AnomalyVFM -- Transforming Vision Foundation Models into Zero-Shot Anomaly Detectors | Paper Citation Record

Observation aa2c132e-b5b9-4625-967a-4eb77a786857 · outbound

This paper cites Zero-shot versus many-shot: Unsupervised texture anomaly detection.

AnomalyVFM -- Transforming Vision Foundation Models into Zero-Shot Anomaly Detectors Zero-shot versus many-shot: Unsupervised texture anomaly detection

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T10:47:45.639077Z

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Observation 4182ec06-3eb8-4f33-89ed-00f7fd9c01a8 · outbound

This paper cites Efficien- tAD: Accurate Visual Anomaly Detection at Millisecond- Level Latencies.

AnomalyVFM -- Transforming Vision Foundation Models into Zero-Shot Anomaly Detectors Efficien- tAD: Accurate Visual Anomaly Detection at Millisecond- Level Latencies

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T10:47:45.646890Z

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No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 42e6d01f-be28-4483-b057-e4c49aa3703e · outbound

This paper cites Improving Unsupervised Defect Segmentation by Applying Structural Similarity to Autoencoders.

AnomalyVFM -- Transforming Vision Foundation Models into Zero-Shot Anomaly Detectors Improving Unsupervised Defect Segmentation by Applying Structural Similarity to Autoencoders

Reference 3

Resolution: verified exact; local_arxiv, observed 2026-05-16T10:47:45.448752Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 6aaa9f67-dcb8-49c3-b37d-a9fc84570250 · outbound

This paper cites MVTec AD–A Comprehensive Real-World Dataset for Unsupervised Anomaly Detection.

AnomalyVFM -- Transforming Vision Foundation Models into Zero-Shot Anomaly Detectors MVTec AD–A Comprehensive Real-World Dataset for Unsupervised Anomaly Detection

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T10:47:45.649451Z

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No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 987b7185-0a5b-4087-82d5-2a5bcd0d8251 · outbound

This paper cites Wm-dova maps for accurate polyp highlighting in colonoscopy: Validation vs.

AnomalyVFM -- Transforming Vision Foundation Models into Zero-Shot Anomaly Detectors Wm-dova maps for accurate polyp highlighting in colonoscopy: Validation vs

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T10:47:45.651851Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 198e1598-1c49-43c5-a211-6ea1b453d191 · outbound

This paper cites Language Models are Realistic Tabular Data Generators.

AnomalyVFM -- Transforming Vision Foundation Models into Zero-Shot Anomaly Detectors Language Models are Realistic Tabular Data Generators

Reference 6

Resolution: verified exact; arxiv_id, observed 2026-05-16T10:47:45.453001Z

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Observation 3f32051a-b339-404f-97d4-c2a24b3d895c · outbound

This paper cites Mixed supervision for surface-defect detection: From weakly to fully supervised learning.Computers in Industry, 129: 103459.

AnomalyVFM -- Transforming Vision Foundation Models into Zero-Shot Anomaly Detectors Mixed supervision for surface-defect detection: From weakly to fully supervised learning.Computers in Industry, 129: 103459

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T10:47:45.644269Z

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Observation 6917ce3d-6cb6-4f74-ad47-bbe75c2e1f0a · outbound

This paper cites Segment Any Anomaly without Training via Hybrid Prompt Regularization.

AnomalyVFM -- Transforming Vision Foundation Models into Zero-Shot Anomaly Detectors Segment Any Anomaly without Training via Hybrid Prompt Regularization

Reference 8

Resolution: verified exact; arxiv_id, observed 2026-05-16T10:47:45.444905Z

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Observation 8661f5df-862c-42c5-a050-79e0d3f38557 · outbound

This paper cites AdaCLIP: Adapting CLIP with hybrid learnable prompts for zero-shot anomaly detection.

AnomalyVFM -- Transforming Vision Foundation Models into Zero-Shot Anomaly Detectors AdaCLIP: Adapting CLIP with hybrid learnable prompts for zero-shot anomaly detection

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T10:47:45.641683Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 0932bb63-d035-44e8-817e-c4f0240d20b3 · outbound

This paper cites Back on track: Bundle adjustment for dynamic scene re- construction.

AnomalyVFM -- Transforming Vision Foundation Models into Zero-Shot Anomaly Detectors Back on track: Bundle adjustment for dynamic scene re- construction

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T10:47:45.795367Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 04a14f05-cb93-4de1-9742-a58ab130703c · outbound

This paper cites Clip-AD: A Language-Guided Staged Dual-Path Model for Zero-shot Anomaly Detection.

