SAMamba: Adaptive State Space Modeling with Hierarchical Vision for Infrared Small Target Detection

Changwei Wang; Chuan Ren; Rongtao Xu; Shibiao Xu; ShuChen Zheng; Wenhao Xu; Zherui Zhang

REVIEW 4 major objections 4 minor 47 references

SAMamba: Adaptive State Space Modeling with Hierarchical Vision for Infrared Small Target Detection

T0 review · 4 major / 4 minor · reviewed 2026-08-07 · deepseek-v4-flash

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Pith's one-line read SAMamba claims state-of-the-art infrared small-target detection on three public benchmarks by pairing SAM2's frozen hierarchical encoder with Mamba's selective state-space modeling.

desk verdict Plausible new SOTA for infrared small target detection, but the comparison protocol against baselines is underspecified and must be checked. read the letter →

arxiv 2505.23214 v1 pith:ZPRMNEBS submitted 2025-05-29 cs.CV cs.AI

Wenhao Xu , Shuchen Zheng , Changwei Wang , Zherui Zhang , Chuan Ren , Rongtao Xu , Shibiao Xu This is my paper

classification cs.CVcs.AI

keywords infraredsmalltargetdetectionsegmentanythingmodelvisionmambastatespacemodelsdomainadaptationfeaturefusionU-Netobject

verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

The paper sets out to show that combining SAM2's hierarchical feature learning with Mamba's selective state-space sequence modeling yields a detector that outperforms existing CNN-based methods for infrared small target detection, where targets occupy under 0.15% of the image and blend into clutter. The authors claim this is achieved by freezing the SAM2 encoder and adding three lightweight modules that adapt features to the infrared domain, model global context with linear complexity, and preserve small-target detail during multi-scale fusion. Across NUAA-SIRST, IRSTD-1k, and NUDT-SIRST, the reported IoU, nIoU, and F1 scores exceed all compared methods. If the comparisons hold up under matched training protocols, the result matters because it gives a straightforward recipe for adapting a large vision foundation model to a sparse, low-contrast detection task without full fine-tuning.

What carries the argument

The load-bearing machinery is a three-part adaptation of a frozen SAM2 encoder. The FS-Adapter re-weights tokens by cosine similarity to a learnable task embedding and then mixes channels through a learned matrix, inserted before each Hiera block to close the natural-to-infrared gap. The CSI module splits skip-connection features into four channel groups, runs each through a Vision Mamba state-space block, recombines same-index channels across groups, and applies channel and spatial attention, giving global context at linear cost. The DPCF module upsamples deep features and uses a single learnable scalar expanded into spatial-channel gates to fuse high- and low-resolution segments, so the network can choose detail versus context per location.

What would settle it

Retrain ISNet, HCFNet, and UIUNet with SAMamba's exact training recipe, including 1024x1024 dynamic crops, Adam, and SoftIoU plus Dice plus Focal loss for 300 epochs, then compare on NUAA-SIRST; if their IoU climbs to within about one point of SAMamba's 81.08, the claimed architectural advantage is a protocol artifact, whereas a persistent gap of several points would support the central claim.

Watch

Extended reading notes

Core claim

The paper's central claim is that SAMamba, a U-Net with a frozen SAM2 Hiera-S encoder and three lightweight modules, achieves the best published results on NUAA-SIRST, IRSTD-1k, and NUDT-SIRST across IoU, nIoU, and F1. On NUAA-SIRST it reports 81.08% IoU, 79.17% nIoU, and 89.55% F1; on IRSTD-1k 73.53% IoU, 68.99% nIoU, and 84.75% F1; on NUDT-SIRST 93.13% IoU, 93.15% nIoU, and 96.44% F1. The authors attribute the gains to domain adaptation via the FS-Adapter, long-range context via the CSI module's Mamba-based state-space interaction, and detail preservation via DPCF's adaptive gating fusion.

Load-bearing premise

The load-bearing premise is that the baseline methods in Table 1 were trained and evaluated under the same protocol as SAMamba, including 1024x1024 resolution, dynamic cropping, Adam optimization, and the combined SoftIoU plus Dice plus Focal loss; if the published baseline numbers used a different protocol, the reported margins could reflect training conditions rather than the architecture.

