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REVIEW 4 major objections 5 minor 27 references

Segment Any Class (SAC): Multi-Class Few-Shot Semantic Segmentation via Class Region Proposals

T0 review · 4 major / 5 minor · reviewed 2026-08-12 · deepseek-v4-flash

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Pith's one-line read A training-free pipeline of frozen DINOv2 features and SAM point prompts claims to outperform gradient-trained multi-class few-shot segmenters on COCO-20i, with the gap widening as class count grows.

desk verdict Plausible training-free multi-class FSS pipeline that beats trained baselines on COCO-20i, but the unspecified filtering thresholds make the headline numbers hard to trust. read the letter →

arxiv 2411.13774 v1 pith:TWKR27J3 submitted 2024-11-21 cs.CV

Hussni Mohd Zakir , Eric Tatt Wei Ho This is my paper · ORCID
classification cs.CV
keywords SegmentAnythingModelmulti-classfew-shotsemanticsegmentationin-contextlearningfoundationadaptationtraining-freepromptingclassregionproposalscosinesimilarityCOCO-20i
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

This paper sets out to show that a vision foundation model can be adapted to multi-class few-shot semantic segmentation without updating any weights. Instead of training a mask decoder or prompt encoder, the method, Segment Any Class (SAC), uses a frozen feature extractor (DINOv2) to build per-class prototype clusters from support images, computes per-pixel cosine similarity on the query image, and converts the resulting Class Region Proposals into positive and negative point prompts for the frozen segmentation model SAM. The authors report that this prompt-only pipeline beats the gradient-trained baselines DCAMA and Label Anything on the COCO-20i benchmark, and that the gap widens as the number of classes grows (e.g., 37.9 mIoU for SAC versus 13.7 for Label Anything and 4.7 for DCAMA at 20-way 1-shot). If the claim holds, task adaptation of foundation models for new classes need not rewrite weights, which would make adaptation immune to catastrophic forgetting and fast enough for online use.

What carries the argument

The load-bearing machinery is the Class Region Proposal (CRP), built from Class-Representative Feature Arrays (CRFAs). Support images pass through frozen DINOv2; pixels of each class (plus a background class) are k-means clustered into $n_{\text{cluster}}=5$ prototype vectors; cosine similarity between the query embedding and each prototype yields per-pixel score maps; Otsu thresholding keeps only high-similarity pixels; inter-class overlap filtering and average-cosine filtering discard conflicting or low-confidence masks. These region proposals become SAM point prompts, with positive points from clustered proposal locations paired with negative points from the background proposal, so the frozen SAM mask decoder does the segmentation.

What would settle it

Run the full pipeline on COCO-20i with the support set's class masks replaced by random masks of the same shape. If mIoU stays far above the chance level, the class signal is not coming from the support annotations and the Class Region Proposal geometry is doing no work; if the score collapses, the mechanism depends on the claimed feature alignment.

Watch

Extended reading notes

Core claim

SAC's central claim is that elementary feature geometry in a frozen self-supervised vision model is sufficient to adapt a class-agnostic segmenter to a new multi-class segmentation task. The method clusters support pixels per class (and background) into Class-Representative Feature Arrays, computes cosine-similarity maps between query embeddings and each array, thresholds these maps with Otsu, and uses the surviving regions as class region proposals. Positive point prompts are drawn from each proposal, negative prompts from a background region proposal, and spatially clustered to create prompt sets for SAM; overlapping low-confidence masks are filtered. On COCO-20i, SAC reports 1-way 1-shot mIoU of 49.2, within about two points of DCAMA's 50.9, while at 5-way it reaches 44.0 against DCAMA's 16.7 and Label Anything's 27.7, and at 20-way 37.9 against 4.7 and 13.7 respectively, with no weight updates.

Load-bearing premise

The argument rests on the premise that DINOv2's frozen embeddings place pixels of the same class close together in cosine space on unseen query images, so that the cluster prototypes and the fixed Otsu and overlap thresholds yield point prompts that land on true class instances; if the features do not transfer, or the thresholds are miscalibrated, every downstream mask inherits the error.

