Toward Long-Tailed Online Anomaly Detection through Class-Agnostic Concepts | Paper Citation Record

Observation 89ed104e-0305-4cdf-96d0-660613f13f83 · outbound

This paper cites CableInspect-AD: An expert- annotated anomaly detection dataset.

Toward Long-Tailed Online Anomaly Detection through Class-Agnostic Concepts CableInspect-AD: An expert- annotated anomaly detection dataset

Reference 1

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Observation d43d208f-cacb-4048-8c9e-aa5ccd256e81 · outbound

This paper cites Dual-path frequency discriminators for few-shot anomaly detection.Knowledge- Based Systems, 2024.

Toward Long-Tailed Online Anomaly Detection through Class-Agnostic Concepts Dual-path frequency discriminators for few-shot anomaly detection.Knowledge- Based Systems, 2024

Reference 2

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Observation 4a1c38fd-9cf4-4c45-894f-e7ce1a350dc5 · outbound

This paper cites The RSNA-ASNR-MICCAI BraTS 2021 Benchmark on Brain Tumor Segmentation and Radiogenomic Classification.

Toward Long-Tailed Online Anomaly Detection through Class-Agnostic Concepts The RSNA-ASNR-MICCAI BraTS 2021 Benchmark on Brain Tumor Segmentation and Radiogenomic Classification

Reference 3

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Observation bf241d17-0584-4289-ade4-a94ab95d21e3 · outbound

This paper cites BMAD: Benchmarks for med- ical anomaly detection.

Toward Long-Tailed Online Anomaly Detection through Class-Agnostic Concepts BMAD: Benchmarks for med- ical anomaly detection

Reference 4

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Observation c242c33d-caa3-4bcb-9a92-34cd841bb6eb · outbound

This paper cites MVTec AD–a comprehensive real-world dataset for unsupervised anomaly detection.

Toward Long-Tailed Online Anomaly Detection through Class-Agnostic Concepts MVTec AD–a comprehensive real-world dataset for unsupervised anomaly detection

Reference 5

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Observation aceefc85-d53f-4117-98ba-9b37b46e4a81 · outbound

This paper cites The liver tumor segmentation benchmark (lits).

Toward Long-Tailed Online Anomaly Detection through Class-Agnostic Concepts The liver tumor segmentation benchmark (lits)

Reference 6

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Observation afd10c94-7f5b-4916-9bbf-1cebbcd3b111 · outbound

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

Toward Long-Tailed Online Anomaly Detection through Class-Agnostic Concepts Segment Any Anomaly without Training via Hybrid Prompt Regularization

Reference 7

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Observation 451ac339-7fed-44e0-b369-95bb97edd32f · outbound

This paper cites A Survey on Visual Anomaly Detection: Challenge, Approach, and Prospect.

Toward Long-Tailed Online Anomaly Detection through Class-Agnostic Concepts A Survey on Visual Anomaly Detection: Challenge, Approach, and Prospect

Reference 8

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Observation 32793536-dfba-45ad-ad8c-8b19c2869a8c · outbound

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

Toward Long-Tailed Online Anomaly Detection through Class-Agnostic Concepts AdaCLIP: Adapt- ing CLIP with hybrid learnable prompts for zero-shot anomaly detection

Reference 9

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Observation 2cdc66dd-64b9-44d4-9763-0194751a339a · outbound

This paper cites Human-Free Automated Prompting for Vision-Language Anomaly Detection: Prompt Optimization with Meta-guiding Prompt Scheme.

Toward Long-Tailed Online Anomaly Detection through Class-Agnostic Concepts Human-Free Automated Prompting for Vision-Language Anomaly Detection: Prompt Optimization with Meta-guiding Prompt Scheme

Reference 10

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Observation 4d108bb2-d6a7-42c8-83a7-6a1aa245c268 · outbound

This paper cites A unified anomaly synthesis strategy with gradient ascent for industrial anomaly detection and localization.

Toward Long-Tailed Online Anomaly Detection through Class-Agnostic Concepts A unified anomaly synthesis strategy with gradient ascent for industrial anomaly detection and localization

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-06T15:06:24.403632Z

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Observation 503cec6b-8f00-42fb-a66e-2e2239624cee · outbound

This paper cites Sub-Image Anomaly Detection with Deep Pyramid Correspondences.