Editorial extensions

If this is right

A frozen SAM2 Hiera-S encoder, adapted by lightweight modules, is sufficient to beat fully trained CNN detectors on three ISTD benchmarks.
The Mamba-based CSI module's linear-complexity global context modeling is a practical alternative to attention for high-resolution infrared inputs.
The component ablations attribute the improvement to each module: +0.89 IoU from FS-Adapter, +2.47 from CSI, and +2.28 from DPCF over the Hiera-based baseline.
At 1024x1024 input, SAMamba's 37.18M-parameter Hiera-S variant runs at 6.39 FPS on an RTX 3090, comparable to ISNet and HCFNet while reporting higher accuracy.
Domain adaptation through token-level selection plus channel mixing can be reused in other tasks that use a frozen foundation-model encoder on a different imaging modality.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

Editorial inference: if the protocol-matched comparison confirms the margins, the FS-Adapter design could transfer directly to other low-contrast or non-visible-spectrum tasks, such as radar or hyperspectral segmentation, where only small labeled sets exist.
Editorial inference: the paper's own limitation analysis suggests the next testable extension is video, where feeding temporal frames through the same CSI state-space blocks could exploit motion cues, a direction the authors mention as future work.
Editorial inference: because DPCF uses a single scalar expanded to gates, replacing it with per-channel learned gates might further improve fusion on heterogeneous backgrounds; this is a cheap experiment the paper does not run.
Editorial inference: the large gain on NUDT-SIRST (93.13 IoU) relative to CNN baselines may reflect the synthetic dataset's clean target shapes, so a real-world low-SCR benchmark would clarify whether the architecture's advantage persists under sensor noise.

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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Desk Editor's Note

read the letter

SAMamba is a serious, workmanlike entry in the infrared small target detection line. The new bit is concrete: a frozen SAM2/Hiera encoder with adapter modules, Vision Mamba blocks inside the skip connections, and a gated fusion in the decoder. The three named modules (FS-Adapter, CSI, DPCF) are not in prior work, and the ablation shows each one adds a meaningful, monotonic chunk of IoU on NUAA-SIRST. That is real evidence the architecture, not just the backbone, is carrying weight. The paper also ships code and states its limitations clearly, including failure cases on complex backgrounds and low SCR.

The central SOTA claim is plausible but not nailed down. The paper never says whether the baselines in Table 1 were retrained under SAMamba's 1024x1024, dynamic-crop, Adam, SoftIoU+Dice+Focal protocol. If those numbers are copied from the original papers (the common practice in this subfield), then the +1.0 IoU margin over ISNet and HCFNet on NUAA-SIRST could be largely a protocol artifact — Figure 6(a) shows resolution alone moves SAMamba's IoU by several points, so a one-point margin is within that range. This is the load-bearing soft spot, and it needs a sentence or a supplementary table to fix.

There are smaller issues worth naming. The DPCF gate is described as spatially adaptive, but alpha is a single scalar per stage that gets broadcast — the "spatial" language oversells it. The hyperparameter sensitivity study (Table 3) is run on the NUAA-SIRST test set, which is the same benchmark where the headline numbers are reported; that is a mild selection-on-test smell, common in this literature but still worth a flag. There are no error bars or significance tests, and the FS-Adapter text is a bit tangled — equation (6) is written as token selection but the prose says channel re-weighting.

None of this sinks the paper. The architecture is sensible, the gains are consistent across three datasets, and the ablations back the components. It deserves a serious referee, and I'd want the baseline-protocol question resolved before trusting the SOTA claim. If the baselines were actually retrained under the same protocol, this is a clean, useful result. If not, it is still a decent architecture but the margin over ISNet/HCFNet is unproven.

Recommended: send to peer review; ask for baseline protocol details, error bars, and a de-overclaimed description of the DPCF gate.

Referee Report

4 major / 4 minor

Summary. The paper proposes SAMamba, an infrared small target detection (ISTD) architecture that combines a frozen SAM2 Hiera-S backbone with a new Feature Selection Adapter (FS-Adapter), a Cross-Channel State-Space Interaction (CSI) module based on Vision Mamba in the skip connections, and a Detail-Preserving Contextual Fusion (DPCF) module in the decoder. The method is evaluated on three ISTD benchmarks (NUAA-SIRST, IRSTD-1k, NUDT-SIRST) with IoU, nIoU, and F1 metrics. The authors report state-of-the-art results on all three datasets, together with component ablations, hyperparameter sensitivity studies, and a computational analysis. The central empirical claim is that SAMamba consistently outperforms prior methods, with margins of roughly 1 IoU point over ISNet and HCFNet on NUAA-SIRST and larger margins on IRSTD-1k and NUDT-SIRST.

