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REVIEW 4 major objections 5 minor 50 references

Balancing Conservatism and Aggressiveness: Prototype-Affinity Hybrid Network for Few-Shot Segmentation

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

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Pith's one-line read A hybrid network fuses a conservative prototype predictor with an aggressive affinity learner to improve few-shot segmentation accuracy on unseen classes.

desk verdict PAHNet is a solid, incremental FSS contribution whose hybrid predictor idea is real but whose evaluation needs more transparency before I'd trust the magnitude of the gains. read the letter →

arxiv 2507.19140 v1 pith:IAU2G5XO submitted 2025-07-25 cs.CV

Tianyu Zou , Shengwu Xiong , Ruilin Yao , Yi Rong This is my paper · ORCID
classification cs.CV
keywords few-shotsemanticsegmentationprototypelearningaffinityforeground-backgroundmismatchattentionscorecalibrationprototype-guidedfeatureenhancementPASCAL-5iCOCO-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

Few-shot segmentation asks a model to segment never-seen object classes from a single or a few labelled examples. The paper starts from an observed split: prototype-learning methods are conservative (they rarely activate background but miss parts of the object), while affinity-learning methods are aggressive (they cover more of the object but wrongly activate background). The central claim is that these two behaviours are complementary, and that a frozen prototype predictor can be used to calibrate each attention block of an affinity learner through a Prototype-guided Feature Enhancement module and an Attention Score Calibration module, reducing foreground-background mismatches. On PASCAL-5i and COCO-20i, the resulting PAHNet improves two affinity baselines, SCCAN and HDMNet, by margins of about 2 to 5 points in mean IoU across 1-shot and 5-shot settings. If correct, this gives a recipe for upgrading existing affinity models with a cheap, frozen prior.

What carries the argument

The load-bearing machinery is a pair of modules placed inside each attention block of the affinity learner. PFE (Prototype-guided Feature Enhancement) builds two prototypes per block—one from the affinity learner's own foreground prediction, one from the frozen prototype predictor's soft query mask—and fuses the resulting enhanced features with a 1x1 convolution and a residual connection, so foreground information is amplified in both support and query features before cross-attention. ASC (Attention Score Calibration) computes a pixel-level correlation matrix between the soft query mask and the support ground-truth mask, uses it to down-weight suspicious attention scores, and then hard-masks to $-\infty$ any query-support pair that is confidently foreground in one and background in the other, so those pairs contribute nothing to the attention output. Together they translate the predictor's conservatism into a spatial prior that the aggressive affinity learner can use.

What would settle it

Take the 1-shot PASCAL-5i setup and replace the frozen predictor's soft mask on one novel fold with a deliberately corrupted version—for example, set all query foreground probabilities below 0.1 for objects of that fold—then rerun PAHNet; if mIoU does not drop back toward or below the unmodified affinity baseline, the reliability assumption is not load-bearing.

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Extended reading notes

Core claim

On its own terms, the paper claims that the aggressive over-activation of affinity learning models stems from a foreground-background (FG-BG) mismatch in cross-attention, and that this can be corrected by injecting the conservative soft prediction of a pre-trained prototype model into every attention block. Two lightweight modules do the injection: PFE strengthens foreground features in both support and query representations, and ASC re-weights cross-attention scores so that definitely mismatched FG-BG pairs are masked out. The authors report that adding these modules to SCCAN raises mIoU on PASCAL-5i from 66.8 to 71.6 (1-shot) and from 70.3 to 75.3 (5-shot), and adding them to HDMNet raises it from 69.4 to 71.5 and 71.8 to 76.1; on COCO-20i the corresponding gains are 3.9/2.7 points for SCCAN and 3.6/3.9 points for HDMNet. The discovery is not a new backbone but a reusable correction mechanism.

Load-bearing premise

The load-bearing premise is that the frozen prototype predictor's soft query mask reliably marks foreground for unseen classes; if that mask is wrong or low-confidence, PFE will amplify background noise and ASC will either fail to remove true mismatches or remove correct matches, and the reported gains should shrink.

Editorial extensions

If this is right

  • Equipping an affinity learner with PFE and ASC turns a frozen prototype predictor into a general upgrade: both SCCAN and HDMNet improve on PASCAL-5i and COCO-20i in 1-shot and 5-shot settings.
  • Gains are larger in the 5-shot setting than the 1-shot setting—for example, 4.3 vs 2.1 points for HDMNet on PASCAL-5i—because more support samples give the prototype predictor a more accurate soft mask.
  • Ablations show PFE alone contributes 1.9 points and ASC alone 2.6 points over the baseline on PASCAL-5i 1-shot, and together they add 3.2 points, so the two mechanisms are complementary rather than redundant.
  • Because the modules sit inside attention blocks without changing the core attention operations, PAHNet can be layered onto different affinity architectures, as demonstrated with SCCAN and HDMNet.