Toward Long-Tailed Online Anomaly Detection through Class-Agnostic Concepts Sub-Image Anomaly Detection with Deep Pyramid Correspondences

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-06T15:06:24.407310Z

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Observation 8a4daed3-a86e-4337-88b6-e14cf1808bb1 · outbound

This paper cites Microsoft Copilot, 2023.

Toward Long-Tailed Online Anomaly Detection through Class-Agnostic Concepts Microsoft Copilot, 2023

Reference 13

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Observation 85145034-1843-41f0-9919-d95f3de7fa8a · outbound

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

Toward Long-Tailed Online Anomaly Detection through Class-Agnostic Concepts PaDiM: a patch distribution modeling framework for anomaly detection and localization

Reference 14

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Observation 3b14db45-d729-4dc0-a132-8e7ddbdb161b · outbound

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

Toward Long-Tailed Online Anomaly Detection through Class-Agnostic Concepts Anomaly detection via reverse distillation from one-class embedding

Reference 15

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Observation b7a70748-717a-42d9-9e3e-cf8014e2213a · outbound

This paper cites Continual learning for anomaly detection in surveillance videos.

Toward Long-Tailed Online Anomaly Detection through Class-Agnostic Concepts Continual learning for anomaly detection in surveillance videos

Reference 16

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Observation 5bb3771f-d3ed-4873-b3a3-f45585defd37 · outbound

This paper cites Transformers: Align model docu- mentation.

Toward Long-Tailed Online Anomaly Detection through Class-Agnostic Concepts Transformers: Align model docu- mentation

Reference 17

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Observation 6e24a02c-66af-4c75-a961-f89d8e8568f3 · outbound

This paper cites ChangeChip: A reference-based unsupervised change de- tection for PCB defect detection.

Toward Long-Tailed Online Anomaly Detection through Class-Agnostic Concepts ChangeChip: A reference-based unsupervised change de- tection for PCB defect detection

Reference 18

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Observation 3050249a-4db8-459e-b35e-0c7786763d72 · outbound

This paper cites TransFusion–a transparency-based diffusion model for anomaly detection.

Toward Long-Tailed Online Anomaly Detection through Class-Agnostic Concepts TransFusion–a transparency-based diffusion model for anomaly detection

Reference 19

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Observation 47a3b7fa-49f4-4940-a097-f2a9811b3c0f · outbound

This paper cites Leveraging vector-quantized variational autoencoder inner metrics for anomaly detection.

Toward Long-Tailed Online Anomaly Detection through Class-Agnostic Concepts Leveraging vector-quantized variational autoencoder inner metrics for anomaly detection

Reference 20

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Observation e9a18568-99bd-4b21-aa27-09c62c38a58d · outbound

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

Toward Long-Tailed Online Anomaly Detection through Class-Agnostic Concepts Learning to detect multi-class anomalies with just one normal image prompt

Reference 21

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Observation 555185e9-f467-40c2-b9e0-feb38847393d · outbound

This paper cites Towards Total Online Unsupervised Anomaly Detection and Localization in Industrial Vision.

Toward Long-Tailed Online Anomaly Detection through Class-Agnostic Concepts Towards Total Online Unsupervised Anomaly Detection and Localization in Industrial Vision

Reference 22

Resolution: verified exact; local_arxiv, observed 2026-08-06T15:06:25.673128Z

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Observation b8245ceb-9518-435e-ae7f-e9f7be8dde1f · outbound

This paper cites Real-time evaluation in online continual learning: A new hope.

Toward Long-Tailed Online Anomaly Detection through Class-Agnostic Concepts Real-time evaluation in online continual learning: A new hope

Reference 23

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Observation de0432bd-4cab-4ffb-8e9b-06580ccc4a83 · outbound

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

Toward Long-Tailed Online Anomaly Detection through Class-Agnostic Concepts Filo: Zero-shot anomaly detection by fine-grained description and high- quality localization

Reference 24

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Observation d14a1a43-1116-4539-bd66-a58b9980b8ca · outbound

This paper cites AnomalyGPT: Detecting in- dustrial anomalies using large vision-language models.

Toward Long-Tailed Online Anomaly Detection through Class-Agnostic Concepts AnomalyGPT: Detecting in- dustrial anomalies using large vision-language models

Reference 25

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Observation 6342e330-054b-4d99-b355-abcb219fa22b · outbound

This paper cites Few-shot anomaly-driven generation for anomaly classification and segmentation.