Editorial extensions

If this is right

  • Adding a new class to SAC requires only adding its support images and re-running the clustering; no retraining or weight update is needed, so previously learned classes cannot be overwritten.
  • Segmentation accuracy degrades far more slowly as the number of classes grows: SAC drops from 49.2 mIoU at 1-way to 37.9 at 20-way (1-shot), whereas DCAMA falls from 50.9 to 4.7 and Label Anything from 43.1 to 13.7.
  • Because the pipeline is prompt-only, the same frozen SAM and DINOv2 weights can be swapped between tasks online, without storing per-task model copies.
  • The ablation attributes most of the gain to the feature extractor: replacing DINOv2 with SAM's image encoder drops 1-way 1-shot mIoU from 45.5 to 26.6, indicating that the frozen features, not the mask decoder, carry the adaptation.
  • The method accommodates any N-way K-shot configuration, so the same machinery applies to one-shot, five-shot, or any other support-set size without changing the algorithm.

Reading between the lines

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

  • A testable consequence the paper does not pursue: because the proposal generator is prompt-agnostic, the same CRFAs should drive newer point-promptable segmenters such as SAM 2, which would isolate where the gain comes from.
  • The fixed hyperparameters ($n_{\text{cluster}}=5$, Otsu thresholding, overlap and confidence cutoffs) are calibrated on COCO-20i; on a benchmark with dissimilar class scales, the same defaults may miscalibrate, so measuring per-class accuracy by object size would reveal the boundary of the claim.
  • The paper implicitly redefines adaptation as prompt selection rather than weight update; under that definition the model cannot forget because there is no learned state to overwrite, but it also cannot repair a weak feature extractor, so the ceiling is whatever DINOv2's frozen features can separate.
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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 proposes Segment Any Class (SAC), a training-free method for multi-class few-shot semantic segmentation. SAC uses DINOv2 features extracted from support images to build class-representative feature arrays, computes per-pixel cosine similarity between query features and these arrays, applies Otsu thresholding and additional filtering to obtain Class Region Proposals, and then automatically generates positive/negative point prompts to drive the frozen SAM model. The authors evaluate SAC on COCO-20i for 1-way to 20-way settings and report that it outperforms gradient-trained baselines (DCAMA and Label Anything) in multi-way settings, with an especially large margin at 20-way, while remaining competitive in 1-way.

Significance. If the reported results are robust, SAC would be a valuable demonstration that prompt-only adaptation of vision foundation models can compete with or exceed gradient-based adaptation for multi-class few-shot segmentation, with advantages such as immunity to catastrophic forgetting and rapid online adaptation. The paper provides a useful conceptual contribution by extending single-class prompt-based methods like Matcher and PerSAM to multi-class settings, and it evaluates on a standard benchmark with 1000 episodes per fold. The ablation study in Table III gives some support for the importance of the proposed filtering components. However, the central quantitative claim currently rests on underspecified and potentially tuned thresholds, and the evaluation lacks statistical grounding, so the significance cannot be fully assessed from the manuscript as written.

major comments (4)
  1. [Section III-B and Section IV] The two thresholds that control inter-class overlap filtering and the rejection of masks based on average cosine similarity are never specified. The text says masks are removed if overlap 'exceeds a certain threshold' and if average cosine similarity is 'below a predefined threshold', but no values are given and no sensitivity analysis is provided. This is load-bearing because, for classes absent from a query image, Otsu thresholding will still partition the similarity distribution and produce spurious positive regions; whether those regions survive into SAM masks depends entirely on these thresholds. The reported 20-way margin over Label Anything (37.9 vs 13.7) could therefore be a consequence of threshold tuning rather than a general property of the prompt-only approach. The authors should report the exact threshold values and include an analysis showing that the high-N-way advantage is stable across a range of thresholds.
  2. [Abstract and Section V-C, Table II] The claim of 'superior results over state-of-the-art methods' is overstated relative to the paper's own data. Table I shows that DCAMA outperforms SAC in both 1-way 1-shot (50.9 vs 49.2) and 1-way 5-shot (58.3 vs 58.0), while Table II shows SAC ahead only starting at 2-way. The abstract and Section V-C should be reworded to state that SAC is competitive in 1-way and superior in multi-way settings, so that the central claim matches the evidence.
  3. [Section IV and Section V-C] The evaluation is limited to a single benchmark (COCO-20i) and no error bars, confidence intervals, or multiple-seed results are reported. Since the experiments use 1000 randomly selected episodes per fold, the differences between methods—especially the smaller gaps at 1-way and 2-way—could be within noise. To support a state-of-the-art claim, the authors should report variance statistics (e.g., standard deviation or confidence interval over episode draws) and ideally evaluate on at least one additional benchmark, such as PASCAL-5i, to demonstrate that the multiclass advantage generalizes.
  4. [Section III-B] The prompt generation procedure is described at a high level but lacks implementation details needed for reproduction. Specifically, the paper does not state how the positive-point grid is constructed, how many positive points are used per class, how the spatial clustering of positive points is performed (what clustering algorithm and what parameters), or how 'the average cosine similarity inside a mask' is computed. These details are essential for reproducing the method and for assessing whether the reported performance depends on specific heuristic choices beyond the thresholds mentioned above.
minor comments (5)
  1. [Section I and Section V-D] There are several typographical issues, including 's ´ans' in the Section V-D heading and 'Fig1' without a space in Section III. These should be corrected.
  2. [References] Reference [13] is malformed, containing repeated 'DINOv' text and an incomplete title; this should be fixed. Also, the paper cites 'MFNET' in Section IV without defining it or providing a reference, so the reader cannot tell what protocol is being followed.
  3. [Equation (2)] The notation in Equation (2) is unclear: the left-hand side defines R^c as a set indexed over support images, but the right-hand side uses F^c_i and M^c_i without clearly defining how these relate to the original image embeddings and labels. Clarifying the indexing would help readability.
  4. [Section III-B, Equation (4)] Equation (4) produces n_cluster similarity maps per class, but the text then says 'for each class, we assign each pixel to the class that has the highest cosine similarity score in the similarity map' without explaining how the per-cluster maps are aggregated into a single per-class similarity map. This should be specified.
  5. [Section IV] The paper states that background class is excluded from mIoU 'in alignment with the MFNET approach', but this choice is not further justified or compared with alternative evaluation protocols; a brief explanation or citation would help.

