Bridge Feature Matching and Cross-Modal Alignment with Mutual-filtering for Zero-shot Anomaly Detection | Paper Citation Record

Observation fe32147b-db3a-4a43-918a-1df33f4d7593 · outbound

This paper cites Pni: Indus- trial anomaly detection using position and neighborhood in- formation.

Bridge Feature Matching and Cross-Modal Alignment with Mutual-filtering for Zero-shot Anomaly Detection Pni: Indus- trial anomaly detection using position and neighborhood in- formation

Reference 2

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Observation 29e271f8-46a1-4461-a90e-976eeae13579 · outbound

This paper cites Dual-path frequency discriminators for few-shot anomaly detection.

Bridge Feature Matching and Cross-Modal Alignment with Mutual-filtering for Zero-shot Anomaly Detection Dual-path frequency discriminators for few-shot anomaly detection

Reference 3

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Observation 32d4a9cf-70ab-4e9e-85bd-900dea1ba3e4 · outbound

This paper cites Mvtec ad–a comprehensive real-world dataset for unsupervised anomaly detection.

Bridge Feature Matching and Cross-Modal Alignment with Mutual-filtering for Zero-shot Anomaly Detection Mvtec ad–a comprehensive real-world dataset for unsupervised anomaly detection

Reference 4

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Observation b696cee6-e007-4a28-80e5-2f5ee8b97384 · outbound

This paper cites The mvtec anomaly detection dataset: a comprehensive real-world dataset for unsupervised anomaly detection.

Bridge Feature Matching and Cross-Modal Alignment with Mutual-filtering for Zero-shot Anomaly Detection The mvtec anomaly detection dataset: a comprehensive real-world dataset for unsupervised anomaly detection

Reference 5

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Observation b1e08200-9070-44a0-9221-a2fc00ca7fe4 · outbound

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

Bridge Feature Matching and Cross-Modal Alignment with Mutual-filtering for Zero-shot Anomaly Detection A Survey on Visual Anomaly Detection: Challenge, Approach, and Prospect

Reference 6

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Observation e3fa16cd-dad5-4d11-8c94-c31dcf231f3d · outbound

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

Bridge Feature Matching and Cross-Modal Alignment with Mutual-filtering for Zero-shot Anomaly Detection Adaclip: Adapting clip with hybrid learnable prompts for zero-shot anomaly de- tection

Reference 7

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Observation 735d69b0-3d46-4f19-ba75-a2906ff1948c · outbound

This paper cites A Unified Anomaly Synthesis Strategy with Gradient Ascent for Industrial Anomaly Detection and Localization.

Bridge Feature Matching and Cross-Modal Alignment with Mutual-filtering for Zero-shot Anomaly Detection A Unified Anomaly Synthesis Strategy with Gradient Ascent for Industrial Anomaly Detection and Localization

Reference 8

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Observation 5850c8cc-022a-4b39-8f94-510d4528ef6b · outbound

This paper cites APRIL-GAN: A Zero-/Few-Shot Anomaly Classification and Segmentation Method for CVPR 2023 VAND Workshop Challenge Tracks 1&2: 1st Place on Zero-shot AD and 4th Place on Few-shot AD.

Bridge Feature Matching and Cross-Modal Alignment with Mutual-filtering for Zero-shot Anomaly Detection APRIL-GAN: A Zero-/Few-Shot Anomaly Classification and Segmentation Method for CVPR 2023 VAND Workshop Challenge Tracks 1&2: 1st Place on Zero-shot AD and 4th Place on Few-shot AD

Reference 9

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Observation 3333646c-b3a1-4e23-86b8-6fa2ebe7881c · outbound

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

Bridge Feature Matching and Cross-Modal Alignment with Mutual-filtering for Zero-shot Anomaly Detection CLIP-AD: A Language-Guided Staged Dual-Path Model for Zero-shot Anomaly Detection

Reference 10

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Observation 215919a2-2363-48af-b330-8d59b912ecc2 · outbound

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

Bridge Feature Matching and Cross-Modal Alignment with Mutual-filtering for Zero-shot Anomaly Detection Padim: a patch distribution modeling framework for anomaly detection and localization

Reference 11

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Observation dee24cfe-c331-4cf6-941f-5bf2ee5bfb2b · outbound

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

Bridge Feature Matching and Cross-Modal Alignment with Mutual-filtering for Zero-shot Anomaly Detection Anomaly detection via reverse distillation from one-class embedding

Reference 12

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Observation f452aed3-423c-409e-9633-0c3a71278de1 · outbound

This paper cites An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale.

