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REVIEW 5 major objections 5 minor 74 references

Few-Shot Pattern Detection via Template Matching and Regression

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

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Pith's one-line read A template-matching detector that preserves the spatial layout of exemplars outperforms prototype-based few-shot counting and detection on RPINE, FSCD-147, and FSCD-LVIS.

desk verdict TMR is a clean template-matching detector that beats prototype-based FSCD methods on non-object patterns, but the RPINE split-overlap question needs to be resolved before the cross-dataset claims can be trusted. read the letter →

arxiv 2508.17636 v1 pith:GJVAUMYO submitted 2025-08-25 cs.CV cs.AI

Eunchan Jo , Dahyun Kang , Sanghyun Kim , Yunseon Choi , Minsu Cho This is my paper · ORCID
classification cs.CVcs.AI
keywords few-shotpatterndetectiontemplatematchingobjectcountingandboundingboxregressionrepeatedRPINEdatasetprototypecross-datasetgeneralization
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 tries to establish that few-shot pattern detection—finding every instance of a pattern given only a few exemplars—does not need the sophisticated prototype-fusion machinery used by recent few-shot counting and detection methods. The authors argue that collapsing support exemplars into a pooled prototype throws away the geometry of the pattern, and that a classic template-matching correlation followed by a light regression head preserves that geometry and detects non-object patterns such as textures, object parts, and nameless repeated elements. To test this, they introduce RPINE, a multi-pattern dataset spanning object-like and non-object repetitions, and report that their TMR detector beats previous state-of-the-art methods on RPINE, FSCD-147, and FSCD-LVIS while transferring better across datasets. If right, a much simpler detector is also the more general one, and benchmark design has been quietly enforcing an object bias.

What carries the argument

The load-bearing mechanism is two-dimensional channel-wise template matching: for each location in the image feature map $\mathbf{F}$, the template feature $\mathbf{T}$ is centered there and multiplied channel-by-channel to produce the template-matching feature $\mathbf{F}_{\mathrm{TM}}$, which is concatenated with $\mathbf{F}$ and fed to the box head. This preserves the exemplar's spatial layout instead of collapsing it into a prototype vector. The second mechanism is support-conditioned regression: the box head predicts only shift and scale factors applied to the support exemplar's width and height, so localization is anchored to the exemplar's own size. The third is adaptive template extraction: RoIAlign crops the template at a size that rounds the exemplar box up to the feature-grid alignment, keeping the template in spatial correspondence with the query feature map. A frozen ViT backbone supplies the features, and only six learnable layers sit on top.

What would settle it

Run an image-level deduplication of RPINE training images against the FSCD-147 and FSCD-LVIS test sets, retrain TMR on the cleaned split, and re-run the cross-dataset evaluation reported in Table 4; if the AP advantage over GeCo on FSCD-147 shrinks to noise, the generalization claim was an artifact of train/test overlap rather than spatial template matching.

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Extended reading notes

Core claim

On the paper's own terms, the central discovery is that spatial layout itself is the reusable cue for few-shot pattern detection. TMR crops a template feature from the image feature map at the exemplar's bounding box using adaptive-size RoIAlign, correlates it channel-wise with the full image feature map to produce a template-matching feature, concatenates that with the image feature, and lets a small convolutional-plus-linear head predict presence scores and support-conditioned box parameters, namely shift and scale relative to the exemplar box. Because the template is never averaged into a vector, shape and internal structure survive the matching step. The paper reports that this design outperforms prototype-based FSCD systems on RPINE, FSCD-147, and FSCD-LVIS, and that the gap is largest on RPINE, where non-object patterns dominate. It also reports that the optional SAM-decoder refinement helps on object-centric FSCD-147 but hurts on RPINE, which the authors read as evidence that object-edge priors are a liability for general pattern detection.

Load-bearing premise

The central claim rests on RPINE being a fair, leakage-free testbed: if its training images overlap the FSCD-147 or FSCD-LVIS evaluation sets, or its crowd annotations are too inconsistent to be treated as ground truth, the reported cross-dataset and non-object gains could be artifacts of the benchmark rather than of template matching.

Editorial extensions

If this is right

  • Prototype-based matching is not required for state-of-the-art few-shot counting and detection; correlation of spatially intact templates is enough, which should redirect attention to spatial-preserving representations.
  • The same pipeline detects non-object patterns, including parts, textures, and geometric motifs, so few-shot detection can move beyond object categories to arbitrary user-specified patterns.
  • Cross-dataset behavior improves because matching relies on structure rather than training-set semantics; the paper's cross-evaluations show TMR losing less when moving between RPINE, FSCD-147, and FSCD-LVIS.
  • Edge-based refinement from the SAM decoder is not a free lunch: it helps on object benchmarks and degrades RPINE, so future systems should condition refinement on whether the target pattern is edge-delimited.
  • Multi-scale feature maps add further gains, implying the reported single-scale numbers understate the approach's ceiling.

