PB-IAD: Utilizing multimodal foundation models for semantic industrial anomaly detection in dynamic manufacturing environments | Paper Citation Record

Observation 9d96b2cf-78d7-4e8e-b5f1-72fa0c031761 · outbound

This paper cites Markatos, A.

PB-IAD: Utilizing multimodal foundation models for semantic industrial anomaly detection in dynamic manufacturing environments Markatos, A

Reference 1

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Observation e287f5a6-9ba9-43f2-9c3f-6c1c5cdf2b66 · outbound

This paper cites Montgomery, Introduction to statistical quality control, Eighth edition, Wiley, Hoboken, NJ, 2019.

PB-IAD: Utilizing multimodal foundation models for semantic industrial anomaly detection in dynamic manufacturing environments Montgomery, Introduction to statistical quality control, Eighth edition, Wiley, Hoboken, NJ, 2019

Reference 2

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This paper cites Chandola, A.

PB-IAD: Utilizing multimodal foundation models for semantic industrial anomaly detection in dynamic manufacturing environments Chandola, A

Reference 3

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Observation 7c2dddd9-02f8-45b3-b120-c3ae5ab1af4f · outbound

This paper cites Chatterjee, R.

PB-IAD: Utilizing multimodal foundation models for semantic industrial anomaly detection in dynamic manufacturing environments Chatterjee, R

Reference 4

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This paper cites Deep Time Series Models: A Comprehensive Survey and Benchmark.

PB-IAD: Utilizing multimodal foundation models for semantic industrial anomaly detection in dynamic manufacturing environments Deep Time Series Models: A Comprehensive Survey and Benchmark

Reference 5

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This paper cites Juerging, P.M.

PB-IAD: Utilizing multimodal foundation models for semantic industrial anomaly detection in dynamic manufacturing environments Juerging, P.M

Reference 6

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Observation 8ef6346c-3d51-471e-a186-dc7d1be70926 · outbound

This paper cites Greco, B.

PB-IAD: Utilizing multimodal foundation models for semantic industrial anomaly detection in dynamic manufacturing environments Greco, B

Reference 7

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Observation 077587d2-98f8-4071-8f38-d87d8f1a3d28 · outbound

This paper cites Can Multimodal Large Language Models be Guided to Improve Industrial Anomaly Detection?.

PB-IAD: Utilizing multimodal foundation models for semantic industrial anomaly detection in dynamic manufacturing environments Can Multimodal Large Language Models be Guided to Improve Industrial Anomaly Detection?

Reference 8

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This paper cites GPTs are GPTs: An Early Look at the Labor Market Impact Potential of Large Language Models.

PB-IAD: Utilizing multimodal foundation models for semantic industrial anomaly detection in dynamic manufacturing environments GPTs are GPTs: An Early Look at the Labor Market Impact Potential of Large Language Models

Reference 9

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Observation 1a245522-9fc3-430d-b694-6c185869e8d3 · outbound

This paper cites Anomaly Detection for Industrial Applications, Its Challenges, Solutions, and Future Directions: A Review.

PB-IAD: Utilizing multimodal foundation models for semantic industrial anomaly detection in dynamic manufacturing environments Anomaly Detection for Industrial Applications, Its Challenges, Solutions, and Future Directions: A Review

Reference 10

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Observation e9482623-4ffd-4a97-a71f-f5314e2f3aee · outbound

This paper cites Abdullahi, K.

PB-IAD: Utilizing multimodal foundation models for semantic industrial anomaly detection in dynamic manufacturing environments Abdullahi, K

Reference 11

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Observation 1d1bc18e-b6dd-449f-a834-21e85dc310e7 · outbound

This paper cites Evaluating Large Language Models on Time Series Feature Understanding: A Comprehensive Taxonomy and Benchmark.

PB-IAD: Utilizing multimodal foundation models for semantic industrial anomaly detection in dynamic manufacturing environments Evaluating Large Language Models on Time Series Feature Understanding: A Comprehensive Taxonomy and Benchmark

Reference 12

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Observation 774dbb31-cda6-4826-93af-5a0ec2348e02 · outbound

This paper cites Raiaan, M.S.H.