AnomalyVFM -- Transforming Vision Foundation Models into Zero-Shot Anomaly Detectors Clip-AD: A Language-Guided Staged Dual-Path Model for Zero-shot Anomaly Detection

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T10:47:45.839426Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation e9f129cf-06c6-4d16-8e5a-47514e892661 · outbound

This paper cites an unresolved cited work.

AnomalyVFM -- Transforming Vision Foundation Models into Zero-Shot Anomaly Detectors Unresolved cited work

Reference 12

Resolution: unresolved; raw_fallback, observed 2026-05-16T10:47:45.743741Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 0afdf707-21ee-4a5d-a63a-4334ef897ad9 · outbound

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

AnomalyVFM -- Transforming Vision Foundation Models into Zero-Shot Anomaly Detectors Padim: a patch distribution modeling framework for anomaly detection and localization

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T10:47:45.656885Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation f8d24d22-46fa-4773-9142-01086a30f90e · outbound

This paper cites Outlier detec- tion by ensembling uncertainty with negative objectness.

AnomalyVFM -- Transforming Vision Foundation Models into Zero-Shot Anomaly Detectors Outlier detec- tion by ensembling uncertainty with negative objectness

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T10:47:45.718350Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation d91f135c-66ec-4edd-92c1-105489e51636 · outbound

This paper cites Anomaly Detection via Re- verse Distillation from One-Class Embedding.

AnomalyVFM -- Transforming Vision Foundation Models into Zero-Shot Anomaly Detectors Anomaly Detection via Re- verse Distillation from One-Class Embedding

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T10:47:45.716368Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 8846080c-303c-4ef0-842e-8520a3177630 · outbound

This paper cites Few- shot defect image generation via defect-aware feature ma- nipulation.Proceedings of the AAAI Conference on Artificial Intelligence, 37(1):571–578.

AnomalyVFM -- Transforming Vision Foundation Models into Zero-Shot Anomaly Detectors Few- shot defect image generation via defect-aware feature ma- nipulation.Proceedings of the AAAI Conference on Artificial Intelligence, 37(1):571–578

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T10:47:45.765101Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation b0e2c1b2-a8af-4107-8843-6328ad2343d1 · outbound

This paper cites Scaling Rectified Flow Transformers for High-Resolution Image Synthesis.

AnomalyVFM -- Transforming Vision Foundation Models into Zero-Shot Anomaly Detectors Scaling Rectified Flow Transformers for High-Resolution Image Synthesis

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T10:47:45.688686Z

Source-reported events for the cited work

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Observation d37427b5-abb0-4f70-a8c1-82a564aa2541 · outbound

This paper cites TransFusion–a Transparency-based Diffusion Model for Anomaly Detection.

AnomalyVFM -- Transforming Vision Foundation Models into Zero-Shot Anomaly Detectors TransFusion–a Transparency-based Diffusion Model for Anomaly Detection

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T10:47:45.693485Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 5219ee0e-baa4-48b3-9f2e-17b0a706f995 · outbound

This paper cites SALAD – Semantics-Aware Logical Anomaly Detection.

AnomalyVFM -- Transforming Vision Foundation Models into Zero-Shot Anomaly Detectors SALAD – Semantics-Aware Logical Anomaly Detection

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T10:47:45.770601Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation f580063f-f562-461a-82be-2c1166d69439 · outbound

This paper cites Multi- task learning for thyroid nodule segmentation with thyroid region prior.

AnomalyVFM -- Transforming Vision Foundation Models into Zero-Shot Anomaly Detectors Multi- task learning for thyroid nodule segmentation with thyroid region prior

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T10:47:45.674097Z

Source-reported events for the cited work

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Observation 0cae5aca-7c70-4ae9-86e0-46c71b95ad5b · outbound

This paper cites an unresolved cited work.

AnomalyVFM -- Transforming Vision Foundation Models into Zero-Shot Anomaly Detectors Unresolved cited work

Reference 21

Resolution: unresolved; raw_fallback, observed 2026-05-16T10:47:45.690862Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 8e6cde57-5cc6-4dd2-ac6c-a2522846cca1 · outbound

This paper cites The 9 endotect 2020 challenge: evaluation and comparison of clas- sification, segmentation and inference time for endoscopy.