Reading between the lines

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

  • The same hybridisation could be applied to other prototype/affinity pairs; the paper tests only SSP with SCCAN and HDMNet, so a natural extension is to swap in stronger or weaker prototype predictors to see whether gains scale with predictor quality.
  • A direct test of the reliability assumption would corrupt the frozen predictor's soft mask (e.g., add noise or force confident mispredictions on a novel class) and measure how much of the gain survives; the paper does not quantify this degradation.
  • Because ASC only needs a query-side soft prior and a support-side ground-truth mask, the mechanism could transfer to other settings that provide a coarse prior, such as interactive segmentation or box-supervised segmentation.
  • The observed 5-shot > 1-shot gain pattern suggests the bottleneck is predictor accuracy on novel classes; fine-tuning the predictor on base-class episodes or using a stronger backbone might push the gains further, at the cost of the 'frozen' simplicity.
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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. This manuscript proposes PAHNet, a hybrid few-shot segmentation framework that combines a frozen prototype-learning predictor (SSP) with a trainable affinity-learning segmenter (SCCAN or HDMNet). The method inserts two modules into each attention block: a Prototype-guided Feature Enhancement (PFE) module, which fuses prototype- and affinity-derived foreground prototypes into the support/query features, and an Attention Score Calibration (ASC) module, which re-weights and hard-masks cross-attention scores using the prototype predictor's soft query mask. The central claim is that prototype learners make conservative (low false-positive, high false-negative) predictions while affinity learners make aggressive (high false-positive, lower false-negative) predictions, and that combining them balances these tendencies. Experiments on PASCAL-5i and COCO-20i report consistent mIoU/FB-IoU gains over SCCAN and HDMNet baselines (e.g., +4.8%/+5.0% over SCCAN on PASCAL-5i, +3.9%/+2.7% on COCO-20i, 1-shot/5-shot), plus ablations of the two modules and a predictor-swap experiment.

Significance. If the reported gains hold, the paper's core observation that prototype and affinity predictors have complementary error characteristics is useful and could influence future FSS design. The proposed integration is simple and shown to be compatible with two different affinity learners, which suggests a general recipe rather than a method tied to one baseline. The release of code is a concrete reproducibility asset. The main value is empirical: the paper identifies a clear failure-mode asymmetry and demonstrates, on standard benchmarks, that injecting conservative prototype guidance into an aggressive affinity learner improves accuracy. However, the significance is tempered by the absence of statistical reliability measures (single runs, no error bars) and by an ablation design whose baseline ('BAM') is undefined, which currently prevents the reader from isolating the contribution of each module as cleanly as the text claims.

