Paper Citation Record · LEDGER

FastRef:Fast Prototype Refinement for Few-Shot Industrial Anomaly Detection

As of 19 August 2026, this Paper Citation Record lists 43 of 43 outbound references and 2 inbound Pith citation observations for arXiv:2506.21398.

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

pith.paper-citation-record.v1
2506.21398 v1

Coverage vector

measured 43 of 43 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-06T22:33:54.832028Z

measured 45 of 45 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-19T06:32:44.657259+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-15T17:22:14.130790Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-06-30T13:54:44.027524Z

Reference resolution

43 of 43 outbound references displayed

verified exact1
verified fuzzy28
unresolved14
parse uncertain0
malformed identifier0
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation d67f649c-1db5-4012-897e-fc9bce9b7a57 · outbound

This paper cites Mvtec ad–a com- prehensive real-world dataset for unsupervised anomaly detection.

FastRef:Fast Prototype Refinement for Few-Shot Industrial Anomaly Detection Mvtec ad–a com- prehensive real-world dataset for unsupervised anomaly detection

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:34:00.888359Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation d6ca8457-8e46-496c-8710-5a5b5783dba4 · outbound

This paper cites Convex optimization: Algorithms and complexity.Foundations and Trends®in Machine Learning, 8(3-4):231–357, 2015.

FastRef:Fast Prototype Refinement for Few-Shot Industrial Anomaly Detection Convex optimization: Algorithms and complexity.Foundations and Trends®in Machine Learning, 8(3-4):231–357, 2015

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:34:00.536788Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 72648e61-bacd-4d9c-bcbf-aa872511d91b · outbound

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

FastRef:Fast Prototype Refinement for Few-Shot Industrial Anomaly Detection Sub-Image Anomaly Detection with Deep Pyramid Correspondences

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-06T22:33:48.590112Z

Source-reported events for the cited work

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Observation 6d9997f1-d538-4933-87b4-e0b0a33619f1 · outbound

This paper cites Sinkhorn distances: Lightspeed computation of optimal transport.Ad- vances in neural information processing systems, 26, 2013.

FastRef:Fast Prototype Refinement for Few-Shot Industrial Anomaly Detection Sinkhorn distances: Lightspeed computation of optimal transport.Ad- vances in neural information processing systems, 26, 2013

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:34:00.319823Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 3dd45a67-fed9-41d6-9d74-248a0f616f55 · outbound

This paper cites Anomalydino: Boostingpatch-basedfew-shotanomalydetectionwithdinov2.

FastRef:Fast Prototype Refinement for Few-Shot Industrial Anomaly Detection Anomalydino: Boostingpatch-basedfew-shotanomalydetectionwithdinov2

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:34:00.037755Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation c967aa8f-deae-4c8d-8d77-6f57dc0e6303 · outbound

This paper cites Padim: a patch distribution modeling framework for anomaly detection and localization.

FastRef:Fast Prototype Refinement for Few-Shot Industrial Anomaly Detection Padim: a patch distribution modeling framework for anomaly detection and localization

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:33:59.716581Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation c12438d7-9726-41f4-8d4f-9c60a337732b · outbound

This paper cites Fas- trecon: Few-shot industrial anomaly detection via fast feature reconstruction.

FastRef:Fast Prototype Refinement for Few-Shot Industrial Anomaly Detection Fas- trecon: Few-shot industrial anomaly detection via fast feature reconstruction

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:33:59.386232Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation ccc131e4-5347-4b59-afed-94a3b7b2f78f · outbound

This paper cites Memorizingnormalitytodetectanomaly: Memory- augmented deep autoencoder for unsupervised anomaly detection.

FastRef:Fast Prototype Refinement for Few-Shot Industrial Anomaly Detection Memorizingnormalitytodetectanomaly: Memory- augmented deep autoencoder for unsupervised anomaly detection

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:33:59.199331Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 5d30ebb8-2c37-49a4-8fae-c21affe6caf1 · outbound

This paper cites Anomalygpt: Detecting industrial anomalies using large vision-language models.

FastRef:Fast Prototype Refinement for Few-Shot Industrial Anomaly Detection Anomalygpt: Detecting industrial anomalies using large vision-language models

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:33:58.983617Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation f88576ce-6631-4c0b-a354-906d77d79715 · outbound

This paper cites DiAD: A Diffusion-based Framework for Multi-class Anomaly Detection.