AnomalyVFM -- Transforming Vision Foundation Models into Zero-Shot Anomaly Detectors The 9 endotect 2020 challenge: evaluation and comparison of clas- sification, segmentation and inference time for endoscopy

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T10:47:45.832163Z

Source-reported events for the cited work

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Observation c3ebcd5b-4edf-4467-a444-c0d5f2d36e1c · outbound

This paper cites LoRA: Low-rank adaptation of large language models.

AnomalyVFM -- Transforming Vision Foundation Models into Zero-Shot Anomaly Detectors LoRA: Low-rank adaptation of large language models

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T10:47:45.667021Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation c0e59851-0ad3-4b2b-a242-67e393341caf · outbound

This paper cites Anomalyd- iffusion: Few-shot anomaly image generation with diffusion model.Proceedings of the AAAI Conference on Artificial Intelligence, 38(8):8526–8534.

AnomalyVFM -- Transforming Vision Foundation Models into Zero-Shot Anomaly Detectors Anomalyd- iffusion: Few-shot anomaly image generation with diffusion model.Proceedings of the AAAI Conference on Artificial Intelligence, 38(8):8526–8534

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T10:47:45.709751Z

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Observation dbce31e5-bb95-4c7d-92ad-f43b6faf75ca · outbound

This paper cites GPT-4o System Card.

AnomalyVFM -- Transforming Vision Foundation Models into Zero-Shot Anomaly Detectors GPT-4o System Card

Reference 25

Resolution: verified exact; local_arxiv, observed 2026-05-16T10:47:45.456460Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation b9e4b040-8096-4534-a791-67411581c75b · outbound

This paper cites WinCLIP: Zero- /Few-Shot Anomaly Classification and Segmentation.

AnomalyVFM -- Transforming Vision Foundation Models into Zero-Shot Anomaly Detectors WinCLIP: Zero- /Few-Shot Anomaly Classification and Segmentation

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T10:47:45.827149Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 34caf55d-7950-4948-bef0-3aa4b44534ae · outbound

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

AnomalyVFM -- Transforming Vision Foundation Models into Zero-Shot Anomaly Detectors Deep learning-based defect detection of metal parts: evaluating current methods in complex condi- tions

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T10:47:45.792827Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 9eae8eba-ee6d-4ff3-8f06-bf3768188614 · outbound

This paper cites Kvasir-seg: A segmented polyp dataset.

AnomalyVFM -- Transforming Vision Foundation Models into Zero-Shot Anomaly Detectors Kvasir-seg: A segmented polyp dataset

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T10:47:45.813462Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation f0e4711a-3cff-4fcf-8feb-60c9ee346743 · outbound

This paper cites an unresolved cited work.

AnomalyVFM -- Transforming Vision Foundation Models into Zero-Shot Anomaly Detectors Unresolved cited work

Reference 29

Resolution: parse uncertain; raw_fallback, observed 2026-05-16T10:47:45.767524Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation ab4a9fcd-4736-4801-a65d-e62902051ffe · outbound

This paper cites Vi- sual prompt tuning.

AnomalyVFM -- Transforming Vision Foundation Models into Zero-Shot Anomaly Detectors Vi- sual prompt tuning

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T10:47:45.820533Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation a965488a-3768-4114-962a-02480efa35e3 · outbound

This paper cites Brain tumor detec- tion using mri images.Brain, 3(2):146–150.

AnomalyVFM -- Transforming Vision Foundation Models into Zero-Shot Anomaly Detectors Brain tumor detec- tion using mri images.Brain, 3(2):146–150

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T10:47:45.776572Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 26e2a632-20af-4e19-8c34-068b8148322d · outbound

This paper cites Diffusion Models for Open-Vocabulary Segmen- tation.

AnomalyVFM -- Transforming Vision Foundation Models into Zero-Shot Anomaly Detectors Diffusion Models for Open-Vocabulary Segmen- tation

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T10:47:45.711912Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 75054e15-fe8c-4cf0-aa62-fcbb2b30b707 · outbound

This paper cites Repurpos- ing diffusion-based image generators for monocular depth estimation.

AnomalyVFM -- Transforming Vision Foundation Models into Zero-Shot Anomaly Detectors Repurpos- ing diffusion-based image generators for monocular depth estimation

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T10:47:45.705170Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 8be87eca-bbaa-4272-96a3-2edfa9a50e2e · outbound

This paper cites Multi-task learning using uncertainty to weigh losses for scene geome- try and semantics.

AnomalyVFM -- Transforming Vision Foundation Models into Zero-Shot Anomaly Detectors Multi-task learning using uncertainty to weigh losses for scene geome- try and semantics

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T10:47:45.815813Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 43ca7e98-66fd-43a1-91b6-2161083cf766 · outbound

This paper cites Segment any- thing.

