Paper Citation Record · LEDGER

Defect-aware Hybrid Prompt Optimization via Progressive Tuning for Zero-Shot Multi-type Anomaly Detection and Segmentation

As of 9 August 2026, this Paper Citation Record lists 50 of 50 outbound references and 1 inbound Pith citation observation for arXiv:2512.09446.

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

pith.paper-citation-record.v1
2512.09446 v3

Coverage vector

measured 50 of 50 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-03T17:28:44.745087Z

measured 51 of 51 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-09T06:31:02.800959+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-07-02T13:55:24.194420Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-02T13:56:59.028784Z

Reference resolution

50 of 50 outbound references displayed

verified exact0
verified fuzzy0
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External citation measurements

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Outbound references

Observation 1e1445ef-1523-4a76-b7f9-46f20962aaa9 · outbound

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

Defect-aware Hybrid Prompt Optimization via Progressive Tuning for Zero-Shot Multi-type Anomaly Detection and Segmentation Mvtec ad–a comprehensive real-world dataset for unsupervised anomaly detection

Reference 1

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Observation 79e1d942-5a62-4d9e-b6fc-463bf4701d66 · outbound

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

Defect-aware Hybrid Prompt Optimization via Progressive Tuning for Zero-Shot Multi-type Anomaly Detection and Segmentation Adaclip: Adapting clip with hybrid learnable prompts for zero-shot anomaly de- tection

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-03T17:28:39.101215Z

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Observation d5d5101f-f506-4c8f-8d72-246140ce0b2b · 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.

Defect-aware Hybrid Prompt Optimization via Progressive Tuning for Zero-Shot Multi-type Anomaly Detection and Segmentation 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 3

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Observation 617bea45-2d60-4433-b986-1dfae20b1764 · outbound

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

Defect-aware Hybrid Prompt Optimization via Progressive Tuning for Zero-Shot Multi-type Anomaly Detection and Segmentation Sub-Image Anomaly Detection with Deep Pyramid Correspondences

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-03T17:28:39.335652Z

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Observation de20e6df-7d31-49b4-8b87-8fcc4e40a00c · outbound

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

Defect-aware Hybrid Prompt Optimization via Progressive Tuning for Zero-Shot Multi-type Anomaly Detection and Segmentation Padim: a patch distribution modeling framework for anomaly detection and localization

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-03T17:28:39.428767Z

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Observation 866717b9-40ab-421e-9cb1-abbec01c0fee · outbound

This paper cites Simclip: Refining image-text alignment with simple prompts for zero-/few-shot anomaly detection.

Defect-aware Hybrid Prompt Optimization via Progressive Tuning for Zero-Shot Multi-type Anomaly Detection and Segmentation Simclip: Refining image-text alignment with simple prompts for zero-/few-shot anomaly detection

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-03T17:28:39.551809Z

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Observation fd08ec47-e340-4a30-aaab-91402ed7fdbb · outbound

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

Defect-aware Hybrid Prompt Optimization via Progressive Tuning for Zero-Shot Multi-type Anomaly Detection and Segmentation Anomaly detection via reverse distillation from one-class embedding

Reference 7

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Observation a00b5621-f477-4372-b2cc-6ec7a76f8c65 · outbound

This paper cites Generative adversarial networks.Commu- nications of the ACM, 63(11):139–144, 2020.

Defect-aware Hybrid Prompt Optimization via Progressive Tuning for Zero-Shot Multi-type Anomaly Detection and Segmentation Generative adversarial networks.Commu- nications of the ACM, 63(11):139–144, 2020

Reference 8

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Observation dfa0d3fc-081d-4e6d-84c7-35a64cabeb6e · outbound

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

Defect-aware Hybrid Prompt Optimization via Progressive Tuning for Zero-Shot Multi-type Anomaly Detection and Segmentation Filo: Zero-shot anomaly detection by fine-grained description and high-quality local- ization

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-03T17:28:39.925690Z

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Observation 2a6f0413-3096-4845-b9c4-a461f7abb0c3 · outbound

This paper cites Openclip.If you use this software, please cite it as below, 7,.