Reading between the lines

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

  • If RPINE survives independent image-level deduplication against the FSCD test sets, the cross-dataset gains imply that spatial template matching is a domain-general prior rather than a dataset-specific trick; a testable extension is applying TMR to repeated-structure tasks such as lattice detection or texture synthesis.
  • A natural extension the paper leaves implicit is rotation invariance: channel-wise template matching is translation-equivariant but not rotation-equivariant, so pairing it with steerable or rotation-equivariant features should close the gap the authors acknowledge on rotated patterns.
  • The frozen-backbone, shallow-head design means adapting TMR to a new domain could be done by swapping the backbone rather than retraining the detector; the SEM demo suggests microscopy and industrial inspection are plausible near-term targets, though the paper does not benchmark them systematically.
  • Because few-shot inference simply runs the same matching per exemplar and merges by NMS, TMR should remain stable as the number of shots grows; a useful stress test is whether this unlearned aggregation beats learned fusion when exemplars are noisy or occluded.
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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

5 major / 5 minor

Summary. The paper proposes TMR, a few-shot pattern detector built from template matching plus a lightweight regression/classification head on a frozen backbone. A support exemplar is cropped with RoIAlign and correlated channel-wise with the image feature map, and box regression is conditioned on the exemplar's size; optional SAM-decoder post-processing is examined. The authors also introduce RPINE, a new crowd-annotated dataset of repeated patterns intended to cover non-object patterns, and report experiments on RPINE, FSCD-147, and FSCD-LVIS, including cross-dataset evaluation. The central claims are that TMR outperforms state-of-the-art few-shot counting/detection methods on all three benchmarks and generalizes across datasets.

Significance. If the claims hold, the paper makes a useful contribution: a simple, efficient detector that preserves exemplar spatial layout, a new benchmark covering non-object patterns, and strong cross-dataset generalization would be of clear value to the few-shot counting/detection community. The paper has concrete strengths: the architecture is simple and clearly described; the ablations in Tables 7 and 8 directly test the role of template matching and support-conditioned regression; the complexity comparison in Table 9 is informative; and the authors report using official code for baselines. However, the headline claim of outperforming state-of-the-art on all three benchmarks is not consistently supported by the paper's own tables, and the validity of RPINE as a clean benchmark and of the cross-dataset conclusions depends on a split-disjointness condition that is neither stated nor verified. These issues are load-bearing for the paper's central claims.

major comments (5)
  1. [Abstract and Sec. 6.3, Table 6] The abstract states that TMR 'outperforms the state-of-the-art methods on the three benchmarks,' but Table 6 does not support this on FSCD-147. On the one-shot setting, TMR's AP is 43.15 versus GeCo's 43.11, a negligible difference, and TMR's MAE is 11.63 versus GeCo's 8.10, substantially worse; in the three-shot setting, TMR's AP is 44.43 versus 43.42, but its MAE is 13.78 versus 7.91. The paper should either revise the abstract and the 'on par' language in Sec. 6.3 or provide a statistical argument that the AP difference is meaningful despite the much larger MAE deficit.
  2. [Sec. 5 and Sec. 6.4, Table 4] The RPINE dataset is constructed from images from FSC-147, FSCD-LVIS, Countbench, Wikiart, Frieze, and Wallpaper, but the paper never states whether the 3,925 RPINE training images are disjoint from the FSCD-147 and FSCD-LVIS evaluation images used in Table 4. Since FSCD-147 derives from FSC-147 and FSCD-LVIS is itself one of the listed RPINE sources, overlap is a concrete possibility. If RPINE training images include FSCD-147 or FSCD-LVIS test images, the claimed cross-dataset generalization results (e.g., RPINE-trained TMR reaching 41.39 AP on FSCD-147 versus GeCo's 36.99) could partly reflect training on the test distribution. The authors need to provide image-ID-level disjointness verification and a released split to rule this out.
  3. [Sec. 5 and Sec. 6.3, Tables 2 and 3] No error bars or repeated-run statistics are reported for any of the main comparisons. The claimed 'large margin' on RPINE in Table 2 and 'significantly outperforms' on FSCD-LVIS in Table 3 are based on single numbers; the paper should report means and variances over multiple training runs, or at least state the number of runs, so the reader can judge whether the differences are meaningful. This is especially important because the FSCD-147 result in Table 6 is already within noise of the baseline on AP.
  4. [Sec. 5, RPINE annotation protocol] The RPINE annotation protocol assigns three annotators per image and includes 'all the annotated patterns as ground truth,' but the paper does not report inter-annotator agreement, how conflicting or duplicate boxes across annotators are resolved, or the distribution of the number of patterns per image. Since RPINE is a new benchmark and is used to support the main claim of superiority on non-object patterns, the reliability of its ground truth should be documented; otherwise the benchmark's validity is unverified.
  5. [Sec. 6.2 and Sec. 8.3] The model uses several hyperparameters that are apparently chosen on the evaluation benchmarks: the presence threshold tau is set to 0.4 for RPINE and 0.3 for FSCD-147, the margin m is fixed at 0.1, and the feature-map upscaling factor is selected by validation performance. The paper should clarify whether these values were tuned on the test sets or on held-out validation data, and report sensitivity to them. Since the FSCD-147 comparison in Table 6 is close, threshold tuning on the test set would be a fairness concern.
minor comments (5)
  1. [Sec. 1, Introduction] There is a garbled or corrupted passage: 'TMR consists only of a few�� � and linear projections' should be a readable description of the module count, such as 'a few convolutional and linear projection layers.'
  2. [Table 1] The table header uses 'pattern-tiling' and 'repetition' categories that are defined only in the caption; the definitions are helpful but would be clearer if the terms were also defined in the main text before the table.
  3. [Table 6] The row label 'C-DETR�' appears with a stray symbol; the symbol's meaning (presumably the model includes the SAM decoder) is not explained for this row.
  4. [Sec. 6.2, Implementation details] The sentence about the backbone mentions the 'SAM-ViT/H of the patch size ��' with corrupted characters; the patch size and feature dimensions should be given as explicit numbers.
  5. [Sec. 6.3, RPINE results] Table 2 reports TMR with and without the SAM decoder, and the version without SD has lower AP but better MAE; the discussion of which configuration is the primary one for the RPINE claim would benefit from a direct statement of the recommended configuration and its ablation status.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: TMR's derivation and evaluation are self-contained against external benchmarks, and no prediction reduces to a fitted input or self-citation chain.