AnomalyVFM -- Transforming Vision Foundation Models into Zero-Shot Anomaly Detectors Segment any- thing

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T10:47:45.722814Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation b2478547-c393-42f9-b792-18de28b47f90 · outbound

This paper cites Dataset Enhancement with Instance-Level Augmentations.

AnomalyVFM -- Transforming Vision Foundation Models into Zero-Shot Anomaly Detectors Dataset Enhancement with Instance-Level Augmentations

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T10:47:45.659489Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation a06cd634-81b7-4ac5-bd3a-c774bf01e41f · outbound

This paper cites Flux.https://github.com/ black-forest-labs/flux.

AnomalyVFM -- Transforming Vision Foundation Models into Zero-Shot Anomaly Detectors Flux.https://github.com/ black-forest-labs/flux

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T10:47:45.707538Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 58a7d2a8-566e-4dc3-a447-8a885aca762e · outbound

This paper cites Zero-Shot Anomaly Detection via Batch Normalization.Advances in Neural Information Processing Systems, 36.

AnomalyVFM -- Transforming Vision Foundation Models into Zero-Shot Anomaly Detectors Zero-Shot Anomaly Detection via Batch Normalization.Advances in Neural Information Processing Systems, 36

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T10:47:45.669636Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 1801933c-0ef7-440c-b1a6-768f98ac22cb · outbound

This paper cites PromptAD: Learning Prompts with only Normal Samples for Few-Shot Anomaly Detection.

AnomalyVFM -- Transforming Vision Foundation Models into Zero-Shot Anomaly Detectors PromptAD: Learning Prompts with only Normal Samples for Few-Shot Anomaly Detection

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T10:47:45.790256Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 50b419e1-0da3-4162-9a88-980ecefbbb53 · outbound

This paper cites PromptAD: Learning Prompts with only Normal Samples for Few-Shot Anomaly Detection.

AnomalyVFM -- Transforming Vision Foundation Models into Zero-Shot Anomaly Detectors PromptAD: Learning Prompts with only Normal Samples for Few-Shot Anomaly Detection

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T10:47:45.785488Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 68c3c6b6-4d80-40d1-875c-2dea6e91912c · outbound

This paper cites Scaling & Shifting Your Features: A New Baseline for Efficient Model Tuning.Advances in Neural Information Processing Systems, 35:109–123.

AnomalyVFM -- Transforming Vision Foundation Models into Zero-Shot Anomaly Detectors Scaling & Shifting Your Features: A New Baseline for Efficient Model Tuning.Advances in Neural Information Processing Systems, 35:109–123

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T10:47:45.698260Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 03396105-a4c0-408e-b6a6-30a8c5f7c7c5 · outbound

This paper cites Focal loss for dense object detection.

AnomalyVFM -- Transforming Vision Foundation Models into Zero-Shot Anomaly Detectors Focal loss for dense object detection

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T10:47:45.818442Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 9ef876db-9f37-415c-b279-5e82e1b8d157 · outbound

This paper cites Can OOD Object Detectors Learn from Founda- tion Models? InEuropean Conference on Computer Vision, pages 213–231.

AnomalyVFM -- Transforming Vision Foundation Models into Zero-Shot Anomaly Detectors Can OOD Object Detectors Learn from Founda- tion Models? InEuropean Conference on Computer Vision, pages 213–231

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T10:47:45.746227Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 726ce46c-3cd5-4ed2-a9fa-417f215646d6 · outbound

This paper cites Grounding DINO: Marrying dino with grounded pre-training for open-set object detection.

AnomalyVFM -- Transforming Vision Foundation Models into Zero-Shot Anomaly Detectors Grounding DINO: Marrying dino with grounded pre-training for open-set object detection

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T10:47:45.734550Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 4c881af4-486b-47f0-b663-a7194af1b69e · outbound

This paper cites SimpleNet: A Simple Network for Image Anomaly Detec- tion and Localization.

AnomalyVFM -- Transforming Vision Foundation Models into Zero-Shot Anomaly Detectors SimpleNet: A Simple Network for Image Anomaly Detec- tion and Localization

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T10:47:45.702651Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 5b589013-9840-4ae1-b999-1a5f4aab99e4 · outbound

This paper cites RePaint: Inpainting using denoising diffusion probabilistic models.