major comments (4)
  1. [Sec. 3.3, Eqs. (7)-(8)] The hard-masking rule in Eq. (8) sets A_cross(i,j) = -inf whenever phi(M_tilde_pro_q)(i) = 0 and phi(M_s)(j) = 1, i.e., every query pixel that SSP scores below gamma_bg=0.3 is forbidden from attending to any support foreground pixel. The paper's own characterization of SSP as a conservative predictor (higher FN, Figure 2) implies that a substantial fraction of true foreground query pixels will have M_pro_q < 0.3, so this path does not only remove FG-BG mismatches; it also suppresses true FG-FG correlations. The manuscript provides no statistics on SSP's per-pixel recall on novel classes, no distribution of M_pro_q over true foreground pixels, and no sensitivity analysis over gamma_bg or gamma_fg. Because the central mechanism is claimed to mitigate the affinity learner's over-aggressiveness while preserving foreground activation, the magnitude of this collateral suppression is load-bearing. Please report the fraction of true-FG query pixels masked by Eq. (8), a sensitivity study of gamma_bg and gamma_fg, and either a continuous soft-mask variant or an ablation that replaces the hard mask with re-weighting only.
  2. [Sec. 4.3, Table 4] The ablation section states that experiments are conducted 'with the combination of SCCAN and BAM as the baseline,' yet BAM is not defined anywhere in the paper or references. The table reports a baseline mIoU of 68.4 and then shows gains from PFE, ASC, and both, but without knowing what BAM is, the reader cannot determine what architecture the PFE/ASC modules are added to, which attention blocks they modify, or how the baseline relates to the SCCAN results in Table 1. This makes the module ablation non-interpretable. Please either define BAM explicitly (including its source or its relation to SCCAN) or recast the ablation on the exact SCCAN baseline used elsewhere.
  3. [Sec. 4.2, Tables 1-2] All reported numbers are single-run results with no variance over random seeds or episode sets. The claimed improvements are often in the 1-5 mIoU range, which is exactly the scale where run-to-run variability in FSS benchmarks can be comparable to the reported gains. Without error bars or at least multiple-seed statistics, the significance of the headline improvements cannot be assessed. Please report mean and standard deviation over at least three seeds (or the conventional protocol used by recent FSS papers) for at least the main tables and the key ablations.
  4. [Sec. 3.3, last paragraph] The training loss is not described in the paper; the text defers to the Supplementary Material ('we adopt the same loss function as in AENet. Please refer to the Supplementary Material for more details of our training loss.'). For a journal version, the loss function is part of the central training setup, and the reader should not be expected to reconstruct it from a citation or an unavailable supplement. Please include the loss formulation in the main text or, if space is a concern, state the exact configuration (e.g., cross-entropy plus any auxiliary losses) as part of the implementation details.
minor comments (5)
  1. [Sec. 1] There is a typo in the caption of Figure 2: 'subtigure' should be 'subfigure', and in the same caption 'Affinity learning methods exhibit higher FP but lower FN (aggressive), while prototype learning methods achieve lower FP but higher FN (conservative)' is followed by a period, but the final statement 'conservative' appears without a closing period; please harmonize the caption formatting.
  2. [Sec. 3.3] In Eq. (7), the notation 'M_pro_q (i,j)' is used inconsistently: the left-hand side is a scalar threshold result but the right-hand side mixes a scalar threshold comparison with a value assignment; this is understandable but please define the domain (e.g., for each pixel) explicitly to avoid ambiguity.
  3. [Sec. 4.3] The text says 'FFS tasks' in the conclusion and 'FFS' in the introduction abstract; the correct acronym is FSS (few-shot segmentation). Please correct throughout.
  4. [Tables 1 and 2] In the table headers, 'SCCAN+PAHNetOurs' and 'HDMNet+PAHNetOurs' are run together as a single token; insert a space or italicize 'Ours' for readability.
  5. [Sec. 4.3, Table 3] The predictor-swap experiment changes the predictor from SSP to SCCAN but does not vary the temperature or thresholds; the claim that SSP's conservatism (rather than its specific accuracy) drives the improvement would be stronger if the analysis also reported the FP/FN statistics of the two predictors on the novel-class evaluation episodes, as done in Figure 2 for the training distribution.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: PAHNet's claims rest on external baselines and held-out benchmark evaluations, not on self-referential derivation.

full rationale

PAHNet's central claim is an empirical architecture comparison: fusing frozen SSP soft predictions into an affinity learner via PFE and ASC improves mIoU on PASCAL-5i and COCO-20i. The soft masks M_pro_q are an external input from the officially released SSP model, not derived from PAHNet's own outputs, and the affinity learner is trained with the standard supervised segmentation objective (same loss as AENet). Equations (5), (7), and (8) define the fusion and masking operations from M_pro_q and M_s; these are module definitions, not predictions that reduce to fitted parameters. Hyperparameters tau, gamma_fg, and gamma_bg are hand-set constants, and Table 4 ablates the modules rather than re-fitting a target. Table 3's predictor swap (SSP to SCCAN) tests whether conservatism matters, and it is an ablation, not a circular justification. The paper does not rely on self-citations: SSP, SCCAN, HDMNet, and AENet are external prior works by other authors. The skeptic's concern that SSP's false negatives may be hard-masked by Eq. (8) is a robustness/correctness risk about an unverified predictor-reliability assumption, not a circularity: the assumption is stated and empirically tested indirectly, and the result is not equivalent to its inputs by construction. Hence no circular step can be exhibited.

Assumptions & free parameters 3 free parameters · 4 assumptions · 0 invented entities

The central claim rests mainly on standard FSS assumptions and on the reliability of a pretrained predictor. Free parameters tau, gamma_fg, and gamma_bg are chosen by hand and are not justified by sensitivity analysis; they affect Eq. (2), Eq. (7), and therefore the attention calibration.

