Paper Citation Record · LEDGER

AdaptCLIP: Adapting CLIP for Universal Visual Anomaly Detection

As of 17 August 2026, this Paper Citation Record lists 60 of 60 outbound references and 4 inbound Pith citation observations for arXiv:2505.09926.

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

pith.paper-citation-record.v1
2505.09926 v2

Coverage vector

measured 60 of 60 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-15T21:25:26.406925Z

measured 64 of 64 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-17T06:30:58.91139+00:00

measured 4 of 4 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-07-15T12:56:18.518533Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-04T19:50:10.571308Z

Reference resolution

60 of 60 outbound references displayed

verified exact0
verified fuzzy57
unresolved2
parse uncertain0
malformed identifier1
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 6eb1ac44-5647-46d0-9c1a-bcf7ecb66178 · outbound

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

AdaptCLIP: Adapting CLIP for Universal Visual Anomaly Detection Zero-shot versus many-shot: Unsupervised texture anomaly detection

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:25:27.296918Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation f3bace4c-3ccc-42ba-845f-ba56b8667c6d · outbound

This paper cites Generalized denoising auto-encoders as generative models.

AdaptCLIP: Adapting CLIP for Universal Visual Anomaly Detection Generalized denoising auto-encoders as generative models

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:25:27.282860Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 73dbbc39-5169-4eac-b018-09d5a7e055d7 · outbound

This paper cites MVTec-AD: A comprehensive real-world dataset for unsupervised anomaly detection.

AdaptCLIP: Adapting CLIP for Universal Visual Anomaly Detection MVTec-AD: A comprehensive real-world dataset for unsupervised anomaly detection

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:25:27.268302Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 8d1586c1-16b1-4bf8-9231-d127b3e98257 · outbound

This paper cites Uninformed Students: Student-teacher anomaly detection with discriminative latent embeddings.

AdaptCLIP: Adapting CLIP for Universal Visual Anomaly Detection Uninformed Students: Student-teacher anomaly detection with discriminative latent embeddings

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:25:27.253957Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 59d1aeb1-f189-4e67-9987-fa0f3c442f9a · outbound

This paper cites The mvtec 3d-ad dataset for unsupervised 3d anomaly detection and localization.

AdaptCLIP: Adapting CLIP for Universal Visual Anomaly Detection The mvtec 3d-ad dataset for unsupervised 3d anomaly detection and localization

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:25:27.238959Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 8dcfd2a0-8ac3-4f76-a504-44f930cf6d3b · outbound

This paper cites Adaclip: Adapting clip with hybrid learnable prompts for zero-shot anomaly de- tection.

AdaptCLIP: Adapting CLIP for Universal Visual Anomaly Detection Adaclip: Adapting clip with hybrid learnable prompts for zero-shot anomaly de- tection

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:25:27.224689Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation fc09df6b-c386-4c29-9a91-db9a41776a9b · outbound

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

AdaptCLIP: Adapting CLIP for Universal Visual Anomaly Detection PaDiM: a patch distribution modeling framework for anomaly detection and localization

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:25:27.211349Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 348adc03-1737-4d24-8c2a-dd42346c89b1 · outbound

This paper cites Anomaly detection via reverse distillation from one-class embedding.

AdaptCLIP: Adapting CLIP for Universal Visual Anomaly Detection Anomaly detection via reverse distillation from one-class embedding

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:25:27.196948Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 078a155e-3e14-4cd5-b28c-60cfb7b7f3e6 · outbound

This paper cites Catching both gray and black swans: Open-set supervised anomaly detection.

AdaptCLIP: Adapting CLIP for Universal Visual Anomaly Detection Catching both gray and black swans: Open-set supervised anomaly detection

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:25:27.182524Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 4cc72d61-b635-4db6-9155-6b30b431e020 · outbound

This paper cites FastRecon: Few-shot industrial anomaly detection via fast feature reconstruction.