Bridge Feature Matching and Cross-Modal Alignment with Mutual-filtering for Zero-shot Anomaly Detection An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale

Reference 13

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Observation e062b93a-af44-4314-9081-60e0079623fb · outbound

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

Bridge Feature Matching and Cross-Modal Alignment with Mutual-filtering for Zero-shot Anomaly Detection Filo: Zero-shot anomaly detection by fine-grained description and high-quality local- ization

Reference 14

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Observation b4ca9623-634c-4aeb-bc9d-fda8cefa79ae · outbound

This paper cites Cflow-ad: Real-time unsupervised anomaly detection with localization via conditional normalizing flows.

Bridge Feature Matching and Cross-Modal Alignment with Mutual-filtering for Zero-shot Anomaly Detection Cflow-ad: Real-time unsupervised anomaly detection with localization via conditional normalizing flows

Reference 15

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Observation 4453393b-e76d-40f0-9770-b265ece90248 · outbound

This paper cites MambaAD: Exploring State Space Models for Multi-class Unsupervised Anomaly Detection.

Bridge Feature Matching and Cross-Modal Alignment with Mutual-filtering for Zero-shot Anomaly Detection MambaAD: Exploring State Space Models for Multi-class Unsupervised Anomaly Detection

Reference 16

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Observation 595970f5-a33c-4e27-a7eb-6b4d5026a88f · outbound

This paper cites Deep residual learning for image recognition.

Bridge Feature Matching and Cross-Modal Alignment with Mutual-filtering for Zero-shot Anomaly Detection Deep residual learning for image recognition

Reference 17

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Observation bcc6252c-9832-4412-a547-ae9fa9d465ee · outbound

This paper cites Anomalyd- iffusion: Few-shot anomaly image generation with diffusion model.

Bridge Feature Matching and Cross-Modal Alignment with Mutual-filtering for Zero-shot Anomaly Detection Anomalyd- iffusion: Few-shot anomaly image generation with diffusion model

Reference 18

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Observation 5161f416-7155-46c2-978d-41486e0030c6 · outbound

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

Bridge Feature Matching and Cross-Modal Alignment with Mutual-filtering for Zero-shot Anomaly Detection Winclip: Zero- /few-shot anomaly classification and segmentation

Reference 19

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Observation 1a54c288-6e1e-4028-a99c-ad479f2bbcc4 · outbound

This paper cites ClearCLIP: Decomposing CLIP Representations for Dense Vision-Language Inference.

Bridge Feature Matching and Cross-Modal Alignment with Mutual-filtering for Zero-shot Anomaly Detection ClearCLIP: Decomposing CLIP Representations for Dense Vision-Language Inference

Reference 20

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Observation d9ee6db1-e214-46a4-90c3-cc270d8e41bd · outbound

This paper cites ProxyCLIP: Proxy Attention Improves CLIP for Open-Vocabulary Segmentation.

Bridge Feature Matching and Cross-Modal Alignment with Mutual-filtering for Zero-shot Anomaly Detection ProxyCLIP: Proxy Attention Improves CLIP for Open-Vocabulary Segmentation

Reference 21

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Observation 6464c8b0-8677-4702-aac0-f2945f69d386 · outbound

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

Bridge Feature Matching and Cross-Modal Alignment with Mutual-filtering for Zero-shot Anomaly Detection Pyramid- flow: High-resolution defect contrastive localization using pyramid normalizing flow

Reference 22

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Observation a494e71d-147d-4c24-8caf-d47b3e5aaa11 · outbound

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

Bridge Feature Matching and Cross-Modal Alignment with Mutual-filtering for Zero-shot Anomaly Detection Zero-shot anomaly detection via batch normalization

Reference 23

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Observation c1fe39d6-36aa-4998-9a5c-539b4ae54368 · outbound

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

Bridge Feature Matching and Cross-Modal Alignment with Mutual-filtering for Zero-shot Anomaly Detection Zero-shot anomaly detection via batch normalization

Reference 24

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Observation 7c326da9-a1fe-44c2-a2be-48f1cc688331 · outbound

