UniADC: A Unified Framework for Anomaly Detection and Classification | Paper Citation Record

Observation 11931503-5e94-4645-bb11-c49ee556d1ef · outbound

This paper cites Deep learning for unsupervised anomaly localization in industrial images: A survey,.

UniADC: A Unified Framework for Anomaly Detection and Classification Deep learning for unsupervised anomaly localization in industrial images: A survey,

Reference 1

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Observation 6a26a331-b9e9-40c8-8a1d-dcf651065843 · outbound

This paper cites Gan-based anomaly detection: A review,.

UniADC: A Unified Framework for Anomaly Detection and Classification Gan-based anomaly detection: A review,

Reference 2

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Observation 44458dc5-4436-476e-9988-2ef3f67feab3 · outbound

This paper cites A survey of methods for automated quality control based on images,.

UniADC: A Unified Framework for Anomaly Detection and Classification A survey of methods for automated quality control based on images,

Reference 3

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Observation 5fd3cc06-b450-48ce-800b-41a48ec79b22 · outbound

This paper cites Deep industrial image anomaly detection: A survey,.

UniADC: A Unified Framework for Anomaly Detection and Classification Deep industrial image anomaly detection: A survey,

Reference 4

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Observation c821d335-4b9f-444a-8c6f-c75b3e67dd8c · outbound

This paper cites Mvrec: A general few-shot defect classification model using multi-view region- context,.

UniADC: A Unified Framework for Anomaly Detection and Classification Mvrec: A general few-shot defect classification model using multi-view region- context,

Reference 5

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Observation 0033428f-cc2d-4f38-a1cd-26105e48fcb2 · outbound

This paper cites Anomalyncd: Towards novel anomaly class discovery in industrial scenarios,.

UniADC: A Unified Framework for Anomaly Detection and Classification Anomalyncd: Towards novel anomaly class discovery in industrial scenarios,

Reference 6

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Observation 861885ed-088a-42c7-bfbd-4d9fd599526a · outbound

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

UniADC: A Unified Framework for Anomaly Detection and Classification Winclip: Zero-/few-shot anomaly classification and segmentation,

Reference 7

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Observation af12b5bb-bb2b-47d1-a5e1-4076fe0f53bd · outbound

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

UniADC: A Unified Framework for Anomaly Detection and Classification Promptad: Learning prompts with only normal samples for few-shot anomaly detection,

Reference 8

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Observation 234838dd-2043-48d1-96e3-a7d834b01a6c · outbound

This paper cites Anomalygpt: Detecting industrial anomalies using large vision-language models,.

UniADC: A Unified Framework for Anomaly Detection and Classification Anomalygpt: Detecting industrial anomalies using large vision-language models,

Reference 9

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Observation 94d2e629-1e6c-4ea6-9d7e-d32ffe7991a5 · outbound

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

UniADC: A Unified Framework for Anomaly Detection and Classification Kernel-aware graph prompt learning for few-shot anomaly detection,

Reference 10

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Observation 50e6fa4e-0a88-48c0-b52d-1fb404702bcc · outbound

This paper cites Medi- clip: Adapting clip for few-shot medical image anomaly detection,.

UniADC: A Unified Framework for Anomaly Detection and Classification Medi- clip: Adapting clip for few-shot medical image anomaly detection,

Reference 11

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Observation 1af5dbc6-1680-4d38-a172-11a13097374d · outbound

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

UniADC: A Unified Framework for Anomaly Detection and Classification Adapting visual-language models for generalizable anomaly detection in medical images,

Reference 12

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Observation 1af6b7c2-1d00-4f86-85bc-989f08acb700 · outbound

This paper cites High- resolution image synthesis with latent diffusion models,.

UniADC: A Unified Framework for Anomaly Detection and Classification High- resolution image synthesis with latent diffusion models,

Reference 13

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Observation 39d19839-1b61-44a4-97ec-365f2bd74489 · outbound

This paper cites Adding conditional control to text-to-image diffusion models,.

UniADC: A Unified Framework for Anomaly Detection and Classification Adding conditional control to text-to-image diffusion models,

Reference 14

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Observation 0439b4af-ff3e-4451-ab12-b90848081ef0 · outbound

This paper cites Surface defect saliency of magnetic tile,.

