Foundation Models and Transformers for Anomaly Detection: A Survey | Paper Citation Record

Observation 6de50b0f-e558-491a-a3f5-b712f8bbb102 · outbound

This paper cites Rafiqul Islam.

Foundation Models and Transformers for Anomaly Detection: A Survey Rafiqul Islam

Reference 1

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Observation b33657c0-30da-4e53-ae5c-9fefdfbb6732 · outbound

This paper cites doi: https://doi.org/10.1016/j.

Foundation Models and Transformers for Anomaly Detection: A Survey doi: https://doi.org/10.1016/j

Reference 3

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Observation 8ff44637-db97-466e-84c9-edb60e06e990 · outbound

This paper cites OpenFlamingo: An Open-Source Framework for Training Large Autoregressive Vision-Language Models.

Foundation Models and Transformers for Anomaly Detection: A Survey OpenFlamingo: An Open-Source Framework for Training Large Autoregressive Vision-Language Models

Reference 5

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Observation d0711552-5a3c-40ef-8ab3-68b6fc03ab3d · outbound

This paper cites On the Opportunities and Risks of Foundation Models.

Foundation Models and Transformers for Anomaly Detection: A Survey On the Opportunities and Risks of Foundation Models

Reference 9

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This paper cites Behzad Bozorgtabar, Dwarikanath Mahapatra, and Jean-Philippe Thiran.

Foundation Models and Transformers for Anomaly Detection: A Survey Behzad Bozorgtabar, Dwarikanath Mahapatra, and Jean-Philippe Thiran

Reference 10

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This paper cites doi: https://doi.org/10.1016/j.

Foundation Models and Transformers for Anomaly Detection: A Survey doi: https://doi.org/10.1016/j

Reference 11

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Observation 47e6d330-381f-4097-8c0a-48f2aab9ae69 · outbound

This paper cites Deep Learning for Anomaly Detection: A Survey.

Foundation Models and Transformers for Anomaly Detection: A Survey Deep Learning for Anomaly Detection: A Survey

Reference 13

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Observation 37f36c2e-d66d-447f-873e-3bee9fd02252 · outbound

This paper cites MobileViT: Light-weight, General-purpose, and Mobile-friendly Vision Transformer.

Foundation Models and Transformers for Anomaly Detection: A Survey MobileViT: Light-weight, General-purpose, and Mobile-friendly Vision Transformer

Reference 14

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Observation ad10f51a-4cb4-4589-a246-206cfeb4b831 · outbound

This paper cites Tevad: Im- proved video anomaly detection with captions.

Foundation Models and Transformers for Anomaly Detection: A Survey Tevad: Im- proved video anomaly detection with captions

Reference 15

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Observation 4582dda2-7ae3-4f33-9bb1-47724f9cd714 · outbound

This paper cites Matan Jacob Cohen and Shai Avidan.

Foundation Models and Transformers for Anomaly Detection: A Survey Matan Jacob Cohen and Shai Avidan

Reference 16

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Observation cde10241-4238-4268-ac31-ac50a650ff13 · outbound

This paper cites doi: 10.1142/s0129065722500307.

Foundation Models and Transformers for Anomaly Detection: A Survey doi: 10.1142/s0129065722500307

Reference 17

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Observation 189bbed4-f110-4aae-a478-c70520f47a1b · outbound

This paper cites doi: 10.5220/0011669400003417.

Foundation Models and Transformers for Anomaly Detection: A Survey doi: 10.5220/0011669400003417

Reference 18

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Observation 5137a19e-aa8f-4abd-9350-be67e3de4cdd · outbound

This paper cites doi: https://doi.org/10.1016/j.optlastec.2023.110296.

Foundation Models and Transformers for Anomaly Detection: A Survey doi: https://doi.org/10.1016/j.optlastec.2023.110296

Reference 19

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Observation 946c122f-63ca-4c63-bcb8-75fb4b3a32bd · outbound

This paper cites Enhancing few-shot video anomaly detection with key-frame selection and relational cross transformers.

Foundation Models and Transformers for Anomaly Detection: A Survey Enhancing few-shot video anomaly detection with key-frame selection and relational cross transformers

Reference 20

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Observation b8f90f99-6822-4abf-aa9f-e608acc7c441 · outbound

This paper cites Yan Fu, Bao Yang, and Ou Ye.