full rationale

The paper's central derivation is a template-matching and regression architecture whose components—RoIAlign-based template extraction, channel-wise correlation, support-conditioned box regression, and BCE/gIoU losses—are defined independently of the reported benchmark numbers. The method is evaluated on FSCD-147, FSCD-LVIS, and the newly introduced RPINE dataset, with comparisons to independently published FSCD methods using their official code. No load-bearing claim is justified by citing only the authors' own prior work, and no 'uniqueness theorem' or ansatz is imported via self-citation. The ablation studies compare variants (e.g., prototype matching vs. template matching, direct vs. support-conditioned regression) in a way that is empirically meaningful rather than tautological. The only notable concern is external validity: RPINE is assembled partly from FSC-147 and FSCD-LVIS source images, and the paper does not state whether RPINE training images overlap FSCD-147 or FSCD-LVIS evaluation splits used in Table 4. That is a potential data-leakage risk affecting the generalization claim, but it is not a circularity of the derivation chain: the method's outputs are not constructed from the benchmark labels, and the cross-dataset comparison is a stated assumption about split disjointness rather than an equation-level reduction. Under the review rules, such an unverified external condition does not by itself constitute circular reasoning, so the appropriate finding is no significant circularity.

Assumptions & free parameters 3 free parameters · 4 assumptions · 1 invented entities

TMR is an empirical architecture; it introduces no new physical entities. The key burden is that the authors both propose the method and construct the RPINE benchmark used to demonstrate its superiority, and several hyperparameters are tuned on validation sets.

free parameters (3)
  • Extended center margin m = 0.05
    Hyperparameter defining the positive presence region around each ground-truth box center; fixed in all experiments (Sec. 8.3).
  • Presence threshold tau = 0.4 on RPINE, 0.3 on FSCD-147
    Inference threshold for low-confidence box removal before NMS; chosen per dataset (Sec. 6.2).
  • Feature map upscaling = 32x32 to 128x128 bilinear interpolation
    Upsampling factor chosen so small instances are detectable on the frozen ViT feature map (Sec. 6.2, Tab. 11).
assumptions (4)
  • domain assumption Frozen SAM-ViT/H features are a sufficient representation space for matching arbitrary patterns.
    Invoked in Sec. 4 and 6.2; TMR's entire matching and regression operate on this frozen backbone.
  • domain assumption RoIAlign with the template box rounded up to a grid-aligned region preserves spatial alignment and translation correspondence.
    Sec. 8.2; if rounding breaks alignment, Eq. 1's channel-wise correlation would not localize correctly.
  • domain assumption The RPINE crowd-sourced annotations following the stated criteria form a valid ground truth for pattern detection, with no leakage from its source images into the FSCD evaluation splits.
    Sec. 5; the benchmark results and cross-dataset generalization claim depend on this validity and disjointness.
  • domain assumption Presence supervision via a rhombus of margin 0.05 around each ground-truth center is an adequate training signal.
    Sec. 8.3; this defines the positive set for the BCE loss and affects localization behavior.
invented entities (1)
  • RPINE dataset
    purpose: A new benchmark of repeated patterns, including non-object patterns, used to evaluate TMR and to claim cross-dataset generalization.
    The dataset is introduced in Sec. 5 but no release link or download URL is given, so an external group cannot independently verify the annotations or splits.

