Content-Adaptive Mamba (CAM) Overview

• Content-Adaptive Mamba is a dynamic state-space model design that adapts sequence formation and token grouping based on image or video content.
• It deploys mechanisms like adaptive token reorganization, learned spatial ordering, and content-dependent fusion of global and local features.
• Empirical results show significant BD-rate reductions, improved PSNR, and competitive segmentation metrics across various benchmarks.

to=arxiv_search.search 天天彩票中奖{"query":"Content-Adaptive Mamba CMIC (Chen et al., 4 Aug 2025) Deform-Mamba (Ji et al., 2024) MambaVSR (He et al., 13 Jun 2025) DART (Yin et al., 12 Jun 2025) CMamba (Wu et al., 7 Feb 2025) CAMS (Khan et al., 2024)", "max_results": 10} to=arxiv_search.search 北京赛车开奖ing 的天天彩票{"query":"(Ji et al., 2024) Deform-Mamba Network for MRI Super-Resolution", "max_results": 5} to=arxiv_search.search 天天中彩票大奖ീയություններից 天天爱彩票怎么json to=arxiv_search.search хадоу problems 代理娱乐 to=arxiv_search.search 大发娱乐 天天送ing 手机上天天中彩票իշտjson 彩票直属{"query":"(Chen et al., 4 Aug 2025) Content-Adaptive Mamba for Learned Image Compression", "max_results": 5} Content-Adaptive Mamba (CAM) denotes a class of state-space-model architectures in which Mamba-style sequence modeling is made dependent on input content rather than relying only on fixed rasterization, fixed local operators, or fixed fusion rules. In its explicit formulation for learned image compression, CAM is a dynamic SSM that combines content-aware token reorganization with global priors prompting (Chen et al., 4 Aug 2025). Across adjacent literature, closely related designs appear as content-adaptive local sampling in Deform-Mamba for MRI super-resolution, content-aware scanning in MambaVSR, dynamic fusion of SSM and CNN branches in CMamba, and adaptive tokenization in DART (Ji et al., 2024, He et al., 13 Jun 2025, Wu et al., 7 Feb 2025, Yin et al., 12 Jun 2025). This suggests that CAM is best understood not as a single canonical block but as a broader design pattern for making sequence formation, local feature extraction, and global context propagation conditional on image or video content.

1. Conceptual definition and scope

In the narrow, explicit sense introduced by CMIC, CAM modifies two aspects of vanilla Mamba that are problematic for images: the linearization of a 2D feature map into a sequence, and the strict causality of token interactions. The resulting module keeps the global receptive field and $O(N)$ complexity of Mamba while clustering and reordering tokens by content similarity and injecting cluster-level prompts into the SSM readout (Chen et al., 4 Aug 2025). The same paper characterizes vanilla Mamba as content-agnostic because it relies on fixed and predefined selective scans.

A broader CAM interpretation emerges from several neighboring architectures. Deform-Mamba treats content adaptivity as dynamically shifted and modulated local sampling through a modulated deformable convolution branch fused with a vision Mamba branch (Ji et al., 2024). MambaVSR treats it as learned spatiotemporal scan construction through Shared Compass Construction and Content-Aware Sequentialization (He et al., 13 Jun 2025). CMamba treats it as dynamic fusion of global content extracted by SSM blocks and local details captured by CNN blocks (Wu et al., 7 Feb 2025). DART treats it as adaptive partitioning of images into content-dependent patches of varying sizes before the Mamba backbone (Yin et al., 12 Jun 2025). Although these mechanisms differ, they share the same structural objective: to let the SSM operate on a representation whose ordering, locality, or emphasis reflects the current sample.

This diversity also clarifies what CAM is not. It is not limited to pure Mamba backbones, because several successful CAM-like systems are hybrid convolution–SSM or attention–SSM models (Wu et al., 7 Feb 2025, Xue et al., 5 Sep 2025). It is also not restricted to one domain: the literature spans MRI super-resolution, video super-resolution, learned image compression, cloud detection, raw video demoireing, camouflaged object detection, and cell detection (Ji et al., 2024, He et al., 13 Jun 2025, Chen et al., 4 Aug 2025, Xue et al., 5 Sep 2025, Xu et al., 2024, Li et al., 31 Jul 2025, Liu et al., 25 Dec 2025).

2. Principal mechanisms of content adaptivity

One recurrent mechanism is content-adaptive local modeling. In Deform-Mamba, local information is extracted by a modulated deformable convolution whose receptive field changes according to learned offsets and modulation scalars: $$Y(p)=\sum_{k=1}^K w_k \cdot X\left(p+p_k+\Delta p_k\right) \cdot \Delta m_k,$$ with $\Delta p_k = \operatorname{Conv}(\mathbf{X})$ and $$\Delta m_k = \sigma(\operatorname{Conv}(\mathbf{X}))$$ (Ji et al., 2024). This makes local sampling depend on edges, textures, and structures rather than a fixed convolution grid.