Circularity Check

0 steps flagged · score 0.0 of 10

No circularity found: SAC's predictions are derived from held-out query features plus support labels, not from the target labels or from a self-citation chain.

full rationale

The paper's derivation chain is self-contained and does not reduce to its inputs by construction. Support-set ground-truth labels enter only in Equation (2), where DINOv2 features are stratified by class masks to build Class-Representative Feature Arrays. Equation (3) clusters those stratified support features, and Equation (4) computes cosine similarity between the frozen query embedding and these CRFAs. The resulting class region proposals are filtered by Otsu thresholding, inter-class overlap filtering, and an average-cosine threshold, and then converted into point prompts for SAM. Query ground-truth labels appear only in the evaluation metric (mIoU), never in any equation that generates proposals, prompts, or masks. Thus there is no self-definitional equality between the claimed prediction and the inputs. The method performs no gradient training on the test classes, and the comparison to DCAMA and Label Anything is an empirical benchmark rather than a derivation forced by the method's assumptions. There are no load-bearing self-citations: references to DINOv2, SAM, Matching Networks, PerSAM, and Matcher are external prior works, and the authors do not invoke any of their own uniqueness theorems or prior results to forbid alternatives. Concerns about the unspecified overlap and average-cosine threshold values, the fixed n_cluster=5, and the transferability of DINOv2 features are correctness, robustness, and reproducibility risks, not circularity, because even if those hyperparameters were tuned on the benchmark, that would be overfitting or evaluation leakage rather than a logical equivalence between inputs and outputs. The conclusion that prompt-only adaptation can outperform gradient-learning methods is an empirical claim supported by the reported experiments, not a statement that is true by definition. Accordingly, no circular step can be exhibited, and the appropriate circularity score is 0.

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

The method introduces no physical or metaphysical entities; CRP, CRFA, and BGRP are algorithmic constructs rather than postulated objects. The main hand-set quantities are n_cluster=5, two undisclosed filtering thresholds, and spatial clustering choices in prompt generation. The central empirical claim also assumes DINOv2 features transfer to query images and that SAM responds well to automatically placed points.

free parameters (4)
  • n_cluster = 5
    Number of class-representative feature clusters per class, set manually in Section IV; affects CRP quality and no sensitivity study is reported.
  • inter-class overlap threshold = not disclosed
    Masks overlapping another class region above 'a certain threshold' are removed in Section III-B; the value is not given.
  • average cosine similarity threshold = not disclosed
    Masks whose mean internal cosine similarity falls below 'a predefined threshold' are discarded in Section III-B; the value is not given.
  • positive-point spatial clustering parameters = not disclosed
    Positive points are clustered spatially to form prompt sets 'inspired by [16]' in Section III-B; the number of clusters and prompt group sizes are not stated.
assumptions (5)
  • domain assumption DINOv2's frozen features transfer semantic class distinctions to query images not seen during support extraction.
    Section III-A and III-B: CRFAs from support images are matched to query pixels by cosine similarity; no feature adaptation is performed.
  • domain assumption Cosine similarity in DINOv2 embedding space is a valid measure of class-region membership.
    Section III-B, Eq. 4: similarity maps are built as Fq C^T and used to assign pixels to classes.
  • domain assumption SAM produces accurate masks when given point prompts inside a true class region.
    Section III-B: all final class masks come from SAM prompted with CRP-derived points; if SAM fails, the pipeline fails regardless of CRP quality.
  • domain assumption Otsu thresholding is appropriate for filtering low-similarity pixels in cosine similarity maps.
    Section III-B, intra-class filtering: Otsu is applied to per-class cosine similarities to retain only high-similarity pixels.
  • domain assumption The support images are representative of the class appearance distribution in the query images.
    Section III-A: class prototypes are built purely from the given support set; this is the standard few-shot assumption.

