## 1 Introduction  
Semantic segmentation is a crucial task in computer vision that involves assigning semantic labels to each pixel in an image. From the Fully Convolutional Networks (FCNs) proposed by [PERSON] [1], the per-pixel classification method has become the mainstream solution for semantic segmentation. The typical FCN architecture replaces the fully connected layer [2] behind a traditional CNN with a convolution layer, allowing the network to achieve semantic segmentation through per-pixel classification. Following this approach, many excellent methods have emerged. PSPNet [3] and HRNet [4] use a multi-branch CNN approach to achieve multi-resolution fusion, enhancing the network's feature extraction capability. Metaformer [5] and SegNeXt [6] demonstrate the effectiveness of the ViT [7] structure for feature fusion. However, these methods usually only focus on improving the performance of network feature extraction, while ignoring the inherent limitations of per-pixel classification methods. The per-pixel classification method directly restores the features captured by the backbone to the ground-truth (GT) size. Despite its simplicity and effectiveness, this method does not fully capitalize on the features captured by the network. SparseInst [8] modifies the traditional decoder into a dual-branch structure, enhancing network performance by combining the IAM branch and the mask branch. PIDNet [9] further upgrades the decoder to a tri-branch structure, utilizing features extracted by the backbone to simultaneously generate semantic, spatial, and boundary information, and integrates these three types of information to improve network performance. To change this situation, MaskFormer [10] proposes a method that adapts mask classification, originally designed for instance segmentation, to semantic segmentation. This approach unifies instance and semantic segmentation. Different from semantic segmentation, instance segmentation requires further distinction of different instances within the same category. Mask classification methods generate a collection of masks, with each mask corresponding to a distinct class. In this more flexible and effective way, the network can identify and categorize objects within an image on their masks or segmented regions. Following MaskFormer, Oneformer [11] further unifies semantic, panoptic, and instance segmentation. Oneformer integrates the training of multiple tasks into the same network architecture, enabling a single network to handle multiple tasks. Additionally, it uses task tokens to condition the model, allowing the model to guide the tasks during training and dynamically adapt to tasks during inference. Comparing the networks mentioned above with traditional semantic segmentation networks, we have noticed that these networks replace per-pixel classification methods with more complex and effective ones, and use corresponding designs to support the implementation of these methods. MaskFormer replaces per-pixel classification with mask classification. To make the network architecture compatible with the mask classification method, it builds upon the backbone by adding two new decoders. One decoder is used for extracting per-pixel embedding, while the other is used for extracting per-segment embedding. Oneformer is based on mask classification and designs a segmentation decoder suitable for multiple tasks. To support this segmentation decoder, a task token module is added to the network. However, both of these networks have their drawbacks. MaskFormer uses the simplest form of mask classification, leaving room for further improvements of this design. On the other hand, OneFormer incorporates many complex structures to handle multiple tasks, but these structures are not simple or efficient enough. This motivates us to design a more efficient network to replace these per-pixel classification networks. To tackle the issues outlined earlier, this paper proposes a network that utilizes modified mask classification combined with an adaptive dilated convolutional (MCDCNet).