A second mechanism is content-adaptive sequence construction. MambaVSR replaces rigid raster or temporal ordering with a learned spatial order derived from intra-frame semantic connectivity graphs and spectral clustering, then interleaves temporally aligned features along that order (He et al., 13 Jun 2025). CMIC performs cosine-based K-means clustering over tokens, groups tokens by cluster, and feeds the reordered sequence into a Dynamic SSM (Chen et al., 4 Aug 2025). DART moves content adaptivity even earlier, learning a 2D importance distribution and using differentiable quantile partitioning so that high-score regions receive more, smaller patches and low-score regions receive fewer, larger patches (Yin et al., 12 Jun 2025).

A third mechanism is content-adaptive fusion of local and global branches. CMamba formalizes this through its Content-Adaptive SSM module. A VSS branch produces $\mathcal{F}_{SSM}$, a ResBlock branch produces $\mathcal{F}_{CNN}$, global max pooling summarizes the merged feature, and two MLPs generate channel-wise fusion weights: $$\alpha = \frac{\exp(\mathcal{F}_\alpha)}{\exp(\mathcal{F}_\alpha)+\exp(\mathcal{F}_\beta)}, \qquad \beta = \frac{\exp(\mathcal{F}_\beta)}{\exp(\mathcal{F}_\alpha)+\exp(\mathcal{F}_\beta)},$$ followed by

$$y = w\big(\alpha \cdot \mathcal{F}_{SSM} + \beta \cdot \mathcal{F}_{CNN}\big).$$

This allows the model to favor SSM features when global structure dominates and CNN features when high-frequency detail is critical (Wu et al., 7 Feb 2025).

A fourth mechanism is adaptive gating in spatial, channel, or scale space. CD-Mamba uses dual-attention skip connections with PAM and CAM to reweight spatial positions and channels according to current image content (Xue et al., 5 Sep 2025). CellMamba’s Triple-Mapping Adaptive Coupling computes branch-specific idiosyncratic attention maps and a consensus attention map, then modulates features multiplicatively (Liu et al., 25 Dec 2025). Vcamba uses phase-guided gating in the frequency domain so that phase motion guides amplitude selection before inverse FFT reconstruction (Li et al., 31 Jul 2025).

3. State-space formulation and sequence modeling

The mathematical core of CAM remains the state-space recurrence used by Mamba-style models. Deform-Mamba presents the continuous-time and discretized SSM as

$$h^{\prime}(t)=\mathbf{A} h(t)+\mathbf{B} x(t), \quad y(t)=\mathbf{C} h(t),$$

and after zero-order hold,

$$h_t = \overline{\mathbf{A}} h_{t-1} + \overline{\mathbf{B}} x_t, \quad y_t = \mathbf{C} h_t,$$

with a structured convolution view that yields linear complexity in sequence length (Ji et al., 2024). MambaVSR preserves this recurrence but changes what counts as the sequence index $$Y(p)=\sum_{k=1}^K w_k \cdot X\left(p+p_k+\Delta p_k\right) \cdot \Delta m_k,$$0: tokens are ordered along a content-aware spatiotemporal path rather than a fixed raster (He et al., 13 Jun 2025).

CMIC makes the adaptation explicit at the SSM level. After content-aware token reorganization, it runs a Dynamic SSM on the reordered sequence $$Y(p)=\sum_{k=1}^K w_k \cdot X\left(p+p_k+\Delta p_k\right) \cdot \Delta m_k,$$1 and modulates the output kernel with prompt-derived priors: $$Y(p)=\sum_{k=1}^K w_k \cdot X\left(p+p_k+\Delta p_k\right) \cdot \Delta m_k,$$2 Here $$Y(p)=\sum_{k=1}^K w_k \cdot X\left(p+p_k+\Delta p_k\right) \cdot \Delta m_k,$$3 is obtained from a learnable prompt dictionary indexed by cluster assignments, so the readout becomes cluster-conditioned (Chen et al., 4 Aug 2025). This does not discard causality in the recurrence, but it changes sequence geometry and readout semantics so that content-similar tokens interact at shorter effective distance.