Defect-aware Hybrid Prompt Optimization via Progressive Tuning for Zero-Shot Multi-type Anomaly Detection and Segmentation Openclip.If you use this software, please cite it as below, 7,

Reference 10

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Observation d9409d02-c158-42a0-8eda-c4941212fce7 · outbound

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

Defect-aware Hybrid Prompt Optimization via Progressive Tuning for Zero-Shot Multi-type Anomaly Detection and Segmentation Winclip: Zero- /few-shot anomaly classification and segmentation

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-03T17:28:40.225221Z

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Observation 6303fc3e-bc74-4665-8e34-4c2cf4de33bf · outbound

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

Defect-aware Hybrid Prompt Optimization via Progressive Tuning for Zero-Shot Multi-type Anomaly Detection and Segmentation Deep learning-based defect detection of metal parts: evaluating current methods in complex condi- tions

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-03T17:28:40.345330Z

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Observation d2b39e2f-88d9-454f-b9f7-44b267591c2f · outbound

This paper cites How can we know what language models know?Trans- actions of the Association for Computational Linguistics, 8: 423–438, 2020.

Defect-aware Hybrid Prompt Optimization via Progressive Tuning for Zero-Shot Multi-type Anomaly Detection and Segmentation How can we know what language models know?Trans- actions of the Association for Computational Linguistics, 8: 423–438, 2020

Reference 13

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Observation 59605650-9336-4260-a5f2-0cc132e12df1 · outbound

This paper cites Maple: Multi-modal prompt learning.

Defect-aware Hybrid Prompt Optimization via Progressive Tuning for Zero-Shot Multi-type Anomaly Detection and Segmentation Maple: Multi-modal prompt learning

Reference 14

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Observation b5259443-df91-4344-adb8-64b9212f99ef · outbound

This paper cites Adam: A Method for Stochastic Optimization.

Defect-aware Hybrid Prompt Optimization via Progressive Tuning for Zero-Shot Multi-type Anomaly Detection and Segmentation Adam: A Method for Stochastic Optimization

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-03T17:28:40.683123Z

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Observation 9204adca-4d09-435f-80f5-6ce7d609c7ae · outbound

This paper cites Auto-encoding vari- ational bayes, 2013.

Defect-aware Hybrid Prompt Optimization via Progressive Tuning for Zero-Shot Multi-type Anomaly Detection and Segmentation Auto-encoding vari- ational bayes, 2013

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-03T17:28:40.799542Z

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Observation 89db07f6-ffef-4f51-bc0b-fcb754421a49 · outbound

This paper cites Segment any- thing.

Defect-aware Hybrid Prompt Optimization via Progressive Tuning for Zero-Shot Multi-type Anomaly Detection and Segmentation Segment any- thing

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-03T17:28:40.915067Z

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Observation 03563075-eff1-47ee-bf40-36021911fe41 · outbound

This paper cites Generalization and network design strate- gies.Connectionism in perspective, 19(143-155):18, 1989.

Defect-aware Hybrid Prompt Optimization via Progressive Tuning for Zero-Shot Multi-type Anomaly Detection and Segmentation Generalization and network design strate- gies.Connectionism in perspective, 19(143-155):18, 1989

Reference 18

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Observation 05277449-beb6-455e-89d0-4f91af441cb9 · outbound

This paper cites The Power of Scale for Parameter-Efficient Prompt Tuning.

Defect-aware Hybrid Prompt Optimization via Progressive Tuning for Zero-Shot Multi-type Anomaly Detection and Segmentation The Power of Scale for Parameter-Efficient Prompt Tuning

Reference 19

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Observation 84584541-ff74-4d5f-a475-95cea1c11ef3 · outbound

This paper cites Clipsam: Clip and sam collaboration for zero-shot anomaly segmentation.Neurocomputing, 618: 129122, 2025.

Defect-aware Hybrid Prompt Optimization via Progressive Tuning for Zero-Shot Multi-type Anomaly Detection and Segmentation Clipsam: Clip and sam collaboration for zero-shot anomaly segmentation.Neurocomputing, 618: 129122, 2025

Reference 20

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Observation e0b1ab3b-ee41-4a7b-bd29-4505150c5d7b · outbound

This paper cites Dice Loss for Data-imbalanced NLP Tasks.

Defect-aware Hybrid Prompt Optimization via Progressive Tuning for Zero-Shot Multi-type Anomaly Detection and Segmentation Dice Loss for Data-imbalanced NLP Tasks

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-03T17:28:41.373771Z

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Observation 8501638c-3aae-4462-99c8-74136e091c43 · outbound

This paper cites Prefix-Tuning: Optimizing Continuous Prompts for Generation.