AdaptCLIP: Adapting CLIP for Universal Visual Anomaly Detection FastRecon: Few-shot industrial anomaly detection via fast feature reconstruction

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:25:27.168691Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation fa6b755a-bdc7-49bc-b527-4fba2556b597 · outbound

This paper cites Learning to detect multi-class anomalies with just one normal image prompt.

AdaptCLIP: Adapting CLIP for Universal Visual Anomaly Detection Learning to detect multi-class anomalies with just one normal image prompt

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:25:27.154369Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 8f54a421-f123-4239-a50f-d3733e97a5e3 · outbound

This paper cites Memorizing normality to detect anomaly: Memory-augmented deep autoencoder for unsupervised anomaly detection.

AdaptCLIP: Adapting CLIP for Universal Visual Anomaly Detection Memorizing normality to detect anomaly: Memory-augmented deep autoencoder for unsupervised anomaly detection

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:25:27.140434Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 0be4c10b-3c0b-483b-ba94-1abda18271d3 · outbound

This paper cites Br35h: Brain tumor detection 2020, 2020.

AdaptCLIP: Adapting CLIP for Universal Visual Anomaly Detection Br35h: Brain tumor detection 2020, 2020

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:25:27.126315Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 730a17e6-9481-4817-874a-81ac6c8f3318 · outbound

This paper cites Divide-and-Assemble: Learning block-wise memory for unsupervised anomaly detection.

AdaptCLIP: Adapting CLIP for Universal Visual Anomaly Detection Divide-and-Assemble: Learning block-wise memory for unsupervised anomaly detection

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:25:27.112569Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 212dbd67-f602-42c6-83d4-84a7b24cc58b · outbound

This paper cites Registration based few-shot anomaly detection.

AdaptCLIP: Adapting CLIP for Universal Visual Anomaly Detection Registration based few-shot anomaly detection

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:25:27.096841Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation f946ee2b-1f75-451c-8491-b636bc744af7 · outbound

This paper cites WinCLIP: Zero-/few-shot anomaly classification and segmentation.

AdaptCLIP: Adapting CLIP for Universal Visual Anomaly Detection WinCLIP: Zero-/few-shot anomaly classification and segmentation

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:25:27.082409Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 996f6cd3-86cc-477f-8047-d17231810219 · outbound

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

AdaptCLIP: Adapting CLIP for Universal Visual Anomaly Detection Deep learning-based defect detection of metal parts: evaluating current methods in complex condi- tions

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:25:27.067973Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation a2506228-4a2e-4d6b-8f8f-64bc0dbeec75 · outbound

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

AdaptCLIP: Adapting CLIP for Universal Visual Anomaly Detection Kvasir-seg: A segmented polyp dataset

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:25:27.054257Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation efa31e5c-57d0-4c65-9869-b25d63939b1c · outbound

This paper cites Pyramid- Flow: High-resolution defect contrastive localization using pyramid normalizing flow.

AdaptCLIP: Adapting CLIP for Universal Visual Anomaly Detection Pyramid- Flow: High-resolution defect contrastive localization using pyramid normalizing flow

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:25:27.039858Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 8f509e85-f39f-4587-a3c0-85ebe3d4be55 · outbound

This paper cites Zero-shot anomaly detection via batch normalization.

AdaptCLIP: Adapting CLIP for Universal Visual Anomaly Detection Zero-shot anomaly detection via batch normalization

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:25:27.025483Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation a1128d41-99f8-45b9-9cbf-ef4e9a6c1a53 · outbound

This paper cites CutPaste: Self-supervised learning for anomaly de- tection and localization.

AdaptCLIP: Adapting CLIP for Universal Visual Anomaly Detection CutPaste: Self-supervised learning for anomaly de- tection and localization

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:25:27.010442Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 7eebde31-1d12-4ef9-8e1f-026e443682d1 · outbound

This paper cites MuSc: Zero-shot industrial anomaly classification and segmentation with mutual scoring of the unlabeled images.