FastRef:Fast Prototype Refinement for Few-Shot Industrial Anomaly Detection DiAD: A Diffusion-based Framework for Multi-class Anomaly Detection

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-06T22:33:49.554945Z

Source-reported events for the cited work

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Observation 7cf2cf2a-79c1-4fe0-8fb5-945f09f18f4a · outbound

This paper cites Maskr-cnn.

FastRef:Fast Prototype Refinement for Few-Shot Industrial Anomaly Detection Maskr-cnn

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:33:58.794194Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 44b579db-3c09-42d8-95fc-e956b7b3780b · outbound

This paper cites Registration based few-shot anomaly detection.

FastRef:Fast Prototype Refinement for Few-Shot Industrial Anomaly Detection Registration based few-shot anomaly detection

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:33:58.600195Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 0d819646-5220-4fce-b682-3e517a0bd283 · outbound

This paper cites Winclip: Zero-/few-shot anomaly classification and segmentation.

FastRef:Fast Prototype Refinement for Few-Shot Industrial Anomaly Detection Winclip: Zero-/few-shot anomaly classification and segmentation

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:33:58.443613Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 00b03f05-e662-4ae7-8f2f-282781ecf599 · outbound

This paper cites Deep learning-based defect detection of metal parts: evaluating current methods in complex conditions.

FastRef:Fast Prototype Refinement for Few-Shot Industrial Anomaly Detection Deep learning-based defect detection of metal parts: evaluating current methods in complex conditions

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:33:58.277423Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 105dd8e7-dcf5-4548-9346-e672c064c156 · outbound

This paper cites Anomaly detection for predictive maintenance in industry 4.0-a survey.

FastRef:Fast Prototype Refinement for Few-Shot Industrial Anomaly Detection Anomaly detection for predictive maintenance in industry 4.0-a survey

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:33:58.066521Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 341b7865-db6c-4941-a9aa-217f50a2ebef · outbound

This paper cites Promptad: Learning prompts with only normal samples for few-shot anom- aly detection.

FastRef:Fast Prototype Refinement for Few-Shot Industrial Anomaly Detection Promptad: Learning prompts with only normal samples for few-shot anom- aly detection

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:33:57.909291Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 53243fd0-4c1a-4401-b024-15de82df4752 · outbound

This paper cites Deep industrial image anomaly detection: A survey.Machine Intelligence Research, 21(1):104–135, 2024.

FastRef:Fast Prototype Refinement for Few-Shot Industrial Anomaly Detection Deep industrial image anomaly detection: A survey.Machine Intelligence Research, 21(1):104–135, 2024

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-06T22:33:50.490783Z

Source-reported events for the cited work

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Observation e783961c-18a6-457a-8b4f-0690b5b3cb1e · outbound

This paper cites Ttt++: When does self-supervised test-time training fail or thrive? Advances in Neural Information Processing Systems, 34:21808–21820, 2021.

FastRef:Fast Prototype Refinement for Few-Shot Industrial Anomaly Detection Ttt++: When does self-supervised test-time training fail or thrive? Advances in Neural Information Processing Systems, 34:21808–21820, 2021

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:33:57.706076Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 2543ccec-f207-4ee6-8aa9-be14c0674f23 · outbound

This paper cites Swin transformer: Hierarchical vision transformer using shifted windows.

FastRef:Fast Prototype Refinement for Few-Shot Industrial Anomaly Detection Swin transformer: Hierarchical vision transformer using shifted windows

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-06T22:33:50.706988Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 128295d6-821c-474f-bf59-ce10b8bfbd66 · outbound

This paper cites Simplenet: A simple network for image anomaly detection and localization.

FastRef:Fast Prototype Refinement for Few-Shot Industrial Anomaly Detection Simplenet: A simple network for image anomaly detection and localization

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:33:57.550928Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 93eddd04-90e9-47e0-8485-6c3d68db2e8f · outbound

This paper cites Hierarchical Vector Quantized Transformer for Multi-class Unsupervised Anomaly Detection.

FastRef:Fast Prototype Refinement for Few-Shot Industrial Anomaly Detection Hierarchical Vector Quantized Transformer for Multi-class Unsupervised Anomaly Detection

Reference 21

Resolution: verified exact; local_arxiv, observed 2026-08-06T22:33:55.037510Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation c78c28cb-ad54-4978-9a50-69179c994165 · outbound

This paper cites Hierarchical vector quantized transformer for multi-class unsupervised anomaly de- tection.Advances in Neural Information Processing Systems, 36, 2024.

