Paper Citation Record · LEDGER

Automatic Prompt Generation and Grounding Object Detection for Zero-Shot Image Anomaly Detection

As of 19 August 2026, this Paper Citation Record lists 25 of 25 outbound references and 0 inbound Pith citation observations for arXiv:2411.19220.

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

pith.paper-citation-record.v1
2411.19220 v1

Coverage vector

measured 25 of 25 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-12T10:27:30.237818Z

measured 25 of 25 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-19T06:32:44.657259+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

25 of 25 outbound references displayed

verified exact0
verified fuzzy16
unresolved9
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 28c42720-7889-459b-9fd3-288cf66571c2 · outbound

This paper cites Unseen Visual Anomaly Generation.

Automatic Prompt Generation and Grounding Object Detection for Zero-Shot Image Anomaly Detection Unseen Visual Anomaly Generation

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-12T10:27:30.077476Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 532cec7e-cae1-4954-a68b-02f010e6a2f4 · outbound

This paper cites A framework for industrial inspection system using deep learning,.

Automatic Prompt Generation and Grounding Object Detection for Zero-Shot Image Anomaly Detection A framework for industrial inspection system using deep learning,

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T10:27:30.725595Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 0cdcacb1-9fee-4dac-8645-702deddb8d74 · outbound

This paper cites Defect detection methods for industrial products using deep learning techniques: A review,.

Automatic Prompt Generation and Grounding Object Detection for Zero-Shot Image Anomaly Detection Defect detection methods for industrial products using deep learning techniques: A review,

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T10:27:30.708594Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 7f857162-8036-4ae7-b838-248223fb3b72 · outbound

This paper cites Learn- ing transferable visual models from natural language supervision,.

Automatic Prompt Generation and Grounding Object Detection for Zero-Shot Image Anomaly Detection Learn- ing transferable visual models from natural language supervision,

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T10:27:30.689532Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation ee79e182-221f-4edd-ab1e-95d6057cb73b · outbound

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

Automatic Prompt Generation and Grounding Object Detection for Zero-Shot Image Anomaly Detection Winclip: Zero-/few-shot anomaly classification and segmentation,

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-12T10:27:30.110229Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6cfb8422-9b0f-4037-8381-c9620e16c3e5 · outbound

This paper cites Language models are few-shot learners,.

Automatic Prompt Generation and Grounding Object Detection for Zero-Shot Image Anomaly Detection Language models are few-shot learners,

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T10:27:30.660888Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation a8398f91-1893-441a-b7c8-d79a8a00a3c0 · outbound

This paper cites Grounding DINO: Marrying DINO with Grounded Pre-Training for Open-Set Object Detection.

Automatic Prompt Generation and Grounding Object Detection for Zero-Shot Image Anomaly Detection Grounding DINO: Marrying DINO with Grounded Pre-Training for Open-Set Object Detection

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-12T10:27:30.126134Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 487c147e-3963-4698-8de1-0d5da6cdda53 · outbound

This paper cites Drocc: Deep robust one-class classification,.

Automatic Prompt Generation and Grounding Object Detection for Zero-Shot Image Anomaly Detection Drocc: Deep robust one-class classification,

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T10:27:30.643899Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 4c153fb0-0e1e-4b7d-970e-30547801627d · outbound

This paper cites A spectrogram image-based network anomaly detection system using deep convolutional neural network,.

Automatic Prompt Generation and Grounding Object Detection for Zero-Shot Image Anomaly Detection A spectrogram image-based network anomaly detection system using deep convolutional neural network,

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T10:27:30.627155Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation ae0728ec-fad4-437d-8862-b8b0703bcd21 · outbound

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

Automatic Prompt Generation and Grounding Object Detection for Zero-Shot Image Anomaly Detection LLaMA: Open and Efficient Foundation Language Models

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-12T10:27:30.148801Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3992b41a-e43b-4239-9041-d69e2672433b · outbound

This paper cites Visual instruc- tion tuning,.

Automatic Prompt Generation and Grounding Object Detection for Zero-Shot Image Anomaly Detection Visual instruc- tion tuning,

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T10:27:30.608821Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation ea312699-828c-40b5-adfd-620528c748c7 · outbound

This paper cites Hugginggpt: Solving ai tasks with chatgpt and its friends in hugging face,.

