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Paper Citation Record · LEDGER

Sub-Image Anomaly Detection with Deep Pyramid Correspondences

As of 21 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 42 inbound Pith citation observations for arXiv:2005.02357.

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

pith.paper-citation-record.v1
2005.02357 v3

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measured 0 of 0 reference resolution

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measured 42 of 42 standing notices

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Source: scholarly_work_events, retraction_status_cache, observed 2026-08-21T06:32:19.484+00:00

measured 42 of 42 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-16T12:23:55.770015Z

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Source: arxiv_reference, observed 2026-07-04T19:40:06.738342Z

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Pith citing papers

Observation e79689e0-356b-4f2b-8ab2-b6219aaf2067 · inbound

Subspace-Guided Feature Reconstruction for Unsupervised Anomaly Localization cites this paper.

Subspace-Guided Feature Reconstruction for Unsupervised Anomaly Localization Sub-Image Anomaly Detection with Deep Pyramid Correspondences

Reference 10

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arxiv_id, observed 2026-05-24T06:46:03.008509Z

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Observation 26205791-3f92-43d6-aa86-73f81c7451e7 · inbound

FUN-AD: Fully Unsupervised Learning for Anomaly Detection with Noisy Training Data cites this paper.

FUN-AD: Fully Unsupervised Learning for Anomaly Detection with Noisy Training Data Sub-Image Anomaly Detection with Deep Pyramid Correspondences

Reference 8

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source=pdf_text observed=2026-08-12T13:37:39.887501Z digest=sha256:27c3258ed884b5adfd0a8d44f243085d1dc354dae1b93e693545900f5d2b3342

Observation 92063b95-949e-4b23-9b06-101059673807 · inbound

Background-Aware Defect Generation for Robust Industrial Anomaly Detection cites this paper.

Background-Aware Defect Generation for Robust Industrial Anomaly Detection Sub-Image Anomaly Detection with Deep Pyramid Correspondences

Reference 4

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source=arxiv_source observed=2026-08-12T13:39:56.136148Z digest=sha256:30881c61c1283819f6caded4c66b5465962cf420f4509a836d197354021cc92d

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

Automatic Prompt Generation and Grounding Object Detection for Zero-Shot Image Anomaly Detection cites this paper.

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

Reference 23

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source=pdf_text observed=2026-08-12T10:27:30.226424Z digest=sha256:1cc832cdddf24a3a01ae7884cbab132f06d8791494b138a2682eb03042ff0d8f

Observation acc79398-c1ec-49fd-a4b0-c8934bc5e6ea · inbound

ONER: Online Experience Replay for Incremental Anomaly Detection cites this paper.

ONER: Online Experience Replay for Incremental Anomaly Detection Sub-Image Anomaly Detection with Deep Pyramid Correspondences

Reference 7

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source=pdf_text observed=2026-08-11T22:03:23.833466Z digest=sha256:6a9072c5024e0c283f6e5f2017b83b147d02a4ade1ff06da5071dd774dfb8b51

Observation 3af79dfe-79b9-4ff0-89ad-741ad0cb18ea · inbound

Towards Zero-shot 3D Anomaly Localization cites this paper.

Towards Zero-shot 3D Anomaly Localization Sub-Image Anomaly Detection with Deep Pyramid Correspondences

Reference 11

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source=pdf_text observed=2026-08-11T21:39:11.800307Z digest=sha256:9a57ecc658baefdfd7f1157eb989a177867ce31a862386cd930053e01679d46d

Observation b62a0b29-9604-4d71-8f9d-d61650ab1e2f · inbound

Filter or Compensate: Towards Invariant Representation from Distribution Shift for Anomaly Detection cites this paper.

Filter or Compensate: Towards Invariant Representation from Distribution Shift for Anomaly Detection Sub-Image Anomaly Detection with Deep Pyramid Correspondences

Reference 3

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source=pdf_text observed=2026-08-11T16:24:32.233024Z digest=sha256:a65903806f5ed0880d9b3ea5d7bfcdb4d00fea21ecd8ba496a9a32b307035cfb

Observation f70148dd-57a4-467f-8ab2-4e0a20045861 · inbound

Revisiting Multimodal Fusion for 3D Anomaly Detection from an Architectural Perspective cites this paper.