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

Bridge Feature Matching and Cross-Modal Alignment with Mutual-filtering for Zero-shot Anomaly Detection Cutpaste: Self-supervised learning for anomaly de- tection and localization

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:27:21.063198Z

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Observation 39d5fbe3-e170-4c0c-b46f-b9a828cbc12e · outbound

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

Bridge Feature Matching and Cross-Modal Alignment with Mutual-filtering for Zero-shot Anomaly Detection MuSc: Zero-Shot Industrial Anomaly Classification and Segmentation with Mutual Scoring of the Unlabeled Images

Reference 26

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Observation 19cc05ff-4b95-4d4d-8720-7146c35dffff · outbound

This paper cites A Closer Look at the Explainability of Contrastive Language-Image Pre-training.

Bridge Feature Matching and Cross-Modal Alignment with Mutual-filtering for Zero-shot Anomaly Detection A Closer Look at the Explainability of Contrastive Language-Image Pre-training

Reference 27

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Observation 647b55d5-3a92-41ec-9849-bcef779bbb38 · outbound

This paper cites Omni-frequency channel- 9 selection representations for unsupervised anomaly detec- tion.

Bridge Feature Matching and Cross-Modal Alignment with Mutual-filtering for Zero-shot Anomaly Detection Omni-frequency channel- 9 selection representations for unsupervised anomaly detec- tion

Reference 28

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Observation 49f99f2a-b2bf-4ffb-bcda-8837bd5cd0af · outbound

This paper cites Deep indus- trial image anomaly detection: A survey.

Bridge Feature Matching and Cross-Modal Alignment with Mutual-filtering for Zero-shot Anomaly Detection Deep indus- trial image anomaly detection: A survey

Reference 29

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Observation e6759558-47a5-44e7-b32b-e3cf65972de8 · outbound

This paper cites Deep indus- trial image anomaly detection: A survey.

Bridge Feature Matching and Cross-Modal Alignment with Mutual-filtering for Zero-shot Anomaly Detection Deep indus- trial image anomaly detection: A survey

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:27:20.676353Z

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Observation e42002b3-4c19-4a6b-94f1-07d10d6783f3 · outbound

This paper cites Reb: Re- ducing biases in representation for industrial anomaly detec- tion.

Bridge Feature Matching and Cross-Modal Alignment with Mutual-filtering for Zero-shot Anomaly Detection Reb: Re- ducing biases in representation for industrial anomaly detec- tion

Reference 31

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Observation 262aeedf-04ea-49b8-be76-37790066a8db · outbound

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

Bridge Feature Matching and Cross-Modal Alignment with Mutual-filtering for Zero-shot Anomaly Detection Vt-adl: A vision trans- former network for image anomaly detection and localiza- tion

Reference 32

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Observation 419472eb-f492-4f8f-ba4b-52c424dca8f8 · outbound

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

Bridge Feature Matching and Cross-Modal Alignment with Mutual-filtering for Zero-shot Anomaly Detection VCP-CLIP: A visual context prompting model for zero-shot anomaly segmentation

Reference 33

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Observation 7ccf6089-b72b-4cbe-9f2d-52d1807e1aed · outbound

This paper cites Learning transferable visual models from natural language supervi- sion.

Bridge Feature Matching and Cross-Modal Alignment with Mutual-filtering for Zero-shot Anomaly Detection Learning transferable visual models from natural language supervi- sion

Reference 34

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Observation c7b28d66-c39b-4cb4-8082-7a64fd03b95e · outbound

This paper cites Towards total recall in industrial anomaly detection.

Bridge Feature Matching and Cross-Modal Alignment with Mutual-filtering for Zero-shot Anomaly Detection Towards total recall in industrial anomaly detection

Reference 35

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Observation 06690642-33c9-4b31-a96d-bde04164d22e · outbound

This paper cites Optimizing PatchCore for Few/many-shot Anomaly Detection.

Bridge Feature Matching and Cross-Modal Alignment with Mutual-filtering for Zero-shot Anomaly Detection Optimizing PatchCore for Few/many-shot Anomaly Detection

Reference 36

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Observation db9c67ed-c9be-4be1-974b-6fb3f0ddca87 · outbound

This paper cites Ex- plore the potential of clip for training-free open vocabulary semantic segmentation.

