Paper Citation Record · LEDGER

Evaluating Vision Transformer Models for Visual Quality Control in Industrial Manufacturing

As of 13 August 2026, this Paper Citation Record lists 34 of 34 outbound references and 0 inbound Pith citation observations for arXiv:2411.14953.

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

pith.paper-citation-record.v1
2411.14953 v1

Coverage vector

measured 34 of 34 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-12T14:44:17.204725Z

measured 34 of 34 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-12T06:34:41.77262+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

34 of 34 outbound references displayed

verified exact7
verified fuzzy3
unresolved22
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External citation measurements

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Outbound references

Observation a6d2f611-4b28-49c6-970d-aba502e0effb · outbound

This paper cites an unresolved cited work.

Evaluating Vision Transformer Models for Visual Quality Control in Industrial Manufacturing Unresolved cited work

Reference 1

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Observation df5cc8c6-844a-4d05-b861-86b85ff6681e · outbound

This paper cites PNI : Industrial Anomaly Detection using Position and Neighborhood Information.

Evaluating Vision Transformer Models for Visual Quality Control in Industrial Manufacturing PNI : Industrial Anomaly Detection using Position and Neighborhood Information

Reference 2

Resolution: verified exact; local_arxiv, observed 2026-08-12T14:44:17.911017Z

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Observation f53b7911-80d0-4a42-9c3f-8cf96ebdbfc0 · outbound

This paper cites International Journal of Computer Vision129(4), 1038–1059 (2021).

Evaluating Vision Transformer Models for Visual Quality Control in Industrial Manufacturing International Journal of Computer Vision129(4), 1038–1059 (2021)

Reference 3

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Observation 5bc7be99-f47f-4b8d-afcc-0dc02a9cc087 · outbound

This paper cites In: 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

Evaluating Vision Transformer Models for Visual Quality Control in Industrial Manufacturing In: 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

Reference 4

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Observation bbb4dc95-f754-4422-bffb-a074cef69f71 · outbound

This paper cites Workingpaper, Aston University (1994).

Evaluating Vision Transformer Models for Visual Quality Control in Industrial Manufacturing Workingpaper, Aston University (1994)

Reference 5

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Observation 8d535f68-854b-4677-851f-741dc0ed8855 · outbound

This paper cites Electronics 11(15), 2306 (2022).

Evaluating Vision Transformer Models for Visual Quality Control in Industrial Manufacturing Electronics 11(15), 2306 (2022)

Reference 6

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Observation 159ebdaf-feb6-428e-8ef0-a963895327c6 · outbound

This paper cites In: 2009 IEEE conference on computer vision and pattern recognition.

Evaluating Vision Transformer Models for Visual Quality Control in Industrial Manufacturing In: 2009 IEEE conference on computer vision and pattern recognition

Reference 7

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Observation 9a1bf9cf-d69e-4fb2-b4f8-3f02aac95d0e · outbound

This paper cites Density estimation using Real NVP.

Evaluating Vision Transformer Models for Visual Quality Control in Industrial Manufacturing Density estimation using Real NVP

Reference 8

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Observation 000d654b-7847-4e34-a19b-95fdd3e5236d · outbound

This paper cites An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale.

Evaluating Vision Transformer Models for Visual Quality Control in Industrial Manufacturing An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale

Reference 9

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Observation a6452594-1d5f-47e7-b815-61a8c261bb99 · outbound

This paper cites Computer Vision and Image Understanding 195 (2020).

Evaluating Vision Transformer Models for Visual Quality Control in Industrial Manufacturing Computer Vision and Image Understanding 195 (2020)

Reference 10

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Observation 954619e0-30ee-41ca-a52c-35f5ad1af2c2 · outbound

This paper cites CFLOW-AD: Real-Time Unsupervised Anomaly Detection with Localization via Conditional Normalizing Flows.