Revisiting Multimodal Fusion for 3D Anomaly Detection from an Architectural Perspective Sub-Image Anomaly Detection with Deep Pyramid Correspondences

Reference 10

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source=arxiv_source observed=2026-08-11T05:42:58.366507Z digest=sha256:ef2116bf68ee141361ed91928f101d3ef5a0a9a131e8d6cbbee20c0d7888d2dc

Observation 2c7b4f0f-a4e4-44d9-8606-69399b0a0179 · inbound

Patch-aware Vector Quantized Codebook Learning for Unsupervised Visual Defect Detection cites this paper.

Patch-aware Vector Quantized Codebook Learning for Unsupervised Visual Defect Detection Sub-Image Anomaly Detection with Deep Pyramid Correspondences

Reference 31

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source=pdf_text observed=2026-08-10T20:14:17.657045Z digest=sha256:ac9f8abe1c34a27623231d44e6657bc06ec96021d98a4b3f0014b29b2cb306c8

Observation ab8c0c92-cec1-49c1-b8e6-6881cf958624 · inbound

Sequential PatchCore: Anomaly Detection for Surface Inspection using Synthetic Impurities cites this paper.

Sequential PatchCore: Anomaly Detection for Surface Inspection using Synthetic Impurities Sub-Image Anomaly Detection with Deep Pyramid Correspondences

Reference 34

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source=pdf_text observed=2026-08-10T19:57:03.361981Z digest=sha256:4c4f5943b5e7a5237a018f290a80198223ca344f7b1923bd753aeed2b3752be3

Observation ec9d770a-ac0e-4392-93da-904c63e7d7a4 · inbound

Exploring Few-Shot Defect Segmentation in General Industrial Scenarios with Metric Learning and Vision Foundation Models cites this paper.

Exploring Few-Shot Defect Segmentation in General Industrial Scenarios with Metric Learning and Vision Foundation Models Sub-Image Anomaly Detection with Deep Pyramid Correspondences

Reference 54

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source=pdf_text observed=2026-08-09T16:13:53.477591Z digest=sha256:bafde1387d9a21cfa978867e634e82a6db1d7f0efb16db4c1ba1482e7be55632

Observation 9a4de94c-53ac-40e2-83b4-bbb820db6baf · inbound

Multimodal Task Representation Memory Bank vs. Catastrophic Forgetting in Anomaly Detection cites this paper.

Multimodal Task Representation Memory Bank vs. Catastrophic Forgetting in Anomaly Detection Sub-Image Anomaly Detection with Deep Pyramid Correspondences

Reference 45

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source=pdf_text observed=2026-08-08T16:32:10.078135Z digest=sha256:59f19b4b1b8f3d5c813077b1b824153ba29ae98d8ab9cbca03350c07142f5497

Observation 400cad0b-9b42-41cc-abd4-f80eb0a11a53 · inbound

MathPhys-Guided Coarse-to-Fine Anomaly Synthesis with SQE-Driven Bi-Level Optimization for Anomaly Detection cites this paper.

MathPhys-Guided Coarse-to-Fine Anomaly Synthesis with SQE-Driven Bi-Level Optimization for Anomaly Detection Sub-Image Anomaly Detection with Deep Pyramid Correspondences

Reference 30

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source=pdf_text observed=2026-08-16T12:23:55.770015Z digest=sha256:497bc8575d1aa6536601c688957a4ceacdd96a36339149a680f81ad3805f762d

Observation fd357693-e7af-4c2b-8d27-cf0c198657bb · inbound

LR-IAD:Mask-Free Industrial Anomaly Detection with Logical Reasoning cites this paper.