AnomalyVFM -- Transforming Vision Foundation Models into Zero-Shot Anomaly Detectors RePaint: Inpainting using denoising diffusion probabilistic models

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T10:47:45.810144Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 2b5888bc-0888-407d-b3b3-4197b56fea17 · outbound

This paper cites Exploring intrinsic normal prototypes within a single im- age for universal anomaly detection.

AnomalyVFM -- Transforming Vision Foundation Models into Zero-Shot Anomaly Detectors Exploring intrinsic normal prototypes within a single im- age for universal anomaly detection

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T10:47:45.683546Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 4a5d592a-f039-4b55-85d9-dac862e0de34 · outbound

This paper cites Aa-clip: Enhancing zero-shot anomaly detection via anomaly-aware clip.

AnomalyVFM -- Transforming Vision Foundation Models into Zero-Shot Anomaly Detectors Aa-clip: Enhancing zero-shot anomaly detection via anomaly-aware clip

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T10:47:45.664606Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 1b6dfd66-a66b-47e8-afdb-9cda6563980f · outbound

This paper cites VT-ADL: A vision trans- former network for image anomaly detection and localiza- tion.

AnomalyVFM -- Transforming Vision Foundation Models into Zero-Shot Anomaly Detectors VT-ADL: A vision trans- former network for image anomaly detection and localiza- tion

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T10:47:45.725094Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 3df71a14-863e-4791-95b7-3941fc7f13e5 · outbound

This paper cites an unresolved cited work.

AnomalyVFM -- Transforming Vision Foundation Models into Zero-Shot Anomaly Detectors Unresolved cited work

Reference 50

Resolution: unresolved; raw_fallback, observed 2026-05-16T10:47:45.686162Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation ffbdc5c7-e8bf-4baa-9783-312678c29c29 · outbound

This paper cites Inpainting transformer for anomaly detection.

AnomalyVFM -- Transforming Vision Foundation Models into Zero-Shot Anomaly Detectors Inpainting transformer for anomaly detection

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T10:47:45.676234Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 3c13dafc-be19-464f-8830-4cdc6fe985e6 · outbound

This paper cites Supporting high-level to low-level requirements coverage reviewing with large lan- guage models.

AnomalyVFM -- Transforming Vision Foundation Models into Zero-Shot Anomaly Detectors Supporting high-level to low-level requirements coverage reviewing with large lan- guage models

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T10:47:45.729920Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 175e430b-c57b-401c-8b23-efa5777460a9 · outbound

This paper cites Highly Accurate Dichotomous Im- age Segmentation.

AnomalyVFM -- Transforming Vision Foundation Models into Zero-Shot Anomaly Detectors Highly Accurate Dichotomous Im- age Segmentation

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T10:47:45.799893Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 4d5e799d-c548-4eed-8b1c-7c384bf06b2b · outbound

This paper cites Bayesian Prompt Flow Learning for Zero-Shot Anomaly De- tection.

AnomalyVFM -- Transforming Vision Foundation Models into Zero-Shot Anomaly Detectors Bayesian Prompt Flow Learning for Zero-Shot Anomaly De- tection

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T10:47:45.834398Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 7fcf1bd6-ee0c-4521-89ea-1f6dc96dec75 · outbound

This paper cites Learn- ing Transferable Visual Models From Natural Language Su- pervision.

AnomalyVFM -- Transforming Vision Foundation Models into Zero-Shot Anomaly Detectors Learn- ing Transferable Visual Models From Natural Language Su- pervision

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T10:47:45.671811Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation d95670fd-d6bd-4f73-8cc3-da4f7ee8f8fe · outbound

This paper cites AM-RADIO: Agglomerative vision founda- tion model reduce all domains into one.

AnomalyVFM -- Transforming Vision Foundation Models into Zero-Shot Anomaly Detectors AM-RADIO: Agglomerative vision founda- tion model reduce all domains into one

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T10:47:45.739230Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 7ff6a8df-a8d2-46a5-969a-6e71718f6228 · outbound

This paper cites SuperSim- pleNet: Unifying Unsupervised and Supervised Learning for Fast and Reliable Surface Defect Detection.

AnomalyVFM -- Transforming Vision Foundation Models into Zero-Shot Anomaly Detectors SuperSim- pleNet: Unifying Unsupervised and Supervised Learning for Fast and Reliable Surface Defect Detection

Reference 57

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T10:47:45.756656Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 3c164357-88ee-411d-9fd3-04296102be71 · outbound

This paper cites No Label Left Behind: A Unified Surface Defect Detection model for all Supervision Regimes.Journal of Intelligent Manufacturing.