free parameters (3)
  • Temperature tau in Eq. (2) = 0.1
    Controls the softmax sharpness of affinity-based query predictions; set by hand in Section 4.1 without sensitivity analysis.
  • Foreground threshold gamma_fg in Eq. (7) = 0.7
    Used to declare high-confidence foreground pixels from the frozen predictor's soft mask; chosen by hand in Section 4.1.
  • Background threshold gamma_bg in Eq. (7) = 0.3
    Used to declare high-confidence background pixels from the frozen predictor's soft mask; chosen by hand in Section 4.1.
assumptions (4)
  • domain assumption The frozen SSP model provides reliable foreground probability maps on novel classes.
    M_pro_q is used in Eq. (5) and Eq. (7)-(8) to enhance features and mask attention; no failure analysis is provided for predictor errors.
  • domain assumption Support mask M_s is available at test time and accurately labels foreground and background.
    Standard FSS setup; M_s is used in Eq. (1) and Eq. (6)-(8) to build prototypes and calibrate attention.
  • domain assumption Prototype methods are conservative and affinity methods are aggressive across the FSS family, not only for SSP and SCCAN.
    The design motivation in Section 1 is based on two models and two simplified variants; the paper extrapolates this to the two paradigms.
  • domain assumption Episodic training on base classes transfers to novel-class episodes.
    Standard FSS protocol; training and evaluation follow the standard setup described in Section 3.

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Cite this review

Pith. "Pith review of Balancing Conservatism and Aggressiveness: Prototype-Affinity Hybrid Network for Few-Shot Segmentation." pith.science (2026). https://pith.science/paper/IAU2G5XO

@misc{pith2026250719140,
  author       = {Pith},
  title        = {Pith review of: Balancing Conservatism and Aggressiveness: Prototype-Affinity Hybrid Network for Few-Shot Segmentation},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/IAU2G5XO}},
  note         = {Machine review of arXiv:2507.19140}
}
abstract

This paper studies the few-shot segmentation (FSS) task, which aims to segment objects belonging to unseen categories in a query image by learning a model on a small number of well-annotated support samples. Our analysis of two mainstream FSS paradigms reveals that the predictions made by prototype learning methods are usually conservative, while those of affinity learning methods tend to be more aggressive. This observation motivates us to balance the conservative and aggressive information captured by these two types of FSS frameworks so as to improve the segmentation performance. To achieve this, we propose a **P**rototype-**A**ffinity **H**ybrid **Net**work (PAHNet), which introduces a Prototype-guided Feature Enhancement (PFE) module and an Attention Score Calibration (ASC) module in each attention block of an affinity learning model (called affinity learner). These two modules utilize the predictions generated by a pre-trained prototype learning model (called prototype predictor) to enhance the foreground information in support and query image representations and suppress the mismatched foreground-background (FG-BG) relationships between them, respectively. In this way, the aggressiveness of the affinity learner can be effectively mitigated, thereby eventually increasing the segmentation accuracy of our PAHNet method. Experimental results show that PAHNet outperforms most recently proposed methods across 1-shot and 5-shot settings on both PASCAL-5$^i$ and COCO-20$^i$ datasets, suggesting its effectiveness. The code is available at: [GitHub - tianyu-zou/PAHNet: Balancing Conservatism and Aggressiveness: Prototype-Affinity Hybrid Network for Few-Shot Segmentation (ICCV'25)](https://github.com/tianyu-zou/PAHNet)

Figures

Figures reproduced from arXiv: 2507.19140 by the authors.

Figure 1
Figure 1. Prediction comparison between the prototype learning [PITH_FULL_IMAGE:figures/full_fig_p001_1.png] view at source ↗
Figure 2
Figure 2. Comparison of average FP (False Positive) and FN (False Negative) between prototype and affinity learning methods across the [PITH_FULL_IMAGE:figures/full_fig_p002_2.png] view at source ↗
Figure 3
Figure 3. Architectural overview of PAHNet. It mainly consists of a pre-trained prototype predictor and a trainable affinity learner. The [PITH_FULL_IMAGE:figures/full_fig_p004_3.png] view at source ↗
Figures from the paper (3 more)
Figure 4
Figure 4. Figure 4: Illustration of our PFE module. It enhances the images [PITH_FULL_IMAGE:figures/full_fig_p005_4.png]
Figure 6
Figure 6. Figure 6: Qualitative comparison under 1-shot setting on PASCAL- [PITH_FULL_IMAGE:figures/full_fig_p007_6.png]
Figure 7
Figure 7. Figure 7: Grad-CAM visualization of the output features from the [PITH_FULL_IMAGE:figures/full_fig_p008_7.png]

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Works this paper leans on

50 extracted references · 46 canonical work pages

  1. [1]

    Relevant intrinsic feature enhancement network for few-shot semantic segmentation

    Xiaoyi Bao, Jie Qin, Siyang Sun, Xingang Wang, and Yun Zheng. Relevant intrinsic feature enhancement network for few-shot semantic segmentation. InPro- ceedings of the AAAI Conference on Artificial Intelli- gence, pages 765–773, 2024. 6

    work page 2024

  2. [2]

