Paper Citation Record · LEDGER

A Survey on Visual Anomaly Detection: Challenge, Approach, and Prospect

As of 7 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 19 inbound Pith citation observations for arXiv:2401.16402.

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

pith.paper-citation-record.v1
2401.16402 v1

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 19 of 19 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-07T06:34:17.273281+00:00

measured 19 of 19 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T13:43:19.564441Z

measured 1 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Reference resolution

0 of 0 outbound references displayed

verified exact0
verified fuzzy0
unresolved0
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

9
arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation bfc705f3-9a44-40b2-a0f7-21b20f850b2c · inbound

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

RoBiS: Robust Binary Segmentation for High-Resolution Industrial Images A Survey on Visual Anomaly Detection: Challenge, Approach, and Prospect

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-07T13:43:19.564441Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0d8d4d6b-ef16-4a97-8ae1-00b0847a108b · inbound

INP-Former++: Advancing Universal Anomaly Detection via Intrinsic Normal Prototypes and Residual Learning cites this paper.

INP-Former++: Advancing Universal Anomaly Detection via Intrinsic Normal Prototypes and Residual Learning A Survey on Visual Anomaly Detection: Challenge, Approach, and Prospect

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-07T11:03:10.179143Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6e4df65f-c3a3-4497-90c1-a70de2bb8739 · inbound

IQE-CLIP: Instance-aware Query Embedding for Zero-/Few-shot Anomaly Detection in Medical Domain cites this paper.

IQE-CLIP: Instance-aware Query Embedding for Zero-/Few-shot Anomaly Detection in Medical Domain A Survey on Visual Anomaly Detection: Challenge, Approach, and Prospect

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-07T04:28:33.889507Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation fb3c09f8-c92a-4682-9b20-820db08f938d · inbound

Quantitative Benchmarking of Anomaly Detection Methods in Digital Pathology cites this paper.

Quantitative Benchmarking of Anomaly Detection Methods in Digital Pathology A Survey on Visual Anomaly Detection: Challenge, Approach, and Prospect

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-06T23:10:07.273869Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b1e08200-9070-44a0-9221-a2fc00ca7fe4 · inbound

Bridge Feature Matching and Cross-Modal Alignment with Mutual-filtering for Zero-shot Anomaly Detection cites this paper.

Bridge Feature Matching and Cross-Modal Alignment with Mutual-filtering for Zero-shot Anomaly Detection A Survey on Visual Anomaly Detection: Challenge, Approach, and Prospect

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-06T17:27:14.370852Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0586d6c5-16c4-4d0e-b566-d98a2546d706 · inbound

A Comprehensive Survey for Real-World Industrial Defect Detection: Challenges, Approaches, and Prospects cites this paper.

A Comprehensive Survey for Real-World Industrial Defect Detection: Challenges, Approaches, and Prospects A Survey on Visual Anomaly Detection: Challenge, Approach, and Prospect

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-06T17:21:37.814140Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 451ac339-7fed-44e0-b369-95bb97edd32f · inbound

Toward Long-Tailed Online Anomaly Detection through Class-Agnostic Concepts cites this paper.

Toward Long-Tailed Online Anomaly Detection through Class-Agnostic Concepts A Survey on Visual Anomaly Detection: Challenge, Approach, and Prospect

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-06T15:06:24.391064Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4031733d-72e8-47bf-8187-2146473587c1 · inbound

Normality Calibration in Semi-supervised Graph Anomaly Detection cites this paper.

Normality Calibration in Semi-supervised Graph Anomaly Detection A Survey on Visual Anomaly Detection: Challenge, Approach, and Prospect

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-04T12:48:07.552118Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7c4ae927-032b-43a3-b996-0ed92a823508 · inbound

SGANet: Semantic and Geometric Alignment for Multimodal Multi-view Anomaly Detection cites this paper.

SGANet: Semantic and Geometric Alignment for Multimodal Multi-view Anomaly Detection A Survey on Visual Anomaly Detection: Challenge, Approach, and Prospect

Reference 1

Resolution: verified exact; arxiv_id, observed 2026-05-10T22:05:48.336594Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 0ba0a0f9-19aa-4d8c-845e-e5684879f3b7 · inbound

GroundingAnomaly: Spatially-Grounded Diffusion for Few-Shot Anomaly Synthesis cites this paper.

GroundingAnomaly: Spatially-Grounded Diffusion for Few-Shot Anomaly Synthesis A Survey on Visual Anomaly Detection: Challenge, Approach, and Prospect

Reference 2

Resolution: verified exact; arxiv_id, observed 2026-05-11T00:35:49.404243Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 424e1e87-4438-42b0-a255-3b4132df0fb8 · inbound

Beyond Normal References: Discriminative Few-Shot Anomaly Detection cites this paper.