LR-IAD:Mask-Free Industrial Anomaly Detection with Logical Reasoning Sub-Image Anomaly Detection with Deep Pyramid Correspondences

Reference 21

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source=pdf_text observed=2026-08-16T05:54:46.827535Z digest=sha256:e44c31596d31d8703da64d16303c5f0878269a43b4246c5997fefa1edef40ad9

Observation 72558bc2-114d-4627-8963-44dd327b2198 · inbound

Detect, Classify, Act: Categorizing Industrial Anomalies with Multi-Modal Large Language Models cites this paper.

Detect, Classify, Act: Categorizing Industrial Anomalies with Multi-Modal Large Language Models Sub-Image Anomaly Detection with Deep Pyramid Correspondences

Reference 8

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source=pdf_text observed=2026-08-16T00:53:10.466230Z digest=sha256:12c18b73e01f2ca8fbc48b99e66c0a964a24c21564a6abb59c00681e2b952bdb

Observation f28e1fc6-634e-441d-bfcf-62475d20fe49 · inbound

Examining the Source of Defects from a Mechanical Perspective for 3D Anomaly Detection cites this paper.

Examining the Source of Defects from a Mechanical Perspective for 3D Anomaly Detection Sub-Image Anomaly Detection with Deep Pyramid Correspondences

Reference 2021

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source=pdf_text observed=2026-08-15T22:58:18.552864Z digest=sha256:1b4b6f592b3485f64cae75fe15119e75475d84ef7cb28e6115153250c45cfb15

Observation a270ddd3-8f30-467f-b5ed-26e81c79d07f · 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 Sub-Image Anomaly Detection with Deep Pyramid Correspondences

Reference 75

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Observation 935eaeda-def4-4407-b4b1-553d8cdc7f23 · inbound

HomographyAD: Deep Anomaly Detection Using Self Homography Learning cites this paper.

HomographyAD: Deep Anomaly Detection Using Self Homography Learning Sub-Image Anomaly Detection with Deep Pyramid Correspondences

Reference 7

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source=pdf_text observed=2026-08-07T05:05:35.053876Z digest=sha256:74c52fbf165acda84e7b55590acccd269cead3e89e42f32bb2e101d694d84218

Observation 2a3d23bc-da42-4992-a3dc-92c85e9feefb · inbound

Noise Fusion-based Distillation Learning for Anomaly Detection in Complex Industrial Environments cites this paper.

Noise Fusion-based Distillation Learning for Anomaly Detection in Complex Industrial Environments Sub-Image Anomaly Detection with Deep Pyramid Correspondences

Reference 17

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source=pdf_text observed=2026-08-15T19:34:02.434269Z digest=sha256:db32768f841cf6379aa390b405b98d7a1a5d82897749be076529edc15f1e3ab6

Observation ffb2da41-35a0-4a7b-9d8c-8173fd00dac8 · inbound

Normality Prior Guided Multi-Semantic Fusion Network for Unsupervised Image Anomaly Detection cites this paper.

Normality Prior Guided Multi-Semantic Fusion Network for Unsupervised Image Anomaly Detection Sub-Image Anomaly Detection with Deep Pyramid Correspondences

Reference 24

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Observation 72648e61-bacd-4d9c-bcbf-aa872511d91b · inbound

FastRef:Fast Prototype Refinement for Few-Shot Industrial Anomaly Detection cites this paper.

FastRef:Fast Prototype Refinement for Few-Shot Industrial Anomaly Detection Sub-Image Anomaly Detection with Deep Pyramid Correspondences

Reference 3

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Observation 12ad75c7-f7d6-4e22-b0a0-e7a22fdc5679 · inbound

SAGE: A Visual Language Model for Anomaly Detection via Fact Enhancement and Entropy-aware Alignment cites this paper.

