## 4 Experiments
### Ablation Study  
Without loss of generality, various ablation experiments in this section are conducted on ADE20K training and validation sets. **Ablation on the network design. ** We carefully considered the structural design of the encoder. Based on the data in Table 4, the SEM module brings 8.0 mIoU gains and increases by 0.6 M Params. This indicates that the SEM module significantly improves the mIoU while keeping the Params nearly unchanged, and the SEM module can effectively improve the segmentation performance of the backbone consisting of MSCA. It also can be seen that the IAM-based decoder brings 5.5 mIoU gains and increases by 7.0 M Params. Compared \begin{table}
\begin{tabular}{l l l l l l} \hline \hline
**Methods** & **Reference** & **Backbone** & **Params (M)** & **FLOPs (G)** & **mIoU** \\ \hline PSPNet [3] & CVPR 2017 & MobileNetV2 & 13.7 & 52.2 & 30.1 \\ DeepLabV3+ [16] & CVPR 2017 & MobileNetV2 & 15.4 & 25.8 & 29.8 \\ DeepLabV3+ [16] & CVPR 2017 & EfficientNet & 17.1 & 26.9 & 31.4 \\ LR-ASPP [46] & ICCV 2019 & MobileNetV3 & - & 2.3 & 25.1 \\ HRFormer [49] & NeurIPS 2021 & HRFormer-S & 13.5 & 109.5 & 37.9 \\ SegFormer [42] & NeurIPS 2021 & MiT-B1 & 13.7 & 15.9 & 40.2 \\ SegFormer [42] & NeurIPS 2021 & MiT-B0 & 3.8 & 8.4 & 35.6 \\ SegNeXt-t [6] & NeurIPS 2022 & MSCA & 4.3 & 6.6 & 38.7 \\ \hline MCDCNet & Ours & MSCA & 11.8 & 37.0 & 40.4 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: The results on COCO-Stuff. The settings are the same as those mentioned in Table 2.  
to traditional per-pixel classification heads, the IAM-based decoder greatly enhances the network's ability to analyze features and significantly restores the original image. **Different scales for the backbone's output. ** We test the impact of using different output scales of the backbone on the network's performance. Based on the data in Table 5, using features from \(\{\frac{1}{4},\frac{1}{8},\frac{1}{16},\frac{1}{32}\}\) can achieve the best performance with similar parameter numbers. Using features from \(\{\frac{1}{16},\frac{1}{32}\}\) and \(\{\frac{1}{8},\frac{1}{16},\frac{1}{32}\}\) obtains similar performance, but both are notably lower than using features from \(\{\frac{1}{4},\frac{1}{8},\frac{1}{16},\frac{1}{32}\}\). Using features from \(\{\frac{1}{32}\}\) achieves the worst performance with the fewest Params. Therefore, we use the features from \(\{\frac{1}{4},\frac{1}{8},\frac{1}{16},\frac{1}{32}\}\) in all the experiments. **Ablation on the SEM module design. ** We have given careful consideration to the structural design of SEM. Based on the data in Table 6, using multi-scale dilated convolution (MDC) alone can only achieve 42.2 mIoU with \(11.8M\) Params. When both multi-scale dilated convolutions and SE are included, the accuracy is improved by 0.6 mIoU with exactly the same Params. The SE module further organizes the features obtained from the multi-scale dilated convolution, enhancing the network's performance. After using the S-res block, a gain of 1.6 mIoU is achieved while Params hardly increased. The S-res block performs fusion processing on the features before and after the ADCA module, significantly improving the accuracy of the network. **Ablation on the ADCA module design. ** The ADCA module divides the input features along the channel dimension into three parts, with each part applying different dilated convolutions. To demonstrate its effectiveness, we conduct studies on the ADCA module design, focusing on both the dilation rate and the number of partitions, as shown in Table 7. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & \multicolumn{2}{c}{**Modules**} & \multirow{2}{*}{**Params (M)**} & \multirow{2}{*}{**FLOPs (G)**} & \multirow{2}{*}{**mIoU**} \\
**MDC** & **SE** & & & & \\ \hline ✓ & & & 11.8 & 37.0 & 42.2 \\ ✓ & ✓ & & 11.8 & 37.0 & 42.8 \\ ✓ & ✓ & ✓ & 11.8 & 37.0 & 44.4 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Ablation studies on the design of the SEM module.