Paper Citation Record · LEDGER

Exploring Few-Shot Defect Segmentation in General Industrial Scenarios with Metric Learning and Vision Foundation Models

As of 10 August 2026, this Paper Citation Record lists 54 of 54 outbound references and 1 inbound Pith citation observation for arXiv:2502.01216.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2502.01216 v2

Coverage vector

measured 54 of 54 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-09T16:13:53.477591Z

measured 55 of 55 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-10T06:31:04.303077+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-05-13T20:20:35.694280Z

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: arxiv_reference, observed 2026-05-13T20:23:13.608567Z

Reference resolution

54 of 54 outbound references displayed

verified exact1
verified fuzzy15
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External citation measurements

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Outbound references

Observation 1c36b062-8cc2-4cc3-8244-a845a390ced3 · outbound

This paper cites an unresolved cited work.

Exploring Few-Shot Defect Segmentation in General Industrial Scenarios with Metric Learning and Vision Foundation Models Unresolved cited work

Reference 1

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Observation 1b99f55f-a31b-4869-814d-7c5d49de324a · outbound

This paper cites Chang, Y.

Exploring Few-Shot Defect Segmentation in General Industrial Scenarios with Metric Learning and Vision Foundation Models Chang, Y

Reference 2

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Observation 187c3604-8cb4-4bff-9f31-7066bcdc066d · outbound

This paper cites an unresolved cited work.

Exploring Few-Shot Defect Segmentation in General Industrial Scenarios with Metric Learning and Vision Foundation Models Unresolved cited work

Reference 3

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Observation 5445eb8e-1ac1-4112-8c52-7acf11297424 · outbound

This paper cites an unresolved cited work.

Exploring Few-Shot Defect Segmentation in General Industrial Scenarios with Metric Learning and Vision Foundation Models Unresolved cited work

Reference 4

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Exploring Few-Shot Defect Segmentation in General Industrial Scenarios with Metric Learning and Vision Foundation Models Unresolved cited work

Reference 5

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Observation a3847583-8e5c-411c-b987-470d98d71af8 · outbound

This paper cites an unresolved cited work.

Exploring Few-Shot Defect Segmentation in General Industrial Scenarios with Metric Learning and Vision Foundation Models Unresolved cited work

Reference 6

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Observation 4f4c6c21-ae92-49fc-bbb6-8a16085f3f65 · outbound

This paper cites Personalize Segment Anything Model with One Shot.

Exploring Few-Shot Defect Segmentation in General Industrial Scenarios with Metric Learning and Vision Foundation Models Personalize Segment Anything Model with One Shot

Reference 7

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Observation 8df3bab2-7cab-45a8-95f8-629e54f2d026 · outbound

This paper cites Matcher: Segment Anything with One Shot Using All-Purpose Feature Matching.

Exploring Few-Shot Defect Segmentation in General Industrial Scenarios with Metric Learning and Vision Foundation Models Matcher: Segment Anything with One Shot Using All-Purpose Feature Matching

Reference 8

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Observation d1ecdf7f-2ad7-4810-b90c-23e9aade7001 · outbound

This paper cites an unresolved cited work.

Exploring Few-Shot Defect Segmentation in General Industrial Scenarios with Metric Learning and Vision Foundation Models Unresolved cited work

Reference 9

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Observation 7226ca95-889c-4b9d-ae60-9eebf82ad81c · outbound

This paper cites Hospedales, A.

Exploring Few-Shot Defect Segmentation in General Industrial Scenarios with Metric Learning and Vision Foundation Models Hospedales, A

Reference 10

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Observation 5dde704f-0358-45af-9da0-7ca67b6f435e · outbound

This paper cites an unresolved cited work.

Exploring Few-Shot Defect Segmentation in General Industrial Scenarios with Metric Learning and Vision Foundation Models Unresolved cited work

Reference 11

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Observation 9dcd8786-5298-41eb-a1c4-8826ab0f261e · outbound

This paper cites One-Shot Learning for Semantic Segmentation.

Exploring Few-Shot Defect Segmentation in General Industrial Scenarios with Metric Learning and Vision Foundation Models One-Shot Learning for Semantic Segmentation

Reference 12

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Exploring Few-Shot Defect Segmentation in General Industrial Scenarios with Metric Learning and Vision Foundation Models Unresolved cited work

Reference 13

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Observation 374a7f37-c2ea-4864-8448-db0df7d3c7a4 · outbound

This paper cites an unresolved cited work.

Exploring Few-Shot Defect Segmentation in General Industrial Scenarios with Metric Learning and Vision Foundation Models Unresolved cited work

Reference 14

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Observation 7686958c-a08e-40ec-965b-8259815a8e47 · outbound

This paper cites an unresolved cited work.

Exploring Few-Shot Defect Segmentation in General Industrial Scenarios with Metric Learning and Vision Foundation Models Unresolved cited work

Reference 15

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Observation 332c5ccb-0fb6-47bd-8add-e6b94b583f51 · outbound

This paper cites Medical SAM 2: Segment medical images as video via Segment Anything Model 2.