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Cite this review

Pith. "Pith review of Segment Any Class (SAC): Multi-Class Few-Shot Semantic Segmentation via Class Region Proposals." pith.science (2026). https://pith.science/paper/TWKR27J3

@misc{pith2026241113774,
  author       = {Pith},
  title        = {Pith review of: Segment Any Class (SAC): Multi-Class Few-Shot Semantic Segmentation via Class Region Proposals},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/TWKR27J3}},
  note         = {Machine review of arXiv:2411.13774}
}
read the original abstract

The Segment-Anything Model (SAM) is a vision foundation model for segmentation with a prompt-driven framework. SAM generates class-agnostic masks based on user-specified instance-referring prompts. However, adapting SAM for automated segmentation -- where manual input is absent -- of specific object classes often requires additional model training. We present Segment Any Class (SAC), a novel, training-free approach that task-adapts SAM for Multi-class segmentation. SAC generates Class-Region Proposals (CRP) on query images which allows us to automatically generate class-aware prompts on probable locations of class instances. CRPs are derived from elementary intra-class and inter-class feature distinctions without any additional training. Our method is versatile, accommodating any N-way K-shot configurations for the multi-class few-shot semantic segmentation (FSS) task. Unlike gradient-learning adaptation of generalist models which risk the loss of generalization and potentially suffer from catastrophic forgetting, SAC solely utilizes automated prompting and achieves superior results over state-of-the-art methods on the COCO-20i benchmark, particularly excelling in high N-way class scenarios. SAC is an interesting demonstration of a prompt-only approach to adapting foundation models for novel tasks with small, limited datasets without any modifications to the foundation model itself. This method offers interesting benefits such as intrinsic immunity to concept or feature loss and rapid, online task adaptation of foundation models.

Figures

Figures reproduced from arXiv: 2411.13774 by the authors.

Figure 1
Figure 1. An overview of Segment Any-Class experiments. Our method was evaluated across various N-way K-shot segmentation scenarios, utilizing mean Intersection￾over-Union (mIoU) as the performance metric, specifically calculated on the test classes. In alignment with the MFNET approach, we excluded the background class from the mIoU calculation and relegated foreground misclassification to be under false positives or false n… view at source ↗
Figure 2
Figure 2. Qualitative results of 5-way 1-shot segmentation on COCO- [PITH_FULL_IMAGE:figures/full_fig_p006_2.png] view at source ↗

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Works this paper leans on

27 extracted references · 8 canonical work pages

  1. [13]

    Visual In-Context Prompting Zero-shot Video Object and Part Segmentation Visual Prompting Referring Segmentation 8 Visual Prompting Generic Segmentation,

    F. Li, Q. Jiang, H. Zhang, T. Ren, S. Liu, X. Zou, H. Xu, H. Li, C. Li, J. Yang, L. Zhang, J. Gao, and D. DINOv DINOv DINOv DINOv, “Visual In-Context Prompting Zero-shot Video Object and Part Segmentation Visual Prompting Referring Segmentation 8 Visual Prompting Generic Segmentation,” Tech. Rep., 2023. [Online]. Available: https://github.com/UX-Decoder/DINOv

    work page 2023

  2. [1]

    BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding,

    J. Devlin, M.-W. Chang, K. Lee, and K. Toutanova, “BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding,” 10

    work page

  3. [2]

    Language Models are Few-Shot Learners,

    T. B. Brown, B. Mann, N. Ryder, M. Subbiah, J. Kaplan, P. Dhariwal, A. Neelakantan, P. Shyam, G. Sastry, A. Askell, S. Agarwal, A. Herbert- V oss, G. Krueger, T. Henighan, R. Child, A. Ramesh, D. M. Ziegler, J. Wu, C. Winter, C. Hesse, M. Chen, E. Sigler, M. Litwin, S. Gray, B. Chess, J. Clark, C. Berner, S. McCandlish, A. Radford, I. Sutskever, and D. Am...