UniADC: A Unified Framework for Anomaly Detection and Classification Surface defect saliency of magnetic tile,

Reference 15

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Observation 1e7d766d-6a46-4b6f-8413-ba7a37cffb56 · outbound

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

UniADC: A Unified Framework for Anomaly Detection and Classification A unified anomaly synthesis strategy with gradient ascent for industrial anomaly detection and localization,

Reference 16

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Observation 15fdb7fc-0194-408d-9176-a07c5ec83862 · outbound

This paper cites Towards total recall in industrial anomaly detection,.

UniADC: A Unified Framework for Anomaly Detection and Classification Towards total recall in industrial anomaly detection,

Reference 17

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Observation acb8dc73-f3a0-4983-9ae6-fc3278f02431 · outbound

This paper cites Padim: a patch dis- tribution modeling framework for anomaly detection and localization,.

UniADC: A Unified Framework for Anomaly Detection and Classification Padim: a patch dis- tribution modeling framework for anomaly detection and localization,

Reference 18

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Observation 0a1f7c93-a6b6-4dda-a9b8-c88d007097ed · outbound

This paper cites Anomaly detection and localization via reverse distillation with latent anomaly suppression,.

UniADC: A Unified Framework for Anomaly Detection and Classification Anomaly detection and localization via reverse distillation with latent anomaly suppression,

Reference 19

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Observation 36f83fff-2588-4469-b176-90b5ae5aba2e · outbound

This paper cites Ura-net: Uncertainty-integrated anomaly perception and restoration attention network for unsupervised anomaly detection,.

UniADC: A Unified Framework for Anomaly Detection and Classification Ura-net: Uncertainty-integrated anomaly perception and restoration attention network for unsupervised anomaly detection,

Reference 20

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Observation 570c79bd-b718-4cf1-8867-38c29f957c70 · outbound

This paper cites Towards High-Resolution Industrial Image Anomaly Detection.

UniADC: A Unified Framework for Anomaly Detection and Classification Towards High-Resolution Industrial Image Anomaly Detection

Reference 21

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Observation f76639ee-066b-4823-a0be-cde30107a16f · outbound

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

UniADC: A Unified Framework for Anomaly Detection and Classification Few-shot anomaly- driven generation for anomaly classification and segmentation,

Reference 22

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Observation d7b22d3d-4f14-4d85-89af-42cb883eb250 · outbound

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

UniADC: A Unified Framework for Anomaly Detection and Classification Explicit boundary guided semi-push-pull contrastive learning for supervised anomaly detection,

Reference 23

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Observation cea2c110-d43c-41a9-91f9-7961559a5848 · outbound

This paper cites Normal-abnormal guided generalist anomaly detection,.

UniADC: A Unified Framework for Anomaly Detection and Classification Normal-abnormal guided generalist anomaly detection,

Reference 24

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Observation 5f5a9b61-2fb4-416b-b5a8-59fe1435fd92 · outbound

This paper cites Alpha-clip: A clip model focusing on wherever you want,.

UniADC: A Unified Framework for Anomaly Detection and Classification Alpha-clip: A clip model focusing on wherever you want,

Reference 25

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Observation 76b067cb-9b45-4893-b146-e7b7ae04f79c · outbound

This paper cites Mul- tiads: Defect-aware supervision for multi-type anomaly detection and segmentation in zero-shot learning,.

UniADC: A Unified Framework for Anomaly Detection and Classification Mul- tiads: Defect-aware supervision for multi-type anomaly detection and segmentation in zero-shot learning,

Reference 26

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Observation 61c41622-1d1d-460d-943e-09c0200b102f · outbound

This paper cites A noise robust method based on completed local binary patterns for hot-rolled steel strip surface defects,.

UniADC: A Unified Framework for Anomaly Detection and Classification A noise robust method based on completed local binary patterns for hot-rolled steel strip surface defects,

Reference 27

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Observation 1a91c4a8-c969-4a0a-88fc-4a39fbf0df69 · outbound

This paper cites Fabric defect classification using proto- typical network of few-shot learning algorithm,.

UniADC: A Unified Framework for Anomaly Detection and Classification Fabric defect classification using proto- typical network of few-shot learning algorithm,

Reference 28

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Observation 5c790abd-4c99-4fe2-80e4-0136803e1395 · outbound

This paper cites AnomalyPainter: Vision-Language-Diffusion Synergy for Zero-Shot Realistic and Diverse Industrial Anomaly Synthesis.

