# A Survey on Visual Mamba
## 3 Mamba for Vision
### Mamba with Other Architectures
#### 3.3.1 Mamba with Convolution  
Convolution, being a widely employed technique, possesses the advantageous property of capturing local information. Consequently, it is frequently integrated with Mamba to augment its capabilities. To construct a new fundamental block, the identity branch of the residual block and convolution layers are commonly employed to enhance the Mamba block. This integration is aimed at enhancing the representational capability of the model and its effectiveness in tasks requiring a thorough comprehension of visual data, by combining localized details captured by Mamba blocks with overarching global features. By incorporating a residual learning framework into the VMamba model, RES-VMamba [48] was the first to utilize both local and global state features that were part of the original VMamba architectural design. MambaIR [57] introduced the residue state space (RSS) block, incorporating scale residual connections, a convolutionary layer, and channel attention atop the VSS block. LMamSUet [45] incorporates residual connections with ViM at both pixel-level and patch-level. nmMamba [70] introduced the Res-Mamba block, which merges the Mamba block with a convolution layer, batch normalization, ReLU activation, residual connections, and weight sharing among channels and spatial dimensions using a Siamese input. SegMamba [71] introduced the TSMamba block, which can enhance the Tri-orientated Mamba with layer normalization, gated spatial convolutional layers, and residual connections. MambaMIR [56] introduced the AMS block group, which enhances feature extraction for reconstruction and \begin{table}
\begin{tabular}{c c} \hline \hline
**Scanning Mechanisms** & **Method** \\ \hline \multirow{5}{*}{BiDirectional Scan [26]} & Vision Mamba [26], Motion Mamba [34] \\  & HARMamba [40], MMA [41], VL-Mamb[42] \\  & Video Mamba Suite [43], Point Mamba [44] \\  & \multicolumn{1}{c}{Mda-UNet [45]} \\  & \multicolumn{1}{c}{Motion-Guided Dual-Camera Tracker [46]} \\ \hline \multirow{5}{*}{Cross-Scan [27]} & VMamba [27],VL-Mamb[42], VMRNN [47] \\  & RES-VMAMBA [48], Sigma [49], ReMamber [50] \\  & Mamba-UNet [51], Semi-Mamba-UNet [52] \\  & VMambaMorph [53], ChangeMamba [54] \\  & H-vmunet [55], MambMR[56], MambLR [57] \\  & Serpent [58], Mamb-HUNet [59], TM-UNet [60] \\  & Swin-UMamba [61], UltraLight VM-UNet [62] \\  & VM-UNet [63], VM-UNETV-2P2 [64] \\  & MedMamba [65], MM-ISTD [66], ROMs Mamba [67] \\ \hline Continuous 2D Scanning [28] & PlainMamba [28] \\ \hline Local Scan [29] & LocalMamba [29], FreMapMap [68] \\ \hline Efficient 2D Scanning (ES2D) [30] & EfficientVAMba [30] \\ \hline Zigzag Scan [31] & ZigMa [31] \\ \hline Omnidirectional Selective Scan [32] & VrambaIR [32], RS-Mamba [69] \\ \hline
3D BDirectional Scan [33] & VideoMamba [33] \\ \hline Hierarchical Scan [34] & Motion Mamba [34] \\ \hline Spatiotemporal Selective Scan [35] & Vivim [35] \\ \hline Multi-Path Scan [36] & RSMamba [36] \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Summary of the Scanning mechanisms used in visual Mamba. uncertainty estimation by incorporating a convolutional layer and layer normalization ahead of a VSS-based Mamba block called AMSS. MedMamba [65] introduced the SS-Conv-SSM block, which comprises a convolution branch and a VSS branch.