Defect-aware Hybrid Prompt Optimization via Progressive Tuning for Zero-Shot Multi-type Anomaly Detection and Segmentation Prefix-Tuning: Optimizing Continuous Prompts for Generation

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-03T17:28:41.438414Z

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Observation 68a90c82-a303-4178-888d-6b7bfbba0082 · outbound

This paper cites Focal loss for dense object detection.

Defect-aware Hybrid Prompt Optimization via Progressive Tuning for Zero-Shot Multi-type Anomaly Detection and Segmentation Focal loss for dense object detection

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-03T17:28:41.521244Z

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Observation ad9a5a99-47c0-4895-9c72-3e0fbc123086 · outbound

This paper cites Unsupervised Two-Stage Anomaly Detection.

Defect-aware Hybrid Prompt Optimization via Progressive Tuning for Zero-Shot Multi-type Anomaly Detection and Segmentation Unsupervised Two-Stage Anomaly Detection

Reference 24

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Observation 9d9857a7-9fde-4c91-ba01-5238698ad562 · outbound

This paper cites Visualizing data using t-sne.Journal of machine learning research, 9 (Nov):2579–2605, 2008.

Defect-aware Hybrid Prompt Optimization via Progressive Tuning for Zero-Shot Multi-type Anomaly Detection and Segmentation Visualizing data using t-sne.Journal of machine learning research, 9 (Nov):2579–2605, 2008

Reference 25

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Observation 89a75560-120b-43e8-a898-f7c2dc284172 · outbound

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

Defect-aware Hybrid Prompt Optimization via Progressive Tuning for Zero-Shot Multi-type Anomaly Detection and Segmentation Learning transferable visual models from natural language supervi- sion

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-03T17:28:41.842080Z

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Observation dde70cc8-4fb5-48d3-a232-f5da0776f7bb · outbound

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

Defect-aware Hybrid Prompt Optimization via Progressive Tuning for Zero-Shot Multi-type Anomaly Detection and Segmentation Towards to- tal recall in industrial anomaly detection

Reference 27

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Observation e374e0fd-461a-484d-b874-4228f5a1ddcb · outbound

This paper cites MultiADS: Defect-aware Supervision for Multi-type Anomaly Detection and Segmentation in Zero-Shot Learning.

Defect-aware Hybrid Prompt Optimization via Progressive Tuning for Zero-Shot Multi-type Anomaly Detection and Segmentation MultiADS: Defect-aware Supervision for Multi-type Anomaly Detection and Segmentation in Zero-Shot Learning

Reference 28

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Observation 3e104e80-3169-4f91-9d14-f6f87cd195d6 · outbound

This paper cites Two Effects, One Trigger: On the Modality Gap, Object Bias, and Information Imbalance in Contrastive Vision-Language Models.

Defect-aware Hybrid Prompt Optimization via Progressive Tuning for Zero-Shot Multi-type Anomaly Detection and Segmentation Two Effects, One Trigger: On the Modality Gap, Object Bias, and Information Imbalance in Contrastive Vision-Language Models

Reference 29

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Observation 0c006cc5-64fe-408b-9597-348e728a655d · outbound

This paper cites Attention guided anomaly localization in images.

Defect-aware Hybrid Prompt Optimization via Progressive Tuning for Zero-Shot Multi-type Anomaly Detection and Segmentation Attention guided anomaly localization in images

Reference 30

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Observation 5cf7ff43-1741-4df2-8f7e-49f5c72b369b · outbound

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

Defect-aware Hybrid Prompt Optimization via Progressive Tuning for Zero-Shot Multi-type Anomaly Detection and Segmentation Real-iad: A real-world multi-view dataset for benchmarking versatile industrial anomaly detec- tion

Reference 31

Resolution: unresolved; no resolver link, observed 2026-08-03T17:28:42.349604Z

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Observation c16b8770-f821-4b02-84ee-9fdbc0db21a9 · outbound

This paper cites Progressive vi- sual prompt learning with contrastive feature re-formation.

Defect-aware Hybrid Prompt Optimization via Progressive Tuning for Zero-Shot Multi-type Anomaly Detection and Segmentation Progressive vi- sual prompt learning with contrastive feature re-formation

Reference 32

Resolution: unresolved; no resolver link, observed 2026-08-03T17:28:42.467748Z

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Observation 0de807ff-8669-422c-96ac-e003203eeea5 · outbound

This paper cites DFR: Deep Feature Reconstruction for Unsupervised Anomaly Segmentation.