AnomalyVFM -- Transforming Vision Foundation Models into Zero-Shot Anomaly Detectors No Label Left Behind: A Unified Surface Defect Detection model for all Supervision Regimes.Journal of Intelligent Manufacturing

Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T10:47:45.837059Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 8e52d039-8cd1-4e26-840d-8981dc642d9f · outbound

This paper cites High-Resolution Image Synthesis with Latent Diffusion Models.

AnomalyVFM -- Transforming Vision Foundation Models into Zero-Shot Anomaly Detectors High-Resolution Image Synthesis with Latent Diffusion Models

Reference 59

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T10:47:45.787977Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 2ce7f269-1428-4d36-9bb8-c4e8ef22bb6a · outbound

This paper cites Towards To- tal Recall in Industrial Anomaly Detection.

AnomalyVFM -- Transforming Vision Foundation Models into Zero-Shot Anomaly Detectors Towards To- tal Recall in Industrial Anomaly Detection

Reference 60

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T10:47:45.720700Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 40924d86-7ae3-4bef-8549-b61ccc3ed0f9 · outbound

This paper cites Multiresolution knowledge distillation for anomaly detection.

AnomalyVFM -- Transforming Vision Foundation Models into Zero-Shot Anomaly Detectors Multiresolution knowledge distillation for anomaly detection

Reference 61

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T10:47:45.824907Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation ea0ce8ba-2d8a-47f4-b0d1-9cbb093fca9f · outbound

This paper cites DINOv3.

AnomalyVFM -- Transforming Vision Foundation Models into Zero-Shot Anomaly Detectors DINOv3

Reference 62

Resolution: metadata mismatch; local_arxiv, observed 2026-05-16T10:47:45.459816Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 879cb494-4e49-4d77-a966-f07c23dc767f · outbound

This paper cites Segmentation-Based Deep-Learning Approach for Surface-Defect Detection.Journal of Intelligent Manufac- turing.

AnomalyVFM -- Transforming Vision Foundation Models into Zero-Shot Anomaly Detectors Segmentation-Based Deep-Learning Approach for Surface-Defect Detection.Journal of Intelligent Manufac- turing

Reference 63

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T10:47:45.807513Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation a070d4fc-4043-4881-82bc-b747c0a01dc6 · outbound

This paper cites Automated polyp detection in colonoscopy videos using shape and context information.IEEE transactions on medical imaging, 35(2):630–644.

AnomalyVFM -- Transforming Vision Foundation Models into Zero-Shot Anomaly Detectors Automated polyp detection in colonoscopy videos using shape and context information.IEEE transactions on medical imaging, 35(2):630–644

Reference 64

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T10:47:45.754377Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 040f2fbe-586d-4130-83bc-e41e208aba19 · outbound

This paper cites Kernel-aware graph prompt learning for few-shot anomaly detection.

AnomalyVFM -- Transforming Vision Foundation Models into Zero-Shot Anomaly Detectors Kernel-aware graph prompt learning for few-shot anomaly detection

Reference 65

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T10:47:45.797637Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 9e9d651c-df30-44ed-87bd-31f04503b209 · outbound

This paper cites Attention is all you need.Advances in neural information processing systems, 30.

AnomalyVFM -- Transforming Vision Foundation Models into Zero-Shot Anomaly Detectors Attention is all you need.Advances in neural information processing systems, 30

Reference 66

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T10:47:45.714128Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 5e624a05-fd8a-4f83-b020-23c169aec698 · outbound

This paper cites Image-consistent detection of road anomalies as unpredictable patches.

AnomalyVFM -- Transforming Vision Foundation Models into Zero-Shot Anomaly Detectors Image-consistent detection of road anomalies as unpredictable patches

Reference 67

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T10:47:45.843895Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation bfd5a891-4b89-49a0-b7aa-93e3007d551e · outbound

This paper cites Pixood: Pixel- level out-of-distribution detection.

AnomalyVFM -- Transforming Vision Foundation Models into Zero-Shot Anomaly Detectors Pixood: Pixel- level out-of-distribution detection

Reference 68

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T10:47:45.661869Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation c0895ecc-d785-40df-b23c-3ad8ed15f144 · outbound

This paper cites Wan: Open and Advanced Large-Scale Video Generative Models.

AnomalyVFM -- Transforming Vision Foundation Models into Zero-Shot Anomaly Detectors Wan: Open and Advanced Large-Scale Video Generative Models

Reference 69

Resolution: verified exact; local_arxiv, observed 2026-05-16T10:47:45.466981Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation b0ae86a8-d9d6-4dd0-8aa5-68a9d2d364c6 · outbound

This paper cites Real-IAD: A real-world multi-view dataset for benchmarking versatile industrial anomaly detec- tion.