Vision-specific CAM designs also alter how 2D or 3D signals are scanned. Deform-Mamba uses SS2D with four directional scans (Ji et al., 2024). CD-Mamba splits channels into four groups and constructs forward, backward, for-backward, and back-forward sequences before SSM processing (Xue et al., 5 Sep 2025). DemMamba scans spatial features along four diagonal directions and temporal features bidirectionally (Xu et al., 2024). Vcamba adds a spiral low-to-high and high-to-low frequency-domain scan so that sequential order follows the structure of the Fourier plane rather than the pixel grid (Li et al., 31 Jul 2025). This suggests that CAM generalizes the Mamba principle by making scan order itself part of the learned inductive bias.

4. Architectural patterns across application domains

A common macro-architecture is the multi-scale encoder–decoder. Deform-Mamba follows a U-Net-like encoder–bottleneck–decoder with pixel-shuffle pre-upsampling, patch embedding, Deform-Mamba encoder stages, a multi-view context module, and a pure vision Mamba decoder (Ji et al., 2024). CD-Mamba also uses a U-Net-style structure in which shallow convolution handles short-range spatial redundancies while deeper Cloud-SMB blocks model long-range dependencies (Xue et al., 5 Sep 2025). CAMS-Net adopts a convolution- and self-attention-free encoder–decoder in which Mamba Channel Aggregator and Mamba Spatial Aggregator are deployed at different stages, with Linearly Interconnected Factorized Mamba blocks reducing complexity (Khan et al., 2024).

Another pattern is global–local decomposition. MambaVSR’s Global-Local State Space Block combines window self-attention for local detail with SSM-based feature propagation over content-aware sequences for global dependence (He et al., 13 Jun 2025). CMamba parallels this by pairing a VSS block with a ResBlock and learning the branch weights (Wu et al., 7 Feb 2025). Deform-Mamba makes the decomposition especially explicit by adding the outputs of a modulated deform block and a vision Mamba block at each encoder scale (Ji et al., 2024).

A third pattern is domain-specific auxiliary adaptation. DemMamba places an Adaptive Frequency Block inside its Spatial Mamba Block and a Channel Attention Block inside its Temporal Mamba Block, yielding adaptivity across space, time, frequency, and channels (Xu et al., 2024). Vcamba constructs separate spatial-motion and frequency-motion branches and merges them in a space and frequency motion fusion module (Li et al., 31 Jul 2025). CellMamba introduces scale-adaptive weighting in the detection head, while DART externalizes content adaptivity to the tokenizer and leaves the Mamba backbone unchanged (Liu et al., 25 Dec 2025, Yin et al., 12 Jun 2025).

5. Empirical performance and representative instantiations

The strongest explicit CAM results are currently reported in learned image compression. CMIC states that its Content-Adaptive Mamba-based LIC model surpasses VTM-21.0 by $$Y(p)=\sum_{k=1}^K w_k \cdot X\left(p+p_k+\Delta p_k\right) \cdot \Delta m_k,$$4, $$Y(p)=\sum_{k=1}^K w_k \cdot X\left(p+p_k+\Delta p_k\right) \cdot \Delta m_k,$$5, and $$Y(p)=\sum_{k=1}^K w_k \cdot X\left(p+p_k+\Delta p_k\right) \cdot \Delta m_k,$$6 BD-rate on Kodak, Tecnick, and CLIC benchmarks, respectively (Chen et al., 4 Aug 2025). In the same area, CMamba reports BD-Rate improvements over VVC and attributes them to the joint effect of its Content-Adaptive SSM module and Context-Aware Entropy module, which uses SSMs for spatial context and channel-wise autoregression for inter-channel dependency (Wu et al., 7 Feb 2025).

In restoration, MambaVSR reports that it outperforms the Transformer-based method by $$Y(p)=\sum_{k=1}^K w_k \cdot X\left(p+p_k+\Delta p_k\right) \cdot \Delta m_k,$$7 dB PSNR on the REDS dataset with $$Y(p)=\sum_{k=1}^K w_k \cdot X\left(p+p_k+\Delta p_k\right) \cdot \Delta m_k,$$8 fewer parameters, and its ablations isolate gains from Fiedler-based scanning and full Content-Aware scanning (He et al., 13 Jun 2025). Deform-Mamba reports competitive performance on IXI and fastMRI, combining a Deform-Mamba encoder, multi-view context module, and a contrastive edge loss with $$Y(p)=\sum_{k=1}^K w_k \cdot X\left(p+p_k+\Delta p_k\right) \cdot \Delta m_k,$$9 (Ji et al., 2024). DemMamba states that it surpasses state-of-the-art methods by $\Delta p_k = \operatorname{Conv}(\mathbf{X})$0 dB in PSNR while remaining alignment-free (Xu et al., 2024).