    Few shot se- mantic segmentation: a review of methodologies and open challenges.arXiv e-prints, pages arXiv–2304,

    Nico Catalano and Matteo Matteucci. Few shot se- mantic segmentation: a review of methodologies and open challenges.arXiv e-prints, pages arXiv–2304,

    work page

  3. [3]

    Pixel matching network for cross- domain few-shot segmentation

    Hao Chen, Yonghan Dong, Zheming Lu, Yunlong Yu, and Jungong Han. Pixel matching network for cross- domain few-shot segmentation. InProceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pages 978–987, 2024. 6

    work page 2024

  4. [4]

    A transformer-based adaptive prototype matching network for few-shot semantic segmentation

    Sihan Chen, Yadang Chen, Yuhui Zheng, Zhi-Xin Yang, and Enhua Wu. A transformer-based adaptive prototype matching network for few-shot semantic segmentation. InProceedings of the Thirty-Third In- ternational Joint Conference on Artificial Intelligence, pages 659–667, 2024. 6

    work page 2024

  5. [5]

    Query- guided prototype evolution network for few-shot seg- mentation.IEEE Transactions on Multimedia, 2024

    Runmin Cong, Hang Xiong, Jinpeng Chen, Wei Zhang, Qingming Huang, and Yao Zhao. Query- guided prototype evolution network for few-shot seg- mentation.IEEE Transactions on Multimedia, 2024. 6

    work page 2024

  6. [6]

    Imagenet: A large-scale hierarchi- cal image database

    Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, and Li Fei-Fei. Imagenet: A large-scale hierarchi- cal image database. In2009 IEEE conference on com- puter vision and pattern recognition, pages 248–255. Ieee, 2009. 7

    work page 2009

  7. [7]

    The pascal visual object classes (voc) challenge.Interna- tional journal of computer vision, 88:303–338, 2010

    Mark Everingham, Luc Van Gool, Christopher KI Williams, John Winn, and Andrew Zisserman. The pascal visual object classes (voc) challenge.Interna- tional journal of computer vision, 88:303–338, 2010. 6

    work page 2010

  8. [8]

    Self-support few-shot semantic segmentation

    Qi Fan, Wenjie Pei, Yu-Wing Tai, and Chi-Keung Tang. Self-support few-shot semantic segmentation. InEuropean Conference on Computer Vision, pages 701–719. Springer, 2022. 2, 3, 4, 6, 7

    work page 2022

Show all 50 references
  1. [9]

    Semantic contours from inverse detectors

    Bharath Hariharan, Pablo Arbel ´aez, Lubomir Bour- dev, Subhransu Maji, and Jitendra Malik. Semantic contours from inverse detectors. In2011 international conference on computer vision, pages 991–998. IEEE,

  2. [10]

    Deep residual learning for image recognition

    Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. Deep residual learning for image recognition. InProceedings of the IEEE conference on computer vision and pattern recognition, pages 770–778, 2016. 7

    2016

  3. [11]

    Prototype adaption and projection for few- and zero-shot 3d point cloud semantic segmentation

    Shuting He, Xudong Jiang, Wei Jiang, and Henghui Ding. Prototype adaption and projection for few- and zero-shot 3d point cloud semantic segmentation. IEEE Transactions on Image Processing, 32:3199– 3211, 2023. 2

    2023

  4. [12]

    Cost aggregation with 4d con- volutional swin transformer for few-shot segmenta- tion

    Sunghwan Hong, Seokju Cho, Jisu Nam, Stephen Lin, and Seungryong Kim. Cost aggregation with 4d con- volutional swin transformer for few-shot segmenta- tion. InEuropean Conference on Computer Vision, pages 108–126. Springer, 2022. 2

    2022

  5. [13]

    Attention-based multi-context guiding for few-shot semantic segmen- tation

    Tao Hu, Pengwan Yang, Chiliang Zhang, Gang Yu, Yadong Mu, and Cees GM Snoek. Attention-based multi-context guiding for few-shot semantic segmen- tation. InProceedings of the AAAI conference on ar- tificial intelligence, pages 8441–8448, 2019. 3

    2019

  6. [14]

    Prototypical kernel learning and open-set foreground perception for generalized few-shot semantic segmen- tation

    Kai Huang, Feigege Wang, Ye Xi, and Yutao Gao. Prototypical kernel learning and open-set foreground perception for generalized few-shot semantic segmen- tation. InProceedings of the IEEE/CVF International Conference on Computer Vision, pages 19256–19265,

  7. [15]