SAGE: A Visual Language Model for Anomaly Detection via Fact Enhancement and Entropy-aware Alignment Sub-Image Anomaly Detection with Deep Pyramid Correspondences

Reference 14

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source=pdf_text observed=2026-08-06T18:34:47.828661Z digest=sha256:462813e48ef2941b2dbd6d30d51487866ce09948839dc29af88c7def89b72c69

Observation 067c747c-0f63-4b70-a837-7277f404d5dc · inbound

Generate Aligned Anomaly: Region-Guided Few-Shot Anomaly Image-Mask Pair Synthesis for Industrial Inspection cites this paper.

Generate Aligned Anomaly: Region-Guided Few-Shot Anomaly Image-Mask Pair Synthesis for Industrial Inspection Sub-Image Anomaly Detection with Deep Pyramid Correspondences

Reference 8

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source=pdf_text observed=2026-08-06T17:57:48.680849Z digest=sha256:63e51942fdac95c69115bba5009bd7d95641d6ffb0598a766ceb50b009533606

Observation 0eae4fa2-f7e7-49d8-abac-7cc2b8bac3c2 · inbound

A Roadmap for Climate-Relevant Robotics Research cites this paper.

A Roadmap for Climate-Relevant Robotics Research Sub-Image Anomaly Detection with Deep Pyramid Correspondences

Reference 262

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source=pdf_text observed=2026-08-06T17:07:54.830974Z digest=sha256:65a86a214eee680dec50175c1709478ba07f5376456fe850d498ea378bfb0750

Observation 7d16479a-db96-4acd-8c5d-07c7b3ce9442 · 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 Sub-Image Anomaly Detection with Deep Pyramid Correspondences

Reference 147

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source=pdf_text observed=2026-08-06T17:21:48.967700Z digest=sha256:fb2d10bc2eb633cf07e2139f7f5cc288a0d8634db3c738874902137ff570807a

Observation 503cec6b-8f00-42fb-a66e-2e2239624cee · inbound

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

Toward Long-Tailed Online Anomaly Detection through Class-Agnostic Concepts Sub-Image Anomaly Detection with Deep Pyramid Correspondences

Reference 12

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source=pdf_text observed=2026-08-06T15:06:24.407310Z digest=sha256:f5d53a93f93e0bfc178b159b11511034395605f129242d2de4d1b04547ef5840

Observation 75933a80-2799-464c-8cb6-5639226761d7 · inbound

BridgeNet: A Unified Multimodal Framework for Bridging 2D and 3D Industrial Anomaly Detection cites this paper.

BridgeNet: A Unified Multimodal Framework for Bridging 2D and 3D Industrial Anomaly Detection Sub-Image Anomaly Detection with Deep Pyramid Correspondences

Reference 12

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source=pdf_text observed=2026-08-15T17:59:33.185284Z digest=sha256:88dc51fc636d067af9e3283bb9128e6cbdf51294fa63d5a5117fb1869ff73e9e

Observation 62e54316-5f5d-4e2e-a389-b3ff3a256d24 · inbound

Learning local and global prototypes with optimal transport for unsupervised anomaly detection and localization cites this paper.

Learning local and global prototypes with optimal transport for unsupervised anomaly detection and localization Sub-Image Anomaly Detection with Deep Pyramid Correspondences

Reference 6

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source=pdf_text observed=2026-08-15T17:22:13.999155Z digest=sha256:bb802f7fd8acbcdc84b9e92de9a4b727a93198975983a8b1e5236b72d7714a5f

Observation b2fb6e2c-16d1-4bb2-8b67-85af30506bca · inbound

DictAS: A Framework for Class-Generalizable Few-Shot Anomaly Segmentation via Dictionary Lookup cites this paper.

DictAS: A Framework for Class-Generalizable Few-Shot Anomaly Segmentation via Dictionary Lookup Sub-Image Anomaly Detection with Deep Pyramid Correspondences

Reference 9

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source=arxiv_source observed=2026-08-05T18:59:07.630867Z digest=sha256:4da1f0d9b44d6332bee3a302c82cdf62d0868932b329b6826575915f9bf5da1f

Observation 5fe230a9-48b9-4fe0-b46c-082b2c6cebd5 · inbound

Generative Model-Based Feature Attention Module for Video Action Analysis cites this paper.