Foundation Models and Transformers for Anomaly Detection: A Survey Yan Fu, Bao Yang, and Ou Ye

Reference 21

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This paper cites Filo: Zero-shot anomaly detection by fine-grained description and high-quality localization.

Foundation Models and Transformers for Anomaly Detection: A Survey Filo: Zero-shot anomaly detection by fine-grained description and high-quality localization

Reference 22

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Observation c38dd506-4775-49f9-b01f-03ea3e77bc01 · outbound

This paper cites doi: https://doi.org/10.1016/j.eswa.2021.116429.

Foundation Models and Transformers for Anomaly Detection: A Survey doi: https://doi.org/10.1016/j.eswa.2021.116429

Reference 23

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Observation 9a11af61-55d9-489e-a6ba-5da13a62413d · outbound

This paper cites Jongheon Jeong, Yang Zou, Taewan Kim, Dongqing Zhang, Avinash Ravichandran, and Onkar Dabeer.

Foundation Models and Transformers for Anomaly Detection: A Survey Jongheon Jeong, Yang Zou, Taewan Kim, Dongqing Zhang, Avinash Ravichandran, and Onkar Dabeer

Reference 24

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Observation 343fdf29-7752-4bf6-b74f-efc20b505373 · outbound

This paper cites Chao Jia, Yinfei Yang, Ye Xia, Yi-Ting Chen, Zarana Parekh, Hieu Pham, Quoc V.

Foundation Models and Transformers for Anomaly Detection: A Survey Chao Jia, Yinfei Yang, Ye Xia, Yi-Ting Chen, Zarana Parekh, Hieu Pham, Quoc V

Reference 25

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Observation f73be18a-5baf-4e13-9ee8-6d464a5dfeca · outbound

This paper cites Mistral 7B.

Foundation Models and Transformers for Anomaly Detection: A Survey Mistral 7B

Reference 26

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Observation 64081e69-ca6e-4f2c-baf8-59a4d0b21066 · outbound

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

Foundation Models and Transformers for Anomaly Detection: A Survey MMAD: A Comprehensive Benchmark for Multimodal Large Language Models in Industrial Anomaly Detection

Reference 27

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This paper cites CLIP-TSA: CLIP-Assisted Temporal Self-Attention for Weakly-Supervised Video Anomaly Detection.

Foundation Models and Transformers for Anomaly Detection: A Survey CLIP-TSA: CLIP-Assisted Temporal Self-Attention for Weakly-Supervised Video Anomaly Detection

Reference 28

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Observation 03fa416e-13c3-401e-86b1-943d4954f5f0 · outbound

This paper cites doi: https://doi.org/10.1016/j.knosys.2023.111186.

Foundation Models and Transformers for Anomaly Detection: A Survey doi: https://doi.org/10.1016/j.knosys.2023.111186

Reference 29

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Observation e6fa4d6e-d2d8-401f-aa12-04cd2f7223b0 · outbound

This paper cites doi: 10.1145/3505244.

Foundation Models and Transformers for Anomaly Detection: A Survey doi: 10.1145/3505244

Reference 30

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Observation 3f78db25-9c54-4163-9834-a1ade05d30ce · outbound

This paper cites Convolutional networks and applica- tions in vision.

Foundation Models and Transformers for Anomaly Detection: A Survey Convolutional networks and applica- tions in vision

Reference 31

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This paper cites UniFormaly: Towards Task-Agnostic Unified Framework for Visual Anomaly Detection.

Foundation Models and Transformers for Anomaly Detection: A Survey UniFormaly: Towards Task-Agnostic Unified Framework for Visual Anomaly Detection

Reference 33

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This paper cites CRCL: Causal Representation Consistency Learning for Anomaly Detection in Surveillance Videos.

Foundation Models and Transformers for Anomaly Detection: A Survey CRCL: Causal Representation Consistency Learning for Anomaly Detection in Surveillance Videos

Reference 35

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This paper cites Privacy-Preserving Video Anomaly Detection: A Survey.

Foundation Models and Transformers for Anomaly Detection: A Survey Privacy-Preserving Video Anomaly Detection: A Survey

Reference 36

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This paper cites P-Tuning v2: Prompt Tuning Can Be Comparable to Fine-tuning Universally Across Scales and Tasks.