PB-IAD: Utilizing multimodal foundation models for semantic industrial anomaly detection in dynamic manufacturing environments Raiaan, M.S.H

Reference 13

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Observation 1d795958-303e-4ac8-ac8a-1b2bf58114cd · outbound

This paper cites An Introduction to Vision-Language Modeling.

PB-IAD: Utilizing multimodal foundation models for semantic industrial anomaly detection in dynamic manufacturing environments An Introduction to Vision-Language Modeling

Reference 14

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This paper cites AnomalyGPT: Detecting Industrial Anomalies Using Large Vision-Language Models.

PB-IAD: Utilizing multimodal foundation models for semantic industrial anomaly detection in dynamic manufacturing environments AnomalyGPT: Detecting Industrial Anomalies Using Large Vision-Language Models

Reference 15

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This paper cites Wood (Ed.), Principles of quality costs: Financial measures for strategic implementation of quality management, ASQ Quality Press, Milwaukee, Wisconsin, 2013.

PB-IAD: Utilizing multimodal foundation models for semantic industrial anomaly detection in dynamic manufacturing environments Wood (Ed.), Principles of quality costs: Financial measures for strategic implementation of quality management, ASQ Quality Press, Milwaukee, Wisconsin, 2013

Reference 16

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This paper cites Qiu, Statistical Process Control Charts as a Tool for Analyzing Big Data, in: S.E.

PB-IAD: Utilizing multimodal foundation models for semantic industrial anomaly detection in dynamic manufacturing environments Qiu, Statistical Process Control Charts as a Tool for Analyzing Big Data, in: S.E

Reference 17

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PB-IAD: Utilizing multimodal foundation models for semantic industrial anomaly detection in dynamic manufacturing environments Unresolved cited work

Reference 18

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PB-IAD: Utilizing multimodal foundation models for semantic industrial anomaly detection in dynamic manufacturing environments Unresolved cited work

Reference 19

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Observation 09c6cdc8-76a1-4db7-8f42-42f0841bd121 · outbound

This paper cites GPT-4 Technical Report.

PB-IAD: Utilizing multimodal foundation models for semantic industrial anomaly detection in dynamic manufacturing environments GPT-4 Technical Report

Reference 20

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This paper cites MMAD: A Comprehensive Benchmark for Multimodal Large Language Models in Industrial Anomaly Detection.

PB-IAD: Utilizing multimodal foundation models for semantic industrial anomaly detection in dynamic manufacturing environments MMAD: A Comprehensive Benchmark for Multimodal Large Language Models in Industrial Anomaly Detection

Reference 21

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This paper cites Surbier, G.

PB-IAD: Utilizing multimodal foundation models for semantic industrial anomaly detection in dynamic manufacturing environments Surbier, G

Reference 22

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This paper cites Kernan Freire, LLM-Powered Cognitive Assistants for Knowledge Sharing among Factory Operators, 2025.

PB-IAD: Utilizing multimodal foundation models for semantic industrial anomaly detection in dynamic manufacturing environments Kernan Freire, LLM-Powered Cognitive Assistants for Knowledge Sharing among Factory Operators, 2025

Reference 23

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PB-IAD: Utilizing multimodal foundation models for semantic industrial anomaly detection in dynamic manufacturing environments Unresolved cited work

Reference 24

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This paper cites Weiss, T.M.

PB-IAD: Utilizing multimodal foundation models for semantic industrial anomaly detection in dynamic manufacturing environments Weiss, T.M

Reference 25

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This paper cites Johnson, S.R.

PB-IAD: Utilizing multimodal foundation models for semantic industrial anomaly detection in dynamic manufacturing environments Johnson, S.R

Reference 26

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PB-IAD: Utilizing multimodal foundation models for semantic industrial anomaly detection in dynamic manufacturing environments Unresolved cited work

Reference 27

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PB-IAD: Utilizing multimodal foundation models for semantic industrial anomaly detection in dynamic manufacturing environments Unresolved cited work

Reference 28

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This paper cites Parameter-Efficient Fine-Tuning for Large Models: A Comprehensive Survey.