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Cite this review

Pith. "Pith review of Few-Shot Pattern Detection via Template Matching and Regression." pith.science (2026). https://pith.science/paper/GJVAUMYO

@misc{pith2026250817636,
  author       = {Pith},
  title        = {Pith review of: Few-Shot Pattern Detection via Template Matching and Regression},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/GJVAUMYO}},
  note         = {Machine review of arXiv:2508.17636}
}
read the original abstract

We address the problem of few-shot pattern detection, which aims to detect all instances of a given pattern, typically represented by a few exemplars, from an input image. Although similar problems have been studied in few-shot object counting and detection (FSCD), previous methods and their benchmarks have narrowed patterns of interest to object categories and often fail to localize non-object patterns. In this work, we propose a simple yet effective detector based on template matching and regression, dubbed TMR. While previous FSCD methods typically represent target exemplars as spatially collapsed prototypes and lose structural information, we revisit classic template matching and regression. It effectively preserves and leverages the spatial layout of exemplars through a minimalistic structure with a small number of learnable convolutional or projection layers on top of a frozen backbone We also introduce a new dataset, dubbed RPINE, which covers a wider range of patterns than existing object-centric datasets. Our method outperforms the state-of-the-art methods on the three benchmarks, RPINE, FSCD-147, and FSCD-LVIS, and demonstrates strong generalization in cross-dataset evaluation.

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Reference graph

Works this paper leans on

74 extracted references · 68 canonical work pages

  1. [1]

    The first annotated set of scanning electron microscopy images for nanoscience

    Rossella Aversa, Mohammad Hadi Modarres, Stefano Cozzini, Regina Ciancio, and Alberto Chiusole. The first annotated set of scanning electron microscopy images for nanoscience. Scientific data, 5(1):1–10, 2018. 8

    work page 2018

  2. [2]

    Translation symmetry de- tection: A repetitive pattern analysis approach

    Yunliang Cai and George Baciu. Translation symmetry de- tection: A repetitive pattern analysis approach. In IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), pages 223–228, 2013. 3

    work page 2013

  3. [3]

    A flexible and robust approach for segmenting cell nu- clei from 2d microscopy images using supervised learning and template matching

    Cheng Chen, Wei Wang, John A Ozolek, and Gustavo K Ro- hde. A flexible and robust approach for segmenting cell nu- clei from 2d microscopy images using supervised learning and template matching. Cytometry Part A, 83(5):495–507,

    work page

  4. [4]

    Steerable cnns

    Taco S Cohen and Max Welling. Steerable cnns. arXiv preprint arXiv:1612.08498, 2016. 4

    arXiv 2016

  5. [5]

    An image is worth 16x16 words: Trans- formers for image recognition at scale

    Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Unterthiner, Mostafa Dehghani, Matthias Minderer, Georg Heigold, Syl- vain Gelly, et al. An image is worth 16x16 words: Trans- formers for image recognition at scale. arXiv preprint arXiv:2010.11929, 2020. 5

    arXiv 2010

  6. [6]

    The pascal visual object classes (voc) challenge

    Mark Everingham, Luc Van Gool, Christopher KI Williams, John Winn, and Andrew Zisserman. The pascal visual object classes (voc) challenge. International Journal of Computer Vision (IJCV), 88:303–338, 2010. 1, 5

    work page 2010

  7. [7]

    Few- shot object detection with attention-rpn and multi-relation detector

    Qi Fan, Wei Zhuo, Chi-Keung Tang, and Yu-Wing Tai. Few- shot object detection with attention-rpn and multi-relation detector. In Proc. IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages 4013–4022, 2020. 1, 2, 6, 7

    work page 2020

  8. [8]

    Gen- eralized few-shot object detection without forgetting

    Zhibo Fan, Yuchen Ma, Zeming Li, and Jian Sun. Gen- eralized few-shot object detection without forgetting. In Proc. IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages 4527–4536, 2021. 1, 2

    work page 2021

Show all 74 references
  1. [9]

    2017 iccv challenge: Detecting symmetry in the wild

    Christopher Funk, Seungkyu Lee, Martin R Oswald, Stavros Tsogkas, Wei Shen, Andrea Cohen, Sven Dickinson, and Yanxi Liu. 2017 iccv challenge: Detecting symmetry in the wild. In International Conference on Computer Vision Workshops (ICCVW), pages 1692–1701, 2017. 5

    2017

  2. [10]

    The chemistry and applications of metal- organic frameworks

    Hiroyasu Furukawa, Kyle E Cordova, Michael O’Keeffe, and Omar M Yaghi. The chemistry and applications of metal- organic frameworks. Science, 341(6149):1230444, 2013. 4

    2013

  3. [11]