AdaptCLIP: Adapting CLIP for Universal Visual Anomaly Detection MuSc: Zero-shot industrial anomaly classification and segmentation with mutual scoring of the unlabeled images

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:25:26.996082Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 3791dd4e-d001-483b-9735-bf9e5364e9e8 · outbound

This paper cites PromptAD: Learn- ing prompts with only normal samples for few-shot anomaly detection.

AdaptCLIP: Adapting CLIP for Universal Visual Anomaly Detection PromptAD: Learn- ing prompts with only normal samples for few-shot anomaly detection

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:25:26.981663Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 34fc2a0c-c2cf-4d62-93ed-a5698a34e616 · outbound

This paper cites Diversity-measurable anomaly detection.

AdaptCLIP: Adapting CLIP for Universal Visual Anomaly Detection Diversity-measurable anomaly detection

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:25:26.966021Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 96ec95e4-b2f5-4755-98be-08af2858f41d · outbound

This paper cites SimpleNet: A simple network for image anomaly detection and localization.

AdaptCLIP: Adapting CLIP for Universal Visual Anomaly Detection SimpleNet: A simple network for image anomaly detection and localization

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:25:26.951372Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation ad52a3f4-b818-4742-bc59-a796e3193607 · outbound

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

AdaptCLIP: Adapting CLIP for Universal Visual Anomaly Detection VT-ADL: A vision trans- former network for image anomaly detection and localiza- tion

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:25:26.937538Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation cea737a8-56f7-4543-9f35-a6e6589e0ee2 · outbound

This paper cites OC- GAN: One-class novelty detection using GANs with con- strained latent representations.

AdaptCLIP: Adapting CLIP for Universal Visual Anomaly Detection OC- GAN: One-class novelty detection using GANs with con- strained latent representations

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:25:26.922987Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 8e717a08-6957-45fc-8480-73cd8e1c03ca · outbound

This paper cites Vcp-clip: A visual context prompting model for zero-shot anomaly segmenta- tion.

AdaptCLIP: Adapting CLIP for Universal Visual Anomaly Detection Vcp-clip: A visual context prompting model for zero-shot anomaly segmenta- tion

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:25:26.908069Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 3a647f97-54f6-4cb2-9708-985d7f9cc08f · outbound

This paper cites Learn- ing transferable visual models from natural language super- vision.

AdaptCLIP: Adapting CLIP for Universal Visual Anomaly Detection Learn- ing transferable visual models from natural language super- vision

Reference 29

Resolution: unresolved; no resolver link, observed 2026-08-15T21:25:26.268060Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 521b9f5f-c729-4265-ab6d-e1e5f4cd6531 · outbound

This paper cites Exploring the effect of image enhancement techniques on covid-19 detection using chest x-ray images.

AdaptCLIP: Adapting CLIP for Universal Visual Anomaly Detection Exploring the effect of image enhancement techniques on covid-19 detection using chest x-ray images

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:25:26.882982Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 6eb86ff1-7df5-48fe-8f98-58e658bcc69b · outbound

This paper cites Predictive coding in the visual cortex: a functional interpretation of some extra- classical receptive-field effects.

AdaptCLIP: Adapting CLIP for Universal Visual Anomaly Detection Predictive coding in the visual cortex: a functional interpretation of some extra- classical receptive-field effects

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:25:26.868975Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation eb54b3b0-cc93-41ce-8db3-7ae912d00b5d · outbound

This paper cites Modeling the distribution of normal data in pretrained deep features for anomaly detection.

AdaptCLIP: Adapting CLIP for Universal Visual Anomaly Detection Modeling the distribution of normal data in pretrained deep features for anomaly detection

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:25:26.854328Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 405946e3-5328-4fd8-8c92-9b22f6a1eb58 · outbound

This paper cites Self-supervised predictive con- volutional attentive block for anomaly detection.

