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REVIEW 4 major objections 5 minor 41 references

Promoting SAM for Camouflaged Object Detection via Selective Key Point-based Guidance

T0 review · 4 major / 5 minor · reviewed 2026-08-15 · deepseek-v4-flash

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Pith's one-line read A frozen SAM, prompted with automatically selected contrastive points, matches specialized camouflaged-object detectors, raising NC4K S-measure from 0.406 to 0.836.

desk verdict The headline jump in Table 2 looks real, but the paper does not prove that its selective key point generation is what causes it, and one SOTA claim is contradicted by its own table. read the letter →

arxiv 2505.09123 v1 pith:AOMZ7PWP submitted 2025-05-14 cs.CV

Guoying Liang , Su Yang This is my paper · ORCID
classification cs.CV
keywords camouflagedobjectdetectionSegmentAnythingModelvisualpromptingpointpromptskeyselectionmulti-scalefeaturepredictionprompt-basedsegmentationfrozenfoundation
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

Camouflaged object detection, the task of finding objects that blend into their surroundings, was previously judged too hard for the Segment Anything Model (SAM). This paper sets out to overturn that conclusion, arguing that SAM fails only because it is not prompted properly. The authors build a lightweight prompt generator, PPT-net plus a key-point selection algorithm, that places a positive point on the object and two nearby negative points on the background, and they feed those triples to a frozen SAM. In their experiments, this raises SAM's S-measure on the NC4K test set from 0.406 to 0.836 and yields results comparable to, and on some precision/recall metrics better than, specialist camouflaged-object detectors. If correct, the result matters because COD becomes a coarse localization problem rather than a pixel-perfect boundary problem, letting a frozen general-purpose segmenter replace task-specific architectures.

What carries the argument

The load-bearing mechanism is the contrastive point-prompt triple produced by two cooperating components. PPT-net (Promotion Point Targeting Network) predicts, for each cell of an M-by-M grid, the probability that a camouflaged object occupies that cell, using multi-scale features from a Res2Net encoder augmented by receptive-field blocks. KPS (Key Point Selection) then chooses high-confidence, mutually distant cells as positive points, and for each positive point selects the two nearest low-confidence cells as negative points, bracketing the object's edge. Feeding these triples to SAM, a general-purpose segmentation network whose weights are never updated, converts COD from exact segmentation into coarse localization; a second SAM pass with the first mask as an additional prompt repairs boundary detail.

What would settle it

Rerun the Table 2 experiment with the selected points replaced by randomly sampled points of the same count inside and outside the object: if random prompts recover most of the 0.406-to-0.836 S-measure gain on NC4K, the key-point selection algorithm is not the driver; if they recover almost none, SAM is genuinely relying on the contrastive triples.

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Extended reading notes

Core claim

The paper's central claim is that SAM's poor performance on camouflaged objects is a prompting failure, not an intrinsic limit. The mechanism is a triple of point prompts per selected location: one positive point sampled from high-confidence predictions, and two negative points from the nearest low-confidence grid cells. These triples tell SAM where the object is and roughly where its edge lies, and a second SAM pass uses the first output mask as an additional prompt to refine boundaries. On the paper's reported numbers, NC4K S-measure rises from 0.406 to 0.836, and the method reaches the top of the baseline table on the weighted and mean F-measures for the large datasets while remaining below the best edge-focused E-measures, which the authors attribute to the finite number of prompt points covering only part of the boundary.

Load-bearing premise

The entire pipeline depends on a handful of coarse positive and negative points carrying enough boundary information for SAM to produce a precise, full-boundary mask of a camouflaged object; if point prompts cannot do that for camouflage, the frozen SAM has no way to recover and the method collapses.

Editorial extensions

If this is right

  • If the claim holds, COD no longer requires hand-crafted edge-, wavelet-, or frequency-domain modules: a frozen general-purpose segmenter plus a small point selector reaches comparable performance.
  • SAM's documented struggles in concealed scenes become a prompt-design problem rather than evidence that the model cannot handle the task.
  • On the reported metrics, the largest gains are in precision/recall-weighted measures such as weighted and mean F-measure, so the method's strength is capturing overall shape rather than exact edges.
  • Because SAM stays frozen, adapting to a new concealment setting only requires training the lightweight PPT-net on grid-level probabilities, which is cheaper than training a specialized COD model from scratch.