Significance. If the reported comparisons are fair, SAMamba would be a credible new state of the art for ISTD on the three chosen benchmarks. The paper has several concrete strengths: it releases code, performs systematic ablations that isolate each module's contribution with monotonic gains, includes sensitivity analyses for internal hyperparameters, and provides an explicit computational analysis. The architectural combination of SAM2's hierarchical features with Mamba-based sequence modeling is sensible and timely. However, the significance of the central claim is currently weakened by incomplete evidence about the fairness of the baseline comparisons, by a formal inconsistency in the description of the DPCF gating mechanism, and by the absence of any statistical validation. These issues do not invalidate the general approach, but they need to be resolved before the state-of-the-art claim can be accepted.

major comments (4)

[§4.1 and Table 1] The central claim in §4.3 that SAMamba 'consistently achieves state-of-the-art performance across all three datasets and all three evaluation metrics' rests on Table 1. SAMamba is trained at 1024×1024 resolution with dynamic scaling/cropping, Adam (lr 1e-4), 300 epochs, batch size 2, and a combined SoftIoU+Dice+Focal loss. The paper does not state whether any baseline was retrained under this protocol, and the original ISNet, UIUNet, and HCFNet papers use different resolutions (commonly 512×512) and different training recipes. Figure 6(a) shows that resolution alone can change IoU by several points, so the ~1-point margins on NUAA-SIRST (+1.06 over ISNet, +0.99 over HCFNet) may reflect protocol differences rather than architectural superiority. Please specify the exact source of every baseline number in Table 1 (original publication vs. reimplementation), and if numbers are copied, retrain the main baselines under the same protocol or report both original and matched-protocol results. The same concern applies to Table 4, where baseline FLOPs are reported for a 1024×1024 input without evidence that the baselines were configured and run at that resolution.
[§3.5, Eqs. (13)-(14)] The DPCF module is described as a 'learnable, spatially-adaptive gating mechanism,' but the formal description does not support that claim. A single learnable parameter α' ∈ R^{1×1×1} is introduced and then expanded spatially and channel-wise to α ∈ R^{H×W×C/4}. Expanding a scalar produces a tensor in which all entries are identical, so β = sigmoid(α) is a single scalar value, not a per-location per-channel gate. This contradicts the subsequent statement that the network learns weights 'for each spatial location and channel group.' Please clarify the actual parameterization: either α is a learned tensor with spatial and channel dimensions, in which case the text should say so and Table 4's parameter counts should be updated accordingly, or the gating is a scalar fusion weight, in which case the 'spatially-adaptive' language and the ablation claims in Table 3(c) need to be revised.
[§3.3, Eqs. (6)-(7)] The dimensions in the FS-Adapter token-selection step are inconsistent. F'_t ∈ R^{HW×C} and ξ ∈ R^C, but t_i is written as belonging to R^{HW} while the index i is ranged to C. If t_i is a token vector of length C, the index should run over HW and the notation should be changed accordingly; if t_i is a per-channel spatial map, the cosine similarity sim(t_i, ξ) is undefined because the two arguments have different dimensions. The intended mechanism is recoverable, but the formal description is not self-consistent and should be corrected before the module can be implemented or evaluated from the paper alone.
[§4.4 and Table 1] All main results and ablations are reported as point estimates with no error bars, multiple seeds, or statistical significance tests. The headline margins over ISNet and HCFNet on NUAA-SIRST are only about 1 IoU point, and Figure 6(a) shows that experimental conditions such as input resolution can shift performance by several points. Please report the mean and standard deviation over at least three seeds for the main comparison in Table 1 and for the component ablation in Table 2, and state whether the observed component-wise gains are stable across seeds. This is necessary to support the word 'consistent' in the central claim.

minor comments (4)

[§3.6, Eq. (16)] The variable names in Eq. (16) are reversed relative to standard convention: the text says 'ŷ represents the ground truth, y is the predicted mask,' but the usual notation is that y is the ground truth and ŷ is the prediction. Please swap the definitions or the symbols for clarity.
[References] The UIUNet reference appears twice, once as Wu et al. (2022) and once as Wu et al. (2023), for what appears to be the same paper. Please consolidate the duplicate entry.
[Table 4] The dagger symbol (†) next to ISNet in Table 4 is not explained in the table caption or the text. Please add a footnote describing what it indicates.
[§4.4, Figure 6(c)] The sentence explaining why Hiera-L degrades performance says this is 'likely due to overfitting on the relatively small ISTD datasets,' but no evidence such as training/validation curves or regularization settings is provided. Please either qualify this as a hypothesis or support it with data.

