## 1 Introduction  
Semantic segmentation is a fundamental task in computer vision that involves assigning a semantic label to every pixel of an image, enabling detailed scene understanding. This pixel-wise prediction is vital for numerous applications, most notably in real-timesystems for areas like autonomous driving [1; 2]. Early work using Convolutional Neural Networks (CNNs), including seminal architectures such as Fully Convolutional Networks (FCN) [3] and Convolutional Networks for Biomedical Image Segmentation [4] (U-Net), established the initial approaches for this task, but often faced challenges in achieving both high accuracy and sufficient speed. FCN [3] pioneered end-to-end training models for semantic segmentation, directly generating pixel-level predictions without requiring intermediate processing layers. To address the issues of feature map size reduction and information loss during the convolution process, the encoder-decoder architecture was proposed, which has since been widely adopted in subsequent networks, such as Segmentation Network (SegNet) [5] and U-Net [4]. SegNet [5] employs pooling indices to transfer encoder feature information to the decoder, restoring spatial details, while U-Net [4] improves segmentation accuracy through skip connections. However, both models fail to fully address the contextual information requirements. Recently, Transformer-based techniques have gained significant attention, leading to methods like Segmentation Transformer (SegFormer) [6], Token Pyramid Transformer (TopFormer) [7], and Squeeze-enhanced Axial Transformer (SeaFormer) [8]. SegFormer [6] improves accuracy and speed with a hierarchical Transformer encoder and efficient MLP decoder, but it struggles with handling large-scale datasets due to the complexity of its architecture. TopFormer [7] combines CNNs with transformers for scale-sensitive features, yet its integration of these two components can be computationally demanding and may reduce scalability. SeaFormer [8] utilizes a compressed axial transformer and dual-path structure to optimize performance, though it requires high memory bandwidth, limiting its suitability for real-time applications. Recent advancements in computer vision also aim to develop architectures capable of handling diverse image segmentation tasks without requiring modifications to loss functions or components. Masked-attention Mask Transformer (MaskFormer) [9] proposed a specialized mask classification architecture, setting new benchmarks for semantic and panoptic segmentation, which Masked-attention Mask Transformer for Universal Image Segmentation (Mask2 Former) [10] further enhanced in results and convergence speed. More recently, k-means Mask Transformer for DeepLab (kMaX-DeepLab) [11] explored k-means clustering as an alternative to traditional attention for this task. Despite significant performance improvements and their multi-task capabilities, these models are resource-intensive, posing a major hurdle for real-world deployment on edge devices with limited computational power. In order to alleviate the computational burden and ensure that inference speed meets application requirements, the development of efficient semantic segmentation methods has become a critical research priority. Efficient Neural Network (ENet) [12] designed a low-latency architecture, Image Cascade Network (ICNet) [13] introduced a cascaded feature fusion module, and Bilateral Segmentation Network (BiSeNetV1) [14] and Bilateral Segmentation Network V2 (BiSeNetV2) [15] adopted dual-path architectures to enhance segmentation accuracy and efficiency. Short-Term Dense Concatenate Network (STDCN-Net) [16] improves computational efficiency by progressively compressing the number of feature channels and enhancing the capture of multi-scale semantic information. The Scale-aware Strip Attention Module (SSAM) in Scale-Sensitive Feature Pyramid Network (S\({}^{2}\)-FPN) [17] effectively models long-range dependencies in the context while reducing computational overhead.