Exploring Few-Shot Defect Segmentation in General Industrial Scenarios with Metric Learning and Vision Foundation Models Medical SAM 2: Segment medical images as video via Segment Anything Model 2

Reference 16

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Observation 6fd750ff-4972-41d0-a4c8-01fd648d3b5d · outbound

This paper cites Retrieval-augmented Few-shot Medical Image Segmentation with Foundation Models.

Exploring Few-Shot Defect Segmentation in General Industrial Scenarios with Metric Learning and Vision Foundation Models Retrieval-augmented Few-shot Medical Image Segmentation with Foundation Models

Reference 17

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Source-reported events for the cited work

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Observation 6a0871a8-7091-4d27-a9f2-de32ee68e9f0 · outbound

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Exploring Few-Shot Defect Segmentation in General Industrial Scenarios with Metric Learning and Vision Foundation Models RevSAM2: Prompt SAM2 for Medical Image Segmentation via Reverse-Propagation without Fine-tuning

Reference 18

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Observation 68635d10-6826-4a4a-ba3d-3916cdf7e911 · outbound

This paper cites Bergmann, M.

Exploring Few-Shot Defect Segmentation in General Industrial Scenarios with Metric Learning and Vision Foundation Models Bergmann, M

Reference 19

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Observation 366a427e-8596-4f79-ba57-abdae1625a1d · outbound

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Exploring Few-Shot Defect Segmentation in General Industrial Scenarios with Metric Learning and Vision Foundation Models Unresolved cited work

Reference 20

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Observation e55c9f41-9309-433e-acd1-72eda70ee853 · outbound

This paper cites Jeong, Y.

Exploring Few-Shot Defect Segmentation in General Industrial Scenarios with Metric Learning and Vision Foundation Models Jeong, Y

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T16:13:54.253432Z

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Observation 7de44c2f-1496-49b9-81d5-28830f20a88f · outbound

This paper cites Towards Generic Anomaly Detection and Understanding: Large-scale Visual-linguistic Model (GPT-4V) Takes the Lead.

Exploring Few-Shot Defect Segmentation in General Industrial Scenarios with Metric Learning and Vision Foundation Models Towards Generic Anomaly Detection and Understanding: Large-scale Visual-linguistic Model (GPT-4V) Takes the Lead

Reference 22

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Source-reported events for the cited work

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This paper cites Kirillov, E.

Exploring Few-Shot Defect Segmentation in General Industrial Scenarios with Metric Learning and Vision Foundation Models Kirillov, E

Reference 23

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Observation 8c33a7f3-bfba-451d-9124-d100084f6b55 · outbound

This paper cites Fast Segment Anything.

Exploring Few-Shot Defect Segmentation in General Industrial Scenarios with Metric Learning and Vision Foundation Models Fast Segment Anything

Reference 24

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Exploring Few-Shot Defect Segmentation in General Industrial Scenarios with Metric Learning and Vision Foundation Models SAM 2: Segment Anything in Images and Videos

Reference 25

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Exploring Few-Shot Defect Segmentation in General Industrial Scenarios with Metric Learning and Vision Foundation Models Unresolved cited work

Reference 26

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Observation 924d81f4-6eb6-4dc3-bc11-3eb7b1e60902 · outbound

This paper cites The MVTec 3D-AD Dataset for Unsupervised 3D Anomaly Detection and Localization.

Exploring Few-Shot Defect Segmentation in General Industrial Scenarios with Metric Learning and Vision Foundation Models The MVTec 3D-AD Dataset for Unsupervised 3D Anomaly Detection and Localization

Reference 27

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Exploring Few-Shot Defect Segmentation in General Industrial Scenarios with Metric Learning and Vision Foundation Models Unresolved cited work

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Exploring Few-Shot Defect Segmentation in General Industrial Scenarios with Metric Learning and Vision Foundation Models Unresolved cited work

Reference 29

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Exploring Few-Shot Defect Segmentation in General Industrial Scenarios with Metric Learning and Vision Foundation Models Unresolved cited work

Reference 30

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This paper cites ClipSAM: CLIP and SAM Collaboration for Zero-Shot Anomaly Segmentation.

Exploring Few-Shot Defect Segmentation in General Industrial Scenarios with Metric Learning and Vision Foundation Models ClipSAM: CLIP and SAM Collaboration for Zero-Shot Anomaly Segmentation

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Exploring Few-Shot Defect Segmentation in General Industrial Scenarios with Metric Learning and Vision Foundation Models Revisiting Fine-tuning for Few-shot Learning

Reference 32

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Exploring Few-Shot Defect Segmentation in General Industrial Scenarios with Metric Learning and Vision Foundation Models Unresolved cited work

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Exploring Few-Shot Defect Segmentation in General Industrial Scenarios with Metric Learning and Vision Foundation Models Unresolved cited work

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Exploring Few-Shot Defect Segmentation in General Industrial Scenarios with Metric Learning and Vision Foundation Models Unresolved cited work