Beyond Normal References: Discriminative Few-Shot Anomaly Detection A Survey on Visual Anomaly Detection: Challenge, Approach, and Prospect

Reference 2

Resolution: verified exact; arxiv_id, observed 2026-05-25T04:55:23.664104Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 119bd49d-f4b8-4254-b0a9-56362bd330ec · inbound

Dual Prototype-Conditioned Diffusion Model for Scalable Multi-Class Unsupervised Anomaly Detection in Large Category Spaces cites this paper.

Dual Prototype-Conditioned Diffusion Model for Scalable Multi-Class Unsupervised Anomaly Detection in Large Category Spaces A Survey on Visual Anomaly Detection: Challenge, Approach, and Prospect

Reference 4

Resolution: verified exact; arxiv_id, observed 2026-06-30T13:54:44.040833Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 0f7edb27-980b-461c-bf01-b4aa0f51d615 · inbound

AnomalyAgent: Training-Free Agentic Models for Zero-/Few-Shot Anomaly Detection cites this paper.

AnomalyAgent: Training-Free Agentic Models for Zero-/Few-Shot Anomaly Detection A Survey on Visual Anomaly Detection: Challenge, Approach, and Prospect

Reference 4

Resolution: verified exact; arxiv_id, observed 2026-06-29T08:13:14.940540Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 536d2f97-8c91-4749-9a1a-943838807657 · inbound

HiMatch-AD: DINOv3-driven Hierarchical Matching for Training-free Medical Anomaly Detection cites this paper.

HiMatch-AD: DINOv3-driven Hierarchical Matching for Training-free Medical Anomaly Detection A Survey on Visual Anomaly Detection: Challenge, Approach, and Prospect

Reference 7

Resolution: verified exact; arxiv_id, observed 2026-07-04T08:39:42.031321Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation cd9c8230-8b66-4159-894f-d9a31908934f · inbound

MambaADv2: Evolving Duality-enhanced State Space Model for Unsupervised Anomaly Detection cites this paper.

MambaADv2: Evolving Duality-enhanced State Space Model for Unsupervised Anomaly Detection A Survey on Visual Anomaly Detection: Challenge, Approach, and Prospect

Reference 16

Resolution: metadata mismatch; arxiv_id, observed 2026-07-04T09:49:44.646264Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 4d9bc430-1345-4a97-8376-0cedb2278b18 · inbound

CoGeoAD: Hierarchical Color-Geometric Fusion with Multi-View Attention for Zero-Shot 3D Anomaly Detection cites this paper.

CoGeoAD: Hierarchical Color-Geometric Fusion with Multi-View Attention for Zero-Shot 3D Anomaly Detection A Survey on Visual Anomaly Detection: Challenge, Approach, and Prospect

Reference 7

Resolution: verified exact; arxiv_id, observed 2026-07-04T19:50:10.591635Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 25a0d44a-ee7b-4dd9-ad76-66f1b891629d · inbound

Learning Topology-Aware Representations via Test-Time Adaptation for Anomaly Segmentation cites this paper.

Learning Topology-Aware Representations via Test-Time Adaptation for Anomaly Segmentation A Survey on Visual Anomaly Detection: Challenge, Approach, and Prospect

Reference 4

Resolution: metadata mismatch; arxiv_id, observed 2026-07-01T17:15:50.964359Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation ad0b0165-bd65-4a8d-8b50-dffdbf8e57d2 · inbound

Anomaly Factory 3D: A Modular Framework for Diverse Pseudo-Anomaly Synthesis in Unsupervised 3D Anomaly Detection cites this paper.

Anomaly Factory 3D: A Modular Framework for Diverse Pseudo-Anomaly Synthesis in Unsupervised 3D Anomaly Detection A Survey on Visual Anomaly Detection: Challenge, Approach, and Prospect

Reference 3

Resolution: verified exact; arxiv_id, observed 2026-06-30T07:54:21.172788Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 4297816f-9c2f-49b2-bbc4-68473cd4ae7e · inbound

Robust Zero-shot Anomaly Detection under Limited Auxiliary Anomaly Priors cites this paper.

Robust Zero-shot Anomaly Detection under Limited Auxiliary Anomaly Priors A Survey on Visual Anomaly Detection: Challenge, Approach, and Prospect

Reference 5

Resolution: verified exact; arxiv_id, observed 2026-06-30T07:54:21.686229Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.