    arXiv 2020

  4. [3]

    LLaMA: Open and Efficient Foundation Language Models,

    H. Touvron, T. Lavril, G. Izacard, X. Martinet, M.-A. Lachaux, T. Lacroix, B. Rozi `ere, N. Goyal, E. Hambro, F. Azhar, A. Rodriguez, A. Joulin, E. Grave, and G. Lample, “LLaMA: Open and Efficient Foundation Language Models,” 2 2023. [Online]. Available: http: //arxiv.org/abs/2302.13971

    arXiv 2023

  5. [4]

    Masked Autoencoders Are Scalable Vision Learners,

    K. He, X. Chen, S. Xie, Y . Li, P. Doll ´ar, and R. Girshick, “Masked Autoencoders Are Scalable Vision Learners,” 11 2021. [Online]. Available: http://arxiv.org/abs/2111.06377

    arXiv 2021

  6. [5]

    DINOv2: Learning Robust Visual Features without Supervision,

    M. Oquab, T. Darcet, T. Moutakanni, H. V o, M. Szafraniec, V . Khalidov, P. Fernandez, D. Haziza, F. Massa, A. El-Nouby, M. Assran, N. Ballas, W. Galuba, R. Howes, P.-Y . Huang, S.-W. Li, I. Misra, M. Rabbat, V . Sharma, G. Synnaeve, H. Xu, H. Jegou, J. Mairal, P. Labatut, A. Joulin, and P. Bojanowski, “DINOv2: Learning Robust Visual Features without Supe...

    arXiv 2023

  7. [6]

    Generative Adversarial Networks,

    I. J. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, and Y . Bengio, “Generative Adversarial Networks,” 6 2014. [Online]. Available: http://arxiv.org/abs/1406.2661

    arXiv 2014

  8. [7]

    Conditional Generative Adversarial Nets,

    M. Mirza and S. Osindero, “Conditional Generative Adversarial Nets,” 11 2014. [Online]. Available: http://arxiv.org/abs/1411.1784

    arXiv 2014

Show all 27 references
  1. [8]

    High-Resolution Image Synthesis with Latent Diffusion Models,

    R. Rombach, A. Blattmann, D. Lorenz, P. Esser, and B. Ommer, “High-Resolution Image Synthesis with Latent Diffusion Models,” 12

  2. [9]

    Segment Anything,

    A. Kirillov, E. Mintun, N. Ravi, H. Mao, C. Rolland, L. Gustafson, T. Xiao, S. Whitehead, A. C. Berg, W.-Y . Lo, P. Doll ´ar, and R. Girshick, “Segment Anything,” 4 2023. [Online]. Available: http://arxiv.org/abs/2304.02643

    2023 arXiv

  3. [10]

    Segment Everything Everywhere All at Once,

    X. Zou, J. Yang, H. Zhang, F. Li, L. Li, J. Wang, L. Wang, J. Gao, and Y . J. Lee, “Segment Everything Everywhere All at Once,” 4 2023. [Online]. Available: http://arxiv.org/abs/2304.06718

    2023 arXiv

  4. [11]

    SAM 2: Segment Anything in Images and Videos,

    N. Ravi, V . Gabeur, Y .-T. Hu, R. Hu, C. Ryali, T. Ma, H. Khedr, R. R ¨adle, C. Rolland, L. Gustafson, E. Mintun, J. Pan, K. V . Alwala, N. Carion, C.-Y . Wu, R. Girshick, P. Doll ´ar, and C. Feichtenhofer, “SAM 2: Segment Anything in Images and Videos,” 8 2024. [Online]. Ava...

    2024 arXiv

  5. [12]

    SAM Fails to Segment Anything? – SAM-Adapter: Adapting SAM in Underperformed Scenes: Camouflage, Shadow, Medical Image Segmentation, and More,

    T. Chen, L. Zhu, C. Ding, R. Cao, Y . Wang, Z. Li, L. Sun, P. Mao, and Y . Zang, “SAM Fails to Segment Anything? – SAM-Adapter: Adapting SAM in Underperformed Scenes: Camouflage, Shadow, Medical Image Segmentation, and More,” 4 2023. [Online]. Available: http://arxiv.org/abs/2...

