# A Multi-Path Semantic Segmentation Network Based on Convolutional Attention Guidance
## 4 Experiment
### Visualization  
In Figure 8, the visual results of MCAG on the Cityscapes dataset are presented. The first column displays the input images, the second column represents the corresponding ground truth and the third column shows the segmentation results of the MCAG method, with black rectangular regions indicating detailed displays.  
\begin{table}
\begin{tabular}{l c c c c c c} \hline \hline
**Method** & **Mirror** & **Seat** & **Lamp** & **Box** & **Book** & **Pillow** & **Oven** \\ \hline SegNext-B [7]* & 65.29 & 56.70 & 60.34 & 25.90 & 45.09 & 65.41 & 53.10 \\ MCAG (OURS) & 65.99 & 58.09 & 62.45 & 27.39 & 46.93 & 69.63 & 55.34 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comparison of specific objects with Segnext on ADE20K. The asterisk (*) denotes reproduced results.  
\begin{table}
\begin{tabular}{l l l l} \hline \hline
**Method** & **Params (M)** & **Backbone** & **mIoU (\%)** \\ \hline Segformer-B0 [6] & 3.8 & MiT & 78.1 \\ SETR [5] & 311 & ViT-L & 79.3 \\ MagNet [45] & - & - & 67.57 \\ HyperSeg-S [46] & 10.2 & EfficientNet-B1 & 78.1 \\ AFFormer-base [41] & 3.0 & - & 78.7 \\ HRFormer-S [14] & 13.5 & - & 81.0 \\ SegNext-B [7]* & 27.6 & MSCAN-B & 82.0 \\ FLANet [37] & - & HRNetW48 & 79.7 \\ FASeg [38]* & 67 & R50 & 78.5 \\ StructToken-PWE [42]* & 364 & ViT-L/16 & 81.2 \\ PIDNet-L [44] & 36.9 & - & 80.6 \\ MPCNet [43] & - & R101 & 78.24 \\ LightSeg [47] & 2.44 & - & 76.8 \\ TSG [39] & 72 & Swin-T & 75.8 \\ MCAG (OURS) & 36 & MSCAN-B & 82.51 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparison with SOTA on Cityscapes. The asterisk (*) denotes reproduced results.  
\begin{table}
\begin{tabular}{l l l l} \hline \hline
**Method** & **Param (M)** & **Backbone** & **mIoU (\%)** \\ \hline HRFormer-B [14]* & 56.2 & - & 43.3 \\ HRFormer-S [14] & 13.5 & - & 38.9 \\ AFFormer-base [41] & 3.0 & - & 35.1 \\ SegNext-B [7]* & 27.6 & MSCAN-B & 43.5 \\ SETR [5]* & 311 & ViT-L & 42.7 \\ MCAG (OURS) & 36 & MSCAN-B & 43.6 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparison with SOTA on Coco-Stuff. The asterisk (*) denotes reproduced results.  
It can be observed that MCAG is more effective at identifying both boundary details and overall information. In the first set of images, MCAG successfully recognizes the railing in front of the central part of the bicycle and the seat of the bicycle, as well as pedestrians next to the utility pole. In the second set of images, above the cyclist, MCAG adeptly identifies the less noticeable lamp posts, and effectively segments the two seated individuals in the center of the image from the background bushes. In the third set of images, MCAG achieves satisfactory results in segmenting pedestrians at the end of the road, paying particular attention to details. In the fourth set of images, the model performs outstanding segmentation between the pedestrians in front of the car and the car itself, with clear distinction between the legs of the pedestrians and the road. These remarkable results stem from the robust long-range dependency-parsing ability of MCAG's main path, the subsidiary paths' exceptional focus on image details at high resolutions and the final fusion mechanism's appropriate handling of features extracted at multiple scales.