Mamba-Based Module

• Mamba-based modules are neural sequence processors that use selective state-space models to achieve efficient long-range dependency modeling with linear computational cost.
• They integrate dynamic scanning, input-dependent gating, and compact MLPs, delivering global receptive fields across applications like computer vision, speech, and medical imaging.
• Empirical evaluations confirm notable performance gains in tasks such as crack segmentation and video super-resolution compared to conventional Transformer-based approaches.

A Mamba-based module is a neural sequence-processing component that leverages selective state-space models (SSMs), particularly the Mamba architecture, to achieve efficient modeling of long-range dependencies with linear computational complexity. Mamba-based modules are increasingly adopted in diverse research areas including computer vision, video processing, speech modeling, hyperspectral classification, 3D medical imaging, action recognition, and efficient model adaptation, due to their scalable global receptive field and favorable resource consumption compared to Transformer attention mechanisms. These modules operate by learning discrete-time recurrences wherein the state evolution and output mappings are input-dependent, typically realized via compact gating networks or small multilayer perceptrons.

1. Mathematical Structure and Complexity Characteristics

The canonical Mamba block models a sequence $\{x_t\}_{t=1}^{L}$ using a continuous-time SSM: $$\frac{d}{dt} h(t) = A h(t) + B x(t), \qquad y(t) = C h(t) + D x(t)$$ Discretization yields the per-step update: $$h_t = \overline{A}_t h_{t-1} + \overline{B}_t x_t, \qquad y_t = C_t h_t + D x_t$$ Here, $\overline{A}_t$, $\overline{B}_t$, and $C_t$ are input-dependent, often parameterized via linear layers or small neural networks. Unlike Transformers, which scale as $O(L^2 d)$ for self-attention, Mamba-based modules process each sequence in $O(L d^2)$—linear in the spatial/temporal token length $L$ and hidden/channel dimension $d$ (He et al., 2024, Chen et al., 21 Feb 2025). Structural adaptations (multi-branch scanning, bidirectional recurrence, tree scans) ensure full global context aggregation.

2. Architectural Instantiation and Topological Adaptations

Mamba-based vision blocks often utilize two-dimensional and bidirectional scanning, employing multiple parallel recurrences over different spatial/temporal axes to fully exploit the receptive field. For example, "CrackMamba" deploys a 2D Selective Scan (SS2D) with four parallel passes—left-to-right, right-to-left, top-to-bottom, bottom-to-top—yielding a complete 2D receptive field (He et al., 2024). In medical volumetric segmentation, the "SABMamba" module executes selective Mamba scans along sagittal, coronal, and axial planes, fusing these to enrich anatomical context (Zeng et al., 17 Aug 2025). For hyperspectral image classification, modules partition input features into clusters or local groups, processing each with independent or multi-group Mamba blocks, followed by global fusion (for instance, the Cluster-Guided Spatial Mamba in CSSMamba (Dewis et al., 22 Jan 2026), or the multi-group blocks in HS-Mamba (Peng et al., 22 Apr 2025)).

3. Gating, Attention, and Adaptation Mechanisms

A defining characteristic across recent Mamba-based modules is the integration of attention- or gating-inspired adaptations:

• Attention Map Fusion: Several modules fuse Mamba output with explicit attention-like maps, e.g., CrackMamba's addition of a sigmoid-activated attention map generated via SS2D, which modulates features before a final layer normalization and activation (He et al., 2024).
• Motion-Awareness: In micro-gesture recognition, MSF-Mamba introduces central frame difference (CFD) and local state fusion, learning to weigh local temporal neighborhoods and highlighting motion cues (Li et al., 12 Oct 2025).
• Deformable and Dynamic Scanning: UIS-Mamba builds Dynamic Tree Scan (DTS), dynamically deforming patch scanning paths along a learnable tree structure, and Hidden State Weaken (HSW), which suppresses background states according to normalized cut masks, sharpening instance segmentation in underwater imagery (Cong et al., 1 Aug 2025).
• Adaptor Layers for Memory and Spatial Enhancement: Mamba-Adaptor provides temporal memory augmentation (Adaptor-T) and multi-scale spatial inductive bias (Adaptor-S) around SSM recurrences, directly counteracting long-range forgetting and spatial information loss in standard sequential scanning (Xie et al., 19 May 2025).

