# A Survey on Visual Mamba
## 4 Visual Mamba in Application Fields
### General Visual Mamba
#### 4.1.1 High/Mid-Level Vision
Distinguished from conventional segmentation, RIS entails identifying and segmenting specific objects in images according to textual descriptions. The ReMamber architecture comprises several Mamba Twister blocks, each featuring multiple VSS blocks and a Twisting layer. The Mamba Twister block is a multi-modal feature fusion block that blends textual and visual features into a single output, which is the fused multi-modal feature representation. The last segmentation mask is generated by retrieving intermediate features after every Mamba Twister block and feeding them into a flexible decoder. The VSS layers are tasked with extracting visual features, while the Twisting layer primarily captures effective visual-language interactions. The experiments conducted by authors on various RIS datasets produced cutting-edge outcomes. Sigma [49] presented a novel network tailored for multimodal semantic segmentation tasks. Following each Mamba Twister block, intermediate features are retrieved and input into a flexible decoder, which generates the segmentation mask at the end. Furthermore, a channel-aware Mamba decoder and an attention-based Mamba fusion mechanism were presented. During the decoding phase, the fused features undergo further enhancement through channel-aware VSS (CVSS) blocks, adept at capturing multi-scale long-range information and facilitating cross-modal information integration. Unlike transformers that depend on quadratic complexity attention mechanisms, Mamba, as a pure SSM-based model, excels in handling long sequences with linear complexity and is particularly adept at processing lengthy videos at high resolutions. Vi54 mer [80] serves as a model primarily used for recognizing and classifying long videos, especially for understanding and categorizing lengthy movie clips. Vi54 mer is composed of two primary parts: a multi-scale temporal S4 decoder suited for further long-range temporal reasoning, and a standard Transformer encoder intended for short-distance spatiotemporal feature extraction from videos. The multi-scale temporal S4 decoder is based on SSM and makes use of the ability of the core SSM to identify long-range correlations in consecutive data, in order to reduce the computational cost of the model. The Video Mamba Suite [43] is not a novel method; rather, it investigates and evaluates SSM's potential, embodied by Mamba, in tasks related to comprehending videos. The decomposed bidirectionally Mamba (DBM) block is an improved version of the ViM block that shares the SSM parameters in both scanning directions, while allowing the input projector to be separated. They classify Mamba into four distinct roles for modeling videos and compare it with existing Transformer-based models to evaluate its effectiveness in various video understanding tasks. The 14 models and modules that make up the Video Mamba Suite were used to assess performance on 12 different video comprehension tasks. The experiments showed that Mamba is applicable in video analysis and can be used for more complex, multimodal video understanding challenges. Apart from the Video Mamba Suite, VideoMamba [33] was proposed for video understanding tasks, with a specific focus on addressing the following two major challenges: local redundancy and global dependencies. The study evaluated VideoMamba's capabilities across four key aspects: scalability in the video domain, sensitivity to short-term action recognition, advantages in long-term video understanding, and compatibility with other modalities. To enhance model scalability in the visual domain, a self-distillation strategy is used in VideoMamba. This approach significantly enhances the model's performance as both the model and input sizes increase, without the need for pretraining on large-scale datasets. While the ViM block enhances the model's spatial perception capabilities, VideoMamba extends this capability to 3D video understanding by including spatio-temporal bidirectional scanning.