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Paper Citation Record · LEDGER

Zero-Shot Anomaly Detection with Pre-trained Segmentation Models

As of 23 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2306.09269.

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

pith.paper-citation-record.v1
2306.09269 v1

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 2 of 2 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-23T06:30:58.430688+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-06T21:43:26.520659Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-03T00:17:28.736053Z

Reference resolution

0 of 0 outbound references displayed

  • verified exact0
  • verified fuzzy0
  • unresolved0
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation bbc93374-9223-4006-93de-0626ea3a5c59 · inbound

StackCLIP: Clustering-Driven Stacked Prompt in Zero-Shot Industrial Anomaly Detection cites this paper.

StackCLIP: Clustering-Driven Stacked Prompt in Zero-Shot Industrial Anomaly Detection Zero-Shot Anomaly Detection with Pre-trained Segmentation Models

Reference 2

Resolution
unresolved
no resolver link, observed 2026-08-06T21:43:26.520659Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T21:43:26.520659Z digest=sha256:6d75a54aa66ca23d43571b8b83a33e6678debd88b3d132d9270e14a3c43b4097

Observation b303e0f1-7a00-4e1f-a1c5-d7d6931a6f29 · inbound

Visual Prompting Meets Feature Reconstruction-Based Anomaly Detection with Dual-Teacher Supervision cites this paper.

Visual Prompting Meets Feature Reconstruction-Based Anomaly Detection with Dual-Teacher Supervision Zero-Shot Anomaly Detection with Pre-trained Segmentation Models

Reference 15

Resolution
verified exact
arxiv_id, observed 2026-07-03T00:17:28.737749Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

source=pdf_text observed=2026-06-27T17:23:13.494169Z digest=sha256:1dfa3433b6830bfc5073064be90562a3600df015766b778d29c6c9ce8dd40033