# A Survey on Visual Mamba
## 3 Mamba for Vision
### Mamba with Other Architectures
#### 3.3.1 Mamba with Convolution
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Other Architecture** & **Mamba Method** & **Capability** \\ \hline \hline \multirow{8}{*}{**Convolution**} & RES-VAMMBA [48] & Food vision tasks \\ \cline{2-3}  & MedMamba [65] & Medical images classification tasks \\ \cline{2-3}  & HSIMamba [72] & Hyperspectral images classification tasks \\ \cline{2-3}  & MambaMIR [56] & Medical images reconstruction tasks \\ \cline{2-3}  & MambaIR [57] & Image restoration tasks \\ \cline{2-3}  & VMambaMorph [53] & 3D images registration tasks \\ \cline{2-3}  & FreqMamba [68] & Image deraining tasks \\ \cline{2-3}  & Pan-Mamba [73] & Pan-sharpening tasks \\ \cline{2-3}  & MambaTalk [74] & Gesture synthesis tasks \\ \cline{2-3}  & Samba [75] & Images semantic segmentation tasks \\ \cline{2-3}  & Semi-Mamba-UNet [52], Swin-UMamba [61] & \\  H-rmunet [55], Ultral-Light VM-UNet [62] & Medical images segmentation tasks \\ \cline{2-3}  & LMa-UNet [45], SegMamba [71], T-Mamba [77] & \\  Vivim [35], nnMamba [70], ProMamba [78] & \\ \hline \multirow{2}{*}{**Recurrence**} & VMRNN [47] & Video prediction tasks \\ \cline{2-3}  & VMambaMorph [53] & 3D images registration tasks \\ \hline \multirow{8}{*}{**Attention**} & SSM-ViT [79] & Event camera-based tasks \\ \cline{2-3}  & MMA [41] & Image super-resolution tasks \\ \cline{2-3}  & ViS4 mer [80] & Long movie clip classification tasks \\ \cline{2-3}  & FDVM-Net [81] & Images exposure correction tasks \\ \cline{2-3}  & CMViM [82] & 3D multi-modal representation tasks \\ \cline{2-3}  & Motion-Guided Dual-Camera Tracker [46] & Endoscopy skill evaluation tasks \\ \cline{2-3}  & MambaIR [57] & Image restoration tasks \\ \cline{2-3}  & FreqMamba [68] & Image deraining tasks \\ \cline{2-3}  & 3 DMambaComplete [83] & Point cloud completion tasks \\ \cline{2-3}  & VM-UNET-V2 [64], Weak-Mamba-UNet [76] & \\  UltraLight VM-UNet [62], ProMamba [78] & Medical images segmentation tasks \\ \hline \multirow{8}{*}{**U-Net**} & U-Lambda [84], VUM-Net [85], Mamba-UNet [51] & \\  TM-UNet [60], Semi-Mamba-UNet [52] & \\  Swin-UMamba [61], Weak-Mamba-UNet [76] & \\  LMa-UNet [45], LightM-UNet [86] & Medical images tasks \\ \cline{2-3}  & UltralLight VM-UNet [62], VM-UNET-V2 [64] & \\  H-rmunet [55], Mamba-UNet [59] & \\  VM-UNet [63] & \\ \cline{2-3}  & MambaMIR-GAN [56] & Medical images reconstruction tasks \\ \cline{2-3}  & VmambalR [32] & Image restoration tasks \\ \cline{2-3}  & Motion Mamba [34] & Generation tasks \\ \cline{2-3}  & MambaMorph [87] & Multi-modality registration tasks \\ \cline{2-3}  & FreqMamba [68] & Image deraining tasks \\ \cline{2-3}  & RS-Mamba [69] & Dense image prediction tasks \\ \hline \multirow{4}{*}{**Diffusion**} & DIS [88], ZigMa [31], Motion Mamba [34] & Generation tasks \\ \cline{2-3}  & SSM-based diffusion model [89] & Generation tasks \\ \cline{1-1} \cline{2-3}  & MD-Dose [90] & Radiation dose prediction tasks \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Summary of visual Mamba with other architectures. In particular, specialized Mampa-based blocks have been devised to capture frequency-based information, leveraging techniques from convolutional networks. T-Mamba [77] introduced the Tim block, which integrates frequency-based bandpass filtering atop convolutional shared dual position encoding compensation and a gate selection unit. HSI-Mamba [72] introduced the HyperspectralBiNetworks block, which was derived from ViM but tailored for spectral inputs. Vivim [35] introduced the Temporal Mampa Block, integrating a spatiotemporal version of ViM named ST-Mamba, along with detail-specific FFN, convolution, and layer normalization. FreqMamba [68] introduced the FreqSSM block, incorporating a convolutional layer for the spatial branch, discrete wavelet transformation with the SSM block for the frequency band branch, and a Fourier modeling branch implemented with a convolutional layer. A branch of research directly integrates Mamba blocks with convolutional layers into existing architectures.