AdaptCLIP: Adapting CLIP for Universal Visual Anomaly Detection Self-supervised predictive con- volutional attentive block for anomaly detection

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:25:26.839213Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 843abdc6-3b57-409a-a7ce-b3c2ae3be695 · outbound

This paper cites Towards total recall in industrial anomaly detection.

AdaptCLIP: Adapting CLIP for Universal Visual Anomaly Detection Towards total recall in industrial anomaly detection

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:25:26.824327Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 971f9710-c361-4ace-a842-14b2fa75d27f · outbound

This paper cites Fully convolutional cross-scale-flows for image- based defect detection.

AdaptCLIP: Adapting CLIP for Universal Visual Anomaly Detection Fully convolutional cross-scale-flows for image- based defect detection

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:25:26.809796Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation ad279e75-97bb-4c62-96a2-253b65864e83 · outbound

This paper cites Multiresolution knowledge distillation for anomaly detection.

AdaptCLIP: Adapting CLIP for Universal Visual Anomaly Detection Multiresolution knowledge distillation for anomaly detection

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:25:26.794884Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation a15053bd-1817-4354-b212-91a92949f2f6 · outbound

This paper cites A hierarchical transformation-discriminating generative model for few shot anomaly detection.

AdaptCLIP: Adapting CLIP for Universal Visual Anomaly Detection A hierarchical transformation-discriminating generative model for few shot anomaly detection

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:25:26.780403Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation f201c815-3bc0-4812-bf25-5dbda13d6889 · outbound

This paper cites Segmentation-based deep-learning approach for surface-defect detection.

AdaptCLIP: Adapting CLIP for Universal Visual Anomaly Detection Segmentation-based deep-learning approach for surface-defect detection

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:25:26.766839Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation d4e02952-6423-41fd-80e3-10eb4501b7f9 · outbound

This paper cites A benchmark for endoluminal scene segmentation of colonoscopy images.

AdaptCLIP: Adapting CLIP for Universal Visual Anomaly Detection A benchmark for endoluminal scene segmentation of colonoscopy images

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:25:26.752860Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation da13bf25-c6eb-4149-905f-7f159a35881f · outbound

This paper cites Extracting and composing robust features with denoising autoencoders.

AdaptCLIP: Adapting CLIP for Universal Visual Anomaly Detection Extracting and composing robust features with denoising autoencoders

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:25:26.738904Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation afd2a5c7-109d-4e7f-9e41-0954557c1dd5 · outbound

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

AdaptCLIP: Adapting CLIP for Universal Visual Anomaly Detection Real-iad: A real-world multi-view dataset for benchmarking versatile industrial anomaly detec- tion

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:25:26.726096Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation dd5727aa-48c6-4c38-804d-773ec2c93fd2 · outbound

This paper cites Student-teacher feature pyramid matching for anomaly de- tection.

AdaptCLIP: Adapting CLIP for Universal Visual Anomaly Detection Student-teacher feature pyramid matching for anomaly de- tection

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:25:26.712328Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 299c1186-e215-4ce0-adb9-7577b23ad324 · outbound

This paper cites Glanc- ing at the patch: Anomaly localization with global and local feature comparison.

AdaptCLIP: Adapting CLIP for Universal Visual Anomaly Detection Glanc- ing at the patch: Anomaly localization with global and local feature comparison

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:25:26.698054Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation f8a2cc79-7387-4c39-87c7-1d585ab693a7 · outbound

This paper cites Pushing the limits of fewshot anomaly detection in industry vision: Graphcore.

AdaptCLIP: Adapting CLIP for Universal Visual Anomaly Detection Pushing the limits of fewshot anomaly detection in industry vision: Graphcore

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:25:26.684040Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 70fae959-285d-4b9f-b2be-e56b8ef16a0e · outbound

This paper cites Learning semantic context from nor- mal samples for unsupervised anomaly detection.