Bridge Feature Matching and Cross-Modal Alignment with Mutual-filtering for Zero-shot Anomaly Detection Ex- plore the potential of clip for training-free open vocabulary semantic segmentation

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:27:19.817238Z

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Observation d465a01a-f653-41e0-a7b6-a80392351313 · outbound

This paper cites Attention guided anomaly localization in images.

Bridge Feature Matching and Cross-Modal Alignment with Mutual-filtering for Zero-shot Anomaly Detection Attention guided anomaly localization in images

Reference 38

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Observation 18e10504-2474-4508-ab7a-f32e48f4276e · outbound

This paper cites Sclip: Rethinking self-attention for dense vision-language inference.

Bridge Feature Matching and Cross-Modal Alignment with Mutual-filtering for Zero-shot Anomaly Detection Sclip: Rethinking self-attention for dense vision-language inference

Reference 39

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Observation 3a3df126-354b-45e4-a2fe-92aca8870c11 · outbound

This paper cites Anoddpm: Anomaly detection with de- noising diffusion probabilistic models using simplex noise.

Bridge Feature Matching and Cross-Modal Alignment with Mutual-filtering for Zero-shot Anomaly Detection Anoddpm: Anomaly detection with de- noising diffusion probabilistic models using simplex noise

Reference 40

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Observation b9157a87-1363-43fb-9e43-5b733e5d7085 · outbound

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

Bridge Feature Matching and Cross-Modal Alignment with Mutual-filtering for Zero-shot Anomaly Detection A unified model for multi-class anomaly detection

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:27:19.108932Z

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Observation 35439a9c-a628-4f2b-b555-6016750ce4dd · outbound

This paper cites Draem- a discriminatively trained reconstruction embedding for sur- face anomaly detection.

Bridge Feature Matching and Cross-Modal Alignment with Mutual-filtering for Zero-shot Anomaly Detection Draem- a discriminatively trained reconstruction embedding for sur- face anomaly detection

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:27:19.004225Z

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Observation 655acc46-4bff-427d-8d08-9904aa7be194 · outbound

This paper cites Exploring Plain ViT Reconstruction for Multi-class Unsupervised Anomaly Detection.

Bridge Feature Matching and Cross-Modal Alignment with Mutual-filtering for Zero-shot Anomaly Detection Exploring Plain ViT Reconstruction for Multi-class Unsupervised Anomaly Detection

Reference 43

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Observation bf4b6810-ac4b-4a15-8f39-0c71858e8946 · outbound

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

Bridge Feature Matching and Cross-Modal Alignment with Mutual-filtering for Zero-shot Anomaly Detection A Comprehensive Library for Benchmarking Multi-class Visual Anomaly Detection

Reference 44

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Observation 4cf17e85-1b6a-4a0e-a2f6-d43ef1cf75ce · outbound

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

Bridge Feature Matching and Cross-Modal Alignment with Mutual-filtering for Zero-shot Anomaly Detection Realnet: A feature selection network with realistic synthetic anomaly for anomaly detection

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:27:18.835479Z

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Observation 73c7fbe4-9433-47ec-b9f1-96925f7a928f · outbound

This paper cites Dual-Image Enhanced CLIP for Zero-Shot Anomaly Detection.

Bridge Feature Matching and Cross-Modal Alignment with Mutual-filtering for Zero-shot Anomaly Detection Dual-Image Enhanced CLIP for Zero-Shot Anomaly Detection

Reference 46

Resolution: verified exact; local_arxiv, observed 2026-08-06T17:27:18.253363Z

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Observation adf0f691-df51-4a37-8ce9-05835e059306 · outbound

This paper cites Anomalyclip: Object-agnostic prompt learn- ing for zero-shot anomaly detection.

Bridge Feature Matching and Cross-Modal Alignment with Mutual-filtering for Zero-shot Anomaly Detection Anomalyclip: Object-agnostic prompt learn- ing for zero-shot anomaly detection

Reference 47

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Observation f0f852fc-bae0-4e9c-bd6a-87397c6a2f4f · outbound

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

Bridge Feature Matching and Cross-Modal Alignment with Mutual-filtering for Zero-shot Anomaly Detection Spot-the-difference self-supervised pre- training for anomaly detection and segmentation

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:27:18.672800Z

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