Reading between the lines

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

  • A natural experiment the paper does not run is a sensitivity check: if selected key points are perturbed by one grid cell, a large S-measure drop would show the precision of the point selector matters more than SAM's tolerance for coarse prompts.
  • The same contrastive-point recipe may transfer to other low-contrast segmentation tasks, such as medical lesions or objects in satellite imagery, where a coarse locator could replace a domain-specific decoder.
  • The grid-density comparison suggests an adaptive or non-uniform candidate grid concentrated near predicted object locations could cover small or fragmented objects better than a fixed uniform grid.
  • The authors attribute some remaining errors to objects split into isolated parts, which points to a specific testable weakness: recall on multi-part objects may drop because KPS spreads only a handful of positive points across the entire image.
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Editorial analysis

A structured set of objections, weighed in public.

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

Referee Report

4 major / 5 minor

Summary. The paper addresses the question of whether the Segment Anything Model (SAM) can be made to work for Camouflaged Object Detection (COD) through automatically generated point prompts. It proposes a two-part prompt generation pipeline: PPT-net, a lightweight network that predicts, for each cell of a uniform M×M grid, the probability that a camouflaged object is present at the cell center; and a Key Point Selection (KPS) algorithm that converts these probability scores into triples of one positive and two negative points. The prompts are fed to a frozen SAM, followed by a second SAM pass in which the first mask is used as a mask prompt, following the SAMAug idea. Experiments on COD10K, NC4K, and CAMO compare the method against specialized COD baselines and report large gains over unprompted SAM (e.g., NC4K S-measure from 0.406 to 0.836, Table 2). The paper claims new state-of-the-art Fm_beta on all three datasets and Fw_beta on two datasets, and positions the work as the first to facilitate a big model for COD.

Significance. If the central attribution is verified, the paper makes a useful practical point: a frozen generalist segmenter, guided by inexpensive point-level localization, can approach specialized COD models, reducing the need for heavily engineered COD architectures. The quantitative effect in Table 2 is striking, and the training setup is honest in an important respect: PPT-net is trained on averaged ground-truth masks of the training split, SAM is frozen, and the final mask comes from SAM on held-out test images, so there is no self-referential circularity. The main gap is that the experiments do not isolate the proposed selective key-point mechanism from trivial prompt baselines; the missing controls are essential before the paper can claim that selective key points, rather than SAM's own promptable segmentation capability, are responsible for the observed improvement. The SOTA claims also need to be reconciled with the paper's own Table 1.