FastRef:Fast Prototype Refinement for Few-Shot Industrial Anomaly Detection Hierarchical vector quantized transformer for multi-class unsupervised anomaly de- tection.Advances in Neural Information Processing Systems, 36, 2024

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:33:57.393038Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 1fa4decd-322e-4c25-93cb-0d1aadbf1c88 · outbound

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

FastRef:Fast Prototype Refinement for Few-Shot Industrial Anomaly Detection DINOv2: Learning Robust Visual Features without Supervision

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-06T22:33:51.211770Z

Source-reported events for the cited work

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Observation 237c454a-f321-41f7-a99e-96e99c4b2320 · outbound

This paper cites The matrix cookbook.Technical University of Denmark, 7(15):510, 2008.

FastRef:Fast Prototype Refinement for Few-Shot Industrial Anomaly Detection The matrix cookbook.Technical University of Denmark, 7(15):510, 2008

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-06T22:33:51.350016Z

Source-reported events for the cited work

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Observation a1124c38-368b-4153-906d-a5f11f03543b · outbound

This paper cites Computational optimal transport: With applications to data science.Foundations and Trends®in Machine Learning, 11(5-6):355–607, 2019.

FastRef:Fast Prototype Refinement for Few-Shot Industrial Anomaly Detection Computational optimal transport: With applications to data science.Foundations and Trends®in Machine Learning, 11(5-6):355–607, 2019

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-06T22:33:51.470682Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2ce41bb9-1081-4309-96b4-8802d655cfa6 · outbound

This paper cites Learning transferable visual models from natural language supervision.

FastRef:Fast Prototype Refinement for Few-Shot Industrial Anomaly Detection Learning transferable visual models from natural language supervision

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-06T22:33:51.626009Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d02cc696-c7c0-45b3-9356-37424d811b1b · outbound

This paper cites Towards total recall in industrial anomaly detection.

FastRef:Fast Prototype Refinement for Few-Shot Industrial Anomaly Detection Towards total recall in industrial anomaly detection

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:33:57.203917Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation c4641163-c5fd-4d56-ad5d-1d70c5eda4df · outbound

This paper cites Optimizing PatchCore for Few/many-shot Anomaly Detection.

FastRef:Fast Prototype Refinement for Few-Shot Industrial Anomaly Detection Optimizing PatchCore for Few/many-shot Anomaly Detection

Reference 28

Resolution: unresolved; no resolver link, observed 2026-08-06T22:33:53.440953Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a5f2d385-1827-422f-8507-9e97ff67cffc · outbound

This paper cites Active Learning for Convolutional Neural Networks: A Core-Set Approach.

FastRef:Fast Prototype Refinement for Few-Shot Industrial Anomaly Detection Active Learning for Convolutional Neural Networks: A Core-Set Approach

Reference 29

Resolution: unresolved; no resolver link, observed 2026-08-06T22:33:53.528808Z

Source-reported events for the cited work

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Observation 19a02799-0cb7-4c8d-a20b-565b6c0c1d98 · outbound

This paper cites Prototypicalnetworksforfew-shotlearning.

FastRef:Fast Prototype Refinement for Few-Shot Industrial Anomaly Detection Prototypicalnetworksforfew-shotlearning

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:33:57.011466Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation cec02c39-fae0-4439-a754-41925e328828 · outbound

This paper cites Test- time training with self-supervision for generalization under distribution shifts.

FastRef:Fast Prototype Refinement for Few-Shot Industrial Anomaly Detection Test- time training with self-supervision for generalization under distribution shifts

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:33:56.782708Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 30a4150b-6010-4ef1-907d-0b399dc3087f · outbound

This paper cites Learning to compare: Relation network for few-shot learning.

FastRef:Fast Prototype Refinement for Few-Shot Industrial Anomaly Detection Learning to compare: Relation network for few-shot learning

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:33:56.565663Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 8c85f32d-879f-4912-ba72-b2d2f6879925 · outbound

This paper cites EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks.

FastRef:Fast Prototype Refinement for Few-Shot Industrial Anomaly Detection EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks

Reference 33

Resolution: unresolved; no resolver link, observed 2026-08-06T22:33:53.864432Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 28f9946a-3863-4f6b-8370-6e091186d457 · outbound

This paper cites Foct: Few-shot industrial anomaly detection with foreground- aware online conditional transport.

FastRef:Fast Prototype Refinement for Few-Shot Industrial Anomaly Detection Foct: Few-shot industrial anomaly detection with foreground- aware online conditional transport

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:33:56.398248Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 57a2ce7e-18ff-481c-a4de-b3dd556fa1c8 · outbound

This paper cites Real-iad: A real-world multi-view dataset for benchmarking versatile industrial anomaly detection.