    Learning what not to segment: A new perspec- tive on few-shot segmentation

    Chunbo Lang, Gong Cheng, Binfei Tu, and Junwei Han. Learning what not to segment: A new perspec- tive on few-shot segmentation. InProceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 8057–8067, 2022. 7

    2022

  8. [16]

    Beyond the prototype: Divide-and-conquer proxies for few-shot segmentation.arXiv preprint arXiv:2204.09903, 2022

    Chunbo Lang, Binfei Tu, Gong Cheng, and Junwei Han. Beyond the prototype: Divide-and-conquer proxies for few-shot segmentation.arXiv preprint arXiv:2204.09903, 2022. 2

    2022 arXiv

  9. [17]

    Pseudo-label: The simple and efficient semi-supervised learning method for deep neural networks

    Dong-Hyun Lee et al. Pseudo-label: The simple and efficient semi-supervised learning method for deep neural networks. InWorkshop on challenges in rep- resentation learning, ICML, page 896. Atlanta, 2013. 1

    2013

  10. [18]

    Adaptive prototype learning and allocation for few-shot seg- mentation

    Gen Li, Varun Jampani, Laura Sevilla-Lara, Deqing Sun, Jonghyun Kim, and Joongkyu Kim. Adaptive prototype learning and allocation for few-shot seg- mentation. InProceedings of the IEEE/CVF con- ference on computer vision and pattern recognition, pages 8334–8343, 2021. 3

    2021

  11. [19]

    Microsoft coco: Com- mon objects in context

    Tsung-Yi Lin, Michael Maire, Serge Belongie, James Hays, Pietro Perona, Deva Ramanan, Piotr Doll ´ar, and C Lawrence Zitnick. Microsoft coco: Com- mon objects in context. InComputer Vision–ECCV 2014: 13th European Conference, Zurich, Switzer- land, September 6-12, 2014, Procee...

    2014

  12. [20]

    Fecanet: Boost- ing few-shot semantic segmentation with feature- enhanced context-aware network.IEEE Transactions on Multimedia, 25:8580–8592, 2023

    Huafeng Liu, Pai Peng, Tao Chen, Qiong Wang, Yazhou Yao, and Xian-Sheng Hua. Fecanet: Boost- ing few-shot semantic segmentation with feature- enhanced context-aware network.IEEE Transactions on Multimedia, 25:8580–8592, 2023. 6

    2023

  13. [21]

    Dynamic proto- type convolution network for few-shot semantic seg- mentation

    Jie Liu, Yanqi Bao, Guo-Sen Xie, Huan Xiong, Jan- Jakob Sonke, and Efstratios Gavves. Dynamic proto- type convolution network for few-shot semantic seg- mentation. InProceedings of the IEEE/CVF Con- ference on Computer Vision and Pattern Recognition, pages 11553–11562, 2022. 3

    2022

  14. [22]

    Fully convolutional networks for semantic segmen- tation

    Jonathan Long, Evan Shelhamer, and Trevor Darrell. Fully convolutional networks for semantic segmen- tation. InProceedings of the IEEE conference on computer vision and pattern recognition, pages 3431– 3440, 2015. 1

    2015

  15. [23]

    Simpler is better: Few-shot semantic segmentation with classifier weight trans- former

    Zhihe Lu, Sen He, Xiatian Zhu, Li Zhang, Yi-Zhe Song, and Tao Xiang. Simpler is better: Few-shot semantic segmentation with classifier weight trans- former. InProceedings of the IEEE/CVF International Conference on Computer Vision, pages 8741–8750,

  16. [24]

    Hyper- correlation squeeze for few-shot segmentation

    Juhong Min, Dahyun Kang, and Minsu Cho. Hyper- correlation squeeze for few-shot segmentation. InPro- ceedings of the IEEE/CVF international conference on computer vision, pages 6941–6952, 2021. 3

    2021

  17. [25]

    Feature weight- ing and boosting for few-shot segmentation

    Khoi Nguyen and Sinisa Todorovic. Feature weight- ing and boosting for few-shot segmentation. InPro- ceedings of the IEEE/CVF International Conference on Computer Vision, pages 622–631, 2019. 6

    2019

  18. [26]

    Hierarchi- cal dense correlation distillation for few-shot segmen- tation

    Bohao Peng, Zhuotao Tian, Xiaoyang Wu, Chengyao Wang, Shu Liu, Jingyong Su, and Jiaya Jia. Hierarchi- cal dense correlation distillation for few-shot segmen- tation. InProceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 23641–23651, 2023. 3, 6, 7

    2023

  19. [27]

    Few-shot seg- mentation propagation with guided networks.arXiv preprint arXiv:1806.07373, 2018