major comments (4)
  1. [§4.2, Table 1] The paper states in §4.2 that "our approach establishes a new state-of-the-art in terms of Fm_beta on all the 3 datasets," but Table 1 does not support this. On CAMO, the proposed method obtains Fm_beta=0.790 while ZoomNet obtains 0.794; on the same dataset Fw_beta is 0.742, tied with SiNetV2 and below ZoomNet's 0.752. The abstract's claim of results "over the existing methods on 3 data sets under 6 metrics" is also broader than what the table shows. Please correct the SOTA claims to name the exact metrics and datasets on which the method leads, and discuss the CAMO shortfall.
  2. [§3.4, Table 2 vs. Tables 4/5] The headline evidence (Table 2: NC4K S-measure 0.406 to 0.836) compares unprompted SAM with the full pipeline, which includes both the KPS point triples and the second SAM pass with mask feedback. The ablations show that the second pass adds only about 0.005 in S-measure (Table 5) and the 10 negative points add about 0.003 (Table 4), but no experiment isolates the proposed PPT-net/KPS prompt generator from trivial prompt baselines. A control with uniformly sampled grid points, a single centroid positive point, or oracle points derived from the ground-truth mask is needed to verify that "selective key point" selection, rather than SAM's own ability to segment from any interior point, is responsible for the gain. Without such controls, the central attribution in the title and contributions is not established.
  3. [§3.3, Algorithm 1] Algorithm 1 is not executable as written because line 4 computes arg max over d(C_i, P) while P is initialized to the empty set; the distance to an empty set is undefined, so the first positive point cannot be selected by the stated rule. The surrounding text says the first PP is the point with the highest prediction score, but this rule does not appear in the pseudocode. Also, lines 17-18 use d(C_i, P_j) with P_j a single point, while the earlier definition d(C_i, P) is for a collection; please clarify the notation for point-to-point and point-to-set distances. These issues make the central selection algorithm non-reproducible as described.
  4. [§4.3, Tables 3-5] The ablations select the grid size, number of positive/negative points, and number of SAM iterations by performance on the COD10K test split, but the paper does not describe a held-out validation split or a hyperparameter selection procedure. For example, Table 3 shows that 32×32 candidates outperform 16×16 (S-measure 0.834 vs. 0.826), yet the main results use 16×16. Since these hyperparameters directly determine the prompts, the reported numbers may reflect test-set tuning; please report how K, M, the confidence thresholds in Eq. (6), and the number of iterations were chosen, and provide sensitivity results on NC4K and CAMO.
minor comments (5)
  1. [Throughout] The term "promotion" is used throughout where "prompt" is standard (e.g., abstract, §1, Fig. 2 caption); please define this terminology at first use to avoid confusion.
  2. [§3.3] There is a typo in "receptive filed" which should be "receptive field," and in §4.3 "has a higher chance" should be "have a higher chance."
  3. [References] References [19] and [20] are the same paper (Le et al., Anabranch network for camouflaged object segmentation); please merge them.
  4. [§4.1] Please specify the exact train/test split of COD10K (the table reports 2,026 test images) and clarify how NC4K and CAMO are used for evaluation; the sentence "we train our method only on COD10K data set" is ambiguous.
  5. [Abstract and §1] The claim that this is "the first work to facilitate big model for COD" is not substantiated against prior prompt-based SAM-COD adaptations; please cite and discuss the relevant literature or soften the novelty claim.

Circularity Check

0 steps flagged · score 0.0 of 10

No circularity: PPT-net is trained on coarsened ground truth, but evaluation is on held-out data with frozen SAM; no self-citation is load-bearing and no prediction reduces to its inputs by construction.

full rationale

The paper's chain is: PPT-net predicts point-presence probabilities from multi-scale features (Eqs. 1-5), supervised by MSE against average-pooled ground-truth masks (Eq. 3); KPS converts these scores into positive/negative point triples (Algorithm 1); frozen SAM produces the final segmentation, optionally with a second mask-feedback pass (Sec. 3.4). The only sense in which the supervision signal resembles the evaluation target is that AvgPooling(Mask) is a coarsened version of the ground truth, but this is ordinary supervised training on a training split followed by evaluation on held-out test sets; the final SAM output is never set equal to the training target by construction. No parameter is fitted to the test metric and then reported as a prediction. The paper's references to SAM, SinetV2, and SAMAug are external prior works, not the present authors' own results, and no load-bearing claim rests on a self-citation or on an imported uniqueness theorem. The skeptic's concern that Table 2 does not include a trivial-prompt or random-point control is a legitimate experimental-isolation concern about whether the gain is caused by the selective KPS points, but an omitted control is not circularity: nothing in the paper's equations forces the improvement to be equivalent to its inputs. The ablations (Tables 4 and 5) show small contributions from additional negative points and mask feedback, but they do not define the headline result into existence. The paper also honestly acknowledges limitations in edge-focused metrics and multi-part objects, which further indicates the results are not simply renamed inputs. Therefore the derivation is self-contained with respect to the circularity criteria, and the score is 0.

Assumptions & free parameters 5 free parameters · 4 assumptions · 0 invented entities

The central claim rests on a handful of hand-set hyperparameters (K, grid size, thresholds, iteration count) and on the unproven premise that frozen SAM obeys coarse point prompts for camouflage imagery. None of these are derived; they are set by small ablations performed on the COD10K test set, which inflates apparent performance. No new physical or conceptual entities are introduced.