Foundation Models and Transformers for Anomaly Detection: A Survey P-Tuning v2: Prompt Tuning Can Be Comparable to Fine-tuning Universally Across Scales and Tasks

Reference 37

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Foundation Models and Transformers for Anomaly Detection: A Survey doi: https://doi.org/10.1016/j.engappai.2023.107810

Reference 38

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This paper cites doi: 10.1109/tpami.2023.3322604.

Foundation Models and Transformers for Anomaly Detection: A Survey doi: 10.1109/tpami.2023.3322604

Reference 39

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This paper cites Anomaly Detection in Video Using Predictive Convolutional Long Short-Term Memory Networks.

Foundation Models and Transformers for Anomaly Detection: A Survey Anomaly Detection in Video Using Predictive Convolutional Long Short-Term Memory Networks

Reference 40

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Foundation Models and Transformers for Anomaly Detection: A Survey Vt-adl: A vision transformer network for image anomaly detection and localization

Reference 42

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This paper cites 2021.9576231.

Foundation Models and Transformers for Anomaly Detection: A Survey 2021.9576231

Reference 43

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This paper cites Generalized Out-of-Distribution Detection and Beyond in Vision Language Model Era: A Survey.

Foundation Models and Transformers for Anomaly Detection: A Survey Generalized Out-of-Distribution Detection and Beyond in Vision Language Model Era: A Survey

Reference 44

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Foundation Models and Transformers for Anomaly Detection: A Survey Unresolved cited work

Reference 45

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This paper cites OpenAI, Josh Achiam, Steven Adler, Sandhini Agarwal, and et al.

Foundation Models and Transformers for Anomaly Detection: A Survey OpenAI, Josh Achiam, Steven Adler, Sandhini Agarwal, and et al

Reference 46

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Foundation Models and Transformers for Anomaly Detection: A Survey Maxime Oquab, Timothée Darcet, Théo Moutakanni, Huy Vo, Marc Szafraniec, Vasil Khalidov, Pierre Fernandez, Daniel Haziza, Francisco Massa, Alaaeldin El-Nouby, et al

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Foundation Models and Transformers for Anomaly Detection: A Survey Yatian Pang, Wenxiao Wang, Francis E

Reference 48

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Foundation Models and Transformers for Anomaly Detection: A Survey Very Deep Convolutional Networks for Large-Scale Image Recognition

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Foundation Models and Transformers for Anomaly Detection: A Survey Large Language Models for Forecasting and Anomaly Detection: A Systematic Literature Review

Reference 51

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This paper cites Masato Tamura.

Foundation Models and Transformers for Anomaly Detection: A Survey Masato Tamura

Reference 52

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This paper cites LLaMA: Open and Efficient Foundation Language Models.

Foundation Models and Transformers for Anomaly Detection: A Survey LLaMA: Open and Efficient Foundation Language Models

Reference 53

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Foundation Models and Transformers for Anomaly Detection: A Survey Unresolved cited work

Reference 54

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Foundation Models and Transformers for Anomaly Detection: A Survey CogVLM: Visual Expert for Pretrained Language Models

Reference 55

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Foundation Models and Transformers for Anomaly Detection: A Survey Anodfdnet: A deep feature difference network for anomaly detection, 2022b

Reference 56

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This paper cites VadCLIP: Adapting Vision-Language Models for Weakly Supervised Video Anomaly Detection.

Foundation Models and Transformers for Anomaly Detection: A Survey VadCLIP: Adapting Vision-Language Models for Weakly Supervised Video Anomaly Detection

Reference 58

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Foundation Models and Transformers for Anomaly Detection: A Survey Limits to visual representational correspondence be- tween convolutional neural networks and the human brain

Reference 59

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This paper cites Attention-based mis- aligned spatiotemporal auto-encoder for video anomaly detection.

Foundation Models and Transformers for Anomaly Detection: A Survey Attention-based mis- aligned spatiotemporal auto-encoder for video anomaly detection

Reference 60

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Observation 4e70c0fe-65d6-4051-b943-9463fe917ada · outbound

This paper cites The Dawn of LMMs: Preliminary Explorations with GPT-4V(ision).

Foundation Models and Transformers for Anomaly Detection: A Survey The Dawn of LMMs: Preliminary Explorations with GPT-4V(ision)

Reference 61

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This paper cites Visual anomaly detection via dual-attention trans- former and discriminative flow, 2023a.

