## 4 Experiments
### Visualization  
In Figure 5, we present a visual comparison of different algorithms on two randomly selected images from validation set of ADE20K. From the top image, we can see that these networks classify categories like sky, buildings, and trees quite clearly, but their classification performance is poor for the grandstand area. Only MCDCNet is able to clearly distinguish most of the grandstand locations and also classify the handrails within the grandstand with high accuracy. Compared to the other networks, MCDCNet more accurately determines the position of small objects in the large background, demonstrating its excellent extraction of spatial information. The focus of the image in 2 nd row is at the second bridge hole in the center of the image. This area includes green trees above and green grass below, which have high similarity in features. In addition, the area is surrounded by the building category on three sides, significantly affecting the segmentation performance of the network. It is apparent that only MCDCNet accurately segments this part of the \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Partition** & **Dilation Rate** & **Params (M)** & **mIoU** \\ \hline
1 & (1) & 11.8 & 42.6 \\ \hline
2 & (1, 2) & 11.8 & 43.8 \\
3 & (1, 2, 3) & 11.8 & 44.4 \\
4 & (1, 2, 3, 4) & 11.8 & 43.4 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Ablation studies on the design of the column number of ADCA block. All convolutions’ kernel size is 3. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Dilation Rate** & **Params (M)** & **FLOPs (G)** & **mIoU** \\ \hline (1, 2, 3) & 11.8 & 37.0 & 44.4 \\ (1, 3, 5) & 11.8 & 37.0 & 41.9 \\ (2, 3, 4) & 11.8 & 37.0 & 43.3 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Ablation studies on the design of dilation rates of dilated convolution in the ADCA module. \begin{table}
\begin{tabular}{c c c c} \hline \hline \multicolumn{3}{c}{**Modules**} & **Params (M)** & **mIoU** \\ \hline ✓ & & 10.3 & 42.5 \\  & ✓ & 11.8 & 44.4 \\ ✓ & ✓ & 12.0 & 42.7 \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Ablation studies on the design of the simple-res module. image, showcasing its effectiveness in segmenting similar categories and multi-categories within a small area. In Figure 6, we present the visualization comparison results of different network architectures on two randomly selected images from the ADE20K validation set. In the images above, we can see that when the network only includes MSCA, it cannot accurately identify the shower. This is due to the network's insufficient ability to capture spatial information, which greatly affects its judgment of small objects. After adding SEM, the network's ability to capture spatial information is enhanced, but it still struggles to make good judgments on objects that are close to each other and similar in appearance. For example, the sink on the left side of the image is only well-segmented under the MCDCNet architecture. This demonstrates that the MCDCNet architecture enhances the decoder's utilization of features extracted by the backbone. Similarly, in the second image, only MCDCNet achieves the most detailed segmentation of the light on the ceiling, and only MCDCNet distinguishes between the open door and the scene outside the door. In summary, MSCA provides semantic information to the network, SEM enhances the network's learning of spatial information, and IAM fully utilizes all the information obtained previously. Therefore, the MCDCNet architecture achieves better results. Figure 5: Visualization results on ADE20k. We compare MCDCNet with TopFormer, SegNeXt-tiny, and SegNeXt-small in the two images above. The top image shows that MCDCNet predicts large areas more accurately. The bottom image demonstrates that MCDCNet classifies categories with similar features more accurately. Figure 6: Visualization results on ADE20k. We visualized the ablation experiments for the network architecture component. There are three network structures in total: MSCA, MSCA + SEM, and MSCA + SEM + IAM.