Circularity Check

0 steps flagged · score 2.0 of 10

No derivation-level circularity; the SOTA claim is an external empirical comparison, with only a minor non-load-bearing self-citation and a mild NUAA-SIRST hyperparameter selection loop.

full rationale

Walking the claimed derivation chain, SAMamba's three components are specified as explicit forward computations (Eqs. 6-15) and are evaluated against external ground-truth benchmarks (NUAA-SIRST, IRSTD-1k, NUDT-SIRST) with standard metrics and a standard combined SoftIoU+Dice+Focal loss; the central SOTA claim in Section 4.3 is an empirical comparison, not a quantity derived by construction from the model's own definitions. The self-citation to HCFNet (Xu et al., 2024) appears only as related-work context and as a Table 1 baseline; no load-bearing argument, uniqueness theorem, or ansatz is imported from it, so this is not circular. One in-scope concern is that Section 4.4 selects the default CSI head count (4) and DPCF segment count (4) by maximizing IoU on the same NUAA-SIRST dataset whose 81.08% IoU is then reported as the headline in Table 1; this is a mild evaluation-selection loop that should be disclosed, and it makes the NUAA-SIRST margin optimistic rather than out-of-sample. It is not, however, a construction-level circularity, because the reported number is a measured outcome of training a fixed architecture, not the selection criterion itself, and the IRSTD-1k and NUDT-SIRST results are not part of that selection. Baseline comparability at 1024x1024 resolution (Section 4.1 vs Table 1) is a protocol-fairness question rather than a circularity question. Overall, the derivation is self-contained against external benchmarks, and the only circularity-adjacent issues are minor.

Assumptions & free parameters 4 free parameters · 4 assumptions · 0 invented entities

The paper introduces no new physical entities; the three modules are architectural components validated internally through ablations, which do not constitute independent evidence in the sense used for physical theories. The free parameters are architecture choices and a per-stage gate scalar selected via test-set evaluation.

free parameters (4)

CSI heads = 4
The number of parallel Vision Mamba heads in the CSI module; selected via hyperparameter sweep on NUAA-SIRST (Table 3a), with IoU ranging from 79.05 (1 head) to 81.08 (4 heads).
DPCF segments = 4
The number of channel segments in the DPCF fusion module; selected via sweep on NUAA-SIRST (Table 3b), with IoU ranging from 79.35 (1 segment) to 81.08 (4 segments).
DPCF gating scalar alpha' = learned per fusion stage
A single 1x1x1 learnable scalar broadcast to HxWxC/4 to form the gate beta in Eq. 13. This is the entirety of the claimed spatially adaptive mechanism; it is a per-stage constant rather than a per-pixel adaptive weight.
Input resolution = 1024x1024
Chosen via resolution study (Fig 6a) showing performance plateaus at 1024x1024; affects both accuracy and computational cost.

assumptions (4)

domain assumption Pre-trained SAM2 Hiera features trained on natural images transfer to infrared small targets via lightweight adapters.
The whole FS-Adapter design and frozen-backbone strategy in Sections 3.2 and 3.3 assume that frozen natural-image features plus small learned adapters can bridge the domain gap to infrared imagery. If this transfer fails, the domain adaptation claim collapses.
domain assumption The U-Net encoder-decoder with skip connections is an appropriate base architecture for ISTD.
The baseline and all ablations (Table 2) build on U-Net; the paper does not justify this structural choice beyond common practice in ISTD segmentation.
standard math The Mamba/ViM state-space formulation (Eq. 2-4) is correct and stable as used in the CSI module.
The paper relies on the published Mamba and Vision Mamba formulations without proving or re-deriving them; any instability or mismatch in the selective scan implementation would affect the CSI module.
domain assumption The reported benchmark splits are respected and the test sets are not used for model selection.
The paper states standard dataset splits (Section 4.1), but Section 4.4 performs hyperparameter selection on NUAA-SIRST, the same dataset whose test numbers are reported as headline results. This creates a potential optimism bias.