4. Application Domains and Representative Module Examples

Mamba-based modules are deployed in structurally diverse settings:

• Semantic Segmentation: Mamba modules are used for crack detection, medical image segmentation, and instance segmentation in challenging environments, e.g., CrackMamba (He et al., 2024), SABMamba (Zeng et al., 17 Aug 2025), UIS-Mamba (Cong et al., 1 Aug 2025).
• Video and Burst Processing: In VSRM (Tran et al., 28 Jun 2025), DAMB blocks perform bidirectional spatio-temporal scanning, and cross-mamba alignment adapts feature warping. BurstMamba models keyframe and temporal information with SSM recurrences, enhanced via optical flow serialization and wavelet-based parameterization (Unal et al., 25 Mar 2025).
• Classification and Recognition: MSF-Mamba fuses spatiotemporal local windows for micro-gesture recognition (Li et al., 12 Oct 2025); TSkel-Mamba builds hybrid spatial Transformer–temporal Mamba pipelines for skeleton-based action recognition, using multi-scale temporal interaction (MTI) operators (Liu et al., 12 Dec 2025).
• Hyperspectral Image Processing: CSSMamba and HS-Mamba implement clustering, dual-branch spectral-spatial encoding, and multi-group Mamba integration to handle high-dimensional spectral–spatial sequences efficiently (Dewis et al., 22 Jan 2026, Peng et al., 22 Apr 2025).
• Adaption/Distillation: TransMamba establishes universal adaptation from pre-trained Transformer models by two-stage feature alignment and weight subcloning, including cross-modal "Cross-Mamba" blocks for vision–language fusion (Chen et al., 21 Feb 2025).

5. Empirical Validation and Efficiency

Mamba-based modules repeatedly demonstrate empirical gains:

• Crack segmentation: CrackMamba yields superior miIoU and miDice with reduced parameter and computational cost compared to CNN and Transformer-based baselines (e.g., +5.84% miIoU on SteelCrack) (He et al., 2024).
• Video super-resolution: VSRM achieves SOTA PSNR/SSIM while maintaining lower FLOPs than Transformer networks, leveraging global SSM scanning and frequency-domain supervision (Tran et al., 28 Jun 2025).
• Micro-gesture and action recognition: MSF-Mamba and TSkel-Mamba outperform CNN/Transformer/TCN baselines with linear-time inference and improved accuracy, especially for challenging long-sequence data (Li et al., 12 Oct 2025, Liu et al., 12 Dec 2025).
• Hyperspectral classification: CSSMamba reports OA of 97.55% (PaviaU), highest of all tested models, and distinct boundary preservation attributed to cluster-guided sequence reduction and attention-driven selection (Dewis et al., 22 Jan 2026).
• Transfer learning and adaptation: Mamba-Adaptor increases Top-1 accuracy and COCO detection metrics with minimal extra parameters or FLOPs, confirming the effectiveness of lightweight temporal and spatial enhancement (Xie et al., 19 May 2025).

6. Analysis of Global Receptive Field and Interpretability

Multiple lines of evidence support the claim that Mamba-based modules attain global receptive fields at linear cost:

• Theoretical unrolling of SSM recurrences reveals full-sequence dependencies in each output token (He et al., 2024).
• Empirical visualization of effective receptive fields, e.g. ERF maps from RepLKNet [Ding et al.], confirm that CrackMamba outputs span the entire spatial domain after training (He et al., 2024).
• In medical and structural segmentation, planar and multi-axis scans synthesize comprehensive anatomical context without resorting to quadratic attention or local kernels (Zeng et al., 17 Aug 2025).
• These properties underpin the state-of-the-art performance observed in sequence modeling tasks across modalities.

7. Prospects and Limitations

Ongoing research highlights extensions such as:

• Advanced memory augmentation, deformable spatial modeling, and multi-modal cross-Mamba fusion (Xie et al., 19 May 2025, Chen et al., 21 Feb 2025).
• Applications in speech reconstruction tasks, where Mamba’s multi-stage mutual information profile is advantageous, while pure classification requires extra decoding (Zhang et al., 2024).
• Efficient adaptation and distillation from large-scale Transformer models—TransMamba achieves comparable downstream performance with just 50–75% of the original training data (Chen et al., 21 Feb 2025).

Potential limitations include challenges in explicit 2D spatial structural encoding, long-range memory retention, and optimal sequence partitioning for irregular or clustered data; various modules address these via hybrid attention mechanisms, clustering, or adaptor layers.

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In summary, the Mamba-based module constitutes a family of neural operators and architectural motifs that enforce global receptive fields and data-dependent interaction in linear time via selective state-space modeling. Their integration of dynamic scanning, attention-like gating, and hardware-aware scheduling allows broad deployment across vision, speech, and multimodal tasks, while empirical benchmarks and theoretical analysis substantiate their computational and representational advantages.