AdaptCLIP: Adapting CLIP for Universal Visual Anomaly Detection Learning semantic context from nor- mal samples for unsupervised anomaly detection

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:25:26.669436Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation a7672ca3-d534-407d-bc94-d849fcf24465 · outbound

This paper cites Focus the Discrepancy: Intra-and inter- correlation learning for image anomaly detection.

AdaptCLIP: Adapting CLIP for Universal Visual Anomaly Detection Focus the Discrepancy: Intra-and inter- correlation learning for image anomaly detection

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:25:26.654084Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 5d51a556-14a4-42a8-9629-d8fe13836f98 · outbound

This paper cites Explicit boundary guided semi-push- pull contrastive learning for supervised anomaly detection.

AdaptCLIP: Adapting CLIP for Universal Visual Anomaly Detection Explicit boundary guided semi-push- pull contrastive learning for supervised anomaly detection

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:25:26.639702Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation ed43d1c2-43a2-43ca-b493-4f3d5de13c08 · outbound

This paper cites A unified model for multi-class anomaly detection.

AdaptCLIP: Adapting CLIP for Universal Visual Anomaly Detection A unified model for multi-class anomaly detection

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:25:26.625153Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 5bceb1b4-7a14-4858-827b-d81c32525d41 · outbound

This paper cites Old is Gold: Redefining the adversari- ally learned one-class classifier training paradigm.

AdaptCLIP: Adapting CLIP for Universal Visual Anomaly Detection Old is Gold: Redefining the adversari- ally learned one-class classifier training paradigm

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:25:26.610659Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation bd9f2638-d0c2-4d34-82c1-47dd53b99f29 · outbound

This paper cites DRAEM: A discriminatively trained reconstruction embed- ding for surface anomaly detection.

AdaptCLIP: Adapting CLIP for Universal Visual Anomaly Detection DRAEM: A discriminatively trained reconstruction embed- ding for surface anomaly detection

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:25:26.596575Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 741ccf52-b222-4a36-b161-0b59cd3f0abc · outbound

This paper cites Re- construction by inpainting for visual anomaly detection.

AdaptCLIP: Adapting CLIP for Universal Visual Anomaly Detection Re- construction by inpainting for visual anomaly detection

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:25:26.582059Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 80f35bed-fb10-4519-8c34-35387bcb0fab · outbound

This paper cites OmniAL: A unified cnn framework for unsuper- vised anomaly localization.

AdaptCLIP: Adapting CLIP for Universal Visual Anomaly Detection OmniAL: A unified cnn framework for unsuper- vised anomaly localization

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:25:26.567680Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 027c93ea-65f4-4d6c-b1d7-b0a05b214bb4 · outbound

This paper cites AnomalyCLIP: Object-agnostic prompt learning for zero-shot anomaly detection.

AdaptCLIP: Adapting CLIP for Universal Visual Anomaly Detection AnomalyCLIP: Object-agnostic prompt learning for zero-shot anomaly detection

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:25:26.552950Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 49ff365f-0ff8-4223-965b-6e450712187b · outbound

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

AdaptCLIP: Adapting CLIP for Universal Visual Anomaly Detection Toward generalist anomaly detection via in-context residual learning with few-shot sam- ple prompts

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:25:26.539189Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 30b998b7-2459-4a6e-b5c2-9b8a0d49b898 · outbound

This paper cites Spot-the-difference self-supervised pre- training for anomaly detection and segmentation.

AdaptCLIP: Adapting CLIP for Universal Visual Anomaly Detection Spot-the-difference self-supervised pre- training for anomaly detection and segmentation

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:25:26.524258Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 463ccba2-7b6e-4dcd-8869-3e22d56ca0d8 · outbound

This paper cites We only use two test datasets for model pre- training and generalization evaluation on other test datasets, and their relevant information is reported in Tab.