Toward Long-Tailed Online Anomaly Detection through Class-Agnostic Concepts Few-shot anomaly-driven generation for anomaly classification and segmentation

Reference 26

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Observation 8fb96994-77bb-4e2e-ab98-3b326cabc2e2 · outbound

This paper cites Absolute-Unified Multi-Class Anomaly Detection via Class-Agnostic Distribution Alignment.

Toward Long-Tailed Online Anomaly Detection through Class-Agnostic Concepts Absolute-Unified Multi-Class Anomaly Detection via Class-Agnostic Distribution Alignment

Reference 27

Resolution: verified exact; local_arxiv, observed 2026-08-06T15:06:25.655152Z

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Observation 2c1dcd65-2da6-4b91-aa9c-7bb2d0c41ef9 · outbound

This paper cites Br35H: Brain tumor detection 2020,.

Toward Long-Tailed Online Anomaly Detection through Class-Agnostic Concepts Br35H: Brain tumor detection 2020,

Reference 28

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Observation b7c29ef9-8995-4a89-b60b-1a7509978757 · outbound

This paper cites OneLLM: One framework to align all modali- ties with language.

Toward Long-Tailed Online Anomaly Detection through Class-Agnostic Concepts OneLLM: One framework to align all modali- ties with language

Reference 29

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Observation 97945ddd-cb55-4a0f-99fa-c96cc6f28a39 · outbound

This paper cites MambaAD: Exploring state space models for multi-class unsupervised anomaly detec- tion.

Toward Long-Tailed Online Anomaly Detection through Class-Agnostic Concepts MambaAD: Exploring state space models for multi-class unsupervised anomaly detec- tion

Reference 30

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Observation cbc2afdd-eac9-44fa-a476-a46fad0b36a4 · outbound

This paper cites A diffusion-based framework for multi-class anomaly detection.

Toward Long-Tailed Online Anomaly Detection through Class-Agnostic Concepts A diffusion-based framework for multi-class anomaly detection

Reference 31

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Observation 2b05d1f6-21ef-4896-9098-80618a619c91 · outbound

This paper cites Deep residual learning for image recognition.

Toward Long-Tailed Online Anomaly Detection through Class-Agnostic Concepts Deep residual learning for image recognition

Reference 32

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Observation 9f88ccc1-935b-41b8-be92-8a29791cfa55 · outbound

This paper cites Learning unified reference rep- resentation for unsupervised multi-class anomaly detection.

Toward Long-Tailed Online Anomaly Detection through Class-Agnostic Concepts Learning unified reference rep- resentation for unsupervised multi-class anomaly detection

Reference 33

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Observation b07a383f-ef73-4fa5-ae50-90eade01e1a3 · outbound

This paper cites Long-tailed anomaly detection with learnable class names.

Toward Long-Tailed Online Anomaly Detection through Class-Agnostic Concepts Long-tailed anomaly detection with learnable class names

Reference 34

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Observation aa015249-eb91-4121-8a05-2fc6fddf56d2 · outbound

This paper cites Automated seg- mentation of macular edema in oct using deep neural net- works.

Toward Long-Tailed Online Anomaly Detection through Class-Agnostic Concepts Automated seg- mentation of macular edema in oct using deep neural net- works

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Observation dbb625ac-6bc9-4800-b5b7-236d66281da6 · outbound

This paper cites Registration based few-shot anomaly detection.

Toward Long-Tailed Online Anomaly Detection through Class-Agnostic Concepts Registration based few-shot anomaly detection

Reference 36

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Observation 6e69525d-a94d-4937-9815-da80bb436461 · outbound

This paper cites Adapting visual- language models for generalizable anomaly detection in medical images.

Toward Long-Tailed Online Anomaly Detection through Class-Agnostic Concepts Adapting visual- language models for generalizable anomaly detection in medical images

Reference 37

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Observation 7f8665c5-327c-4cd9-ac96-b556529b95cb · outbound

This paper cites ReCon- Patch: Contrastive patch representation learning for indus- trial anomaly detection.

Toward Long-Tailed Online Anomaly Detection through Class-Agnostic Concepts ReCon- Patch: Contrastive patch representation learning for indus- trial anomaly detection

Reference 38

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Observation ee3b911d-cefb-427c-b6ad-f7bb84690d3a · outbound

This paper cites Towards open-world object-based anomaly detection via self-supervised outlier synthesis.

