# Containment Control-Guided Boundary Information for Semantic Segmentation
## 4 Experiment
### Ablation Study  
We perform ablation experiments on two variants of CcNet, named CcNet-S and CcNet-L.  
**Features fusion matrix**\(\vec{\mathcal{L}}\)  
The features fusion matrix \(\vec{\mathcal{L}}\) serves as a quantifying tool for the fusion process between boundary and internal features. In this context, we focus on a comprehensive analysis of the impact of different configurations on the features fusion matrix within the CcNet architecture. To establish targeted communication channels between internal and boundary information, we introduce two sets of matrices. Two distinct losses guide the fusion of the D branch, C branch, and B branch, necessitating mappings from two sets of internal semantic information. Thus, for each variant of CcNet, we investigate the influence of three different combinations of internal feature information on the Laplacian matrix: D branch, C branch, and Head. Experimental results are shown in Table 3, indicating consistent effects across both variants of CcNet for different combinations of feature information. The most effective approach involves mapping features from the D branch and C branch, utilizing the generated loss to guide the fusion of features between the self-branch and the B branch. This targeted configuration demonstrates optimal efficiency in leveraging the Laplacian matrix for enhanced fusion of internal and boundary features. These findings provide valuable insights into the role of the features fusion matrix in guiding feature fusion processes within CcNet. **FEU**  
This study aims to assess the performance of the FEU module concerning its placement and to analyze its influence on the two variants of CcNet. The results are shown in Table 4. There are two key variations to the location of the FEU module: the C branch and the Head. The C branch processes contextual information, while the Head processes the final feature information fed to the Head of the network. In the context of different variants, CcNet-S and CcNet-L, the introduction of the C branch into the FEU module significantly improves mIOU. This enhancement is attributed to the inherent rich contextual information of the C \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multicolumn{4}{c}{**FWF**} & \multirow{2}{*}{**mIOU**} \\ \cline{2-2} \cline{4-5}  & **D Branch** & **C Branch** & **Head** & \\ \hline \multirow{3}{*}{CcNet-S} & & ✓ & ✓ & 78.12 \\ \cline{2-5}  & ✓ & & ✓ & 78.16 \\ \cline{2-5}  & ✓ & ✓ & & 78.48 \\ \hline \multirow{3}{*}{CcNet-L} & & ✓ & ✓ & 79.85 \\ \cline{2-5}  & ✓ & & ✓ & 80.55 \\ \cline{1-1} \cline{2-5}  & ✓ & ✓ & & 81.15 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Ablation study of FWF. Figure 7: Channel feature visualization. branch, which undergoes further optimization through the DAPPM module. Hence, the demand for optimizing multi-scale features at this juncture is particularly pronounced. The structure of the FEU is quite complex. Therefore, to more intuitively demonstrate the complexity of the FEU, we conduct comparative experiments based on CcNet-s. Multiple experiments show that CcNet-s can achieve a 5 FPS improvement after removing FEU. This complexity may be due to the additional operations required during the \"split-restructure\" process, which improves feature quality but increases the computational load. However, the FEU can be viewed as a mechanism to balance between higher accuracy (through better feature enhancement) and faster inference speed. By including or excluding the FEU, one can adjust the performance of CcNet depending on the specific application requirements. Additionally, relocating the FEU module from the C branch to the head in the CcNet-S variant raises the mIOU from 78.04 to 78.48. Conversely, for the CcNet-L variant, relocating the FEU module from the C branch to the head results in a sharp mIOU decline to 80.0. This indicates that, as the model size increases, the FEU module processing the results from the FWF module may have a detrimental effect.