Defect-aware Hybrid Prompt Optimization via Progressive Tuning for Zero-Shot Multi-type Anomaly Detection and Segmentation DFR: Deep Feature Reconstruction for Unsupervised Anomaly Segmentation

Reference 33

Resolution: unresolved; no resolver link, observed 2026-08-03T17:28:42.570931Z

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Observation ba0f64ce-c394-4ec3-9929-eeb7e69a9659 · outbound

This paper cites Long-horizon language-conditioned imita- tion learning for robotic manipulation.IEEE/ASME Trans- actions on Mechatronics, pages 1–12, 2025.

Defect-aware Hybrid Prompt Optimization via Progressive Tuning for Zero-Shot Multi-type Anomaly Detection and Segmentation Long-horizon language-conditioned imita- tion learning for robotic manipulation.IEEE/ASME Trans- actions on Mechatronics, pages 1–12, 2025

Reference 34

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Observation 57fb63af-c805-4e8b-90db-b268c5caa787 · outbound

This paper cites Bridging Language and Action: A Survey of Language-Conditioned Robot Manipulation.

Defect-aware Hybrid Prompt Optimization via Progressive Tuning for Zero-Shot Multi-type Anomaly Detection and Segmentation Bridging Language and Action: A Survey of Language-Conditioned Robot Manipulation

Reference 35

Resolution: unresolved; no resolver link, observed 2026-08-03T17:28:42.820866Z

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Observation 9b3dad24-c4e5-4e6f-914b-c6d4994791a9 · outbound

This paper cites Language-conditioned imitation learning with base skill pri- ors under unstructured data.IEEE Robotics and Automation Letters, 9(11):9805–9812, 2024.

Defect-aware Hybrid Prompt Optimization via Progressive Tuning for Zero-Shot Multi-type Anomaly Detection and Segmentation Language-conditioned imitation learning with base skill pri- ors under unstructured data.IEEE Robotics and Automation Letters, 9(11):9805–9812, 2024

Reference 36

Resolution: unresolved; no resolver link, observed 2026-08-03T17:28:42.918355Z

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Observation 4031bced-718c-4d9a-9656-b78a28ec2bec · outbound

This paper cites Predicting the road ahead: A knowledge graph based foundation model for scene under- standing in autonomous driving.

Defect-aware Hybrid Prompt Optimization via Progressive Tuning for Zero-Shot Multi-type Anomaly Detection and Segmentation Predicting the road ahead: A knowledge graph based foundation model for scene under- standing in autonomous driving

Reference 37

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Observation 7312f5c2-52fb-4201-93a6-c0d5200a818a · outbound

This paper cites Conditional prompt learning for vision-language mod- els.

Defect-aware Hybrid Prompt Optimization via Progressive Tuning for Zero-Shot Multi-type Anomaly Detection and Segmentation Conditional prompt learning for vision-language mod- els

Reference 38

Resolution: unresolved; no resolver link, observed 2026-08-03T17:28:43.207774Z

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Observation eb0be11a-ad8c-4f57-962b-08507dafeaab · outbound

This paper cites Learning to prompt for vision-language models.In- ternational Journal of Computer Vision, 130(9):2337–2348,.

Defect-aware Hybrid Prompt Optimization via Progressive Tuning for Zero-Shot Multi-type Anomaly Detection and Segmentation Learning to prompt for vision-language models.In- ternational Journal of Computer Vision, 130(9):2337–2348,

Reference 39

Resolution: unresolved; no resolver link, observed 2026-08-03T17:28:43.356532Z

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Observation e0204525-47c7-46ed-8047-57998c912043 · outbound

This paper cites Pad: A dataset and benchmark for pose-agnostic anomaly detection.Advances in Neural Information Processing Systems, 36:44558–44571,.

Defect-aware Hybrid Prompt Optimization via Progressive Tuning for Zero-Shot Multi-type Anomaly Detection and Segmentation Pad: A dataset and benchmark for pose-agnostic anomaly detection.Advances in Neural Information Processing Systems, 36:44558–44571,

Reference 40

Resolution: unresolved; no resolver link, observed 2026-08-03T17:28:43.459493Z

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Observation 6dcd9ed7-2d6f-4d19-93b7-dfeffa44e1c5 · outbound

This paper cites Anomalyclip: Object-agnostic prompt learn- ing for zero-shot anomaly detection.arXiv preprint arXiv:2310.18961, 2023.

Defect-aware Hybrid Prompt Optimization via Progressive Tuning for Zero-Shot Multi-type Anomaly Detection and Segmentation Anomalyclip: Object-agnostic prompt learn- ing for zero-shot anomaly detection.arXiv preprint arXiv:2310.18961, 2023

Reference 41

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Observation ed3812d7-7ab3-4342-b584-fd6516be398f · outbound

This paper cites an unresolved cited work.