Toward Long-Tailed Online Anomaly Detection through Class-Agnostic Concepts Towards open-world object-based anomaly detection via self-supervised outlier synthesis

Reference 39

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Observation 6d240a20-fbb6-4bec-8ee7-9e33bf18ccc7 · outbound

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

Toward Long-Tailed Online Anomaly Detection through Class-Agnostic Concepts WinCLIP: Zero- /few-shot anomaly classification and segmentation

Reference 40

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Observation 4ac40283-b55e-404d-bc32-8387862c7dce · outbound

This paper cites Scaling up visual and vision-language representation learning with noisy text supervision.

Toward Long-Tailed Online Anomaly Detection through Class-Agnostic Concepts Scaling up visual and vision-language representation learning with noisy text supervision

Reference 41

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Observation e35a8911-e4eb-4641-aea2-b3bdd47ab346 · outbound

This paper cites Brain tumor detec- tion using MRI images.

Toward Long-Tailed Online Anomaly Detection through Class-Agnostic Concepts Brain tumor detec- tion using MRI images

Reference 42

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Observation f2175444-14c0-4e86-966b-93ec3ecf6e9a · outbound

This paper cites Head CT - hemorrhage, 2018.

Toward Long-Tailed Online Anomaly Detection through Class-Agnostic Concepts Head CT - hemorrhage, 2018

Reference 43

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Observation bf5c8173-1a31-4dcd-8707-b2d6e6d9030b · outbound

This paper cites Online continual learning on class incremental blurry task configuration with anytime inference.

Toward Long-Tailed Online Anomaly Detection through Class-Agnostic Concepts Online continual learning on class incremental blurry task configuration with anytime inference

Reference 44

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Observation 4b0040c4-0510-41e1-a1ac-817a90d11ac7 · outbound

This paper cites Miccai multi-atlas la- beling beyond the cranial vault–workshop and challenge.

Toward Long-Tailed Online Anomaly Detection through Class-Agnostic Concepts Miccai multi-atlas la- beling beyond the cranial vault–workshop and challenge

Reference 45

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Observation 190094b2-d114-41a2-800e-288ea761bf3f · outbound

This paper cites Continuous memory representation for anomaly detection.

Toward Long-Tailed Online Anomaly Detection through Class-Agnostic Concepts Continuous memory representation for anomaly detection

Reference 46

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Observation 70898475-a245-4b49-b58e-bdc4cf289caf · outbound

This paper cites AD3: Introducing a score for anomaly detec- tion dataset difficulty assessment using VIADUCT dataset.

Toward Long-Tailed Online Anomaly Detection through Class-Agnostic Concepts AD3: Introducing a score for anomaly detec- tion dataset difficulty assessment using VIADUCT dataset

Reference 47

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Observation 9fcab1f9-b02b-4de5-b1eb-a95979a8a64e · outbound

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

Toward Long-Tailed Online Anomaly Detection through Class-Agnostic Concepts CutPaste: Self-supervised learning for anomaly de- tection and localization

Reference 48

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Observation 4cd212ae-a602-4c7a-a7c1-022a765caddc · outbound

This paper cites ClipSAM: CLIP and SAM Collaboration for Zero-Shot Anomaly Segmentation.

Toward Long-Tailed Online Anomaly Detection through Class-Agnostic Concepts ClipSAM: CLIP and SAM Collaboration for Zero-Shot Anomaly Segmentation

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Resolution: verified exact; local_arxiv, observed 2026-08-06T15:06:25.635748Z

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Observation 51dc7f09-5250-42cb-8269-a5fecfdd835e · outbound

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

Toward Long-Tailed Online Anomaly Detection through Class-Agnostic Concepts MuSc: Zero-shot industrial anomaly classification and segmenta- tion with mutual scoring of the unlabeled images

Reference 50

Resolution: unresolved; no resolver link, observed 2026-08-06T15:06:24.554839Z

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Observation 698df711-218b-48dd-92d6-7028b94e0ead · outbound

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

Toward Long-Tailed Online Anomaly Detection through Class-Agnostic Concepts PromptAD: Learning prompts with only normal samples for few-shot anomaly detection

Reference 51

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Observation 193aeb56-51c2-4abe-b8ce-0b34a102960e · outbound

This paper cites FADE: Few-shot/zero-shot anomaly detection engine using large vision-language model.

Toward Long-Tailed Online Anomaly Detection through Class-Agnostic Concepts FADE: Few-shot/zero-shot anomaly detection engine using large vision-language model

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Observation 60855bd5-2301-42e5-af0c-b471b8258311 · outbound

This paper cites COFT-AD: Contrastive fine-tuning for few-shot anomaly detection.