    Kate Rakelly, Evan Shelhamer, Trevor Darrell, Alexei A Efros, and Sergey Levine. Few-shot seg- mentation propagation with guided networks.arXiv preprint arXiv:1806.07373, 2018. 2

    2018 arXiv

  20. [28]

    One-shot learning for semantic seg- mentation.arXiv preprint arXiv:1709.03410, 2017

    Amirreza Shaban, Shray Bansal, Zhen Liu, Irfan Essa, and Byron Boots. One-shot learning for semantic seg- mentation.arXiv preprint arXiv:1709.03410, 2017. 1, 3, 6

    2017 arXiv

  21. [29]

    Dense cross-query-and-support attention weighted mask aggregation for few-shot segmenta- tion

    Xinyu Shi, Dong Wei, Yu Zhang, Donghuan Lu, Munan Ning, Jiashun Chen, Kai Ma, and Yefeng Zheng. Dense cross-query-and-support attention weighted mask aggregation for few-shot segmenta- tion. InEuropean Conference on Computer Vision, pages 151–168. Springer, 2022. 3

    2022

  22. [30]

    A comprehensive survey of few-shot learning: Evolution, applications, chal- lenges, and opportunities.ACM Computing Surveys, 55(13s):1–40, 2023

    Yisheng Song, Ting Wang, Puyu Cai, Subrota K Mon- dal, and Jyoti Prakash Sahoo. A comprehensive survey of few-shot learning: Evolution, applications, chal- lenges, and opportunities.ACM Computing Surveys, 55(13s):1–40, 2023. 1

    2023

  23. [31]

    Pixel-by-pixel cross-domain align- ment for few-shot semantic segmentation

    Antonio Tavera, Fabio Cermelli, Carlo Masone, and Barbara Caputo. Pixel-by-pixel cross-domain align- ment for few-shot semantic segmentation. InProceed- ings of the IEEE/CVF Winter Conference on Applica- tions of Computer Vision, pages 1626–1635, 2022. 2

    2022

  24. [32]

    Prior guided feature enrichment network for few-shot segmenta- tion.IEEE transactions on pattern analysis and ma- chine intelligence, 44(2):1050–1065, 2020

    Zhuotao Tian, Hengshuang Zhao, Michelle Shu, Zhicheng Yang, Ruiyu Li, and Jiaya Jia. Prior guided feature enrichment network for few-shot segmenta- tion.IEEE transactions on pattern analysis and ma- chine intelligence, 44(2):1050–1065, 2020. 3

    2020

  25. [33]

    Few-shot semantic segmentation with democratic attention net- works

    Haochen Wang, Xudong Zhang, Yutao Hu, Yandan Yang, Xianbin Cao, and Xiantong Zhen. Few-shot semantic segmentation with democratic attention net- works. InComputer Vision–ECCV 2020: 16th Euro- pean Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XIII 16, pages 73...

    2020

  26. [34]

    Rethinking prior informa- tion generation with clip for few-shot segmentation

    Jin Wang, Bingfeng Zhang, Jian Pang, Honglong Chen, and Weifeng Liu. Rethinking prior informa- tion generation with clip for few-shot segmentation. InProceedings of the IEEE/CVF Conference on Com- puter Vision and Pattern Recognition, pages 3941– 3951, 2024. 6

    2024

  27. [35]

    Panet: Few-shot image seman- tic segmentation with prototype alignment

    Kaixin Wang, Jun Hao Liew, Yingtian Zou, Daquan Zhou, and Jiashi Feng. Panet: Few-shot image seman- tic segmentation with prototype alignment. Inpro- ceedings of the IEEE/CVF international conference on computer vision, pages 9197–9206, 2019. 2, 3

    2019

  28. [36]

    Rethink- ing the correlation in few-shot segmentation: A buoys view

    Yuan Wang, Rui Sun, and Tianzhu Zhang. Rethink- ing the correlation in few-shot segmentation: A buoys view. InProceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 7183–7192, 2023. 2

    2023

  29. [37]

    Scale-aware graph neural network for few- shot semantic segmentation

    Guo-Sen Xie, Jie Liu, Huan Xiong, and Ling Shao. Scale-aware graph neural network for few- shot semantic segmentation. InProceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 5475–5484, 2021. 3

    2021

  30. [38]

    Doubly deformable aggregation of covariance matri- ces for few-shot segmentation

    Zhitong Xiong, Haopeng Li, and Xiao Xiang Zhu. Doubly deformable aggregation of covariance matri- ces for few-shot segmentation. InEuropean Confer- ence on Computer Vision, pages 133–150. Springer,

  31. [39]