    Unsuper- vised learning of high-order structural semantics from im- ages

    Jizhou Gao, Yin Hu, Jinze Liu, and Ruigang Yang. Unsuper- vised learning of high-order structural semantics from im- ages. In Proc. IEEE International Conference on Computer Vision (ICCV), pages 2122–2129. IEEE, 2009. 3

    2009

  4. [12]

    Fast r-cnn

    Ross Girshick. Fast r-cnn. In Proc. IEEE International Conference on Computer Vision (ICCV), pages 1440–1448,

  5. [13]

    Flow cytometry: an introduction

    Alice L Givan. Flow cytometry: an introduction. Flow cytometry protocols, pages 1–29, 2011. 4

    2011

  6. [14]

    Courier Dover Publications, 1987

    Branko Gr ¨unbaum and Geoffrey Colin Shephard.Tilings and patterns. Courier Dover Publications, 1987. 3

    1987

  7. [15]

    Niff: Alleviating forgetting in generalized few-shot object detection via neu- ral instance feature forging

    Karim Guirguis, Johannes Meier, George Eskandar, Matthias Kayser, Bin Yang, and J ¨urgen Beyerer. Niff: Alleviating forgetting in generalized few-shot object detection via neu- ral instance feature forging. In Proc. IEEE Conference on Computer Vision and Pattern Recognition (C...

    2023

  8. [16]

    Lvis: A dataset for large vocabulary instance segmentation

    Agrim Gupta, Piotr Dollar, and Ross Girshick. Lvis: A dataset for large vocabulary instance segmentation. In Proc. IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages 5356–5364, 2019. 1

    2019

  9. [17]

    Query adaptive few-shot object detec- tion with heterogeneous graph convolutional networks

    Guangxing Han, Yicheng He, Shiyuan Huang, Jiawei Ma, and Shih-Fu Chang. Query adaptive few-shot object detec- tion with heterogeneous graph convolutional networks. In Proc. IEEE International Conference on Computer Vision (ICCV), pages 3263–3272, 2021. 2

    2021

  10. [18]

    Meta faster r-cnn: Towards accurate few-shot object detection with attentive feature alignment

    Guangxing Han, Shiyuan Huang, Jiawei Ma, Yicheng He, and Shih-Fu Chang. Meta faster r-cnn: Towards accurate few-shot object detection with attentive feature alignment. In Proc. AAAI Conference on Artificial Intelligence (AAAI), pages 780–789, 2022. 1

    2022

  11. [19]

    Few-shot object detection with fully cross-transformer

    Guangxing Han, Jiawei Ma, Shiyuan Huang, Long Chen, and Shih-Fu Chang. Few-shot object detection with fully cross-transformer. In Proc. IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages 5321–5330,

  12. [20]

    Reg- ular texture analysis as statistical model selection

    Junwei Han, Stephen J McKenna, and Ruixuan Wang. Reg- ular texture analysis as statistical model selection. In Proc. European Conference on Computer Vision (ECCV), pages 242–255. Springer, 2008. 3

    2008

  13. [21]

    Discovering texture regularity as a higher-order cor- respondence problem

    James Hays, Marius Leordeanu, Alexei A Efros, and Yanxi Liu. Discovering texture regularity as a higher-order cor- respondence problem. In Proc. European Conference on Computer Vision (ECCV), pages 522–535. Springer, 2006. 3

    2006

  14. [22]

    Mask r-cnn

    Kaiming He, Georgia Gkioxari, Piotr Doll ´ar, and Ross Gir- shick. Mask r-cnn. In Proc. IEEE International Conference on Computer Vision (ICCV), pages 2961–2969, 2017. 1, 3

    2017

  15. [23]

    Point segment and count: A gen- eralized framework for object counting

    Zhizhong Huang, Mingliang Dai, Yi Zhang, Junping Zhang, and Hongming Shan. Point segment and count: A gen- eralized framework for object counting. In Proc. IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2024. 1, 3, 4, 6, 7, 8

    2024

  16. [24]

    Few-shot object detection via feature reweighting

    Bingyi Kang, Zhuang Liu, Xin Wang, Fisher Yu, Jiashi Feng, and Trevor Darrell. Few-shot object detection via feature reweighting. In Proc. IEEE International Conference on Computer Vision (ICCV), pages 8420–8429, 2019. 2

    2019

  17. [25]

    Berg, Wan-Yen Lo, Piotr Dol- lar, and Ross Girshick

    Alexander Kirillov, Eric Mintun, Nikhila Ravi, Hanzi Mao, Chloe Rolland, Laura Gustafson, Tete Xiao, Spencer Whitehead, Alexander C. Berg, Wan-Yen Lo, Piotr Dol- lar, and Ross Girshick. Segment anything. In Proc. IEEE International Conference on Computer Vision (ICCV), 2023. 3...