AnomalyVFM -- Transforming Vision Foundation Models into Zero-Shot Anomaly Detectors Real-IAD: A real-world multi-view dataset for benchmarking versatile industrial anomaly detec- tion

Reference 70

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T10:47:45.782796Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 07af258d-f5bc-459a-8b2f-5a8c74100ab1 · outbound

This paper cites DUST3R: Geometric 3d vision made easy.

AnomalyVFM -- Transforming Vision Foundation Models into Zero-Shot Anomaly Detectors DUST3R: Geometric 3d vision made easy

Reference 71

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T10:47:45.727349Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 1f7ab777-0588-4d55-82ab-e9cdb2fbccd1 · outbound

This paper cites LLM-powered data augmentation for enhanced cross- lingual performance.

AnomalyVFM -- Transforming Vision Foundation Models into Zero-Shot Anomaly Detectors LLM-powered data augmentation for enhanced cross- lingual performance

Reference 72

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T10:47:45.762398Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 1356326b-8589-42a5-9e1d-09cdc3c23b6f · outbound

This paper cites Weakly supervised learn- ing for industrial optical inspection.

AnomalyVFM -- Transforming Vision Foundation Models into Zero-Shot Anomaly Detectors Weakly supervised learn- ing for industrial optical inspection

Reference 73

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T10:47:45.654554Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation d603721e-8fcc-4c69-b8ab-c2013348b86a · outbound

This paper cites Qwen-Image Technical Report.

AnomalyVFM -- Transforming Vision Foundation Models into Zero-Shot Anomaly Detectors Qwen-Image Technical Report

Reference 74

Resolution: verified exact; local_arxiv, observed 2026-05-16T10:47:45.463415Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation b63f627f-d631-462b-a109-c54abc4d7a4a · outbound

This paper cites Group normalization.

AnomalyVFM -- Transforming Vision Foundation Models into Zero-Shot Anomaly Detectors Group normalization

Reference 75

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T10:47:45.749289Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 3c74bfed-6e37-4da5-8fc4-59cdc339db2d · outbound

This paper cites Memseg: A semi- supervised method for image surface defect detection using differences and commonalities.Engineering Applications of Artificial Intelligence, 119:105835.

AnomalyVFM -- Transforming Vision Foundation Models into Zero-Shot Anomaly Detectors Memseg: A semi- supervised method for image surface defect detection using differences and commonalities.Engineering Applications of Artificial Intelligence, 119:105835

Reference 76

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T10:47:45.822964Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 62e23a6d-d6e2-4f93-9669-d93ab8253016 · outbound

This paper cites Defect spectrum: A granular look of large-scale defect datasets with rich seman- tics.

AnomalyVFM -- Transforming Vision Foundation Models into Zero-Shot Anomaly Detectors Defect spectrum: A granular look of large-scale defect datasets with rich seman- tics

Reference 77

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T10:47:45.773511Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation ce0dcb15-6784-44f7-aa17-8f5ada73a702 · outbound

This paper cites GPT3Mix: Leveraging large- scale language models for text augmentation.

AnomalyVFM -- Transforming Vision Foundation Models into Zero-Shot Anomaly Detectors GPT3Mix: Leveraging large- scale language models for text augmentation

Reference 78

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T10:47:45.695760Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 98444095-bd75-48c9-a598-b57dbcb0ddb4 · outbound

This paper cites an unresolved cited work.

AnomalyVFM -- Transforming Vision Foundation Models into Zero-Shot Anomaly Detectors Unresolved cited work

Reference 79

Resolution: unresolved; raw_fallback, observed 2026-05-16T10:47:45.779418Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation e1d8170c-829d-4396-be4a-eb0e749f1645 · outbound

This paper cites DRÆM - a Discriminatively Trained Reconstruction Embedding for Surface Anomaly Detection.

AnomalyVFM -- Transforming Vision Foundation Models into Zero-Shot Anomaly Detectors DRÆM - a Discriminatively Trained Reconstruction Embedding for Surface Anomaly Detection

Reference 80

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T10:47:45.736695Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation fa52716a-0fbc-47e8-86f8-df0aa2bdbfa8 · outbound

This paper cites Recon- struction by inpainting for visual anomaly detection.Pattern Recognition, 112:107706.

