# A Survey on Visual Mamba
## 3 Mamba for Vision
### Comparison of Mamba Models and Other State-of-the-Art Models
#### 3.4.3 Analysis and Comparison in Semantic Segmentation Tasks  
The ADE20K dataset was used for semantic segmentation tasks. As shown in Table 6, we used the mIoU metric to compare and analyze the performance of various models on this dataset. mIoU, or mean intersection over union, is the standard metric for measuring model performance in segmentation tasks, reflecting the model's accuracy in handling complex scenes and objects of different scales. In semantic segmentation tasks, CNN models exhibit relatively stable mIoU performance under both single-scale (SS) and multi-scale (MS) settings. ConvNeXt [104] models perform slightly better than ResNet [98] models, with ConvNeXt-B achieving a mIoU (SS) of 49.1 and a mIoU (MS) of 49.9. Compared to other models, CNNs offer stable and reliable performance, making them suitable for resource-limited applications, though they may fall short in tasks requiring high precision. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Model** & **Backbone** & **Image Size** & **Params (M)** & **FLOPs (G)** & **mIoU (SS)** & **mIoU (MS)** \\ \hline \multirow{8}{*}{**CNN**} & ResNet-50 [98] & \(512^{2}\) & 67 & 953 & 42.1 & 42.8 \\  & ResNet-101 [98] & \(512^{2}\) & 85 & 1030 & 42.9 & 44.0 \\  & ConvNeXt-T [104] & \(512^{2}\) & 60 & 939 & 46.0 & 46.7 \\  & ConvNeXt-S [104] & \(512^{2}\) & 82 & 1027 & 48.7 & 49.6 \\  & ConvNeXt-B [104] & \(512^{2}\) & 122 & 1170 & 49.1 & 49.9 \\ \hline \multirow{8}{*}{**Transformer**} & Swin-T [91] & \(512^{2}\) & 60 & 945 & 44.4 & 45.8 \\  & Swin-S [91] & \(512^{2}\) & 81 & 1039 & 47.6 & 49.5 \\  & Swin-B [91] & \(512^{2}\) & 121 & 1188 & 48.1 & 49.7 \\  & Focal-T [97] & \(512^{2}\) & 62 & 998 & 45.8 & 47.0 \\  & Focal-S [97] & \(512^{2}\) & 85 & 1130 & 48.0 & 50.0 \\  & Focal-B [97] & \(512^{2}\) & 126 & 1354 & 49.0 & 50.5 \\  & DeiT-S + MLN [105] & \(512^{2}\) & 58 & 1217 & 43.8 & 45.1 \\  & DeiT-B + MLN [105] & \(512^{2}\) & 144 & 2007 & 45.5 & 47.2 \\ \hline \multirow{8}{*}{**Mamba**} & Vim-Ti [26] & \(512^{2}\) & 13 & - & 41.0 & - \\  & Vim-S [26] & \(512^{2}\) & 46 & - & 44.9 & - \\  & V Mamba-T [27] & \(512^{2}\) & 55 & 939 & 47.3 & 48.3 \\  & V Mamba-S [27] & \(512^{2}\) & 76 & 1037 & 49.5 & 50.5 \\  & V Mamba-B [27] & \(512^{2}\) & 110 & 1167 & 50.0 & 51.3 \\  & V Mamba-S [27] & \(640^{2}\) & 76 & 1620 & 50.8 & 50.8 \\  & PlainMamba-L1 [28] & \(640^{2}\) & 35 & 174 & 44.1 & - \\  & PlainMamba-L2 [28] & \(640^{2}\) & 55 & 285 & 46.8 & - \\  & PlainMamba-L3 [28] & \(640^{2}\) & 81 & 419 & 49.1 & - \\  & LocalVim-T [99] & \(512^{2}\) & 36 & 181 & 43.4 & 44.4 \\  & LocalVim-S [29] & \(512^{2}\) & 58 & 297 & 46.4 & 47.5 \\  & LocalV Mamba-T [29] & \(512^{2}\) & 57 & 970 & 47.9 & 49.1 \\  & LocalV Mamba-S [29] & \(512^{2}\) & 81 & 1095 & 50.0 & 51.0 \\  & EfficientV Mamba-T [30] & \(512^{2}\) & 14 & 230 & 38.9 & 39.3 \\  & EfficientV Mamba-S [30] & \(512^{2}\) & 29 & 505 & 41.5 & 42.1 \\  & EfficientV Mamba-B [30] & \(512^{2}\) & 65 & 930 & 46.5 & 47.3 \\  & SiMBA-S [39] & \(512^{2}\) & 62 & 1040 & 49.0 & 49.6 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Comparison of Mamba models and other state-of-the-art models on ADE20K datasets. Among Transformer models, Swin-B [91] stands out on the ADE20K dataset, with a mIoU (SS) of 48.1 and a mIoU (MS) of 49.7. Focal [97] models also perform well, particularly Focal-B, which achieved mIoU (SS) and mIoU (MS) scores of 49.0 and 50.5, respectively. However, Transformer models generally demand significant computational resources. For instance, while delivering excellent performance, Focal-B required 126M parameters and 1354G FLOPs. Similarly, DeiT-B + MLN [105] required 144M parameters and 2007G FLOPs. In the Mamba model category, the VMamba [27] backbones showed outstanding performance under both single-scale and multi-scale tests, particularly VMamba-S (640\({}^{2}\)), which achieved a mIoU (SS) and mIoU (MS) of 50.8. The PlainMamba [28] and LocalVim [29] backbones also provide high mIoU scores with lower parameters and FLOPs, such as PlainMamba-L3, which achieved a mIoU (SS) of 49.1.