Visual Mamba Modules Overview

• Visual Mamba Modules are computational units based on state-space models that use input-adaptive scans and selective recurrence to efficiently capture long-range dependencies in vision tasks.
• They replace quadratic-cost self-attention with linear-complexity recurrence and convolution, enhancing speed, localization, and fusion in various applications.
• Their design integrates multi-directional scanning, dynamic gating, and advanced fusion techniques to boost performance in recognition, segmentation, and video processing.

Visual Mamba Modules are computational units built upon state-space models (SSMs) to deliver highly efficient long-range dependency modeling in computer vision architectures. They systematically replace quadratic-cost self-attention with input-adaptive, linear-complexity recurrence and convolutional mechanisms, most notably in the Mamba, Vim, VMamba, and their numerous variants. These modules leverage the selective scan operator, multi-directional traversals, dynamic parameterization, and advanced fusion mechanisms—including spatial, temporal, and multimodal augments—thus making them foundational for state-of-the-art visual recognition, segmentation, detection, video processing, multimodal reasoning, and specialized applications such as medical imaging and defect inspection.

1. Mathematical Core: State Space Model Foundation

Visual Mamba modules fundamentally derive from continuous- and discrete-time state-space models (SSMs):

$$h'(t) = A h(t) + B x(t), \qquad y(t) = C h(t) + D x(t)$$

Upon zero-order-hold discretization with step $\Delta$, the update for input sequence $x_{1:L}$, hidden state $h_{t}$, and selectivity parameters is:

$$h_t = \bar{A}_t\,h_{t-1} + \bar{B}_t\,x_t, \qquad y_t = C_t\,h_t + D\,x_t$$

where:

• $\bar{A}_t = \exp(\Delta_t A)$
• $$\bar{B}_t = (\Delta_t A)^{-1}(\exp(\Delta_t A) - I)\,\Delta_t B$$
• $B_t$, $C_t$, $\Delta_t$ are input-adaptive via learned parameter heads $s_B(x)$, $s_C(x)$, $s_\Delta(x)$

The scan can be efficiently computed via parallel associative algorithms ($O(\log L)$ steps), making the overall complexity $O(L \cdot n)$ (for $n$ channels or states), as opposed to $O(L^2)$ for traditional attention mechanisms (Ibrahim et al., 11 Feb 2025, Xiao et al., 19 Oct 2024, Wang et al., 14 Oct 2024).

2. Block Structure and Scanning Patterns

Visual Mamba blocks consist of three primary stages:

• Input Projection & Preprocessing: Linear projection or convolution embeds patch or pixel features ($x \to \mathbb{R}^{L \times D}$), followed by normalization (LayerNorm).
• Selective Scan Module: Executes input-dependent SSM scans:
  • Vim: Bidirectional 1D scan over patch tokens (forward & backward).
  • VMamba (VSS Block): Four-directional scans (left-right, right-left, top-bottom, bottom-top) over the spatial feature map.
  • SS2D Module: Implements the above in parallel and merges via averaging or fusion.
• Gated Fusion & Feedforward: Scan outputs gated (element-wise multiplication with learned projections), summed with input (residual), and followed by a pointwise FFN (MLP) (Liu et al., 18 Jan 2024, Nasiri-Sarvi et al., 4 Jul 2024, Wang et al., 14 Oct 2024).

This block-level structure is repeated in a hierarchical pyramid, often interleaved with downsampling/upscaling operations (e.g., patch merging/expanding).

3. Advanced Variants and Efficiency Enhancements

Visual Mamba modules exhibit a diverse set of enhancements addressing locality, parameter efficiency, speed, and robustness:

• Spatial-Mamba: Adds structure-aware state fusion via multi-scale, depthwise dilated convolutions, directly connecting each pixel to 2D neighbors after the scan; improves locality and horizontal/vertical receptive decay (Xiao et al., 19 Oct 2024).
• GroupMamba: Partitions channels into four groups, each scanned along a unique direction with its own SSM instance and modulated via channel affinity gating, reducing parameters by up to 36% while retaining global context (Shaker et al., 18 Jul 2024).
• FastVim: Alternates mean-pooling along spatial axes before each scan, halving parallel scan depth per block and yielding a measured speedup of up to $3.2\times$ on $2048\times2048$ grids (Kapse et al., 1 Feb 2025).
• Mamba-Adaptor: Injects spatial dilation (Adaptor-S) and learnable long-range temporal memory (Adaptor-T) into SSM updates, boosting transfer learning and backbone performance while maintaining $<$10% overhead (Xie et al., 19 May 2025).
• Dynamic Vision Mamba (DyVM): Implements token- and block-level dynamic selection via Gumbel-Softmax gates, reducing FLOPs by 35.2% with $<$2% accuracy loss (Wu et al., 7 Apr 2025).
• MobileMamba: Fuses linear-time global SSMs with Haar wavelet branch (high-frequency enhancement), multi-kernel depthwise conv, and identity passing to maximize throughput and accuracy for lightweight deployments (He et al., 24 Nov 2024).
• Multi-Head Scan (MHS): Projects features into $n$ subspaces and performs $k$ scan routes per head; outputs are fused via coefficient-of-variation gating, allowing parameter/FLOP reduction by 48–56% in segmentation models (Ji, 10 Jun 2024).
• A2Mamba: Deeply integrates multi-scale attention maps (local+adaptive-dilated) with SSM state aggregation via hybrid cross-attention in the MASS (multi-scale attention-augmented SSM) module, outperforming all previous SSM/Transformer variants (Lou et al., 22 Jul 2025).