    2023 arXiv

  6. [14]

    VRP-SAM: SAM with Visual Reference Prompt,

    Y . Sun, J. Chen, S. Zhang, X. Zhang, Q. Chen, G. Zhang, E. Ding, J. Wang, and Z. Li, “VRP-SAM: SAM with Visual Reference Prompt,” 2 2024. [Online]. Available: http://arxiv.org/abs/2402.17726

    2024

  7. [15]

    Personalize Segment Anything Model with One Shot,

    R. Zhang, Z. Jiang, Z. Guo, S. Yan, J. Pan, X. Ma, H. Dong, P. Gao, and H. Li, “Personalize Segment Anything Model with One Shot,” 5

  8. [16]

    Matcher: Segment Anything with One Shot Using All-Purpose Feature Matching,

    Y . Liu, M. Zhu, H. Li, H. Chen, X. Wang, and C. Shen, “Matcher: Segment Anything with One Shot Using All-Purpose Feature Matching,” 5 2023. [Online]. Available: http://arxiv.org/abs/2305.13310

    2023 arXiv

  9. [17]

    Exploring Effective Factors for Improving Visual In-Context Learning,

    Y . Sun, Q. Chen, J. Wang, J. Wang, and Z. Li, “Exploring Effective Factors for Improving Visual In-Context Learning,” 4 2023. [Online]. Available: http://arxiv.org/abs/2304.04748

    2023

  10. [18]

    What Makes Good Examples for Visual In-Context Learning?

    Y . Zhang, K. Zhou, and Z. Liu, “What Makes Good Examples for Visual In-Context Learning?” 1 2023. [Online]. Available: http://arxiv.org/abs/2301.13670

    2023 arXiv

  11. [19]

    Dense Cross-Query-and-Support Attention Weighted Mask Aggregation for Few-Shot Segmentation,

    X. Shi, D. Wei, Y . Zhang, D. Lu, M. Ning, J. Chen, K. Ma, and Y . Zheng, “Dense Cross-Query-and-Support Attention Weighted Mask Aggregation for Few-Shot Segmentation,” 7 2022. [Online]. Available: http://arxiv.org/abs/2207.08549

    2022 arXiv

  12. [20]

    Label Anything: Multi-Class Few-Shot Semantic Segmentation with Visual Prompts,

    P. De Marinis, N. Fanelli, R. Scaringi, E. Colonna, G. Fiameni, G. Vessio, and G. Castellano, “Label Anything: Multi-Class Few-Shot Semantic Segmentation with Visual Prompts,” 7 2024. [Online]. Available: http://arxiv.org/abs/2407.02075

    2024 arXiv

  13. [21]

    A Novel Benchmark for Few-Shot Semantic Segmentation in the Era of Foundation Models,

    R. Bensaid, V . Gripon, F. Leduc-Primeau, L. Mauch, G. B. Hacene, and F. Cardinaux, “A Novel Benchmark for Few-Shot Semantic Segmentation in the Era of Foundation Models,” Tech. Rep. [Online]. Available: https://github.com/RedaBensaidDS/Foundation FewShot

  14. [22]

    SegGPT: Segmenting Everything In Context,

    X. Wang, X. Zhang, Y . Cao, W. Wang, C. Shen, and T. Huang, “SegGPT: Segmenting Everything In Context,” 4 2023. [Online]. Available: http://arxiv.org/abs/2304.03284

    2023 arXiv

  15. [23]

    Matching Networks for One Shot Learning,

    O. Vinyals, C. Blundell, T. Lillicrap, K. Kavukcuoglu, and D. Wierstra, “Matching Networks for One Shot Learning,” 6 2016. [Online]. Available: http://arxiv.org/abs/1606.04080

    2016 arXiv

  16. [24]

    Panoptic Segmentation,

    A. Kirillov, K. He, R. Girshick, C. Rother, and P. Doll ´ar, “Panoptic Segmentation,” 1 2018. [Online]. Available: http://arxiv.org/abs/1801. 00868

    2018

  17. [2018]

    Available: http://arxiv.org/abs/1810.04805

    [Online]. Available: http://arxiv.org/abs/1810.04805

    arXiv

  18. [2021]

    Available: http://arxiv.org/abs/2112.10752

    [Online]. Available: http://arxiv.org/abs/2112.10752

    arXiv

  19. [2023]

    Available: http://arxiv.org/abs/2305.03048

    [Online]. Available: http://arxiv.org/abs/2305.03048

    arXiv

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Reviewed August 12, 2026 · model on record in the stance chip above.