Foundation Models and Transformers for Anomaly Detection: A Survey Visual anomaly detection via dual-attention trans- former and discriminative flow, 2023a

Reference 62

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Observation 92897c01-2073-4614-8a5e-1a0404dd2539 · outbound

This paper cites Active Deep Kernel Learning of Molecular Properties: Realizing Dynamic Structural Embeddings.

Foundation Models and Transformers for Anomaly Detection: A Survey Active Deep Kernel Learning of Molecular Properties: Realizing Dynamic Structural Embeddings

Reference 63

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This paper cites Luca Zanella, Willi Menapace, Massimiliano Mancini, Yiming Wang, and Elisa Ricci.

Foundation Models and Transformers for Anomaly Detection: A Survey Luca Zanella, Willi Menapace, Massimiliano Mancini, Yiming Wang, and Elisa Ricci

Reference 64

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Observation f51298b4-68ad-47fb-b4b8-c0a9c8e9a1b6 · outbound

This paper cites Visualizing and understanding convolutional networks.

Foundation Models and Transformers for Anomaly Detection: A Survey Visualizing and understanding convolutional networks

Reference 65

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Observation 57859aad-6aa1-409b-ae1e-51d870f7cdb3 · outbound

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

Foundation Models and Transformers for Anomaly Detection: A Survey Faster Segment Anything: Towards Lightweight SAM for Mobile Applications

Reference 66

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b9d34aba-a19a-4764-a935-16bbdc78c44a · outbound

This paper cites GPT-4V-AD: Exploring Grounding Potential of VQA-oriented GPT-4V for Zero-shot Anomaly Detection.

Foundation Models and Transformers for Anomaly Detection: A Survey GPT-4V-AD: Exploring Grounding Potential of VQA-oriented GPT-4V for Zero-shot Anomaly Detection

Reference 67

Resolution: unresolved; no resolver link, observed 2026-08-06T15:32:52.968330Z

Source-reported events for the cited work

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Observation 056942a5-eccf-4b9b-8e17-17306296e5f8 · outbound

This paper cites doi: https://doi.org/10.1016/j.vrih.2022.07.006.

Foundation Models and Transformers for Anomaly Detection: A Survey doi: https://doi.org/10.1016/j.vrih.2022.07.006

Reference 68

Resolution: unresolved; no resolver link, observed 2026-08-06T15:32:52.971175Z

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Observation c0a20065-7455-46b1-9851-2a5e4c83c6eb · outbound

This paper cites Improved anomaly detection based on loss prediction.

Foundation Models and Transformers for Anomaly Detection: A Survey Improved anomaly detection based on loss prediction

Reference 69

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:32:54.693290Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 510db1a5-67e5-4b0d-b6d3-b307b1a3c081 · outbound

This paper cites Xuan Zhang, Shiyu Li, Xi Li, Ping Huang, Jiulong Shan, and Ting Chen.

Foundation Models and Transformers for Anomaly Detection: A Survey Xuan Zhang, Shiyu Li, Xi Li, Ping Huang, Jiulong Shan, and Ting Chen

Reference 70

Resolution: verified exact; raw_fallback, observed 2026-08-06T15:32:53.328720Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation daa29b1d-2ec1-439f-b57e-41629c5079bf · outbound

This paper cites Yu, and Lichao Sun.

Foundation Models and Transformers for Anomaly Detection: A Survey Yu, and Lichao Sun

Reference 71

Resolution: unresolved; no resolver link, observed 2026-08-06T15:32:52.980037Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e96665cf-1532-4ce5-9068-6ff8b2e97f94 · outbound

This paper cites doi: https://doi.org/10.1016/j.measurement.2024.114216.

Foundation Models and Transformers for Anomaly Detection: A Survey doi: https://doi.org/10.1016/j.measurement.2024.114216

Reference 72

Resolution: metadata mismatch; raw_fallback, observed 2026-08-06T15:32:53.248313Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 07da6526-482b-46db-aa5e-c6f635a3b5b0 · outbound

This paper cites doi: https: //doi.org/10.1016/j.eswa.2022.118269.

Foundation Models and Transformers for Anomaly Detection: A Survey doi: https: //doi.org/10.1016/j.eswa.2022.118269

Reference 73

Resolution: metadata mismatch; raw_fallback, observed 2026-08-06T15:32:53.179834Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 19d8bc76-a618-41a0-b6b6-12085851fe4d · outbound

This paper cites Paul Bergmann, Michael Fauser, David Sattlegger, and Carsten Steger.

