## 1 Introduction
Achieving the balance between accuracy and speed in real-time segmentation remains challenging. To address this balance, certain architectures leverage lightweight convolutional frameworks to improve efficiency and reduce computational costs. Efficient Spatial Pyramid Network (ESPNet) [18] combines pointwise and dilated convolutions, while Efficient Spatial Pyramid Network Version 2 (ESpNetV2) [19] extracts features with a larger receptive field. Rethinking Model Scaling for Convolutional Neural Networks (EfficientNet) [20] employs a model scaling strategy to adjust network depth, width, and resolution. Segmentation Next (SegNeXt) [21] introduces a simple and cost-effective convolutional encoder, and Fast Segmentation Convolutional Neural Network (Fast-SCNN) [22] proposes an efficient downsampling module. Although these structures enhance speed and reduce computational complexity, they often struggle to achieve the desired performance and may result in the loss of spatial details. To overcome this issue, multi-scale feature fusion has become a common strategy to enhance accuracy and robustness. The Pyramid Scene Parsing Network (PSPNet) [23] uses pyramid pooling to extract contextual information, Global Convolutional Network (GCN) [24] employs large-kernel convolutions to increase receptive field size, and Deep Labelling Version 3 Plus (DeepLabV3+) [25] integrates dilated convolutions and Atrous Spatial Pyramid Pooling (ASPP) modules to improve accuracy. However, these high-accuracy networks incur significant computational costs, limiting their applications in fields like autonomous driving. Dual-Resolution Network (DDRNet) [26] improves upon the Residual Resolution Network (Res2 Net) [27] by introducing a Deep Aggregation Pyramid Pooling Module (DAPPM) to enhance context aggregation. Location-Based Normalization with Attention Augmentation (LBN-AA) [28] employs a spatial pyramid (DASPP) inspired by human vision [29] and dilated convolutions to expand the receptive field. Multi-scale integration increases efficiency, but simple architectures may lose detailed features, reducing accuracy. Paddle-Paddle Lite Segmentation Network (PPLiteSeg) [30] uses a pyramid pooling module (SPPM) to enhance accuracy with minimal overhead, but eliminating shortcut connections can impact feature fidelity. More recently, research has focused on achieving both efficiency and multi-task or multi-modal capabilities. Prototype-based Efficient Maskformer (PEM) [31] proposed a real-time architecture for both semantic and panoptic segmentation, while the You Only Segment Once (YOSO) framework [32] further optimized efficiency for multi-task predictions. However, there is still a significant speed gap compared to highly specialized, single-task architectures, indicating a trade-off between multi-task flexibility and peak efficiency. The Bilateral Segmentation Network with Transformer (BiSeNetFormer) [33] adapts efficient two-stream semantic segmentation networks for multi-task capabilities via mask classification, yet demonstrates significantly lower accuracy on complex tasks compared to state-of-the-art methods. Context-guided Semantic Fusion Network (CSFNet) [34] leverages a Cosine Similarity Attention Fusion Module (CS-AFM) and an optimized encoder-decoder; while achieving competitive accuracy in RGB-X semantic segmentation, it does not consistently reach state-of-the-art performance across all scenarios. Deep Common Feature Mining (DCFM) [35] decomposes video features into reusable common and independent features, thereby avoiding time-consuming re-calibration; however, keyframe processing remains the primary latency bottleneck, limiting its suitability for ultra-low-latency applications. Despite the substantial progress observed in contemporary semantic segmentation research, several challenges remain salient. Although CNNs are highly effective for semantic segmentation, they often face challenges such as high computational costs and a trade-off between accuracy and inference speed.