AdaptCLIP: Adapting CLIP for Universal Visual Anomaly Detection We only use two test datasets for model pre- training and generalization evaluation on other test datasets, and their relevant information is reported in Tab

Reference 57

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:25:26.493041Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 6c600588-bba4-4484-bd1c-18284db0a84f · outbound

This paper cites All images are resized to a resolution of 518×518 for training and testing.

AdaptCLIP: Adapting CLIP for Universal Visual Anomaly Detection All images are resized to a resolution of 518×518 for training and testing

Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:25:26.477967Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 6a1b1890-0d61-44c3-9723-d2a1dc7084f6 · outbound

This paper cites Here, we provide more comprehensive com- parisons, including image-level anomaly classification in AUPR and F1max in Tabs.

AdaptCLIP: Adapting CLIP for Universal Visual Anomaly Detection Here, we provide more comprehensive com- parisons, including image-level anomaly classification in AUPR and F1max in Tabs

Reference 59

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:25:26.461239Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 4d6080d0-83c0-4291-b967-8c74c74a46b6 · outbound

This paper cites Here, we show more visualizations for all 91 categories from 8 industrial and 2 medical datasets, as shown in Figs.

AdaptCLIP: Adapting CLIP for Universal Visual Anomaly Detection Here, we show more visualizations for all 91 categories from 8 industrial and 2 medical datasets, as shown in Figs

Reference 60

Resolution: malformed identifier; raw_fallback, observed 2026-08-15T21:25:26.445750Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation d82e51b4-c322-47e3-b5cc-7615316186cb · outbound

This paper cites an unresolved cited work.

AdaptCLIP: Adapting CLIP for Universal Visual Anomaly Detection Unresolved cited work

Reference 2022

Resolution: unresolved; raw_fallback, observed 2026-08-15T21:25:26.507763Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Pith citing papers

Observation bf202570-348f-456a-9eb2-83bce8d52a4f · inbound

Closed form perturbative relativistic modifications to wave-packet dynamics in the quantum harmonic oscillator cites this paper.

Closed form perturbative relativistic modifications to wave-packet dynamics in the quantum harmonic oscillator AdaptCLIP: Adapting CLIP for Universal Visual Anomaly Detection

Reference 16

Resolution: unresolved; no resolver link, observed 2026-07-15T12:56:18.518533Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5bbaae46-1e43-490e-a364-d8b0f5da7dd7 · inbound

EntroAD: Structural Entropy-Guided Prompt Adaptation for Zero-Shot Anomaly Detection cites this paper.

EntroAD: Structural Entropy-Guided Prompt Adaptation for Zero-Shot Anomaly Detection AdaptCLIP: Adapting CLIP for Universal Visual Anomaly Detection

Reference 11

Resolution: verified exact; arxiv_id, observed 2026-06-29T13:13:27.305414Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 4c2baac2-cb00-431d-9668-cd9780683cb7 · inbound

CoGeoAD: Hierarchical Color-Geometric Fusion with Multi-View Attention for Zero-Shot 3D Anomaly Detection cites this paper.

CoGeoAD: Hierarchical Color-Geometric Fusion with Multi-View Attention for Zero-Shot 3D Anomaly Detection AdaptCLIP: Adapting CLIP for Universal Visual Anomaly Detection

Reference 31

Resolution: verified exact; arxiv_id, observed 2026-07-04T19:50:10.572900Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 0e2a68c4-0b17-49a9-89ec-fced22698e6f · inbound

Global Logic and Local Search: Dual-Stream Multimodal In-Context Learning for Verifiable Industrial Anomaly Detection cites this paper.

Global Logic and Local Search: Dual-Stream Multimodal In-Context Learning for Verifiable Industrial Anomaly Detection AdaptCLIP: Adapting CLIP for Universal Visual Anomaly Detection

Reference 13

Resolution: unresolved; no resolver link, observed 2026-07-11T23:45:43.436443Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.