Toward Long-Tailed Online Anomaly Detection through Class-Agnostic Concepts COFT-AD: Contrastive fine-tuning for few-shot anomaly detection

Reference 53

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Observation 40450dee-7bb2-4dff-959c-192d088eca06 · outbound

This paper cites Learning diffusion models for multi-view anomaly detection.

Toward Long-Tailed Online Anomaly Detection through Class-Agnostic Concepts Learning diffusion models for multi-view anomaly detection

Reference 54

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Observation 56850e83-34e9-49cb-8b86-2f93fb5e5faf · outbound

This paper cites Unsupervised continual anomaly detection with contrastively-learned prompt.

Toward Long-Tailed Online Anomaly Detection through Class-Agnostic Concepts Unsupervised continual anomaly detection with contrastively-learned prompt

Reference 55

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Observation fe7049e8-4ae4-40cf-8e31-b5b510a601da · outbound

This paper cites Heterogeneity-aware recurrent neu- ral network for hyperspectral and multispectral image fu- sion.

Toward Long-Tailed Online Anomaly Detection through Class-Agnostic Concepts Heterogeneity-aware recurrent neu- ral network for hyperspectral and multispectral image fu- sion

Reference 56

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Observation 88126336-5d9f-4af2-bd06-a5e2c73f36ef · outbound

This paper cites Hierarchical vector quantized transformer for multi-class unsupervised anomaly detection.

Toward Long-Tailed Online Anomaly Detection through Class-Agnostic Concepts Hierarchical vector quantized transformer for multi-class unsupervised anomaly detection

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Observation 638e5ed3-4e73-4cf2-ab0c-4bf618da64d6 · outbound

This paper cites Review of wafer surface defect detection methods.

Toward Long-Tailed Online Anomaly Detection through Class-Agnostic Concepts Review of wafer surface defect detection methods

Reference 58

Resolution: unresolved; no resolver link, observed 2026-08-06T15:06:24.585045Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2bdf8618-188a-4768-8b48-a58766695824 · outbound

This paper cites Anomaly de- tection through latent space restoration using vector quan- tized variational autoencoders.

Toward Long-Tailed Online Anomaly Detection through Class-Agnostic Concepts Anomaly de- tection through latent space restoration using vector quan- tized variational autoencoders

Reference 59

Resolution: unresolved; no resolver link, observed 2026-08-06T15:06:24.588857Z

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Unavailable: canonical work link unavailable.

Observation 373891c2-88b9-476d-8a91-be5db03598c3 · outbound

This paper cites Mixture of ex- perts: a literature survey.

Toward Long-Tailed Online Anomaly Detection through Class-Agnostic Concepts Mixture of ex- perts: a literature survey

Reference 60

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Observation 4a2b1682-28fa-4b22-a64e-8169aff7e476 · outbound

This paper cites Unsu- pervised, online and on-the-fly anomaly detection for non- stationary image distributions.

Toward Long-Tailed Online Anomaly Detection through Class-Agnostic Concepts Unsu- pervised, online and on-the-fly anomaly detection for non- stationary image distributions

Reference 61

Resolution: unresolved; no resolver link, observed 2026-08-06T15:06:24.596489Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d748cb8e-9d20-4514-99b6-844d3cab02b7 · outbound

This paper cites MoEAD: A parameter- efficient model for multi-class anomaly detection.

Toward Long-Tailed Online Anomaly Detection through Class-Agnostic Concepts MoEAD: A parameter- efficient model for multi-class anomaly detection

Reference 62

Resolution: unresolved; no resolver link, observed 2026-08-06T15:06:24.599962Z

Source-reported events for the cited work

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Observation 6a5e2b89-15f5-477e-9c31-478c1dc2c601 · outbound

This paper cites RGI: Robust GAN- inversion for mask-free image inpainting and unsupervised pixel-wise anomaly detection.

Toward Long-Tailed Online Anomaly Detection through Class-Agnostic Concepts RGI: Robust GAN- inversion for mask-free image inpainting and unsupervised pixel-wise anomaly detection

Reference 63

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a9737e6d-38bf-4bf7-873d-fb3f7926d7fc · outbound

This paper cites ChatGPT, 2023.

Toward Long-Tailed Online Anomaly Detection through Class-Agnostic Concepts ChatGPT, 2023

Reference 64

Resolution: unresolved; no resolver link, observed 2026-08-06T15:06:24.607132Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 464cd2ed-d535-4fda-b5b7-4002c7d16df0 · outbound

This paper cites Deep learning for anomaly detection: A review.