PB-IAD: Utilizing multimodal foundation models for semantic industrial anomaly detection in dynamic manufacturing environments Parameter-Efficient Fine-Tuning for Large Models: A Comprehensive Survey

Reference 29

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PB-IAD: Utilizing multimodal foundation models for semantic industrial anomaly detection in dynamic manufacturing environments Parameter-Efficient Transfer Learning for NLP

Reference 30

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PB-IAD: Utilizing multimodal foundation models for semantic industrial anomaly detection in dynamic manufacturing environments LoRA: Low-Rank Adaptation of Large Language Models

Reference 31

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PB-IAD: Utilizing multimodal foundation models for semantic industrial anomaly detection in dynamic manufacturing environments Parameter-Efficient Fine-Tuning Methods for Pretrained Language Models: A Critical Review and Assessment

Reference 32

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PB-IAD: Utilizing multimodal foundation models for semantic industrial anomaly detection in dynamic manufacturing environments Language Models are Few-Shot Learners

Reference 33

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PB-IAD: Utilizing multimodal foundation models for semantic industrial anomaly detection in dynamic manufacturing environments The Prompt Report: A Systematic Survey of Prompt Engineering Techniques

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This paper cites FD-LLM: Large Language Model for Fault Diagnosis of Machines.

PB-IAD: Utilizing multimodal foundation models for semantic industrial anomaly detection in dynamic manufacturing environments FD-LLM: Large Language Model for Fault Diagnosis of Machines

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This paper cites FaultGPT: Industrial Fault Diagnosis Question Answering System by Vision Language Models.

PB-IAD: Utilizing multimodal foundation models for semantic industrial anomaly detection in dynamic manufacturing environments FaultGPT: Industrial Fault Diagnosis Question Answering System by Vision Language Models

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This paper cites Myriad: Large Multimodal Model by Applying Vision Experts for Industrial Anomaly Detection.

PB-IAD: Utilizing multimodal foundation models for semantic industrial anomaly detection in dynamic manufacturing environments Myriad: Large Multimodal Model by Applying Vision Experts for Industrial Anomaly Detection

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PB-IAD: Utilizing multimodal foundation models for semantic industrial anomaly detection in dynamic manufacturing environments Refining Time Series Anomaly Detectors using Large Language Models

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PB-IAD: Utilizing multimodal foundation models for semantic industrial anomaly detection in dynamic manufacturing environments Singh, J

Reference 39

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PB-IAD: Utilizing multimodal foundation models for semantic industrial anomaly detection in dynamic manufacturing environments WinCLIP: Zero-/Few-Shot Anomaly Classification and Segmentation

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PB-IAD: Utilizing multimodal foundation models for semantic industrial anomaly detection in dynamic manufacturing environments Learning Transferable Visual Models From Natural Language Supervision

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PB-IAD: Utilizing multimodal foundation models for semantic industrial anomaly detection in dynamic manufacturing environments Do LLMs Understand Visual Anomalies? Uncovering LLM's Capabilities in Zero-shot Anomaly Detection

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This paper cites Customizing Visual-Language Foundation Models for Multi-modal Anomaly Detection and Reasoning.

PB-IAD: Utilizing multimodal foundation models for semantic industrial anomaly detection in dynamic manufacturing environments Customizing Visual-Language Foundation Models for Multi-modal Anomaly Detection and Reasoning

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This paper cites Schiele, D.

PB-IAD: Utilizing multimodal foundation models for semantic industrial anomaly detection in dynamic manufacturing environments Schiele, D

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This paper cites RAAD-LLM: Adaptive Anomaly Detection Using LLMs and RAG Integration.

PB-IAD: Utilizing multimodal foundation models for semantic industrial anomaly detection in dynamic manufacturing environments RAAD-LLM: Adaptive Anomaly Detection Using LLMs and RAG Integration

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PB-IAD: Utilizing multimodal foundation models for semantic industrial anomaly detection in dynamic manufacturing environments DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning

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PB-IAD: Utilizing multimodal foundation models for semantic industrial anomaly detection in dynamic manufacturing environments JSONSchemaBench: A Rigorous Benchmark of Structured Outputs for Language Models

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This paper cites https://platform.openai.com/docs/guides/text?api-mode=responses (accessed 28 May 2025).