Foundation Models and Transformers for Anomaly Detection: A Survey Paul Bergmann, Michael Fauser, David Sattlegger, and Carsten Steger

Reference 2009

Resolution: unresolved; no resolver link, observed 2026-08-06T15:32:48.819466Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 44187f40-43ce-4504-a475-8e3aac8bbfab · outbound

This paper cites Yann LeCun, Yoshua Bengio, and Geoffrey Hinton.

Foundation Models and Transformers for Anomaly Detection: A Survey Yann LeCun, Yoshua Bengio, and Geoffrey Hinton

Reference 2010

Resolution: metadata mismatch; raw_fallback, observed 2026-08-06T15:32:54.049554Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 153ebc47-14e2-4368-9189-86f6dcd7815a · outbound

This paper cites Separable Self-attention for Mobile Vision Transformers.

Foundation Models and Transformers for Anomaly Detection: A Survey Separable Self-attention for Mobile Vision Transformers

Reference 2016

Resolution: unresolved; no resolver link, observed 2026-08-06T15:32:52.190864Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0d33dda3-8201-4b4e-9da3-d38188f680ef · outbound

This paper cites Cvt: Introducing convolutions to vision transformers.

Foundation Models and Transformers for Anomaly Detection: A Survey Cvt: Introducing convolutions to vision transformers

Reference 2018

Resolution: unresolved; no resolver link, observed 2026-08-06T15:32:52.942164Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f8cdc5d6-13a9-433e-8562-755b99e46141 · outbound

This paper cites SAM-LAD: Segment Anything Model Meets Zero-Shot Logic Anomaly Detection.

Foundation Models and Transformers for Anomaly Detection: A Survey SAM-LAD: Segment Anything Model Meets Zero-Shot Logic Anomaly Detection

Reference 2019

Resolution: verified exact; local_arxiv, observed 2026-08-06T15:32:53.643810Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 14fd36d9-35e5-404d-a2b8-f6421eba3cb6 · outbound

This paper cites BigScience Workshop.

Foundation Models and Transformers for Anomaly Detection: A Survey BigScience Workshop

Reference 2020

Resolution: malformed identifier; no resolver link, observed 2026-08-06T15:32:48.893251Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8bd67b78-ea24-46e5-bc49-e6d87a166f10 · outbound

This paper cites A-vae: Attention based variational autoencoder for traffic video anomaly detection.

Foundation Models and Transformers for Anomaly Detection: A Survey A-vae: Attention based variational autoencoder for traffic video anomaly detection

Reference 2021

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:32:54.784246Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 4af4e430-7d1c-4db7-a3d9-42f0544c7b70 · outbound

This paper cites Mind the Pad -- CNNs can Develop Blind Spots.

Foundation Models and Transformers for Anomaly Detection: A Survey Mind the Pad -- CNNs can Develop Blind Spots

Reference 2022

Resolution: verified exact; local_arxiv, observed 2026-08-06T15:32:54.681036Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation d37fc4e1-9a45-4adf-a1cd-3e827d2fefb1 · outbound

This paper cites D3D-HOI: Dynamic 3D Human-Object Interactions from Videos.

Foundation Models and Transformers for Anomaly Detection: A Survey D3D-HOI: Dynamic 3D Human-Object Interactions from Videos

Reference 2023

Resolution: unresolved; no resolver link, observed 2026-08-06T15:32:48.747249Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a1a2226f-13a8-47df-b020-48b14c39ec3a · outbound

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

Foundation Models and Transformers for Anomaly Detection: A Survey Towards Generic Anomaly Detection and Understanding: Large-scale Visual-linguistic Model (GPT-4V) Takes the Lead

Reference 2024

Resolution: unresolved; no resolver link, observed 2026-08-06T15:32:49.214107Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3cbfde70-e9c0-42da-8236-5096c636530e · outbound

This paper cites MuSc: Zero-Shot Industrial Anomaly Classification and Segmentation with Mutual Scoring of the Unlabeled Images.

Foundation Models and Transformers for Anomaly Detection: A Survey MuSc: Zero-Shot Industrial Anomaly Classification and Segmentation with Mutual Scoring of the Unlabeled Images

Reference 2025

Resolution: metadata mismatch; local_arxiv, observed 2026-08-06T15:32:53.965219Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Coverage vector

Reference resolution

Outbound references

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