# Swin-APT: An Enhancing Swin-Transformer Adaptor for Intelligent Transportation
## 5 Experiments
### Road Segmentation
#### 5.1.5 Results on CeyMo  
The road object segmentation task experimental results of Swin-APT on the CeyMo [24] dataset are presented. The mIoU and Acc of the lightweight network Swin-L and Swin-APT for 11 object classes are reported in Tables 4 and 5, respectively, to verify the effectiveness of the multi-scale adapter network and the information metric comparison module for inter-frame consistency. Table 4 shows that the best IoU is mostly obtained by the complete Swin-APT. As the configuration transitions from having no additional modules (denoted by \(\mathbf{\mathsf{\kappa}}\) for absent and \(\mathbf{\mathsf{\psi}}\) for present) to having all modules, consistent improvements in IoU across most \begin{table}
\begin{tabular}{c c c c c c c c c c c c c c} \hline \hline \multicolumn{13}{c}{**Module**} & \multicolumn{13}{c}{**Category**} \\ \hline
**Apt** & **InCM** & **MS** & **BL** & **CL** & **DM** & **JB** & **LA** & **PC** & **RA** & **SA** & **SL** & **CLA** & **SRA** \\ \hline \(\mathbf{\mathsf{\kappa}}\) & \(\mathbf{\mathsf{\kappa}}\) & \(\mathbf{\mathsf{\kappa}}\) & 74.84 & 52.76 & 79.36 & 44.42 & 10.83 & 79.64 & 37.81 & 58.32 & 71.28 & 55.86 & 21.75 \\ \(\mathbf{\mathsf{\psi}}\) & \(\mathbf{\mathsf{\psi}}\) & \(\mathbf{\mathsf{\kappa}}\) & 75.72 & 53.49 & 80.18 & 45.26 & 12.91 & 81.76 & 39.04 & 58.61 & 73.34 & 56.12 & 24.51 \\ \(\mathbf{\mathsf{\psi}}\) & \(\mathbf{\mathsf{\psi}}\) & \(\mathbf{\mathsf{\psi}}\) & 76.51 & 53.91 & 80.66 & 45.51 & 14.04 & 82.36 & 40.85 & 59.23 & 74.91 & 56.37 & 25.42 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Comparison results of IoU on the CeyMo dataset. Numbers in bold represents the best performance. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multicolumn{1}{c}{\multirow{2}{*}{**Methods**}} & \multicolumn{1}{c}{**Swin-L**} & \multicolumn{1}{c}{**Swin-L + apt**} & \multicolumn{1}{c}{**Swin-L + InCM**} & \multicolumn{1}{c}{**Swin-L + MS apt**} & \multicolumn{1}{c}{**Swin-APT**} \\ \hline Road & 94.02 & 96.55 & 96.42 & 97.07 & **97.79** \\ Sidewalk & 93.84 & 94.45 & 94.25 & 94.97 & **95.35** \\ Building & 94.58 & 94.98 & 95.84 & 95.92 & **96.19** \\ Fence & 62.52 & 64.32 & 65.84 & 66.94 & **67.31** \\ Pole & 67.18 & 67.99 & 69.56 & 69.43 & **70.57** \\ Sky & 94.37 & 94.93 & 95.68 & 95.47 & **96.10** \\ Car & 94.30 & 94.65 & 95.08 & 95.11 & **95.44** \\ Vegetation & 75.49 & 76.17 & 76.89 & 77.01 & **77.18** \\ Sign & 65.91 & 66.18 & 67.89 & 67.35 & **68.04** \\ Pedestrian & 74.22 & 75.02 & 76.93 & 77.15 & **77.98** \\ Cyclist & 58.15 & 60.41 & 61.07 & 62.18 & **62.83** \\ mIoU & 79.51 & 80.51 & 81.40 & 81.69 & **82.25** \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Comparison results of mIoU on the synthetic SYNTHIA dataset. Numbers in bold represents the best performance. \begin{table}
\begin{tabular}{c c c c c c c c c c c c c c} \hline \hline \multicolumn{13}{c}{**Module**} & \multicolumn{13}{c}{**Category**} \\ \hline
**Apt** & **InCM** & **MS** & **BL** & **CL** & **DM** & **JB** & **LA** & **PC** & **RA** & **SA** & **SL** & **CLA** & **SRA** \\ \hline \(\mathbf{\mathsf{\kappa}}\) & \(\mathbf{\mathsf{\kappa}}\) & \(\mathbf{\mathsf{\kappa}}\) & 74.84 & 52.76 & 79.36 & 44.42 & 10.83 & 79.64 & 37.81 & 58.32 & 71.28 & 55.86 & 21.75 \\ \(\mathbf{\mathsf{\psi}}\) & \(\mathbf{\mathsf{\psi}}\) & \(\mathbf{\mathsf{\kappa}}\) & 75.72 & 53.49 & 80.18 & 45.26 & 12.91 & 81.76 & 39.04 & 58.61 & 73.34 & 56.12 & 24.51 \\ \(\mathbf{\mathsf{\psi}}\) & \(\mathbf{\mathsf{\psi}}\) & \(\mathbf{\mathsf{\psi}}\) & 76.51 & 53.91 & 80.66 & 45.51 & 14.04 & 82.36 & 40.85 & 59.23 & 74.91 & 56.37 & 25.42 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Comparison results of Acc on the CeyMo dataset. Numbers in bold represents the best performance. categories are observed. Swin-APT consistently outperforms the other configurations in almost all categories, including \"BL\", \"DM\", \"JB\", \"PC\", \"RA\", \"SL\", and \"SLA\".