    2023

  18. [26]

    Fast-match: Fast affine template matching

    Simon Korman, Daniel Reichman, Gilad Tsur, and Shai Avidan. Fast-match: Fast affine template matching. In Proc. IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages 2331–2338, 2013. 3, 4

    2013

  19. [27]

    Diamidocarbene-derived palladium and nickel–sulfur clusters

    Minji Lee, Hyunju Noh, and Youngsuk Kim. Diamidocarbene-derived palladium and nickel–sulfur clusters. Chemical Communications, 60(94):13867–13870,

  20. [28]

    Detecting, localizing and grouping repeated scene elements from an image

    Thomas Leung and Jitendra Malik. Detecting, localizing and grouping repeated scene elements from an image. In Proc. European Conference on Computer Vision (ECCV). Springer, 1996. 3

    1996

  21. [29]

    Exploring plain vision transformer backbones for object de- tection, 2022

    Yanghao Li, Hanzi Mao, Ross Girshick, and Kaiming He. Exploring plain vision transformer backbones for object de- tection, 2022. 3

    2022

  22. [30]

    Object count- ing: You only need to look at one

    Hui Lin, Xiaopeng Hong, and Yabin Wang. Object count- ing: You only need to look at one. arXiv preprint arXiv:2112.05993, 2021. 2

    2021 arXiv

  23. [31]

    Ex- tracting periodicity of a regular texture based on autocorre- lation functions

    Hsin-Chih Lin, Ling-Ling Wang, and Shi-Nine Yang. Ex- tracting periodicity of a regular texture based on autocorre- lation functions. Pattern recognition letters, 18(5):433–443,

  24. [32]

    Microsoft coco: Common 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: Common objects in context. In Proc. European Conference on Computer Vision (ECCV), pages 740–755. Springer, 2014. 1, 5

    2014

  25. [33]

    Feature pyramid networks for object detection

    Tsung-Yi Lin, Piotr Doll ´ar, Ross Girshick, Kaiming He, Bharath Hariharan, and Serge Belongie. Feature pyramid networks for object detection. In Proc. IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages 2117–2125, 2017. 1, 3

    2017

  26. [34]

    Tracking dynamic near- regular texture under occlusion and rapid movements

    Wen-Chieh Lin and Yanxi Liu. Tracking dynamic near- regular texture under occlusion and rapid movements. In Proc. European Conference on Computer Vision (ECCV), pages 44–55. Springer, 2006. 3

    2006

  27. [35]

    Grasp recurring patterns from a single view

    Jingchen Liu and Yanxi Liu. Grasp recurring patterns from a single view. In Proc. IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages 2003–2010, 2013. 3

    2003

  28. [36]

    Symmetry detection from realworld images compe- tition 2013: Summary and results

    Jingchen Liu, George Slota, Gang Zheng, Zhaohui Wu, Min- woo Park, Seungkyu Lee, Ingmar Rauschert, and Yanxi Liu. Symmetry detection from realworld images compe- tition 2013: Summary and results. In IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), ...

    2013

  29. [37]

    Patchmatch-based auto- matic lattice detection for near-regular textures

    Siying Liu, Tian-Tsong Ng, Kalyan Sunkavalli, Minh N Do, Eli Shechtman, and Nathan Carr. Patchmatch-based auto- matic lattice detection for near-regular textures. In Proc. IEEE International Conference on Computer Vision (ICCV), pages 181–189, 2015. 3

    2015

  30. [38]

    Near-regular texture analysis and manipulation

    Yanxi Liu, Wen-Chieh Lin, and James Hays. Near-regular texture analysis and manipulation. ACM Transactions on Graphics (TOG), 23(3):368–376, 2004. 3, 5

    2004

  31. [39]

    Decoupled weight decay regularization, 2019

    Ilya Loshchilov and Frank Hutter. Decoupled weight decay regularization, 2019. 5

    2019

  32. [40]

    Class- agnostic counting

    Erika Lu, Weidi Xie, and Andrew Zisserman. Class- agnostic counting. In Asian Conference on Computer Vision (ACCV), pages 669–684. Springer, 2019. 2

    2019

  33. [41]

    Locating macromolecular assemblies in cells by 2d template matching with cistem

    Bronwyn A Lucas, Benjamin A Himes, Liang Xue, Timothy Grant, Julia Mahamid, and Nikolaus Grigorieff. Locating macromolecular assemblies in cells by 2d template matching with cistem. Elife, 10:e68946, 2021. 4

    2021

  34. [42]

    Can sam count anything? an empirical study on sam counting,

    Zhiheng Ma, Xiaopeng Hong, and Qinnan Shangguan. Can sam count anything? an empirical study on sam counting,

  35. [43]

    Adding new tasks to a single net- work with weight transformations using binary masks

    Massimiliano Mancini, Elisa Ricci, Barbara Caputo, and Samuel Rota Bulo. Adding new tasks to a single net- work with weight transformations using binary masks. In European Conference on Computer Vision Workshops (ECCVW), pages 0–0, 2018. 4