Evaluating Vision Transformer Models for Visual Quality Control in Industrial Manufacturing CFLOW-AD: Real-Time Unsupervised Anomaly Detection with Localization via Conditional Normalizing Flows

Reference 11

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Observation c4d31c71-dcf5-4acd-b272-4cb7355389d5 · outbound

This paper cites Deep Residual Learning for Image Recognition.

Evaluating Vision Transformer Models for Visual Quality Control in Industrial Manufacturing Deep Residual Learning for Image Recognition

Reference 12

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Observation 105f8aa8-76fe-46c6-a31b-cacbb7e4551b · outbound

This paper cites ReConPatch : Contrastive Patch Representation Learning for Industrial Anomaly Detection.

Evaluating Vision Transformer Models for Visual Quality Control in Industrial Manufacturing ReConPatch : Contrastive Patch Representation Learning for Industrial Anomaly Detection

Reference 13

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Observation 6b71d274-b980-464e-985d-90c8a11d2590 · outbound

This paper cites AltUB: Alternating Training Method to Update Base Distribution of Normalizing Flow for Anomaly Detection.

Evaluating Vision Transformer Models for Visual Quality Control in Industrial Manufacturing AltUB: Alternating Training Method to Update Base Distribution of Normalizing Flow for Anomaly Detection

Reference 14

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Observation 2ea75fee-aeb9-414d-aba1-fa562be56716 · outbound

This paper cites PyramidFlow: High-Resolution Defect Contrastive Localization using Pyramid Normalizing Flow.

Evaluating Vision Transformer Models for Visual Quality Control in Industrial Manufacturing PyramidFlow: High-Resolution Defect Contrastive Localization using Pyramid Normalizing Flow

Reference 15

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Observation 0a770448-b49f-4d77-a908-8c8e8a140dea · outbound

This paper cites Efficient Self-supervised Vision Transformers for Representation Learning.

Evaluating Vision Transformer Models for Visual Quality Control in Industrial Manufacturing Efficient Self-supervised Vision Transformers for Representation Learning

Reference 16

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Observation e5d913e0-d3b0-4b64-8588-c5774165b6db · outbound

This paper cites Industrial Anomaly Detection and Localization Using Weakly-Supervised Residual Transformers.

Evaluating Vision Transformer Models for Visual Quality Control in Industrial Manufacturing Industrial Anomaly Detection and Localization Using Weakly-Supervised Residual Transformers

Reference 17

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Observation d730616c-7e6e-41f2-97e7-655233a8f83f · outbound

This paper cites EfficientFormer: Vision Transformers at MobileNet Speed.

Evaluating Vision Transformer Models for Visual Quality Control in Industrial Manufacturing EfficientFormer: Vision Transformers at MobileNet Speed

Reference 18

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Observation 2cd3385d-f588-42ab-b938-e84bc9bf5abc · outbound

This paper cites Swin Transformer: Hierarchical Vision Transformer using Shifted Windows.

Evaluating Vision Transformer Models for Visual Quality Control in Industrial Manufacturing Swin Transformer: Hierarchical Vision Transformer using Shifted Windows

Reference 19

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Observation 643a3258-a01d-4c33-b2b5-c894ea389e93 · outbound

This paper cites HaloAE: An HaloNet based Local Transformer Auto-Encoder for Anomaly Detection and Localization.

Evaluating Vision Transformer Models for Visual Quality Control in Industrial Manufacturing HaloAE: An HaloNet based Local Transformer Auto-Encoder for Anomaly Detection and Localization

Reference 20

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Observation 550295f7-b5e0-4a52-bfb7-ecdad406fa91 · outbound

This paper cites In: 30th IEEE/IES International Symposium on Industrial Electronics (ISIE) (June 2021).

Evaluating Vision Transformer Models for Visual Quality Control in Industrial Manufacturing In: 30th IEEE/IES International Symposium on Industrial Electronics (ISIE) (June 2021)

Reference 21

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Observation 52c780c2-c4ae-4192-bf53-d2d2c7d54bce · outbound

This paper cites Towards Total Recall in Industrial Anomaly Detection.