Toward Long-Tailed Online Anomaly Detection through Class-Agnostic Concepts Deep learning for anomaly detection: A review

Reference 65

Resolution: unresolved; no resolver link, observed 2026-08-06T15:06:24.610853Z

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Unavailable: canonical work link unavailable.

Observation 5f735a8b-1922-4eb7-bd6f-fec1987d3dcd · outbound

This paper cites Revisiting deep feature reconstruction for logical and structural industrial anomaly detection.

Toward Long-Tailed Online Anomaly Detection through Class-Agnostic Concepts Revisiting deep feature reconstruction for logical and structural industrial anomaly detection

Reference 66

Resolution: unresolved; no resolver link, observed 2026-08-06T15:06:24.614559Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation afde420c-4e41-409d-a5c0-0c7c9dc10093 · outbound

This paper cites VCP-CLIP: A visual context prompting model for zero-shot anomaly seg- mentation.

Toward Long-Tailed Online Anomaly Detection through Class-Agnostic Concepts VCP-CLIP: A visual context prompting model for zero-shot anomaly seg- mentation

Reference 67

Resolution: unresolved; no resolver link, observed 2026-08-06T15:06:24.618357Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f6080dc1-9aea-4642-a309-461fa745f657 · outbound

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

Toward Long-Tailed Online Anomaly Detection through Class-Agnostic Concepts Learn- ing transferable visual models from natural language super- vision

Reference 68

Resolution: unresolved; no resolver link, observed 2026-08-06T15:06:24.621825Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation cfb28705-0a58-4b08-a2f5-1827708d6023 · outbound

This paper cites DELTA: Decoupling long-tailed online continual learning.

Toward Long-Tailed Online Anomaly Detection through Class-Agnostic Concepts DELTA: Decoupling long-tailed online continual learning

Reference 69

Resolution: unresolved; no resolver link, observed 2026-08-06T15:06:24.625581Z

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Observation 94c3faad-2152-4b83-82fa-d7445fd434cf · outbound

This paper cites Variational infer- ence with normalizing flows.

Toward Long-Tailed Online Anomaly Detection through Class-Agnostic Concepts Variational infer- ence with normalizing flows

Reference 70

Resolution: unresolved; no resolver link, observed 2026-08-06T15:06:24.629473Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6089365d-f7e4-4ddd-8d60-20c601852cc5 · outbound

This paper cites Scaling vision with sparse mix- ture of experts.

Toward Long-Tailed Online Anomaly Detection through Class-Agnostic Concepts Scaling vision with sparse mix- ture of experts

Reference 71

Resolution: unresolved; no resolver link, observed 2026-08-06T15:06:24.633385Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8904cecb-f9a9-48f0-b310-39c8e69cc7f9 · outbound

This paper cites Towards to- tal recall in industrial anomaly detection.

Toward Long-Tailed Online Anomaly Detection through Class-Agnostic Concepts Towards to- tal recall in industrial anomaly detection

Reference 72

Resolution: unresolved; no resolver link, observed 2026-08-06T15:06:24.636974Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation eb5270cd-5d2a-4998-a3b4-0a4b9cd2fd9d · outbound

This paper cites Same same but differnet: Semi-supervised defect detection with normalizing flows.

Toward Long-Tailed Online Anomaly Detection through Class-Agnostic Concepts Same same but differnet: Semi-supervised defect detection with normalizing flows

Reference 73

Resolution: unresolved; no resolver link, observed 2026-08-06T15:06:24.641981Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 63c0a64f-c579-4130-b744-e51efc48dfd4 · outbound

This paper cites Tire defect detection model using ma- chine learning.

Toward Long-Tailed Online Anomaly Detection through Class-Agnostic Concepts Tire defect detection model using ma- chine learning

Reference 74

Resolution: unresolved; no resolver link, observed 2026-08-06T15:06:24.646009Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 12692ee8-cee4-48b2-bf9b-97f7bc7a8535 · outbound

This paper cites Multiresolution knowledge distillation for anomaly detection.

Toward Long-Tailed Online Anomaly Detection through Class-Agnostic Concepts Multiresolution knowledge distillation for anomaly detection

Reference 75

Resolution: unresolved; no resolver link, observed 2026-08-06T15:06:24.649967Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6b0c17c8-9f19-49d3-9605-7fb9d2d53612 · outbound

This paper cites Dissolving is amplifying: Towards fine-grained anomaly detection.

