Paper Citation Record · LEDGER

Exploring Large Vision-Language Models for Robust and Efficient Industrial Anomaly Detection

As of 23 August 2026, this Paper Citation Record lists 29 of 29 outbound references and 0 inbound Pith citation observations for arXiv:2412.00890.

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

pith.paper-citation-record.v1
2412.00890 v1

Coverage vector

measured 29 of 29 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-12T04:56:38.272466Z

measured 29 of 29 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-23T06:30:58.430688+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

29 of 29 outbound references displayed

verified exact0
verified fuzzy16
unresolved12
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 58e013e8-901f-4537-ba98-96df01863158 · outbound

This paper cites In: Findings of the Association for Comput ational Linguistics, ACL 2024, Bangkok, Thailand and virtual meeting, August 11- 16, 2024.

Exploring Large Vision-Language Models for Robust and Efficient Industrial Anomaly Detection In: Findings of the Association for Comput ational Linguistics, ACL 2024, Bangkok, Thailand and virtual meeting, August 11- 16, 2024

Reference 1

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Observation 177e5bdf-6de9-4ed0-9b41-831426b08a40 · outbound

This paper cites Rethinking Visual Dependency in Long-Context Reasoning for Large Vision-Language Models.

Exploring Large Vision-Language Models for Robust and Efficient Industrial Anomaly Detection Rethinking Visual Dependency in Long-Context Reasoning for Large Vision-Language Models

Reference 2

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Observation cdbcaed6-33b3-4b9d-9542-b9f0ed461883 · outbound

This paper cites In: Proceedings of the IEEE/CVF conference on computer vision and pattern recogni tion.

Exploring Large Vision-Language Models for Robust and Efficient Industrial Anomaly Detection In: Proceedings of the IEEE/CVF conference on computer vision and pattern recogni tion

Reference 3

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Observation 5a9ff79e-6690-43d8-a764-fcae4ffedb3d · outbound

This paper cites In: European Conference on Computer Vision.

Exploring Large Vision-Language Models for Robust and Efficient Industrial Anomaly Detection In: European Conference on Computer Vision

Reference 4

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Observation 8655d5e7-f5d3-47d4-8630-c68dc4f1507e · outbound

This paper cites In: Wooldridge, M.J., 12 K.

Exploring Large Vision-Language Models for Robust and Efficient Industrial Anomaly Detection In: Wooldridge, M.J., 12 K

Reference 5

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This paper cites In: Proceedi ngs of the IEEE/CVF conference on computer vision and pattern recognition.

Exploring Large Vision-Language Models for Robust and Efficient Industrial Anomaly Detection In: Proceedi ngs of the IEEE/CVF conference on computer vision and pattern recognition

Reference 7

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Observation de18b211-5117-42b2-b672-7682fedcd394 · outbound

This paper cites In: Meila, M., Zhang, T.

Exploring Large Vision-Language Models for Robust and Efficient Industrial Anomaly Detection In: Meila, M., Zhang, T

Reference 8

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Observation ab2ce85c-cbed-4512-be31-ed7dcc2c82ac · outbound

This paper cites In: Proceedings of the 17th Conference of the Euro pean Chapter of the Association for Computational Linguistics.

Exploring Large Vision-Language Models for Robust and Efficient Industrial Anomaly Detection In: Proceedings of the 17th Conference of the Euro pean Chapter of the Association for Computational Linguistics

Reference 9

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This paper cites In: Proceedings of the 17th Conference of the Eu ropean Chapter of the Association for Computational Linguistics.

Exploring Large Vision-Language Models for Robust and Efficient Industrial Anomaly Detection In: Proceedings of the 17th Conference of the Eu ropean Chapter of the Association for Computational Linguistics

Reference 10

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Observation 759bac4f-0cd0-4efd-9c48-9ba172c4eef1 · outbound

This paper cites In: Findings of the Association for Computational Ling uistics: EACL 2023.

Exploring Large Vision-Language Models for Robust and Efficient Industrial Anomaly Detection In: Findings of the Association for Computational Ling uistics: EACL 2023

Reference 11

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Source-reported events for the cited work

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Observation 747fde13-eae8-4102-8ff6-eb65bb1f0c4a · outbound

This paper cites UGC Care Group I Journal 8(14), 71–75 (2021).

Exploring Large Vision-Language Models for Robust and Efficient Industrial Anomaly Detection UGC Care Group I Journal 8(14), 71–75 (2021)

Reference 12

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Observation dca81ec1-6fc3-49ae-aaf2-f8491cc2cd49 · outbound

This paper cites VisualBERT: A Simple and Performant Baseline for Vision and Language.

Exploring Large Vision-Language Models for Robust and Efficient Industrial Anomaly Detection VisualBERT: A Simple and Performant Baseline for Vision and Language

Reference 13

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Observation e2491d41-2dc0-40ff-81ad-5849ffac88f6 · outbound

This paper cites In: Proceedings of the 60th Annual Meeting o f the Association for Computational Linguistics (Volume 1: Long Papers).

Exploring Large Vision-Language Models for Robust and Efficient Industrial Anomaly Detection In: Proceedings of the 60th Annual Meeting o f the Association for Computational Linguistics (Volume 1: Long Papers)

Reference 14

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Observation 56de7e4a-634e-40c7-a1f0-dcf9e079127f · outbound

This paper cites In: Proceedings of the ACM Web Conference 2022.