Generative Model-Based Feature Attention Module for Video Action Analysis Sub-Image Anomaly Detection with Deep Pyramid Correspondences

Reference 8

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source=pdf_text observed=2026-08-05T19:03:27.699167Z digest=sha256:dd3482e079de9834972f6fa4c1da0fdb9951ffc241e96dffa3354f878e9b72df

Observation 7049db35-2978-413f-b08e-4f0563259d5b · inbound

AutoDetect: Designing an Autoencoder-based Detection Method for Poisoning Attacks on Object Detection Applications in the Military Domain cites this paper.

AutoDetect: Designing an Autoencoder-based Detection Method for Poisoning Attacks on Object Detection Applications in the Military Domain Sub-Image Anomaly Detection with Deep Pyramid Correspondences

Reference 38

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source=pdf_text observed=2026-08-05T11:10:20.776134Z digest=sha256:188081333541db1a50d6ae34b8ba6bdecbf87ae931cba345c79155ab7f4b9ae8

Observation 617bea45-2d60-4433-b986-1dfae20b1764 · inbound

Defect-aware Hybrid Prompt Optimization via Progressive Tuning for Zero-Shot Multi-type Anomaly Detection and Segmentation cites this paper.

Defect-aware Hybrid Prompt Optimization via Progressive Tuning for Zero-Shot Multi-type Anomaly Detection and Segmentation Sub-Image Anomaly Detection with Deep Pyramid Correspondences

Reference 4

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source=pdf_text observed=2026-08-03T17:28:39.335652Z digest=sha256:cb6b8e038c7b4dd372b44968873dc7e10371294dc98b5f9b8e7fb1f1293dd651

Observation d4f9730d-2ec0-4d22-a16c-367db87b21ac · inbound

SubspaceAD: Training-Free Few-Shot Anomaly Detection via Subspace Modeling cites this paper.

SubspaceAD: Training-Free Few-Shot Anomaly Detection via Subspace Modeling Sub-Image Anomaly Detection with Deep Pyramid Correspondences

Reference 10

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arxiv_id, observed 2026-05-15T18:50:16.759266Z

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No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=pdf_text observed=2026-05-15T18:48:08.212050Z digest=sha256:3fc0875f25bed4ed8be759b9b23abbcfccb36f6050d0f3a8cf87ff5eb6bbdf38

Observation 12859edf-9f0d-44ab-a23f-aea41f061a91 · inbound

Align3D-AD: Cross-Modal Feature Alignment and Dual-Prompt Learning for Zero-shot 3D Anomaly Detection cites this paper.

Align3D-AD: Cross-Modal Feature Alignment and Dual-Prompt Learning for Zero-shot 3D Anomaly Detection Sub-Image Anomaly Detection with Deep Pyramid Correspondences

Reference 11

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arxiv_id, observed 2026-05-11T16:31:06.706081Z

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source=pdf_text observed=2026-05-09T16:34:22.826289Z digest=sha256:689350d9e72688596bed7c218d313e2febfbd6556bb2c2511798b5697b0c2c46

Observation d0f8cbca-e57c-4166-9734-79f7261321dd · inbound

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

Beyond Normal References: Discriminative Few-Shot Anomaly Detection Sub-Image Anomaly Detection with Deep Pyramid Correspondences

Reference 65

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arxiv_id, observed 2026-05-25T04:55:23.674385Z

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No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=arxiv_source observed=2026-05-25T04:52:07.114651Z digest=sha256:695a5127a6d3d521f7fb3500aae5af51e6d0fd9db0a78630534ac19438fa54cd

Observation 1a4e88c2-8689-466c-a0a4-d454444686c9 · inbound

Anomaly as Non-Conformity via Training-Free Graph Laplacian Energy Minimization cites this paper.