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Cite this review

Pith. "Pith review of SAMamba: Adaptive State Space Modeling with Hierarchical Vision for Infrared Small Target Detection." pith.science (2026). https://pith.science/paper/ZPRMNEBS

@misc{pith2026250523214,
  author       = {Pith},
  title        = {Pith review of: SAMamba: Adaptive State Space Modeling with Hierarchical Vision for Infrared Small Target Detection},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/ZPRMNEBS}},
  note         = {Machine review of arXiv:2505.23214}
}

read the original abstract

Infrared small target detection (ISTD) is vital for long-range surveillance in military, maritime, and early warning applications. ISTD is challenged by targets occupying less than 0.15% of the image and low distinguishability from complex backgrounds. Existing deep learning methods often suffer from information loss during downsampling and inefficient global context modeling. This paper presents SAMamba, a novel framework integrating SAM2's hierarchical feature learning with Mamba's selective sequence modeling. Key innovations include: (1) A Feature Selection Adapter (FS-Adapter) for efficient natural-to-infrared domain adaptation via dual-stage selection (token-level with a learnable task embedding and channel-wise adaptive transformations); (2) A Cross-Channel State-Space Interaction (CSI) module for efficient global context modeling with linear complexity using selective state space modeling; and (3) A Detail-Preserving Contextual Fusion (DPCF) module that adaptively combines multi-scale features with a gating mechanism to balance high-resolution and low-resolution feature contributions. SAMamba addresses core ISTD challenges by bridging the domain gap, maintaining fine-grained details, and efficiently modeling long-range dependencies. Experiments on NUAA-SIRST, IRSTD-1k, and NUDT-SIRST datasets show SAMamba significantly outperforms state-of-the-art methods, especially in challenging scenarios with heterogeneous backgrounds and varying target scales. Code: https://github.com/zhengshuchen/SAMamba.

Figures

Figures reproduced from arXiv: 2505.23214 by the authors.

**Figure 2.** Overview of the proposed SAMamba framework. The architecture consists of [PITH_FULL_IMAGE:figures/full_fig_p009_2.png] view at source ↗

**Figure 3.** Architecture of the Cross-Channel State-Space Interaction (CSI) module. The [PITH_FULL_IMAGE:figures/full_fig_p010_3.png] view at source ↗

Figures from the paper (4 more)

**Figure 4.** Figure 4: Illustration of the Detail-Preserving Contextual Fusion (DPCF) module. Low [PITH_FULL_IMAGE:figures/full_fig_p013_4.png]

**Figure 5.** Figure 5: Visual examples of representative methods are provided. Pink and green circles [PITH_FULL_IMAGE:figures/full_fig_p016_5.png]

**Figure 6.** Figure 6: Impact of key architectural parameters on SAMamba’s performance. (a) Model [PITH_FULL_IMAGE:figures/full_fig_p021_6.png]

**Figure 7.** Figure 7: Visualization of challenging scenarios illustrating potential limitations. [PITH_FULL_IMAGE:figures/full_fig_p026_7.png]

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Pith tools

Reviewed August 7, 2026 · model on record in the stance chip above.

[1] [1]

write newline

" write newline "" before.all 'output.state := FUNCTION n.dashify 't := "" t empty not t #1 #1 substring "-" = t #1 #2 substring "--" = not "--" * t #2 global.max substring 't := t #1 #1 substring "-" = "-" * t #2 global.max substring 't := while if t #1 #1 substring * t #2 global.max substring 't := if while FUNCTION word.in bbl.in ":" * " " * FUNCTION f...

[2] [2]

write newline

" write newline "" before.all 'output.state := FUNCTION n.dashify 't := "" t empty not t #1 #1 substring "-" = t #1 #2 substring "--" = not "--" * t #2 global.max substring 't := t #1 #1 substring "-" = "-" * t #2 global.max substring 't := while if t #1 #1 substring * t #2 global.max substring 't := if while FUNCTION word.in bbl.in ":" * " " * FUNCTION f...

[3] [3]

write newline

" write newline "" before.all 'output.state := FUNCTION n.dashify 't := "" t empty not t #1 #1 substring "-" = t #1 #2 substring "--" = not "--" * t #2 global.max substring 't := t #1 #1 substring "-" = "-" * t #2 global.max substring 't := while if t #1 #1 substring * t #2 global.max substring 't := if while FUNCTION word.in bbl.in ":" * " " * FUNCTION f...

[4] [4]

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work page 2024

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work page 2024

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arXiv 2024

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author Chen, T. , author Zhu, L. , author Ding, C. , author Cao, R. , author Wang, Y. , author Zhang, S. , author Li, Z. , author Sun, L. , author Zang, Y. , author Mao, P. , year 2023 . title Sam-adapter: Adapting segment anything in underperformed scenes , in: booktitle 2023 IEEE/CVF International Conference on Computer Vision Workshops (ICCVW) , pp. pa...

arXiv 2023