PB-IAD: Utilizing multimodal foundation models for semantic industrial anomaly detection in dynamic manufacturing environments https://platform.openai.com/docs/guides/text?api-mode=responses (accessed 28 May 2025)

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This paper cites https://docs.anthropic.com/en/docs/build-with-claude/prompt-engineering/overview (accessed 28 May 2025).

PB-IAD: Utilizing multimodal foundation models for semantic industrial anomaly detection in dynamic manufacturing environments https://docs.anthropic.com/en/docs/build-with-claude/prompt-engineering/overview (accessed 28 May 2025)

Reference 49

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This paper cites Meier, S.

PB-IAD: Utilizing multimodal foundation models for semantic industrial anomaly detection in dynamic manufacturing environments Meier, S

Reference 50

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PB-IAD: Utilizing multimodal foundation models for semantic industrial anomaly detection in dynamic manufacturing environments Bergmann, K

Reference 51

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PB-IAD: Utilizing multimodal foundation models for semantic industrial anomaly detection in dynamic manufacturing environments Heckler-Kram, J.-H

Reference 52

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This paper cites Scheck, Stripped Wire Dataset, Zenodo, 2025.

PB-IAD: Utilizing multimodal foundation models for semantic industrial anomaly detection in dynamic manufacturing environments Scheck, Stripped Wire Dataset, Zenodo, 2025

Reference 53

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PB-IAD: Utilizing multimodal foundation models for semantic industrial anomaly detection in dynamic manufacturing environments https://doi.org/10.7910/DVN/WBDKN6

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This paper cites Bruhin US 2010/0139351 A1, 2009.

PB-IAD: Utilizing multimodal foundation models for semantic industrial anomaly detection in dynamic manufacturing environments Bruhin US 2010/0139351 A1, 2009

Reference 55

Resolution: unresolved; no resolver link, observed 2026-08-05T18:34:09.264529Z

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Observation f3c70d7d-771f-4a18-9a2a-574d4cc9174a · outbound

This paper cites Towards transparent and data-driven fault detection in manufacturing: A case study on univariate, discrete time series.

PB-IAD: Utilizing multimodal foundation models for semantic industrial anomaly detection in dynamic manufacturing environments Towards transparent and data-driven fault detection in manufacturing: A case study on univariate, discrete time series

Reference 56

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Observation 42856a3c-5a5d-45a0-9624-d228685dae1c · outbound

This paper cites Hofmann, A.

PB-IAD: Utilizing multimodal foundation models for semantic industrial anomaly detection in dynamic manufacturing environments Hofmann, A

Reference 57

Resolution: verified exact; doi, observed 2026-08-05T18:34:10.129667Z

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No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation f941292b-7618-4a36-93d8-55a91f3f3783 · outbound

This paper cites Towards Total Recall in Industrial Anomaly Detection.

PB-IAD: Utilizing multimodal foundation models for semantic industrial anomaly detection in dynamic manufacturing environments Towards Total Recall in Industrial Anomaly Detection

Reference 58

Resolution: unresolved; no resolver link, observed 2026-08-05T18:34:09.593045Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 31194705-677f-4e24-8d77-b0b780e7e513 · outbound

This paper cites Liu, K.M.

PB-IAD: Utilizing multimodal foundation models for semantic industrial anomaly detection in dynamic manufacturing environments Liu, K.M

Reference 59

Resolution: unresolved; no resolver link, observed 2026-08-05T18:34:09.738409Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation afe43cdd-8143-42a1-9834-2149fd8f41c2 · outbound

This paper cites https://scikit- learn.org/stable/modules/generated/sklearn.ensemble.IsolationForest.html (accessed 23 June 2025).

PB-IAD: Utilizing multimodal foundation models for semantic industrial anomaly detection in dynamic manufacturing environments https://scikit- learn.org/stable/modules/generated/sklearn.ensemble.IsolationForest.html (accessed 23 June 2025)

Reference 60

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T18:34:16.699340Z

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No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation a424fc26-dd71-48cc-9493-fedc02fb152d · outbound

This paper cites an unresolved cited work.

PB-IAD: Utilizing multimodal foundation models for semantic industrial anomaly detection in dynamic manufacturing environments Unresolved cited work

Reference 214

Resolution: unresolved; no resolver link, observed 2026-08-05T18:34:05.328680Z

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