Automatic Prompt Generation and Grounding Object Detection for Zero-Shot Image Anomaly Detection Hugginggpt: Solving ai tasks with chatgpt and its friends in hugging face,

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-12T10:27:30.161680Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 41312d4c-5410-4279-b32a-092ca2812a7a · outbound

This paper cites Bounding the Capabilities of Large Language Models in Open Text Generation with Prompt Constraints.

Automatic Prompt Generation and Grounding Object Detection for Zero-Shot Image Anomaly Detection Bounding the Capabilities of Large Language Models in Open Text Generation with Prompt Constraints

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-12T10:27:30.167328Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6140f6ff-7e45-4615-825f-306339fc9891 · outbound

This paper cites Unsupervised prompt tuning for text-driven object detection,.

Automatic Prompt Generation and Grounding Object Detection for Zero-Shot Image Anomaly Detection Unsupervised prompt tuning for text-driven object detection,

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T10:27:30.580267Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 31c28ae1-c452-44ba-bc0c-2108a82e56a9 · outbound

This paper cites Fast r-cnn,.

Automatic Prompt Generation and Grounding Object Detection for Zero-Shot Image Anomaly Detection Fast r-cnn,

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-12T10:27:30.179930Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2d44dfc6-c278-46f9-9c8f-886fe536d848 · outbound

This paper cites You only look once: Unified, real-time object detec- tion,.

Automatic Prompt Generation and Grounding Object Detection for Zero-Shot Image Anomaly Detection You only look once: Unified, real-time object detec- tion,

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T10:27:30.549516Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 493216de-504e-4cd0-b121-9947e1f073ab · outbound

This paper cites Weakly supervised object localization and detection: A survey,.

Automatic Prompt Generation and Grounding Object Detection for Zero-Shot Image Anomaly Detection Weakly supervised object localization and detection: A survey,

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T10:27:30.532796Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 749ad4b5-1a33-430d-a3e9-121ddac46d1c · outbound

This paper cites Zero-shot ground- ing of objects from natural language queries,.

Automatic Prompt Generation and Grounding Object Detection for Zero-Shot Image Anomaly Detection Zero-shot ground- ing of objects from natural language queries,

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T10:27:30.515452Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 9dae926b-6547-4c44-a730-85c28a53a69b · outbound

This paper cites Referring to objects in photographs of natural scenes,.

Automatic Prompt Generation and Grounding Object Detection for Zero-Shot Image Anomaly Detection Referring to objects in photographs of natural scenes,

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T10:27:30.497205Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 960ca2bc-36e5-41cc-87bc-5b804575364f · outbound

This paper cites Clip-vg: Self-paced curriculum adapting of clip for visual grounding,.

Automatic Prompt Generation and Grounding Object Detection for Zero-Shot Image Anomaly Detection Clip-vg: Self-paced curriculum adapting of clip for visual grounding,

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T10:27:30.479544Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation bfb0525a-9642-4197-a7a9-ede32f2b3714 · outbound

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

Automatic Prompt Generation and Grounding Object Detection for Zero-Shot Image Anomaly Detection Mvtec ad–a comprehensive real-world dataset for un- supervised anomaly detection,

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-12T10:27:30.215721Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 95552f51-ef3f-4b58-bf48-c714c454836a · outbound

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

Automatic Prompt Generation and Grounding Object Detection for Zero-Shot Image Anomaly Detection Spot-the-difference self-supervised pre-training for anomaly detection and segmentation,

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T10:27:30.448619Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 79e3e87a-1df0-4a4a-8ff3-a1b895782fba · outbound

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

Automatic Prompt Generation and Grounding Object Detection for Zero-Shot Image Anomaly Detection Sub-Image Anomaly Detection with Deep Pyramid Correspondences

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-12T10:27:30.226424Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 861b7057-ad55-4cf4-aaf9-c49afe961b95 · outbound

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

Automatic Prompt Generation and Grounding Object Detection for Zero-Shot Image Anomaly Detection Padim: A patch distribution modeling framework for anomaly detection and localization,

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T10:27:30.430105Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation f1498666-6e07-4239-904e-fc06fcc6fd50 · outbound

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

Automatic Prompt Generation and Grounding Object Detection for Zero-Shot Image Anomaly Detection Towards total recall in industrial anomaly detection,

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T10:27:30.411020Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Pith citing papers

No inbound Pith citation observations are available.