# A Survey on Visual Mamba
## 4 Visual Mamba in Application Fields
### General Visual Mamba
#### 4.1.1 High/Mid-Level Vision  
The visual Mamba backbone [26; 27; 28; 29; 38] had decent performance in classification, object detection, and segmentation. SSM-ViT [79] was designed for object detection using event cameras. Differently from standard frame-based cameras, event cameras record per-pixel relative brightness changes in a scene as they occur. Therefore, object detection with event cameras requires processing an asynchronous stream of events in a four-dimensional spatio-temporal space. Earlier studies used RNNs architectures with convolutional or attention mechanisms to develop models exhibiting superior performance on downstream tasks using event camera data. However, these models usually suffer from slow training. As a response, the SSM-ViT block was introduced by leveraging an SSM for efficient event-based information processing. It explores two strategies to mitigate aliasing effects when deploying the model at higher frequencies. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Category** & **Sub-Category** & **Method** & **Efficiency** & **Code** \\ \hline \multirow{6}{*}{Segmentation} & Object detection & SSM-ViT [79] & Params 17.5 & ✗ \\ \cline{2-4}  & \multirow{2}{*}{Video understanding} & ReMamber [50] & - & ✗ \\ \cline{2-4}  & & Sigma [49] & - & ✓ \\ \cline{2-4}  & Video classification & ViS4 mer [80] & Memory \$273.6 & ✓ \\ \cline{2-4}  & \multirow{2}{*}{Video classification} & Video Mamba Suite [43] & - & ✓ \\ \cline{2-4}  & & FLOPs & - & \\  & & Video Mamba-T: 7.1 & ✓ \\  & & Video Mamba-S: 28 & & \\  & & Video Mamba-M: 83.1 & & \\ \cline{2-4}  & \multirow{2}{*}{SpikeMba [93]} & - & ✗ \\ \cline{2-4}  & & Cobra [106] & - & ✓ \\ \cline{2-4}  & \multirow{2}{*}{Multi-Modal understanding} & ReMamber [50] & - & ✗ \\ \cline{2-4}  & & VL-Mamba [42] & - & ✗ \\ \hline \multirow{6}{*}{Video prediction} & VMRNN [47] & Params 2.6, FLOPs 0.9 & ✓ \\ \cline{2-4}  & \multirow{2}{*}{HARMamba [40]} & FLOPs & PAMM 22:279.21 & ✗ \\  & & UCL237.83 & & \\  & & UNIMIB HAR238.36 & & \\  & & WISDM-256.52 & & \\ \hline Image super-resolution & MMA [41] & - & ✗ \\ \hline \multirow{6}{*}{Image restoration} & MambaIR [57] & Params 16.7 & ✓ \\ \cline{2-4}  & SERPENT [58] & - & ✗ \\ \cline{2-4}  & \multirow{2}{*}{VramballR [32]} & Params 10.50, FLOPs 20.5 & ✓ \\ \cline{2-4}  & & UVM-Net [85] & Params 19.25 & ✓ \\ \cline{2-4}  & & FreqMamba [68] & Params 14.52 & ✗ \\ \hline Image deblurring & ALGNet [107] & FLOPs 17 & ✗ \\ \hline \multirow{6}{*}{Low-level vision} & MambaTalk [74] & - & ✗ \\ \cline{2-4}  & Motion Mamba [34] & - & ✓ \\ \cline{2-4}  & \multirow{2}{*}{Visual generation} & DS [88] & - & ✓ \\ \cline{2-4}  & & ZigMs [31] & - & ✓ \\ \hline \multirow{6}{*}{Point cloud} & 3 DMambaComplete [83] & Params 34.06, FLOPs 7.12 & ✗ \\ \cline{2-4}  & 3 DMambaIPF [108] & - & ✗ \\ \cline{2-4}  & Point Cloud Mamba [109] & Params 342, FLOPs 45.0 & ✗ \\ \cline{2-4}  & POINT MAMBA [44] & Memory 8550 & ✓ \\ \cline{2-4}  & \multirow{2}{*}{SSPoint Mamba [110]} & Params 12.3, FLOPs 3.6 & ✓ \\ \cline{2-4}  & & ZigMs [31] & - & ✗ \\ \hline \multirow{6}{*}{Video generation} & 3 DMambaComplete [83] & Params 34.06, FLOPs 7.12 & ✗ \\ \cline{2-4}  & 3 DMambaIPF [108] & - & ✗ \\ \cline{1-1} \cline{2-4}  & Point Cloud Mamba [109] & Params 342, FLOPs 45.0 & ✗ \\ \cline{1-1} \cline{2-4}  & POINT MAMBA [44] & Memory 8550 & ✓ \\ \cline{1-1} \cline{2-4}  & SSPoint Mamba [110] & Params 12.3, FLOPs 3.6 & ✓ \\ \cline{1-1} \cline{2-4}  & 3D reconstruction & GAMBA [111] & - & ✗ \\ \hline Video generation & SSM-based diffusion model [89] & - & ✓ \\ \hline \hline \end{tabular} For the efficiency, inference speed is in ms, memory is in MB, Params is in M, and FLOPS is in G.  
\end{table} 
Table 7: \(Cont.\)For referencing image segmentation (RIS), a difficult problem in the field of multi-modal comprehension, ReMamber [50] was introduced, utilizing Mamba's notable advances in efficient training and inference with linear complexity.