Exploring Large Vision-Language Models for Robust and Efficient Industrial Anomaly Detection In: Proceedings of the ACM Web Conference 2022

Reference 15

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Observation 19852c09-dec0-4569-89f8-f635ef29bc6e · outbound

This paper cites UNITER: UNiversal Image-TExt Representation Learning.

Exploring Large Vision-Language Models for Robust and Efficient Industrial Anomaly Detection UNITER: UNiversal Image-TExt Representation Learning

Reference 16

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Observation 1ae1dfb9-fcd2-44ef-8638-f8854024339e · outbound

This paper cites In: 8th Int ernational Conference on L VLM for Industrial Anomaly Detection 13 Learning Representations, ICLR 2020, Addis Ababa, Ethiopi a, April 26-30, 2020.

Exploring Large Vision-Language Models for Robust and Efficient Industrial Anomaly Detection In: 8th Int ernational Conference on L VLM for Industrial Anomaly Detection 13 Learning Representations, ICLR 2020, Addis Ababa, Ethiopi a, April 26-30, 2020

Reference 17

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Observation 6e61e191-a80a-4cd3-afae-f44dc604ceca · outbound

This paper cites In: ICASSP 2021-2021 IEEE Inter national Conference on Acoustics, Speech and Signal Processing (ICASSP).

Exploring Large Vision-Language Models for Robust and Efficient Industrial Anomaly Detection In: ICASSP 2021-2021 IEEE Inter national Conference on Acoustics, Speech and Signal Processing (ICASSP)

Reference 18

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This paper cites In: ICASSP 2022-2022 IEE E International Confer- ence on Acoustics, Speech and Signal Processing (ICASSP).

Exploring Large Vision-Language Models for Robust and Efficient Industrial Anomaly Detection In: ICASSP 2022-2022 IEE E International Confer- ence on Acoustics, Speech and Signal Processing (ICASSP)

Reference 19

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Observation 0bc9a799-f6f3-442f-bcde-d88cb82c8af5 · outbound

This paper cites Advances in neural information processing systems 34, 9694–9705 (2021).

Exploring Large Vision-Language Models for Robust and Efficient Industrial Anomaly Detection Advances in neural information processing systems 34, 9694–9705 (2021)

Reference 20

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Observation e279baa2-bfaf-4a6c-8f6c-45170bc4abfc · outbound

This paper cites Florence: A New Foundation Model for Computer Vision.

Exploring Large Vision-Language Models for Robust and Efficient Industrial Anomaly Detection Florence: A New Foundation Model for Computer Vision

Reference 21

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Observation e33b809c-edb1-47fc-bedb-6e2c9fecc3e9 · outbound

This paper cites Procedia CIRP 93, 1281–1285 (2020).

Exploring Large Vision-Language Models for Robust and Efficient Industrial Anomaly Detection Procedia CIRP 93, 1281–1285 (2020)

Reference 22

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Observation c22a5381-ee91-4192-8880-672d8e9f3777 · outbound

This paper cites In: Tetko, I.V., Kurková, V., Karpov, P., Theis, F.J.

Exploring Large Vision-Language Models for Robust and Efficient Industrial Anomaly Detection In: Tetko, I.V., Kurková, V., Karpov, P., Theis, F.J

Reference 23

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This paper cites In: Practi- cal Applications of Data Processing, Algorithms, and Model ing, pp.

Exploring Large Vision-Language Models for Robust and Efficient Industrial Anomaly Detection In: Practi- cal Applications of Data Processing, Algorithms, and Model ing, pp

Reference 24

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Observation 6d16763d-5c6b-414b-aace-ee9406549a1d · outbound

This paper cites IEEE Trans.

Exploring Large Vision-Language Models for Robust and Efficient Industrial Anomaly Detection IEEE Trans

Reference 25

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Observation 3d53680a-e68f-46df-a279-54826442248d · outbound

This paper cites arXiv preprint arXiv:2405.0 3673 (2024).

Exploring Large Vision-Language Models for Robust and Efficient Industrial Anomaly Detection arXiv preprint arXiv:2405.0 3673 (2024)

Reference 26

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Observation 0253a067-cd5c-4798-8028-7947e4533e46 · outbound

This paper cites In: 2020 International Conference on Electronics and Sustainable C ommunication Systems (ICESC).

Exploring Large Vision-Language Models for Robust and Efficient Industrial Anomaly Detection In: 2020 International Conference on Electronics and Sustainable C ommunication Systems (ICESC)

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T04:56:38.536906Z

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Observation 44707ec3-73c9-426d-b52f-3321598445ff · outbound

This paper cites ISPRS Inter national Journal of Geo-Information 10(3), 177 (2021).

Exploring Large Vision-Language Models for Robust and Efficient Industrial Anomaly Detection ISPRS Inter national Journal of Geo-Information 10(3), 177 (2021)

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T04:56:38.522472Z

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This paper cites In: Bimbo, A.D., Cucchiara, R., Sclaroﬀ, S., Farinella, G.M., M ei, T., Bertini, M., Escalante, H.J., Vezzani, R.

Exploring Large Vision-Language Models for Robust and Efficient Industrial Anomaly Detection In: Bimbo, A.D., Cucchiara, R., Sclaroﬀ, S., Farinella, G.M., M ei, T., Bertini, M., Escalante, H.J., Vezzani, R

Reference 29

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Exploring Large Vision-Language Models for Robust and Efficient Industrial Anomaly Detection I n: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recogni tion

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T04:56:38.507548Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Pith citing papers

No inbound Pith citation observations are available.