Paper Citation Record · LEDGER

SuperAD: A Training-free Anomaly Classification and Segmentation Method for CVPR 2025 VAND 3.0 Workshop Challenge Track 1: Adapt & Detect

As of 8 August 2026, this Paper Citation Record lists 14 of 14 outbound references and 2 inbound Pith citation observations for arXiv:2505.19750.

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

pith.paper-citation-record.v1
2505.19750 v2

Coverage vector

measured 14 of 14 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T14:10:34.604570Z

measured 16 of 16 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-08T06:32:00.761636+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-06-30T21:14:49.787243Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-06-30T21:15:03.938679Z

Reference resolution

14 of 14 outbound references displayed

verified exact0
verified fuzzy8
unresolved6
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation cea34fb1-4222-4892-ba13-1d048a02480c · outbound

This paper cites Efficien- tad: Accurate visual anomaly detection at millisecond-level latencies.

SuperAD: A Training-free Anomaly Classification and Segmentation Method for CVPR 2025 VAND 3.0 Workshop Challenge Track 1: Adapt & Detect Efficien- tad: Accurate visual anomaly detection at millisecond-level latencies

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:10:36.167314Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 5a9499d4-29b7-4355-b9e6-5d9fb7e10a6f · 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.

SuperAD: A Training-free Anomaly Classification and Segmentation Method for CVPR 2025 VAND 3.0 Workshop Challenge Track 1: Adapt & Detect 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 2

Resolution: unresolved; no resolver link, observed 2026-08-07T14:10:33.642713Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 87b7fee5-ca59-4a41-bcb2-6da1e59ec2c8 · outbound

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

SuperAD: A Training-free Anomaly Classification and Segmentation Method for CVPR 2025 VAND 3.0 Workshop Challenge Track 1: Adapt & Detect Padim: a patch distribution modeling framework for anomaly detection and localization

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:10:36.007739Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation f396ec04-30e7-417a-a694-3a73e4067e92 · outbound

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

SuperAD: A Training-free Anomaly Classification and Segmentation Method for CVPR 2025 VAND 3.0 Workshop Challenge Track 1: Adapt & Detect Anomaly detection via reverse distillation from one-class embedding

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:10:35.863106Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 9c4f7583-8be7-48a2-a943-27c6b8fc4474 · outbound

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

SuperAD: A Training-free Anomaly Classification and Segmentation Method for CVPR 2025 VAND 3.0 Workshop Challenge Track 1: Adapt & Detect An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-07T14:10:33.829514Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 959bdbaf-84aa-4826-aff6-6133c5a620c3 · outbound

This paper cites The mvtec ad 2 dataset: Advanced scenarios for unsupervised anomaly detection.

SuperAD: A Training-free Anomaly Classification and Segmentation Method for CVPR 2025 VAND 3.0 Workshop Challenge Track 1: Adapt & Detect The mvtec ad 2 dataset: Advanced scenarios for unsupervised anomaly detection

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-07T14:10:33.901997Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f7c8900e-ea7f-4645-9155-9333dea0b927 · outbound

This paper cites DMAD: Dual Memory Bank for Real-World Anomaly Detection.

SuperAD: A Training-free Anomaly Classification and Segmentation Method for CVPR 2025 VAND 3.0 Workshop Challenge Track 1: Adapt & Detect DMAD: Dual Memory Bank for Real-World Anomaly Detection

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-07T14:10:33.979154Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b7e5e1dc-8854-4aaa-ad5b-86f295dea3ec · outbound

This paper cites Simplenet: A simple network for image anomaly detection and localization.

SuperAD: A Training-free Anomaly Classification and Segmentation Method for CVPR 2025 VAND 3.0 Workshop Challenge Track 1: Adapt & Detect Simplenet: A simple network for image anomaly detection and localization

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:10:35.702578Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 4eeecaf6-d43a-4a78-9484-f6395c0b09b7 · outbound

This paper cites DINOv2: Learning Robust Visual Features without Supervision.

SuperAD: A Training-free Anomaly Classification and Segmentation Method for CVPR 2025 VAND 3.0 Workshop Challenge Track 1: Adapt & Detect DINOv2: Learning Robust Visual Features without Supervision

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-07T14:10:34.131499Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation aaf0e8b3-49a3-4d18-a4c8-a85f9e0f2e28 · outbound

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

SuperAD: A Training-free Anomaly Classification and Segmentation Method for CVPR 2025 VAND 3.0 Workshop Challenge Track 1: Adapt & Detect Towards to- tal recall in industrial anomaly detection

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:10:35.502920Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation dc8120d4-096d-4e10-a568-2bf9c62c1a8a · outbound

This paper cites Revisiting reverse distillation for anomaly detection.

SuperAD: A Training-free Anomaly Classification and Segmentation Method for CVPR 2025 VAND 3.0 Workshop Challenge Track 1: Adapt & Detect Revisiting reverse distillation for anomaly detection

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:10:35.313609Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 90b6c895-b92a-443b-a29c-f7ba00340788 · outbound

This paper cites Wide Residual Networks.

SuperAD: A Training-free Anomaly Classification and Segmentation Method for CVPR 2025 VAND 3.0 Workshop Challenge Track 1: Adapt & Detect Wide Residual Networks

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-07T14:10:34.415397Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 885ae961-0a16-4f90-a5a4-7050da9c40e3 · outbound

This paper cites Dsr– a dual subspace re-projection network for surface anomaly detection.

SuperAD: A Training-free Anomaly Classification and Segmentation Method for CVPR 2025 VAND 3.0 Workshop Challenge Track 1: Adapt & Detect Dsr– a dual subspace re-projection network for surface anomaly detection

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:10:35.144046Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation fdfedd20-cdd8-48eb-bc23-cc329521b959 · outbound

This paper cites Msflow: Multiscale flow-based framework for unsupervised anomaly detection.

SuperAD: A Training-free Anomaly Classification and Segmentation Method for CVPR 2025 VAND 3.0 Workshop Challenge Track 1: Adapt & Detect Msflow: Multiscale flow-based framework for unsupervised anomaly detection

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:10:34.958692Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Pith citing papers

Observation 1cbd359e-c5d5-48e5-a5da-e249d55ad50f · inbound

MuRF: Unlocking the Multi-Scale Potential of Vision Foundation Models cites this paper.

MuRF: Unlocking the Multi-Scale Potential of Vision Foundation Models SuperAD: A Training-free Anomaly Classification and Segmentation Method for CVPR 2025 VAND 3.0 Workshop Challenge Track 1: Adapt & Detect

Reference 13

Resolution: verified exact; arxiv_id, observed 2026-05-15T00:03:20.087346Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 9a8c4ed2-b85a-4e22-b340-5a9cc3bcce9a · inbound

SuperADD: Training-free Class-agnostic Anomaly Segmentation -- CVPR 2026 VAND 4.0 Workshop Challenge Industrial Track cites this paper.

SuperADD: Training-free Class-agnostic Anomaly Segmentation -- CVPR 2026 VAND 4.0 Workshop Challenge Industrial Track SuperAD: A Training-free Anomaly Classification and Segmentation Method for CVPR 2025 VAND 3.0 Workshop Challenge Track 1: Adapt & Detect

Reference 8

Resolution: verified exact; arxiv_id, observed 2026-06-30T21:15:03.940554Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.