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Exploring Few-Shot Defect Segmentation in General Industrial Scenarios with Metric Learning and Vision Foundation Models Unresolved cited work

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Exploring Few-Shot Defect Segmentation in General Industrial Scenarios with Metric Learning and Vision Foundation Models Unresolved cited work

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Exploring Few-Shot Defect Segmentation in General Industrial Scenarios with Metric Learning and Vision Foundation Models Candemir, S

Reference 38

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Exploring Few-Shot Defect Segmentation in General Industrial Scenarios with Metric Learning and Vision Foundation Models Jaeger, A

Reference 39

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Exploring Few-Shot Defect Segmentation in General Industrial Scenarios with Metric Learning and Vision Foundation Models Demir, K

Reference 40

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Exploring Few-Shot Defect Segmentation in General Industrial Scenarios with Metric Learning and Vision Foundation Models Unresolved cited work

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Exploring Few-Shot Defect Segmentation in General Industrial Scenarios with Metric Learning and Vision Foundation Models Unresolved cited work

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Exploring Few-Shot Defect Segmentation in General Industrial Scenarios with Metric Learning and Vision Foundation Models An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale

Reference 43

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Exploring Few-Shot Defect Segmentation in General Industrial Scenarios with Metric Learning and Vision Foundation Models Caron, H

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T16:13:54.001159Z

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Observation 6c8244a3-5f8f-46df-93db-e82513a793d0 · outbound

This paper cites DINOv2: Learning Robust Visual Features without Supervision.

Exploring Few-Shot Defect Segmentation in General Industrial Scenarios with Metric Learning and Vision Foundation Models DINOv2: Learning Robust Visual Features without Supervision

Reference 45

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Observation cd5b9511-4d43-43d4-b014-fd40cd5c4aa9 · outbound

This paper cites Faster Segment Anything: Towards Lightweight SAM for Mobile Applications.

Exploring Few-Shot Defect Segmentation in General Industrial Scenarios with Metric Learning and Vision Foundation Models Faster Segment Anything: Towards Lightweight SAM for Mobile Applications

Reference 46

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This paper cites Xiong, B.

Exploring Few-Shot Defect Segmentation in General Industrial Scenarios with Metric Learning and Vision Foundation Models Xiong, B

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T16:13:53.982464Z

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Observation a183470b-ff48-4c84-923c-dbc08d1f3442 · outbound

This paper cites SAM-Lightening: A Lightweight Segment Anything Model with Dilated Flash Attention to Achieve 30 times Acceleration.

Exploring Few-Shot Defect Segmentation in General Industrial Scenarios with Metric Learning and Vision Foundation Models SAM-Lightening: A Lightweight Segment Anything Model with Dilated Flash Attention to Achieve 30 times Acceleration

Reference 48

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Source-reported events for the cited work

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Observation 2ef9c9b9-ac46-4416-aacc-da58f08ad78f · outbound

This paper cites Tabernik, S.

Exploring Few-Shot Defect Segmentation in General Industrial Scenarios with Metric Learning and Vision Foundation Models Tabernik, S

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T16:13:53.957309Z

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Observation 3e411025-d440-4c2c-80b0-107e7ea259e8 · outbound

This paper cites Wieler, T.

Exploring Few-Shot Defect Segmentation in General Industrial Scenarios with Metric Learning and Vision Foundation Models Wieler, T

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T16:13:53.937814Z

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Observation 5ba11012-ca40-4c41-bac9-a6d845ce55ad · outbound

This paper cites Zhang, R.

Exploring Few-Shot Defect Segmentation in General Industrial Scenarios with Metric Learning and Vision Foundation Models Zhang, R

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T16:13:53.918438Z

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Observation af7e6b4e-f373-4829-bb2e-af7602d558dc · outbound

This paper cites Jocher, A.

Exploring Few-Shot Defect Segmentation in General Industrial Scenarios with Metric Learning and Vision Foundation Models Jocher, A

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T16:13:53.900406Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 5a78c577-e435-4f4b-91af-fd1c10e61e07 · outbound

This paper cites Batzner, L.

Exploring Few-Shot Defect Segmentation in General Industrial Scenarios with Metric Learning and Vision Foundation Models Batzner, L

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T16:13:53.883410Z

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Observation ec9d770a-ac0e-4392-93da-904c63e7d7a4 · outbound

This paper cites Sub-Image Anomaly Detection with Deep Pyramid Correspondences.

Exploring Few-Shot Defect Segmentation in General Industrial Scenarios with Metric Learning and Vision Foundation Models Sub-Image Anomaly Detection with Deep Pyramid Correspondences

Reference 54

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Pith citing papers

Observation 7a2a6d72-c070-4fc8-8c47-40fdf1c38a10 · inbound

UniSpector: Towards Universal Open-set Defect Recognition via Spectral-Contrastive Visual Prompting cites this paper.

UniSpector: Towards Universal Open-set Defect Recognition via Spectral-Contrastive Visual Prompting Exploring Few-Shot Defect Segmentation in General Industrial Scenarios with Metric Learning and Vision Foundation Models

Reference 21

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