AnomalyVFM -- Transforming Vision Foundation Models into Zero-Shot Anomaly Detectors Recon- struction by inpainting for visual anomaly detection.Pattern Recognition, 112:107706

Reference 81

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T10:47:45.829559Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 0cca2f61-76f8-4984-94a4-b4e2b7722b72 · outbound

This paper cites DSR– a dual subspace re-projection network for surface anomaly detection.

AnomalyVFM -- Transforming Vision Foundation Models into Zero-Shot Anomaly Detectors DSR– a dual subspace re-projection network for surface anomaly detection

Reference 82

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T10:47:45.700471Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 5f12217e-ab4a-4e40-a542-222c09e4951c · outbound

This paper cites Cheat- ing depth: Enhancing 3d surface anomaly detection via depth simulation.

AnomalyVFM -- Transforming Vision Foundation Models into Zero-Shot Anomaly Detectors Cheat- ing depth: Enhancing 3d surface anomaly detection via depth simulation

Reference 83

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T10:47:45.760000Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation be319338-7840-4fec-883f-f32caba70cbd · outbound

This paper cites Defect-gan: High-fidelity defect synthesis for automated de- fect inspection.

AnomalyVFM -- Transforming Vision Foundation Models into Zero-Shot Anomaly Detectors Defect-gan: High-fidelity defect synthesis for automated de- fect inspection

Reference 84

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T10:47:45.741423Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation ed92be84-7350-4d6a-879d-14367ea12053 · outbound

This paper cites Synthetic video enhances physical fidelity in video synthesis.

AnomalyVFM -- Transforming Vision Foundation Models into Zero-Shot Anomaly Detectors Synthetic video enhances physical fidelity in video synthesis

Reference 85

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T10:47:45.732078Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 7f309e97-3d4e-4c00-a498-247a1df19e9c · outbound

This paper cites AnomalyCLIP: Object-agnostic Prompt Learning for Zero-shot Anomaly Detection.

AnomalyVFM -- Transforming Vision Foundation Models into Zero-Shot Anomaly Detectors AnomalyCLIP: Object-agnostic Prompt Learning for Zero-shot Anomaly Detection

Reference 86

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T10:47:45.751712Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 2883f68c-c430-433c-a8b6-83abe4256f16 · outbound

This paper cites Msflow: Multiscale flow-based framework for unsupervised anomaly detection.IEEE transactions on neural networks and learning systems.

AnomalyVFM -- Transforming Vision Foundation Models into Zero-Shot Anomaly Detectors Msflow: Multiscale flow-based framework for unsupervised anomaly detection.IEEE transactions on neural networks and learning systems

Reference 87

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T10:47:45.680864Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 55c02441-793c-415f-9a10-cbc84af0b71d · outbound

This paper cites Toward generalist anomaly detection via in-context residual learning with few-shot sam- ple prompts.

AnomalyVFM -- Transforming Vision Foundation Models into Zero-Shot Anomaly Detectors Toward generalist anomaly detection via in-context residual learning with few-shot sam- ple prompts

Reference 88

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T10:47:45.678629Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 5f5e5e46-b88f-4845-84b8-3837bbcdcf02 · outbound

This paper cites Do LLMs Understand Visual Anomalies? Un- covering LLM’s Capabilities in Zero-shot Anomaly Detec- tion.

AnomalyVFM -- Transforming Vision Foundation Models into Zero-Shot Anomaly Detectors Do LLMs Understand Visual Anomalies? Un- covering LLM’s Capabilities in Zero-shot Anomaly Detec- tion

Reference 89

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T10:47:45.802640Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation f9877161-ad93-4459-987b-f7171e6f2552 · outbound

This paper cites Fine-grained abnormality prompt learning for zero- shot anomaly detection.

AnomalyVFM -- Transforming Vision Foundation Models into Zero-Shot Anomaly Detectors Fine-grained abnormality prompt learning for zero- shot anomaly detection

Reference 90

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T10:47:45.804969Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 7f08c9ca-c6b9-4c81-9320-831aec447928 · outbound

This paper cites SPot-the-Difference Self-supervised Pre- training for Anomaly Detection and Segmentation.

AnomalyVFM -- Transforming Vision Foundation Models into Zero-Shot Anomaly Detectors SPot-the-Difference Self-supervised Pre- training for Anomaly Detection and Segmentation

Reference 91

Resolution: verified fuzzy; raw_fallback, observed 2026-05-16T10:47:45.841754Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Coverage vector

Reference resolution

Outbound references

Pith citing papers