4. 2D and Multimodal Extensions

Many Visual Mamba variants generalize classic 1D SSMs to spatial, volumetric, or multimodal contexts:

• V2M: Implements a full 2D SSM via coupled row and column recurrences, parallelized across all four image corners, preserving 2D locality and boosting accuracy by 0.2–0.4% over 1D baselines (Wang et al., 14 Oct 2024).
• MVSMamba: Employs reference-centered, dynamic four-way scans using skip-scan traversal; achieves omnidirectional multi-view feature aggregation with strict linear-time cost for multi-view stereo (Jiang et al., 3 Nov 2025).
• Cross-Mamba (TransMamba): Fuses transformer-derived language tokens with vision SSM features in a shared latent space using SSM-based cross-attention; leverages weight subcloning and bidirectional distillation for fast, efficient architectural transfer (Chen et al., 21 Feb 2025).
• ML-Mamba: MSC module interfaces 2D visual SSM scans (rows/columns, bidirectional or cross-scan) with LLMs via a multimodal connector, enabling competitive reasoning at 3–4$\times$ faster inference speeds than transformer baselines (Huang et al., 29 Jul 2024).
• UAVD-Mamba: Integrates deformable convolution-based tokens, fusion Mamba blocks, cross-enhanced spatial and channel attention for robust multimodal UAV detection, outperforming OAFA by 3.6 mAP (Li et al., 1 Jul 2025).
• VSRM: Alternates spatial-to-temporal/temporal-to-spatial Mamba blocks, with deformable alignment networks for video super-resolution; supervised using a frequency-domain Charbonnier-like loss (Tran et al., 28 Jun 2025).

5. Empirical Performance and Efficiency

Visual Mamba Modules consistently demonstrate favorable trade-offs in accuracy, throughput, and resource usage across benchmarks:

Model	Params (M)	FLOPs (G)	Top-1 (%)	Speedup	Task	Reference
VMamba-T	31	4.9	82.5	2.1x	ImageNet Classify	(Liu et al., 18 Jan 2024)
GroupMamba-T	23	4.6	83.3	−26%	ImageNet Classify	(Shaker et al., 18 Jul 2024)
FastVim-B	98	17.2	81.9	3.2x	High-res Images	(Kapse et al., 1 Feb 2025)
MobileMamba B4†	4.31	83.6	21x	Mobile-efficient	(He et al., 24 Nov 2024)
Spatial-Mamba-B	96	15.8	85.3	—	SOTA SSM Classify	(Xiao et al., 19 Oct 2024)
Mamba-UNet	—	—	0.9281*	—	Med Segmentation	(Wang et al., 7 Feb 2024)

• Dice score.

These modules outperform transformer- and ConvNet-based baselines on COCO, ADE20K, Cityscapes, Kinetics-400, and medical segmentation datasets, with efficiency gains attributed to linear-in-sequence complexity, selective scan, dynamic gating, and specialized aggregation mechanisms.

6. Integration and Application Guidelines

Visual Mamba modules are plug-and-play in existing deep-learning architectures:

• Replace self-attention or convolution blocks with SSM-based scan modules, maintaining pre/post normalization and residual structure.
• For 2D images, use cross-scan (SS2D) or 2D-SSM blocks for patch or pixel tokens; for videos, apply S2T/T2S blocks alternately.
• Multimodal fusion (e.g., vision-language) via Cross-Mamba blocks or multimodal connectors (MSC), with weight/fusion projections to a shared latent space.
• Task-specialized variants (e.g., CrackMamba for defect segmentation) may add explicit attention maps, hybrid convolutions, or deformable attention branches (He et al., 22 Jul 2024).
• For resource-constrained deployments, use channel grouping, interleaved pooling, and multi-head scan mechanisms to control parameters/FLOPs.

7. Open Issues and Future Directions

Current research addresses:

• Optimal scan ordering, locality preservation, and directionality for specific applications (e.g., medical, video, multi-view).
• Theoretical analysis of SSM/attention unification, stability in large-scale models, and interpretability of learned recurrence kernels.
• Task-specific augmentations: deformable tokens, hybrid attention, multi-scale fusion, cross-modal alignment.
• Efficient (subquadratic) training transfer from transformers via weight subcloning/distillation (Chen et al., 21 Feb 2025).
• Extensions to 3D, higher-rank SSMs, spectral domain modeling, and plug-and-play modules for new data modalities.

Visual Mamba Modules provide a flexible, theoretically grounded, and highly efficient alternative to traditional convolutional and transformer layers for global context modeling in vision and multimodal learning. Their empirical gains and architectural adaptability have established them as foundational components in contemporary computer vision research.