free parameters (5)
  • Number of positive prompts K = 5
    Hand-set from ablation on COD10K test (Table 4 shows 5 PPs outperform 3-4).
  • Grid size M = 16 (32x32 ablated but not used in main table)
    Main results use 16x16 grid; Table 3 shows 32x32 better on all metrics, yet 16x16 was used without explanation.
  • Confidence thresholds for L1/L2/L3 = 0.9 / 0.75 / 0.5
    Chosen in Section 3.3 without ablation or justification.
  • Negative points per positive = 2
    Fixed by KPS design; no ablation of 1 vs 3.
  • Number of SAM iterations = 2
    Table 5 shows 2 iterations best on COD10K test; no validation split.
assumptions (4)
  • domain assumption SAM, when given positive and negative point prompts, can segment camouflaged objects accurately enough for competitive COD.
    The entire pipeline freezes SAM and relies on prompt-following; Section 3.4 and Table 2 show the gain, but the premise is the paper's load-bearing bet, contradicting [15,34].
  • domain assumption Average pooling of the binary ground-truth mask over grid cells provides valid supervision for point-presence prediction.
    Training loss (Eq. 3) uses AvgPooling(Mask) as the target; assumes coarse cell-level presence is learnable and sufficient.
  • ad hoc to paper Hand-set confidence thresholds (0.9, 0.75, 0.5) partition points into reliable positive, ambiguous, and background categories in a way that transfers across datasets.
    Section 3.3 defines L1-L4 with these values; no analysis supports their transferability.
  • domain assumption Training only on COD10K generalizes to NC4K and CAMO.
    Section 4.1 follows prior works, but this is an empirical assumption about dataset distribution.

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Cite this review

Pith. "Pith review of Promoting SAM for Camouflaged Object Detection via Selective Key Point-based Guidance." pith.science (2026). https://pith.science/paper/AOMZ7PWP

@misc{pith2026250509123,
  author       = {Pith},
  title        = {Pith review of: Promoting SAM for Camouflaged Object Detection via Selective Key Point-based Guidance},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/AOMZ7PWP}},
  note         = {Machine review of arXiv:2505.09123}
}
read the original abstract

Big model has emerged as a new research paradigm that can be applied to various down-stream tasks with only minor effort for domain adaption. Correspondingly, this study tackles Camouflaged Object Detection (COD) leveraging the Segment Anything Model (SAM). The previous studies declared that SAM is not workable for COD but this study reveals that SAM works if promoted properly, for which we devise a new framework to render point promotions: First, we develop the Promotion Point Targeting Network (PPT-net) to leverage multi-scale features in predicting the probabilities of camouflaged objects' presences at given candidate points over the image. Then, we develop a key point selection (KPS) algorithm to deploy both positive and negative point promotions contrastively to SAM to guide the segmentation. It is the first work to facilitate big model for COD and achieves plausible results experimentally over the existing methods on 3 data sets under 6 metrics. This study demonstrates an off-the-shelf methodology for COD by leveraging SAM, which gains advantage over designing professional models from scratch, not only in performance, but also in turning the problem to a less challenging task, that is, seeking informative but not exactly precise promotions.

Figures

Figures reproduced from arXiv: 2505.09123 by the authors.

Figure 1
Figure 1. Example of prompt.⋆ represents Positive Point and ⋆ represents Negative Point a feature processing module inspired by biological vision to detect camouflaged targets; FEDER [11] employs wavelet decomposition to identify specific target features; [39] proposes a method for detecting subtle differences between targets and backgrounds by examining features in different frequency domains of the image. In general, the ex… view at source ↗
Figure 2
Figure 2. Overall Framework. We generate prompt points based on the input image. SAM generates a camouflaged object segmentation mask according to the image and prompt. The weights of SAM are frozen. Due to the challenging nature of COD, the previous studies [15,34] concluded that SAM as a generic model is not applicable to COD. This study further explores such issue. Our goal is to guide SAM to segment camouflaged objects fr… view at source ↗
Figure 3
Figure 3. Overall architecture of PPT-net. It consists of two key components: The en￾coder to extract multi-scale features, and the predictor to output the probability of the presence of camouflaged targets at each given point. whether a point could be positive/negative. Finally, we select some key points from such candidates as PP or NP according to the predicted probability in association with each candidate point. The over… view at source ↗

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Pith tools

Reviewed August 15, 2026 · model on record in the stance chip above.