Paper Citation Record · LEDGER

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

As of 13 August 2026, this Paper Citation Record lists 15 of 15 outbound references and 0 inbound Pith citation observations for arXiv:2605.14808.

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

pith.paper-citation-record.v1
2605.14808 v1

Coverage vector

measured 15 of 15 reference resolution

Typed states for the displayed outbound observations.

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

measured 15 of 15 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-13T06:32:02.005865+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

15 of 15 outbound references displayed

verified exact5
verified fuzzy9
unresolved0
parse uncertain0
malformed identifier0
metadata mismatch1

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 2120224c-f02a-47c7-a805-33fecdaee62a · outbound

This paper cites A survey of deep learning for industrial visual anomaly detection.Artificial Intelligence Review, 58(9):279,.

SuperADD: Training-free Class-agnostic Anomaly Segmentation -- CVPR 2026 VAND 4.0 Workshop Challenge Industrial Track A survey of deep learning for industrial visual anomaly detection.Artificial Intelligence Review, 58(9):279,

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-07-07T17:44:02.019222Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation f91daa4d-e658-4ea9-ab0d-fa30828649d4 · outbound

This paper cites The mvtec ad 2 dataset: Advanced scenarios for unsupervised anomaly detection.In- ternational Journal of Computer Vision, 134(4):175, 2026.

SuperADD: Training-free Class-agnostic Anomaly Segmentation -- CVPR 2026 VAND 4.0 Workshop Challenge Industrial Track The mvtec ad 2 dataset: Advanced scenarios for unsupervised anomaly detection.In- ternational Journal of Computer Vision, 134(4):175, 2026

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-07-07T17:44:02.015185Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 6701bd51-e8bb-460f-862e-6d9c306bc7e1 · outbound

This paper cites From benchmarks to reality: Advancing visual anomaly detection by the vand 3.0 challenge.arXiv preprint arXiv:2509.17615, 2025.

SuperADD: Training-free Class-agnostic Anomaly Segmentation -- CVPR 2026 VAND 4.0 Workshop Challenge Industrial Track From benchmarks to reality: Advancing visual anomaly detection by the vand 3.0 challenge.arXiv preprint arXiv:2509.17615, 2025

Reference 3

Resolution: verified exact; arxiv_id, observed 2026-06-30T21:15:03.943463Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation eab67cc5-d1a7-42a9-b9bd-0a5fe6ddaa72 · outbound

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

SuperADD: Training-free Class-agnostic Anomaly Segmentation -- CVPR 2026 VAND 4.0 Workshop Challenge Industrial Track Mvtec ad–a comprehensive real-world dataset for unsupervised anomaly detection

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-07-07T17:44:02.017040Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation bfcca251-00b4-4d28-9a2f-bd1c5c14819a · outbound

This paper cites Accurate anomaly localization in challenging industrial settings via a hybrid detection frame- work.

SuperADD: Training-free Class-agnostic Anomaly Segmentation -- CVPR 2026 VAND 4.0 Workshop Challenge Industrial Track Accurate anomaly localization in challenging industrial settings via a hybrid detection frame- work

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-07-07T17:44:02.024983Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 90154ced-fca4-4297-a8bd-10baf977df6d · outbound

This paper cites RoBiS: Robust Binary Segmentation for High-Resolution Industrial Images.

SuperADD: Training-free Class-agnostic Anomaly Segmentation -- CVPR 2026 VAND 4.0 Workshop Challenge Industrial Track RoBiS: Robust Binary Segmentation for High-Resolution Industrial Images

Reference 6

Resolution: verified exact; arxiv_id, observed 2026-06-30T21:15:03.942188Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation dcfc87bb-5069-4634-bcab-d24cf2fd52ab · outbound

This paper cites Exploring intrinsic normal prototypes within a single im- age for universal anomaly detection.

SuperADD: Training-free Class-agnostic Anomaly Segmentation -- CVPR 2026 VAND 4.0 Workshop Challenge Industrial Track Exploring intrinsic normal prototypes within a single im- age for universal anomaly detection

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-07-07T17:44:02.030744Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

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

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

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 2026-08-13T06:32:02.005865+00:00.

Observation f25b7d8c-87ab-41f5-869a-170cc4e3b61a · outbound

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

SuperADD: Training-free Class-agnostic Anomaly Segmentation -- CVPR 2026 VAND 4.0 Workshop Challenge Industrial Track Towards to- tal recall in industrial anomaly detection

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-07-07T17:44:02.021065Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation ce622e4d-8264-4663-966a-fafb9ba1045c · outbound

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

SuperADD: Training-free Class-agnostic Anomaly Segmentation -- CVPR 2026 VAND 4.0 Workshop Challenge Industrial Track DINOv2: Learning Robust Visual Features without Supervision

Reference 10

Resolution: verified exact; local_arxiv, observed 2026-06-30T21:15:03.951311Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation df78d551-58cb-4dba-a690-f2f902ebe4b9 · outbound

This paper cites DINOv3.

SuperADD: Training-free Class-agnostic Anomaly Segmentation -- CVPR 2026 VAND 4.0 Workshop Challenge Industrial Track DINOv3

Reference 11

Resolution: metadata mismatch; local_arxiv, observed 2026-06-30T21:15:03.944931Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation ff4d88de-adbe-4f3c-aa9f-829760ba661b · outbound

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

SuperADD: Training-free Class-agnostic Anomaly Segmentation -- CVPR 2026 VAND 4.0 Workshop Challenge Industrial Track Efficien- tad: Accurate visual anomaly detection at millisecond-level latencies

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-07-07T17:44:02.023013Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation b36cb246-2bac-483b-bcfb-dfa96768471f · outbound

This paper cites An ensemble method for industrial anomaly detection and localization.

SuperADD: Training-free Class-agnostic Anomaly Segmentation -- CVPR 2026 VAND 4.0 Workshop Challenge Industrial Track An ensemble method for industrial anomaly detection and localization

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-07-07T17:44:02.026743Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 940d25b7-21ee-4820-9c72-1d171d22a413 · outbound

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

SuperADD: Training-free Class-agnostic Anomaly Segmentation -- CVPR 2026 VAND 4.0 Workshop Challenge Industrial Track DMAD: Dual Memory Bank for Real-World Anomaly Detection

Reference 14

Resolution: verified exact; arxiv_id, observed 2026-06-30T21:15:03.948274Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 41730d99-c1a2-4c08-967d-8ea5a6e80a3b · outbound

This paper cites Training-free indus- trial defect generation with diffusion models.

SuperADD: Training-free Class-agnostic Anomaly Segmentation -- CVPR 2026 VAND 4.0 Workshop Challenge Industrial Track Training-free indus- trial defect generation with diffusion models

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-07-07T17:44:02.028519Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Pith citing papers

No inbound Pith citation observations are available.