UniADC: A Unified Framework for Anomaly Detection and Classification AnomalyPainter: Vision-Language-Diffusion Synergy for Zero-Shot Realistic and Diverse Industrial Anomaly Synthesis

Reference 29

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Observation ebed6822-27e5-41a1-86fa-7f940ef61e14 · outbound

This paper cites Cutpaste: Self-supervised learning for anomaly detection and localization,.

UniADC: A Unified Framework for Anomaly Detection and Classification Cutpaste: Self-supervised learning for anomaly detection and localization,

Reference 30

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Observation 0adbdfc9-951f-47d2-881a-eb3d22c8d333 · outbound

This paper cites Natural synthetic anomalies for self-supervised anomaly detection and localization,.

UniADC: A Unified Framework for Anomaly Detection and Classification Natural synthetic anomalies for self-supervised anomaly detection and localization,

Reference 31

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Observation d0a3a5c6-a0aa-48f5-96b0-f8ca07da0518 · outbound

This paper cites Normal image guided segmentation framework for unsupervised anomaly detection,.

UniADC: A Unified Framework for Anomaly Detection and Classification Normal image guided segmentation framework for unsupervised anomaly detection,

Reference 32

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Observation 3cfa4a2e-82f5-4f7b-b595-a55e864fa5c6 · outbound

This paper cites Revisiting reverse distillation for anomaly detection,.

UniADC: A Unified Framework for Anomaly Detection and Classification Revisiting reverse distillation for anomaly detection,

Reference 33

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Observation 73bceef5-18d2-4ff0-8b64-49e5c04360ee · outbound

This paper cites Realnet: A feature selection network with realistic synthetic anomaly for anomaly detection,.

UniADC: A Unified Framework for Anomaly Detection and Classification Realnet: A feature selection network with realistic synthetic anomaly for anomaly detection,

Reference 34

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Observation e575a1b7-ceec-42ab-a3c2-8e6e9106f9bc · outbound

This paper cites Unseen visual anomaly gener- ation,.

UniADC: A Unified Framework for Anomaly Detection and Classification Unseen visual anomaly gener- ation,

Reference 35

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Observation 0895ba7f-a397-4b8d-8b90-374d5ac52d08 · outbound

This paper cites Anomagic: Crossmodal prompt-driven zero-shot anomaly generation,.

UniADC: A Unified Framework for Anomaly Detection and Classification Anomagic: Crossmodal prompt-driven zero-shot anomaly generation,

Reference 36

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Observation ed3cc96a-a53d-44cb-b640-d237aa7b5d00 · outbound

This paper cites Few-shot defect image generation via defect-aware feature manipulation,.

UniADC: A Unified Framework for Anomaly Detection and Classification Few-shot defect image generation via defect-aware feature manipulation,

Reference 37

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Observation 1e0830f0-cff1-4466-a09c-8ab70c26d9d1 · outbound

This paper cites Anomalydiffusion: Few-shot anomaly image generation with diffusion model,.

UniADC: A Unified Framework for Anomaly Detection and Classification Anomalydiffusion: Few-shot anomaly image generation with diffusion model,

Reference 38

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Observation c6cdb2bd-71f5-483b-b7dd-67b6fbc6cff1 · outbound

This paper cites Dual-interrelated diffusion model for few-shot anomaly image generation,.

UniADC: A Unified Framework for Anomaly Detection and Classification Dual-interrelated diffusion model for few-shot anomaly image generation,

Reference 39

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Observation c2da993e-166a-41e3-881e-20f99fe9f44f · outbound

This paper cites Defectfill: Realistic defect generation with inpainting diffusion model for visual inspection,.

UniADC: A Unified Framework for Anomaly Detection and Classification Defectfill: Realistic defect generation with inpainting diffusion model for visual inspection,

Reference 40

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Observation 2f677c42-4133-43ca-98c9-5c5c2c0b3bc3 · outbound

This paper cites Brushnet: A plug-and-play image inpainting model with decomposed dual-branch diffusion,.

UniADC: A Unified Framework for Anomaly Detection and Classification Brushnet: A plug-and-play image inpainting model with decomposed dual-branch diffusion,

Reference 41

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Observation 040eb612-cc98-4696-8712-0015ba0938c1 · outbound

This paper cites Image quality assessment: from error visibility to structural similarity,.