FastRef:Fast Prototype Refinement for Few-Shot Industrial Anomaly Detection Real-iad: A real-world multi-view dataset for benchmarking versatile industrial anomaly detection

Reference 35

Resolution: unresolved; no resolver link, observed 2026-08-06T22:33:54.030153Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b6f2f54c-b99b-43df-8e87-df6679e5ed85 · outbound

This paper cites Generalizing from a few examples: A survey on few-shot learning.ACM computing surveys (csur), 53(3):1–34, 2020.

FastRef:Fast Prototype Refinement for Few-Shot Industrial Anomaly Detection Generalizing from a few examples: A survey on few-shot learning.ACM computing surveys (csur), 53(3):1–34, 2020

Reference 36

Resolution: unresolved; no resolver link, observed 2026-08-06T22:33:54.102061Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1a7fccbb-e35a-4730-9221-bab4707137d9 · outbound

This paper cites Learning unsuper- vised metaformer for anomaly detection.

FastRef:Fast Prototype Refinement for Few-Shot Industrial Anomaly Detection Learning unsuper- vised metaformer for anomaly detection

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:33:56.148319Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 355ca069-dcaf-4ada-b004-29436688bbc8 · outbound

This paper cites Anoddpm: Anomaly detection with denoising diffusion probabilistic models using simplex noise.

FastRef:Fast Prototype Refinement for Few-Shot Industrial Anomaly Detection Anoddpm: Anomaly detection with denoising diffusion probabilistic models using simplex noise

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:33:55.945578Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 12640d98-1366-4a3c-90f6-1307e2d67106 · outbound

This paper cites Pushing the limits of fewshot anomaly detection in industry vision: Graphcore.ICLR, 2023.

FastRef:Fast Prototype Refinement for Few-Shot Industrial Anomaly Detection Pushing the limits of fewshot anomaly detection in industry vision: Graphcore.ICLR, 2023

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:33:55.754778Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation be83b5ec-8888-4511-a33a-73bad5fd34b9 · outbound

This paper cites A uni- fied model for multi-class anomaly detection.Advances in Neural Information Processing Systems, 35:4571–4584, 2022.

FastRef:Fast Prototype Refinement for Few-Shot Industrial Anomaly Detection A uni- fied model for multi-class anomaly detection.Advances in Neural Information Processing Systems, 35:4571–4584, 2022

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:33:55.580485Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 1cbaffa6-a272-4df2-ac90-42f36ee0c78f · outbound

This paper cites Wide Residual Networks.

FastRef:Fast Prototype Refinement for Few-Shot Industrial Anomaly Detection Wide Residual Networks

Reference 41

Resolution: unresolved; no resolver link, observed 2026-08-06T22:33:54.606058Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 20b1bf85-9f3a-430e-b2ff-bfd3375f982c · outbound

This paper cites Omnial: A unified cnn framework for unsupervised anomaly localization.

FastRef:Fast Prototype Refinement for Few-Shot Industrial Anomaly Detection Omnial: A unified cnn framework for unsupervised anomaly localization

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:33:55.392795Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 718b8e2e-5363-40fb-8124-8fd3851f77f9 · outbound

This paper cites Spot- the-difference self-supervised pre-training for anomaly detection and segmentation.

FastRef:Fast Prototype Refinement for Few-Shot Industrial Anomaly Detection Spot- the-difference self-supervised pre-training for anomaly detection and segmentation

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:33:55.222538Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Pith citing papers

Observation 5cf9be92-0467-4f44-86e9-4f98551fc169 · inbound

Learning local and global prototypes with optimal transport for unsupervised anomaly detection and localization cites this paper.

Learning local and global prototypes with optimal transport for unsupervised anomaly detection and localization FastRef:Fast Prototype Refinement for Few-Shot Industrial Anomaly Detection

Reference 33

Resolution: unresolved; no resolver link, observed 2026-08-15T17:22:14.130790Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9d82e1a9-1097-4df2-9174-656b9f0320b8 · inbound

Dual Prototype-Conditioned Diffusion Model for Scalable Multi-Class Unsupervised Anomaly Detection in Large Category Spaces cites this paper.

Dual Prototype-Conditioned Diffusion Model for Scalable Multi-Class Unsupervised Anomaly Detection in Large Category Spaces FastRef:Fast Prototype Refinement for Few-Shot Industrial Anomaly Detection

Reference 41

Resolution: verified exact; arxiv_id, observed 2026-06-30T13:54:44.028873Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.