    2018

  36. [44]

    Using symmetry for detecting and locating objects in a picture

    Giovanni Marola. Using symmetry for detecting and locating objects in a picture. Computer Vision, Graphics, and Image Processing, 46(2):179–195, 1989. 3, 4

    1989

  37. [45]

    Robust wide-baseline stereo from maximally stable ex- tremal regions

    Jiri Matas, Ondrej Chum, Martin Urban, and Tom ´as Pa- jdla. Robust wide-baseline stereo from maximally stable ex- tremal regions. Image and vision computing, 22(10):761– 767, 2004

    2004

  38. [46]

    Example-based object detection in images by compo- nents

    Anuj Mohan, Constantine Papageorgiou, and Tomaso Pog- gio. Example-based object detection in images by compo- nents. IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 23(4):349–361, 2001. 3

    2001

  39. [47]

    Few-shot object counting and detection

    Thanh Nguyen, Chau Pham, Khoi Nguyen, and Minh Hoai. Few-shot object counting and detection. In Proc. European Conference on Computer Vision (ECCV). Springer, 2022. 1, 2, 3, 4, 5, 6, 7

    2022

  40. [48]

    Teaching clip to count to ten

    Roni Paiss, Ariel Ephrat, Omer Tov, Shiran Zada, Inbar Mosseri, Michal Irani, and Tali Dekel. Teaching clip to count to ten. In Proc. IEEE International Conference on Computer Vision (ICCV), pages 3170–3180, 2023. 4, 5

    2023

  41. [49]

    Deformed lattice detection in real-world images using mean-shift belief propagation

    Minwoo Park, Kyle Brocklehurst, Robert T Collins, and Yanxi Liu. Deformed lattice detection in real-world images using mean-shift belief propagation. IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 31(10): 1804–1816, 2009. 3

    2009

  42. [50]

    Discovering structural regularity in 3d geometry

    Mark Pauly, Niloy J Mitra, Johannes Wallner, Helmut Pottmann, and Leonidas J Guibas. Discovering structural regularity in 3d geometry. In ACM SIGGRAPH, pages 1– 11, 2008. 3

    2008

  43. [51]

    Dave- a detect-and-verify paradigm for low-shot counting

    Jer Pelhan, Vitjan Zavrtanik, Matej Kristan, et al. Dave- a detect-and-verify paradigm for low-shot counting. In Proc. IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages 23293–23302, 2024. 3, 6, 7

    2024

  44. [52]

    A novel unified architecture for low-shot counting by detection and segmentation

    Jer Pelhan, Alan Lukezic, Vitjan Zavrtanik, and Matej Kris- tan. A novel unified architecture for low-shot counting by detection and segmentation. Advances in Neural Information Processing Systems (NeurIPS), 2025. 1, 2, 3, 4, 5, 6, 7, 8

    2025

  45. [53]

    Flow cytometry: retrospective, fun- damentals and recent instrumentation

    Julien Picot, Coralie L Guerin, Caroline Le Van Kim, and Chantal M Boulanger. Flow cytometry: retrospective, fun- damentals and recent instrumentation. Cytotechnology, 64: 109–130, 2012. 4

    2012

  46. [54]

    E2cnn: An efficient concatenated cnn for classification of surface emg extracted from upper limb

    Muhammad Farrukh Qureshi, Zohaib Mushtaq, Muhammad Zia Ur Rehman, and Ernest Nlandu Kamavuako. E2cnn: An efficient concatenated cnn for classification of surface emg extracted from upper limb. IEEE Sensors Journal, 23(8): 8989–8996, 2023. 4

    2023

  47. [55]

    Vicinal counting networks

    Viresh Ranjan and Minh Hoai. Vicinal counting networks. In Proc. IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages 4221–4230, 2022. 2

    2022

  48. [56]

    Learning to count everything

    Viresh Ranjan, Udbhav Sharma, Thu Nguyen, and Minh Hoai. Learning to count everything. In Proc. IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages 3394–3403, 2021. 2, 1 10

    2021

  49. [57]

    You only look once: Unified, real-time object de- tection

    Joseph Redmon, Santosh Divvala, Ross Girshick, and Ali Farhadi. You only look once: Unified, real-time object de- tection. In Proc. IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages 779–788, 2016. 1

    2016

  50. [58]

    Generalized in- tersection over union: A metric and a loss for bounding box regression

    Hamid Rezatofighi, Nathan Tsoi, JunYoung Gwak, Amir Sadeghian, Ian Reid, and Silvio Savarese. Generalized in- tersection over union: A metric and a loss for bounding box regression. In Proc. IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages 658–666, 2019. 4

    2019

  51. [59]

    Large-scale classifica- tion of fine-art paintings: Learning the right metric on the right feature