Toward Long-Tailed Online Anomaly Detection through Class-Agnostic Concepts Dissolving is amplifying: Towards fine-grained anomaly detection

Reference 76

Resolution: unresolved; no resolver link, observed 2026-08-06T15:06:24.653565Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f2522519-ba90-4a90-9bbd-ac6671079c98 · outbound

This paper cites GeneralAD: Anomaly detection across domains by attending to distorted features.

Toward Long-Tailed Online Anomaly Detection through Class-Agnostic Concepts GeneralAD: Anomaly detection across domains by attending to distorted features

Reference 77

Resolution: unresolved; no resolver link, observed 2026-08-06T15:06:24.657036Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 189ce715-bd63-4e2a-b807-cd8164e1e6d9 · outbound

This paper cites EfficientNet: Rethinking model scaling for convolutional neural networks.

Toward Long-Tailed Online Anomaly Detection through Class-Agnostic Concepts EfficientNet: Rethinking model scaling for convolutional neural networks

Reference 78

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:06:26.526721Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation e819af4c-df5a-4ca4-a775-f78c9b635757 · outbound

This paper cites An incremental unified framework for small defect inspection.

Toward Long-Tailed Online Anomaly Detection through Class-Agnostic Concepts An incremental unified framework for small defect inspection

Reference 79

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:06:26.513965Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 2bdcb882-d38a-464b-b3d9-75c6cb922543 · outbound

This paper cites Revisiting reverse distillation for anomaly detection.

Toward Long-Tailed Online Anomaly Detection through Class-Agnostic Concepts Revisiting reverse distillation for anomaly detection

Reference 80

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:06:26.501823Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation bed54cc2-5684-4b32-a7ef-9a9cb5685342 · outbound

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

Toward Long-Tailed Online Anomaly Detection through Class-Agnostic Concepts LLaMA: Open and Efficient Foundation Language Models

Reference 81

Resolution: unresolved; no resolver link, observed 2026-08-06T15:06:24.671346Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 811b869b-d90b-4ae0-993c-ee82341552ea · outbound

This paper cites Neural discrete representation learning.

Toward Long-Tailed Online Anomaly Detection through Class-Agnostic Concepts Neural discrete representation learning

Reference 82

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:06:26.489366Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation e9ce8263-a7af-412a-8c49-c28f0b45b219 · outbound

This paper cites A comprehensive survey of continual learning: theory, method and application.

Toward Long-Tailed Online Anomaly Detection through Class-Agnostic Concepts A comprehensive survey of continual learning: theory, method and application

Reference 83

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:06:26.477211Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 63b25924-bbfe-4f5f-8394-97ba11b82302 · outbound

This paper cites Few-shot online anomaly detection and segmentation.

Toward Long-Tailed Online Anomaly Detection through Class-Agnostic Concepts Few-shot online anomaly detection and segmentation

Reference 84

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:06:26.465306Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 387660d0-de35-400e-9294-1be207535855 · outbound

This paper cites Weakly supervised learning for industrial optical inspection.

Toward Long-Tailed Online Anomaly Detection through Class-Agnostic Concepts Weakly supervised learning for industrial optical inspection

Reference 85

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:06:26.453472Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 46641ebb-fe93-4154-aa2b-6473ba5b20fb · outbound

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

Toward Long-Tailed Online Anomaly Detection through Class-Agnostic Concepts Defect spectrum: A granular look of large-scale defect datasets with rich seman- tics

Reference 86

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:06:26.441392Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation e312f0fc-6f39-4cb2-88f1-37c271146609 · outbound

This paper cites GLAD: Towards better reconstruction with global and local adaptive diffu- sion models for unsupervised anomaly detection.

Toward Long-Tailed Online Anomaly Detection through Class-Agnostic Concepts GLAD: Towards better reconstruction with global and local adaptive diffu- sion models for unsupervised anomaly detection

Reference 87

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:06:26.429362Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 1c3d783f-68a1-4360-8be6-27942f5cfa21 · outbound

This paper cites Hierarchical gaussian mixture normal- izing flow modeling for unified anomaly detection.

