## 4 Experiments
### Main Experiments  
The MCDCNet is tested on Cityscapes, ADE20K, and COCO-Stuff, and the results are listed in Tables 1, 2, and 3, respectively. In these three tables, we select many networks that use ViT or CNN as decoders for comparison, and the MCDCNet performs well across all three datasets. \begin{table}
\begin{tabular}{l c c c c c} \hline \hline
**Method** & **Reference** & **Backbone** & **Resolutions** & **Params (M)** & **FLOPs (G)** & **mIoU** \\ \hline STDC1 [41] & CVPR 2021 & STDC & 1024 \(\times\) 512 & 14.2 & - & 72.2 \\ STDC1 [41] & CVPR 2021 & STDC & 1536 \(\times\) 768 & 14.2 & - & 74.5 \\ STDC2 [41] & CVPR 2021 & STDC & 1024 \(\times\) 512 & 22.2 & - & 74.2 \\ STDC2 [41] & CVPR 2021 & STDC & 1536 \(\times\) 768 & 22.2 & - & 77.0 \\ SegFormer(h) [42] & NeurIPS 2021 & MiT-B0 & 1024 \(\times\) 512 & 3.8 & 17.7 & 71.9 \\ SegFormer(f) [42] & NeurIPS 2021 & MiT-B0 & 1024 \(\times\) 512 & 22.2 & 125.5 & 76.2 \\ TopFormer-B [43] & CVPR 2022 & MobileNetV2 & 1024 \(\times\) 512 & 5.1 & 3.7 & 70.7 \\ TopFormer-B [43] & CVPR 2022 & MobileNetV2 & 2048 \(\times\) 1024 & 5.1 & 14.9 & 76.3 \\ SeaFormer [44] & ICLR 2023 & SeaFormer-B & 1024 \(\times\) 512 & 8.6 & 3.6 & 72.2 \\ SeaFormer [44] & ICLR 2023 & SeaFormer-B & 2048 \(\times\) 1024 & 8.6 & 14.4 & 77.7 \\ AFFormer [45] & AAAI 2023 & AFFormer-B & 1024 \(\times\) 512 & 3.0 & 8.6 & 73.5 \\ AFFormer [45] & AAAI 2023 & AFFormer-B & 2048 \(\times\) 1024 & 3.0 & 34.4 & 78.7 \\ PIDNet [9] & CVPR 2023 & PIDNet-S & 2048 \(\times\) 1024 & 7.6 & 47.6 & 78.8 \\ PIDNet [9] & CVPR 2023 & PIDNet-M & 2048 \(\times\) 1024 & 34.4 & 197.4 & 80.1 \\ SCTNet [19] & AAAI 2024 & SCTNet-S & 1024 \(\times\) 512 & 4.6 & - & 72.8 \\ SCTNet [19] & AAAI 2024 & SCTNet-B & 1024 \(\times\) 512 & 17.4 & - & 76.5 \\ \hline MCDCNet & Ours & MSCA & 2048 \(\times\) 1024 & 11.8 & 78.4 & 80.3 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: The results on Cityscapes. GFLOPs are tested based on the corresponding resolutions. \begin{table}
\begin{tabular}{l l l c c c} \hline \hline
**Methods** & **Reference** & **Backbone** & **Params (M)** & **FLOPs (G)** & **mIoU** \\ \hline FCN-8s [1] & TPAMI 2017 & MobileNetV2 & 9.8 & 39.6 & 19.7 \\ PSPNet [3] & CVPR 2017 & MobileNetV2 & 13.7 & 52.2 & 29.6 \\ DeepLabV3+ [16] & CVPR 2017 & MobileNetV2 & 15.4 & 25.8 & 38.1 \\ DeepLabV3+ [16] & CVPR 2017 & EfficientNet & 17.1 & 26.9 & 36.2 \\ DeepLabV3+ [16] & CVPR 2017 & ShuffleNetV2-1.5x & 16.9 & 15.3 & 37.6 \\ LR-ASPP [46] & ICCV 2019 & MobileNetV3-Large & 3.2 & 2.0 & 33.1 \\ LR-ASPP [46] & ICCV 2019 & MobileNetV3-Large-r & 1.6 & 1.3 & 32.3 \\ Semantic FPN [47] & CVPR 2019 & ConvMLP-S & 12.8 & 33.8 & 35.8 \\ SegFormer [42] & NeurIPS 2021 & MiT-B1 & 13.7 & 15.9 & 42.2 \\ SegFormer [42] & NeurIPS 2021 & MiT-B0 & 3.8 & 8.4 & 37.4 \\ TopFormer-B [43] & CVPR 2022 & MobileNetV2 & 5.1 & 1.8 & 37.8 \\ SegNeXt-t [6] & NeurIPS 2022 & MSCA & 4.3 & 6.6 & 41.1 \\ SegNeXt-s [6] & NeurIPS 2022 & MSCA & 13.9 & 15.9 & 44.3 \\ SeaFormer-L [44] & ICLR 2023 & SEA-attention & 14.0 & 6.5 & 42.7 \\ ViG-T [48] & Unpublished 2024 & VIG & 8.0 & 14.9 & 43.8 \\ SCTNet-B [19] & AAAI 2024 & CNN & 17.4 & - & 43.0 \\ \hline MCDCNet & Ours & MSCA & 11.8 & 37.0 & 44.4 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: The results on ADE20K. The input image size is 512 \(\times\) 512, and the batch size is 16. **Results on Cityscapes. ** From Table 1, our method achieves the best mIoU of 80.3 with 11.8 M parameters and 78.4 G of computation. The closest result is from PIDNet [9] which uses a three-branch network method and adopts PIDNet-M as the backbone, achieving an mIoU of 80.1 with 7.6 M parameters and 197.4 G of computation. PIDNet uses a multi-branch approach to overcome the constraints of conventional single-branch networks in feature processing. However, this also introduces a significant increase in computational cost. Our method significantly outperforms PIDNet in balancing computation and accuracy. The latest work in 2024, SCTnet [19], addresses semantic segmentation through knowledge distillation.