    Babak Saleh and Ahmed Elgammal. Large-scale classifica- tion of fine-art paintings: Learning the right metric on the right feature. arXiv preprint arXiv:1505.00855, 2015. 4, 5

    2015 arXiv

  52. [60]

    Prototyp- ical networks for few-shot learning

    Jake Snell, Kevin Swersky, and Richard Zemel. Prototyp- ical networks for few-shot learning. Advances in Neural Information Processing Systems (NeurIPS), 30, 2017. 1

    2017

  53. [61]

    Exploiting repetitive object patterns for model compression and completion

    Luciano Spinello, Rudolph Triebel, Dizan Vasquez, Kai O Arras, and Roland Siegwart. Exploiting repetitive object patterns for model compression and completion. In Proc. European Conference on Computer Vision (ECCV), pages 296–309. Springer, 2010. 3

    2010

  54. [62]

    V .J. Stenger. Timeless Reality: Symetry, Simplicity, and Multiple Universes. Prometheus, 2009. 5

    2009

  55. [63]

    Fsce: Few-shot object detection via contrastive pro- posal encoding

    Bo Sun, Banghuai Li, Shengcai Cai, Ye Yuan, and Chi Zhang. Fsce: Few-shot object detection via contrastive pro- posal encoding. In Proc. IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages 7352–7362,

  56. [64]

    Fcos: Fully convolutional one-stage object detection

    Zhi Tian, Chunhua Shen, Hao Chen, and Tong He. Fcos: Fully convolutional one-stage object detection. In Proc. IEEE International Conference on Computer Vision (ICCV), pages 9627–9636, 2019. 1, 4

    2019

  57. [65]

    Rotation-invariant pattern matching using wavelet decomposition

    Du-Ming Tsai and Cheng-Huei Chiang. Rotation-invariant pattern matching using wavelet decomposition. Pattern Recognition Letters, 23(1-3):191–201, 2002. 3, 4

    2002

  58. [66]

    Ef- ficient grouping under perspective skew

    Andreas Turina, Tinne Tuytelaars, and Luc Van Gool. Ef- ficient grouping under perspective skew. In Proc. IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages I–I. IEEE, 2001. 3

    2001

  59. [67]

    Frustratingly simple few-shot object detec- tion

    Xin Wang, Thomas Huang, Joseph Gonzalez, Trevor Darrell, and Fisher Yu. Frustratingly simple few-shot object detec- tion. In Proc. International Conference on Machine Learning (ICML), pages 9919–9928. PMLR, 2020. 2

    2020

  60. [68]

    Few-shot object detection and viewpoint estimation for objects in the wild

    Yang Xiao, Vincent Lepetit, and Renaud Marlet. Few-shot object detection and viewpoint estimation for objects in the wild. TPAMI, 45(3):3090–3106, 2022. 1, 2, 6, 7

    2022

  61. [69]

    Meta r-cnn: Towards general solver for instance-level low-shot learning

    Xiaopeng Yan, Ziliang Chen, Anni Xu, Xiaoxi Wang, Xi- aodan Liang, and Liang Lin. Meta r-cnn: Towards general solver for instance-level low-shot learning. In Proc. IEEE International Conference on Computer Vision (ICCV), pages 9577–9586, 2019. 1, 2

    2019

  62. [70]

    Iterative correlation-based feature refinement for few-shot counting

    Zhiyuan You, Kai Yang, Wenhan Luo, Xin Lu, Lei Cui, and Xinyi Le. Iterative correlation-based feature refinement for few-shot counting. arXiv preprint arXiv:2201.08959, 4,

    arXiv

  63. [71]

    Detect everything with few examples

    Xinyu Zhang, Yuhan Liu, Yuting Wang, and Abdeslam Boularias. Detect everything with few examples. arXiv preprint arXiv:2309.12969, 2023. 2 11 Few-Shot Pattern Detection via Template Matching and Regression Supplementary Material In this supplementary material, we provide addit...

    2023 arXiv

  64. [72]

    Detailed model architecture We provide the details of the model architecture in Tab

    Additional details 8.1. Detailed model architecture We provide the details of the model architecture in Tab. 10. We design our model architecture to be as simple as possi- ble, and there are only 6 learnable layers in total. module structure # params. backbone projection linea...

  65. [73]

    Additional experimental details Qualitative analysis of prototype matching failures. Fig. 11 shows failure cases of the prototype matching 1 ��������������������������������������������������� Figure 11. Noticeable failure cases of prototype matching. Proto- types collapse the...

  66. [74]

    Although TMR effectively handles scale variations, achieving rotation invariance remains a challenging problem, as observed in prior approaches as well [23, 52]

    Future work Rotation invariance. Although TMR effectively handles scale variations, achieving rotation invariance remains a challenging problem, as observed in prior approaches as well [23, 52]. This limitation could be further mitigated by incorporating rotation-invariant dat...

Pith tools

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