Anomaly as Non-Conformity via Training-Free Graph Laplacian Energy Minimization Sub-Image Anomaly Detection with Deep Pyramid Correspondences

Reference 5

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arxiv_id, observed 2026-06-29T13:13:27.454389Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=pdf_text observed=2026-06-29T13:07:09.539291Z digest=sha256:5d7ccd62aa4e59141fee738f5e2f877ac8dc6b4e4a2a5c499e93259002a2b5cb

Observation 8dd06595-a8cb-4f35-b1f1-099857e324f8 · inbound

Uni-RCM: Unified Reference-guided Cross-modal Mapping for Multi-Class Anomaly Detection cites this paper.

Uni-RCM: Unified Reference-guided Cross-modal Mapping for Multi-Class Anomaly Detection Sub-Image Anomaly Detection with Deep Pyramid Correspondences

Reference 24

Resolution
metadata mismatch
arxiv_id, observed 2026-06-29T08:13:14.784998Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=pdf_text observed=2026-06-29T08:11:41.147109Z digest=sha256:2ee22084c165ce826a45a9dbb41e53de8a93e748711b4954fd35ae0a12dc6cac

Observation f646e6a6-b51e-455e-b3e8-95a00bc61661 · 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 Sub-Image Anomaly Detection with Deep Pyramid Correspondences

Reference 45

Resolution
metadata mismatch
arxiv_id, observed 2026-07-04T09:49:44.582550Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=pdf_text observed=2026-06-26T09:26:51.456652Z digest=sha256:36fc73551accd729f6812a9a89602837196f7730caf6e0e4ff5d924976751453

Observation 21282937-214c-4c3e-941b-0f7241da417f · inbound

MATCH: Flow Matching for Multi-View Anomaly Detection cites this paper.

MATCH: Flow Matching for Multi-View Anomaly Detection Sub-Image Anomaly Detection with Deep Pyramid Correspondences

Reference 14

Resolution
verified exact
arxiv_id, observed 2026-07-04T16:29:57.680241Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=pdf_text observed=2026-06-26T00:32:33.173587Z digest=sha256:99da9d5250779505dd87a82e2a243e2e3fb0de2cf8ff7000ae05582529c827b4

Observation f45e7cb1-e110-430b-9cb2-947850565b52 · inbound

Hypergraph Normal World Models for Logical Visual Anomaly Detection cites this paper.

Hypergraph Normal World Models for Logical Visual Anomaly Detection Sub-Image Anomaly Detection with Deep Pyramid Correspondences

Reference 6

Resolution
verified exact
arxiv_id, observed 2026-07-04T19:40:06.739858Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=pdf_text observed=2026-06-25T21:09:19.682153Z digest=sha256:6ba129ff62e5308613f071e6e67f7c9644541d82576f8bc14b7afafc29e09f80

Observation c4d05cb9-b5e6-4352-b23b-c91e37ad4c0f · inbound

UniVAD v2: Unified Visual Anomaly Detection via Support-Conditioned Boundary Construction cites this paper.

UniVAD v2: Unified Visual Anomaly Detection via Support-Conditioned Boundary Construction Sub-Image Anomaly Detection with Deep Pyramid Correspondences

Reference 27

Resolution
metadata mismatch
arxiv_id, observed 2026-06-30T06:54:20.084609Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=pdf_text observed=2026-06-30T06:53:59.441734Z digest=sha256:445e5e46403972c48a74bec8bd59bf31abfe0ca4c4a090e8bc8c4ed892af315d

Observation 37f45059-aeba-4c7f-895a-c5b4fd72ffac · inbound

LIBAD: A Multimodal Anomaly Detection Benchmark for Li-Ion Battery Electrode Manufacturing cites this paper.

LIBAD: A Multimodal Anomaly Detection Benchmark for Li-Ion Battery Electrode Manufacturing Sub-Image Anomaly Detection with Deep Pyramid Correspondences

Reference 5

Resolution
unresolved
no resolver link, observed 2026-08-12T00:42:26.021879Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-12T00:42:26.021879Z digest=sha256:f4467c46610487bfed7b8a48b5a252ad576c0f1c7e9f2a572bf9d51b2b2f9a72