Toward Long-Tailed Online Anomaly Detection through Class-Agnostic Concepts Hierarchical gaussian mixture normal- izing flow modeling for unified anomaly detection

Reference 88

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:06:26.416664Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 703c86b2-e0be-4eae-8bbb-a732184f2cca · outbound

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

Toward Long-Tailed Online Anomaly Detection through Class-Agnostic Concepts A unified model for multi-class anomaly detection

Reference 89

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:06:26.404415Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 061896e5-9e47-4e27-89b5-f950a7bb6271 · outbound

This paper cites FastFlow: Unsupervised Anomaly Detection and Localization via 2D Normalizing Flows.

Toward Long-Tailed Online Anomaly Detection through Class-Agnostic Concepts FastFlow: Unsupervised Anomaly Detection and Localization via 2D Normalizing Flows

Reference 90

Resolution: unresolved; no resolver link, observed 2026-08-06T15:06:24.706782Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6f0d3926-16bd-455f-9885-cfe50a27a3bb · outbound

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

Toward Long-Tailed Online Anomaly Detection through Class-Agnostic Concepts DRAEM-a discriminatively trained reconstruction embed- ding for surface anomaly detection

Reference 91

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:06:26.392444Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 4e9f144e-9b86-4abb-944c-756f8b44e15e · outbound

This paper cites A Systematic Review on Long-Tailed Learning.

Toward Long-Tailed Online Anomaly Detection through Class-Agnostic Concepts A Systematic Review on Long-Tailed Learning

Reference 92

Resolution: verified exact; local_arxiv, observed 2026-08-06T15:06:25.589547Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 3dac0a2e-8d31-41ba-a71f-d72372582570 · outbound

This paper cites Defect-GAN: High-fidelity defect synthesis for automated defect inspection.

Toward Long-Tailed Online Anomaly Detection through Class-Agnostic Concepts Defect-GAN: High-fidelity defect synthesis for automated defect inspection

Reference 93

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:06:26.379292Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation a4eb71bd-a62d-4ee2-b4c5-b66959740f73 · outbound

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

Toward Long-Tailed Online Anomaly Detection through Class-Agnostic Concepts GPT-4V-AD: Exploring Grounding Potential of VQA-oriented GPT-4V for Zero-shot Anomaly Detection

Reference 94

Resolution: unresolved; no resolver link, observed 2026-08-06T15:06:24.722351Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4a689aea-0531-400c-9e58-ac0bfbec23b2 · outbound

This paper cites Exploring plain ViT reconstruction for multi- class unsupervised anomaly detection.

Toward Long-Tailed Online Anomaly Detection through Class-Agnostic Concepts Exploring plain ViT reconstruction for multi- class unsupervised anomaly detection

Reference 95

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:06:26.366583Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation b45fcf4b-96d8-45ff-aeb4-f6812680cb62 · outbound

This paper cites A Comprehensive Library for Benchmarking Multi-class Visual Anomaly Detection.

Toward Long-Tailed Online Anomaly Detection through Class-Agnostic Concepts A Comprehensive Library for Benchmarking Multi-class Visual Anomaly Detection

Reference 96

Resolution: unresolved; no resolver link, observed 2026-08-06T15:06:24.729744Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation efbe44d8-c251-4902-b2c0-be0848f55ff9 · outbound

This paper cites DeSTSeg: Segmentation guided denois- ing student-teacher for anomaly detection.

Toward Long-Tailed Online Anomaly Detection through Class-Agnostic Concepts DeSTSeg: Segmentation guided denois- ing student-teacher for anomaly detection

Reference 97

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:06:26.354201Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation a81cb44e-c014-4261-ba5b-b64825c1889b · outbound

This paper cites Meta-Transformer: A Unified Framework for Multimodal Learning.

Toward Long-Tailed Online Anomaly Detection through Class-Agnostic Concepts Meta-Transformer: A Unified Framework for Multimodal Learning

Reference 98

Resolution: unresolved; no resolver link, observed 2026-08-06T15:06:24.737211Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1f8eba74-3889-4dc3-8fde-eda6fc48f0c3 · outbound

This paper cites OmniAL: A unified cnn framework for unsu- pervised anomaly localization.

Toward Long-Tailed Online Anomaly Detection through Class-Agnostic Concepts OmniAL: A unified cnn framework for unsu- pervised anomaly localization

Reference 99

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:06:26.342179Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation da5e308c-ffc4-48de-98ab-0d3bf8da8314 · outbound

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

Toward Long-Tailed Online Anomaly Detection through Class-Agnostic Concepts AnomalyCLIP: Object-agnostic prompt learning for zero-shot anomaly detection

Reference 100

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:06:26.330123Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

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Reference resolution

Outbound references

Pith citing papers