these regions are more prone to omission or misclassification. This observation reflects the model's difficulty in learning reliable representations for spatially constrained or under-represented categories and highlights the potential benefit of introducing class-aware weighting strategies or attention mechanisms to amplify such features.  
In summary, this error analysis reveals that FP-Deeplab's main performance bottlenecks stem from symmetric structure confusion, boundary localization imprecision, and small-object segmentation weakness. These findings not only provide a clearer picture of the model's current challenges but also inform future directions for improvement, such as integrating boundary-aware refinement, semantic decoupling strategies, and enhanced training schemes for long-tailed categories.  
Figure 7: Normalized confusion matrix of the FP-Deeplab model on the CelebAMask-HQ test set. High values along the diagonal indicate strong prediction accuracy for major facial components such as background, skin, hair, and nose. However, notable inter-class confusion is observed between symmetric or spatially adjacent parts—such as left/right brows, eyes, and lips—ears and earrings, and neck-related categories. This highlights common challenges in fine-grained facial parsing, especially for small or visually similar regions.