## 1 Introduction  
Face parsing, which is also referred to as fine-grained facial semantic segmentation, is a fundamental task in the field of computer vision. Its goal is to perform pixel-level classification of various semantic components in facial images, such as eyes, nose, mouth, eyebrows, and more. Compared with traditional face detection and recognition tasks, face parsing places greater emphasis on structural hierarchy modeling and part-level detail representation, facilitating a high-precision semantic understanding of facial regions. This technique has been widely applied in various practical scenarios, including virtual reality [1], augmented reality [2], intelligent beautification, expression-driven [3] face synthesis, human-computer interaction, and identity recognition, and holds significant research value and application prospects. However, compared to general semantic segmentation tasks [4], face parsing presents more stringent challenges in structural complexity, semantic boundary clarity, and spatial dependency modeling. First, facial components are densely arranged with fine-grained structures, and large inter-individual variations in facial features such as shape, scale, and spatial layout make it difficult for models to learn a unified and effective feature representation. Second, real-world conditions such as facial expression variation, pose changes, lighting fluctuations, and occlusions (e.g., hair and glasses) introduce severe interference in local detail extraction, undermining model robustness. Furthermore, facial regions exhibit strong semantic interdependencies--for instance, structural correlations exist between the eyebrows and eyes and between the lips and mouth region--which demand powerful global modeling capabilities to capture long-range relationships. In boundary modeling, the visual similarity of facial parts, like hairline and eyebrows, to the background in color or texture often leads to boundary ambiguity and misclassification. Especially in high-resolution image processing scenarios [5], coarse feature restoration and boundary information loss during upsampling exacerbate the deficiency in key structural representation, thereby degrading overall segmentation quality. In recent years, despite the significant progress made in semantic segmentation methods based on deep learning, widely adopted strategies such as multi-scale feature extraction, self-attention mechanisms, and global context modeling still face many challenges in facial parsing tasks. Conventional CNN-based networks [6] are limited by their local receptive fields, making it difficult to effectively model long-range dependencies. Although Transformer-based attention mechanisms [7] possess strong contextual modeling capabilities, their high computational cost on high-resolution feature maps often results in resource bottlenecks. Moreover, current upsampling stages generally suffer from coarse feature fusion and loss of boundary details. To address the above challenges, this paper proposes a novel facial parsing network named FP-Deeplab. Built upon the DeepLabv3+ framework [8], the proposed network integrates contextual awareness, multi-scale information fusion, and fine-grained attention-guided strategies to systematically enhance both global structure modeling and local detail preservation in facial semantic segmentation. FP-Deeplab constructs hierarchical modules to enrich semantic representations and recover structural details, which are particularly suitable for challenging scenarios such as blurry facial boundaries, large-scale variations, and hard-to-segment long-tail categories. Specifically, the main contributions of this paper are as follows:  
1. A Cross-Axis Attention mechanism is introduced, which establishes axial attention along both horizontal and vertical spatial dimensions to enhance long-range pixel-level dependency modeling, improving the semantic consistency and segmentation robustness of facial structures. 2.