Defect-aware Hybrid Prompt Optimization via Progressive Tuning for Zero-Shot Multi-type Anomaly Detection and Segmentation Unresolved cited work

Reference 42

Resolution: unresolved; no resolver link, observed 2026-08-03T17:28:43.692925Z

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Observation 8ae772ad-7cce-48b0-a90b-15169493f011 · outbound

This paper cites Fine-grained abnormality prompt learning for zero- shot anomaly detection.arXiv preprint arXiv:2410.10289,.

Defect-aware Hybrid Prompt Optimization via Progressive Tuning for Zero-Shot Multi-type Anomaly Detection and Segmentation Fine-grained abnormality prompt learning for zero- shot anomaly detection.arXiv preprint arXiv:2410.10289,

Reference 43

Resolution: unresolved; no resolver link, observed 2026-08-03T17:28:43.822761Z

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Observation 389300ba-d8a8-4113-9446-47d74956019f · outbound

This paper cites Unsupervised anomaly detection with an enhanced teacher for student- teacher feature pyramid matching.

Defect-aware Hybrid Prompt Optimization via Progressive Tuning for Zero-Shot Multi-type Anomaly Detection and Segmentation Unsupervised anomaly detection with an enhanced teacher for student- teacher feature pyramid matching

Reference 44

Resolution: unresolved; no resolver link, observed 2026-08-03T17:28:43.921994Z

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Observation be436fc3-e23e-48b7-b7f8-1b872e6d2d12 · outbound

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

Defect-aware Hybrid Prompt Optimization via Progressive Tuning for Zero-Shot Multi-type Anomaly Detection and Segmentation Spot-the-difference self-supervised pre- training for anomaly detection and segmentation

Reference 45

Resolution: unresolved; no resolver link, observed 2026-08-03T17:28:44.041860Z

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Observation 5cf086cf-59fb-4111-9827-7d7b6ed85d4c · outbound

This paper cites Table 5 summarizes key statistics of these datasets, includ- ing the number of distinct product classes and the distri- bution of normal and anomalous samples.

Defect-aware Hybrid Prompt Optimization via Progressive Tuning for Zero-Shot Multi-type Anomaly Detection and Segmentation Table 5 summarizes key statistics of these datasets, includ- ing the number of distinct product classes and the distri- bution of normal and anomalous samples

Reference 46

Resolution: unresolved; no resolver link, observed 2026-08-03T17:28:44.177903Z

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Observation 0ed12132-f189-47b3-bd26-79ddde6c37ae · outbound

This paper cites The results of the baselines are taken directly from the respective papers.

Defect-aware Hybrid Prompt Optimization via Progressive Tuning for Zero-Shot Multi-type Anomaly Detection and Segmentation The results of the baselines are taken directly from the respective papers

Reference 47

Resolution: unresolved; no resolver link, observed 2026-08-03T17:28:44.303097Z

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Observation 479158d3-072a-448d-bb93-08495c26f4b0 · outbound

This paper cites implementation details.

Defect-aware Hybrid Prompt Optimization via Progressive Tuning for Zero-Shot Multi-type Anomaly Detection and Segmentation implementation details

Reference 48

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Observation f58d27b2-ad7f-4906-a445-22c9c89a52cd · outbound

This paper cites contam- ination.

Defect-aware Hybrid Prompt Optimization via Progressive Tuning for Zero-Shot Multi-type Anomaly Detection and Segmentation contam- ination

Reference 49

Resolution: unresolved; no resolver link, observed 2026-08-03T17:28:44.602283Z

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Observation 04d77d71-07c3-416e-997a-095ab415f5df · outbound

This paper cites We present six examples of object from VisA, MPDD, MVTec-AD, and Real-IAD datasets.

Defect-aware Hybrid Prompt Optimization via Progressive Tuning for Zero-Shot Multi-type Anomaly Detection and Segmentation We present six examples of object from VisA, MPDD, MVTec-AD, and Real-IAD datasets

Reference 50

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GenAU: Language-Grounded Industrial Anomaly Understanding with Vision-Language Models cites this paper.

GenAU: Language-Grounded Industrial Anomaly Understanding with Vision-Language Models Defect-aware Hybrid Prompt Optimization via Progressive Tuning for Zero-Shot Multi-type Anomaly Detection and Segmentation

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