UniADC: A Unified Framework for Anomaly Detection and Classification Image quality assessment: from error visibility to structural similarity,

Reference 42

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Observation e5f13db4-40d8-4879-bf0b-bcc1de27df7b · outbound

This paper cites Draem-a discriminatively trained reconstruction embedding for surface anomaly detection,.

UniADC: A Unified Framework for Anomaly Detection and Classification Draem-a discriminatively trained reconstruction embedding for surface anomaly detection,

Reference 43

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Observation 1dcde37a-b4fa-4af1-b4e8-5321a0c5cef3 · outbound

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

UniADC: A Unified Framework for Anomaly Detection and Classification AnomalyCLIP: Object- agnostic prompt learning for zero-shot anomaly detection,

Reference 44

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Observation d341d484-a561-4e92-98d2-5f92b5168375 · outbound

This paper cites Focal loss for dense object detection,.

UniADC: A Unified Framework for Anomaly Detection and Classification Focal loss for dense object detection,

Reference 45

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Observation 086a700a-cc05-409f-ad51-702919306dd6 · outbound

This paper cites V-net: Fully convolutional neural networks for volumetric medical image segmentation,.

UniADC: A Unified Framework for Anomaly Detection and Classification V-net: Fully convolutional neural networks for volumetric medical image segmentation,

Reference 46

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Observation f9dda469-bff1-4f10-b3cb-3afbb58b6bd0 · outbound

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

UniADC: A Unified Framework for Anomaly Detection and Classification Toward generalist anomaly detection via in-context residual learning with few-shot sample prompts,

Reference 47

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Observation 868ab326-254e-4898-8f6b-94f80a342d58 · outbound

This paper cites Anomalydino: Boosting patch-based few-shot anomaly detection with dinov2,.

UniADC: A Unified Framework for Anomaly Detection and Classification Anomalydino: Boosting patch-based few-shot anomaly detection with dinov2,

Reference 48

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Observation 7c0b7f5b-5f7e-4ce6-a498-bf0e9df237cd · outbound

This paper cites Mvtec-ad: A comprehensive real-world dataset for unsupervised anomaly detection,.

UniADC: A Unified Framework for Anomaly Detection and Classification Mvtec-ad: A comprehensive real-world dataset for unsupervised anomaly detection,

Reference 49

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Observation d88d0675-e866-4661-8873-09871b2d7564 · outbound

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

UniADC: A Unified Framework for Anomaly Detection and Classification Spot-the- difference self-supervised pre-training for anomaly detection and seg- mentation,

Reference 50

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Observation 0ec26c24-f2b9-45de-8695-19333be044d6 · outbound

This paper cites Denoising diffusion implicit models,.

UniADC: A Unified Framework for Anomaly Detection and Classification Denoising diffusion implicit models,

Reference 51

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Observation 56f004d1-130d-43e8-9808-9289bc46e69b · outbound

This paper cites Bilateral reference for high-resolution dichotomous image segmentation,.

UniADC: A Unified Framework for Anomaly Detection and Classification Bilateral reference for high-resolution dichotomous image segmentation,

Reference 52

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Observation 6d726aa1-7304-4a74-9750-023897775a0f · outbound

This paper cites Learning transferable visual models from natural language supervision,.

UniADC: A Unified Framework for Anomaly Detection and Classification Learning transferable visual models from natural language supervision,

Reference 53

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Observation db4ce52f-d9db-4c44-9deb-47fc22d613b8 · outbound

This paper cites DINOv3.

UniADC: A Unified Framework for Anomaly Detection and Classification DINOv3

Reference 54

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Observation 165283c6-e670-48ea-8cbb-f250e7c7da3a · outbound

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

UniADC: A Unified Framework for Anomaly Detection and Classification Uninformed students: Student-teacher anomaly detection with discriminative latent embeddings,

Reference 55

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Observation 1c0d307a-8043-42cb-b870-e08802c4c8a7 · outbound

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

UniADC: A Unified Framework for Anomaly Detection and Classification Anomaly detection via reverse distillation from one-class embedding,

Reference 56

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Observation c5bf057c-4f75-46c0-baef-65a192ef481c · outbound

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

UniADC: A Unified Framework for Anomaly Detection and Classification Catching both gray and black swans: Open-set supervised anomaly detection,

Reference 57

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