Evaluating Vision Transformer Models for Visual Quality Control in Industrial Manufacturing Towards Total Recall in Industrial Anomaly Detection

Reference 22

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Observation 3d9a4501-a500-4b2e-a51f-ed4c110008d3 · outbound

This paper cites MobileNetV2: Inverted Residuals and Linear Bottlenecks.

Evaluating Vision Transformer Models for Visual Quality Control in Industrial Manufacturing MobileNetV2: Inverted Residuals and Linear Bottlenecks

Reference 23

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Observation 3aa53f02-0d1c-4389-baca-4a43e71e8a30 · outbound

This paper cites IEEE Transactions on Instrumentation and Measurement 71, 1–21 (2022).

Evaluating Vision Transformer Models for Visual Quality Control in Industrial Manufacturing IEEE Transactions on Instrumentation and Measurement 71, 1–21 (2022)

Reference 24

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Observation 65274778-4a20-4e1b-bf54-4f3b8b1480ff · outbound

This paper cites Training data-efficient image transformers & distillation through attention.

Evaluating Vision Transformer Models for Visual Quality Control in Industrial Manufacturing Training data-efficient image transformers & distillation through attention

Reference 25

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This paper cites Scaling Local Self-Attention for Parameter Efficient Visual Backbones.

Evaluating Vision Transformer Models for Visual Quality Control in Industrial Manufacturing Scaling Local Self-Attention for Parameter Efficient Visual Backbones

Reference 26

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Observation f57c436c-d7b8-4a9c-b4da-94a18accb231 · outbound

This paper cites The Interna- tional Journal of Advanced Manufacturing Technology94(9-12), 3465–3471 (2018).

Evaluating Vision Transformer Models for Visual Quality Control in Industrial Manufacturing The Interna- tional Journal of Advanced Manufacturing Technology94(9-12), 3465–3471 (2018)

Reference 27

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Observation 7bb0840f-62eb-44ce-92f5-b76c84ed1bcb · outbound

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Evaluating Vision Transformer Models for Visual Quality Control in Industrial Manufacturing Unresolved cited work

Reference 28

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This paper cites SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformers.

Evaluating Vision Transformer Models for Visual Quality Control in Industrial Manufacturing SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformers

Reference 29

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Evaluating Vision Transformer Models for Visual Quality Control in Industrial Manufacturing 01816.pdf

Reference 30

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Observation 59ca5ffe-55de-4b78-b1c6-0bf75bf952c3 · outbound

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Evaluating Vision Transformer Models for Visual Quality Control in Industrial Manufacturing FastFlow: Unsupervised Anomaly Detection and Localization via 2D Normalizing Flows

Reference 31

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Observation 1e116102-114e-4c62-86c8-cabb0665e7cc · outbound

This paper cites Pattern Recognition Letters 153, 144–150 (2022).

Evaluating Vision Transformer Models for Visual Quality Control in Industrial Manufacturing Pattern Recognition Letters 153, 144–150 (2022)

Reference 32

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Observation f19e431b-9fe6-454e-9415-3b33d00dee5f · outbound

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Evaluating Vision Transformer Models for Visual Quality Control in Industrial Manufacturing Nested Hierarchical Transformer: Towards Accurate, Data-Efficient and Interpretable Visual Understanding

Reference 33

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Observation d71318c8-25a5-4560-bbe9-41e8179b4b24 · outbound

This paper cites In: International Conference on Learning Representations (2018), https: //openreview.net/forum?id=BJJLHbb0- A Generated anomaly maps from different model configurations 18 M.

Evaluating Vision Transformer Models for Visual Quality Control in Industrial Manufacturing In: International Conference on Learning Representations (2018), https: //openreview.net/forum?id=BJJLHbb0- A Generated anomaly maps from different model configurations 18 M

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T14:44:17.923479Z

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Pith citing papers

No inbound Pith citation observations are available.