    Self-calibrated cross attention network for few-shot segmentation

    Qianxiong Xu, Wenting Zhao, Guosheng Lin, and Cheng Long. Self-calibrated cross attention network for few-shot segmentation. InProceedings of the IEEE/CVF International Conference on Computer Vi- sion, pages 655–665, 2023. 2, 3, 6, 7

    2023

  32. [40]

    Hybrid mamba for few-shot segmentation.arXiv preprint arXiv:2409.19613, 2024

    Qianxiong Xu, Xuanyi Liu, Lanyun Zhu, Guosheng Lin, Cheng Long, Ziyue Li, and Rui Zhao. Hybrid mamba for few-shot segmentation.arXiv preprint arXiv:2409.19613, 2024. 6, 7

    2024 arXiv

  33. [41]

    Eliminating feature ambiguity for few-shot segmentation

    Qianxiong Xu, Guosheng Lin, Chen Change Loy, Cheng Long, Ziyue Li, and Rui Zhao. Eliminating feature ambiguity for few-shot segmentation. InEuro- pean Conference on Computer Vision, pages 416–433. Springer, 2025. 3, 6, 7

    2025

  34. [42]

    Prototype mixture models for few-shot semantic segmentation

    Boyu Yang, Chang Liu, Bohao Li, Jianbin Jiao, and Qixiang Ye. Prototype mixture models for few-shot semantic segmentation. InComputer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, Au- gust 23–28, 2020, Proceedings, Part VIII 16, pages 763–778. Springer, 2020. 3

    2020

  35. [43]

    Brinet: Towards bridging the intra-class and inter- class gaps in one-shot segmentation.arXiv preprint arXiv:2008.06226, 2020

    Xianghui Yang, Bairun Wang, Kaige Chen, Xinchi Zhou, Shuai Yi, Wanli Ouyang, and Luping Zhou. Brinet: Towards bridging the intra-class and inter- class gaps in one-shot segmentation.arXiv preprint arXiv:2008.06226, 2020. 3

    2008 arXiv

  36. [44]

    Self- guided and cross-guided learning for few-shot seg- mentation

    Bingfeng Zhang, Jimin Xiao, and Terry Qin. Self- guided and cross-guided learning for few-shot seg- mentation. InProceedings of the IEEE/CVF Con- ference on Computer Vision and Pattern Recognition, pages 8312–8321, 2021. 3

    2021

  37. [45]

    Few-shot segmentation via cycle-consistent transformer.Advances in Neural Information Process- ing Systems, 34:21984–21996, 2021

    Gengwei Zhang, Guoliang Kang, Yi Yang, and Yun- chao Wei. Few-shot segmentation via cycle-consistent transformer.Advances in Neural Information Process- ing Systems, 34:21984–21996, 2021. 3

    2021

  38. [46]

    Mask matching transformer for few-shot segmentation.Ad- vances in Neural Information Processing Systems, 35: 823–836, 2022

    Gengwei Zhang, Shant Navasardyan, Ling Chen, Yao Zhao, Yunchao Wei, Humphrey Shi, et al. Mask matching transformer for few-shot segmentation.Ad- vances in Neural Information Processing Systems, 35: 823–836, 2022. 3

    2022

  39. [47]

    Mfnet: Mul- ticlass few-shot segmentation network with pixel- wise metric learning.IEEE Transactions on Circuits and Systems for Video Technology, 32(12):8586–8598,

    Miao Zhang, Miaojing Shi, and Li Li. Mfnet: Mul- ticlass few-shot segmentation network with pixel- wise metric learning.IEEE Transactions on Circuits and Systems for Video Technology, 32(12):8586–8598,

  40. [48]

    Sg-one: Similarity guidance network for one- shot semantic segmentation.IEEE transactions on cy- bernetics, 50(9):3855–3865, 2020

    Xiaolin Zhang, Yunchao Wei, Yi Yang, and Thomas S Huang. Sg-one: Similarity guidance network for one- shot semantic segmentation.IEEE transactions on cy- bernetics, 50(9):3855–3865, 2020. 3

    2020

  41. [49]

    Fgnet: Towards filling the intra-class and inter-class gaps for few-shot segmentation

    Yuxuan Zhang, Wei Yang, and Shaowei Wang. Fgnet: Towards filling the intra-class and inter-class gaps for few-shot segmentation. InIJCAI, pages 1749–1758,

  42. [50]

    Addressing background context bias in few-shot segmentation through iterative mod- ulation

    Lanyun Zhu, Tianrun Chen, Jianxiong Yin, Simon See, and Jun Liu. Addressing background context bias in few-shot segmentation through iterative mod- ulation. InProceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 3370–3379, 2024. 6, 7

    2024

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