In segmentation and detection, CAMS-Net reports Dice $\Delta p_k = \operatorname{Conv}(\mathbf{X})$1 on the CMR dataset and $\Delta p_k = \operatorname{Conv}(\mathbf{X})$2 on M&Ms-2 with $\Delta p_k = \operatorname{Conv}(\mathbf{X})$3M parameters, while remaining convolution- and self-attention-free (Khan et al., 2024). CD-Mamba reports mIoU $\Delta p_k = \operatorname{Conv}(\mathbf{X})$4, $\Delta p_k = \operatorname{Conv}(\mathbf{X})$5, and ACC $\Delta p_k = \operatorname{Conv}(\mathbf{X})$6 on Biome, and compares favorably with RS-Mamba while using $\Delta p_k = \operatorname{Conv}(\mathbf{X})$7M parameters and $\Delta p_k = \operatorname{Conv}(\mathbf{X})$8G FLOPs versus $\Delta p_k = \operatorname{Conv}(\mathbf{X})$9M and $$\Delta m_k = \sigma(\operatorname{Conv}(\mathbf{X}))$$0G for RS-Mamba (Xue et al., 5 Sep 2025). CellMamba reports $$\Delta m_k = \sigma(\operatorname{Conv}(\mathbf{X}))$$1M parameters, $$\Delta m_k = \sigma(\operatorname{Conv}(\mathbf{X}))$$2 ms inference time, and $$\Delta m_k = \sigma(\operatorname{Conv}(\mathbf{X}))$$3 mAP@50 on CytoDArk0, linking its gains to TMAC and the Adaptive Mamba Head (Liu et al., 25 Dec 2025).

At the tokenizer level, DART improves accuracy by $$\Delta m_k = \sigma(\operatorname{Conv}(\mathbf{X}))$$4 on DeiT and achieves $$\Delta m_k = \sigma(\operatorname{Conv}(\mathbf{X}))$$5 FLOPs reduction with superior performance, while also improving Vim and VideoMamba under fixed or reduced token budgets (Yin et al., 12 Jun 2025). This is significant because it shows that CAM need not be implemented only inside the SSM; it can also arise by changing the token allocation seen by the SSM.

6. Terminological clarifications, limitations, and future directions

A recurring misconception is that CAM always means a pure SSM architecture. The literature does not support that reading. CMamba, CD-Mamba, and several restoration systems are explicitly hybrid, combining SSMs with CNNs, deformable convolutions, attention, or both (Wu et al., 7 Feb 2025, Xue et al., 5 Sep 2025, Ji et al., 2024). Another misconception is that CAM is synonymous with attention-free modeling. CAMS-Net is attention-free and convolution-free, but MambaVSR uses window self-attention, CD-Mamba uses dual attention in skip connections, and CellMamba uses MSA in its fourth stage (Khan et al., 2024, He et al., 13 Jun 2025, Xue et al., 5 Sep 2025, Liu et al., 25 Dec 2025).

The term itself is also not uniform across papers. CMIC uses “Content-Adaptive Mamba” as the formal name of a particular dynamic SSM (Chen et al., 4 Aug 2025). CMamba instead uses “Content-Adaptive SSM” for a global–local fusion module (Wu et al., 7 Feb 2025). DART is not named CAM, yet it makes the Mamba input sequence content-adaptive through differentiable tokenization (Yin et al., 12 Jun 2025). CAMS-Net uses the acronym “CAMS” to mean “Convolution and self-Attention-free Mamba-based semantic Segmentation Network,” which is terminologically distinct from CAM (Khan et al., 2024).

Several limitations are explicit. CMIC notes that current CAM focuses on within-cluster token aggregation and does not impose sophisticated inter-cluster ordering or intra-cluster sorting (Chen et al., 4 Aug 2025). CD-Mamba describes its study as exploratory regarding the optimal fusion ratio of convolution and Cloud-SMB blocks (Xue et al., 5 Sep 2025). MambaVSR implies costs associated with graph construction and dependence on patch alignment quality (He et al., 13 Jun 2025). DART reports that simple MLP scoring was unstable for Vim and VideoMamba, indicating sensitivity between adaptive tokenization and SSM optimization (Yin et al., 12 Jun 2025).

The main future direction suggested across these works is a move from fixed inductive biases toward content-conditioned sequencing and resource allocation. The literature already points to learned scan orders, clustering-based reordering, adaptive frequency scans, scale-adaptive heads, dynamic routing between convolution and Mamba, and token-budget reallocation as viable ingredients (He et al., 13 Jun 2025, Chen et al., 4 Aug 2025, Li et al., 31 Jul 2025, Liu et al., 25 Dec 2025, Yin et al., 12 Jun 2025). A plausible implication is that future CAM systems will combine several of these mechanisms at once: adaptive tokenization before the backbone, adaptive sequencing inside the SSM, and adaptive fusion across local, global, spatial, temporal, and frequency pathways.