Paper Citation Record · LEDGER

Towards High-Resolution Industrial Image Anomaly Detection

As of 22 August 2026, this Paper Citation Record lists 50 of 50 outbound references and 2 inbound Pith citation observations for arXiv:2508.12931.

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

pith.paper-citation-record.v1
2508.12931 v1

Coverage vector

measured 50 of 50 reference resolution

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measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-03T23:17:29.629390Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-06-30T12:54:40.308769Z

Reference resolution

50 of 50 outbound references displayed

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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation ca02cee3-b8f2-4a33-b1d4-08492b23f6d6 · outbound

This paper cites A survey of methods for automated quality control based on images,.

Towards High-Resolution Industrial Image Anomaly Detection A survey of methods for automated quality control based on images,

Reference 1

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

Observation 43eaa551-eceb-4e44-93ec-5be401f50127 · outbound

This paper cites Deep learning for unsupervised anomaly localization in industrial images: A survey,.

Towards High-Resolution Industrial Image Anomaly Detection Deep learning for unsupervised anomaly localization in industrial images: A survey,

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T17:21:44.829777Z

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Observation 8d9c9617-de79-4c43-a89f-16438f8d5962 · outbound

This paper cites Deep industrial image anomaly detection: A survey,.

Towards High-Resolution Industrial Image Anomaly Detection Deep industrial image anomaly detection: A survey,

Reference 3

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Observation 6a492af5-2554-4c16-b1f0-6823174b5717 · outbound

This paper cites Varad: Lightweight high- resolution image anomaly detection via visual autoregressive modeling,.

Towards High-Resolution Industrial Image Anomaly Detection Varad: Lightweight high- resolution image anomaly detection via visual autoregressive modeling,

Reference 4

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Observation c178ffb1-4915-47d4-87d8-3eab18b167ef · outbound

This paper cites Divide and conquer: High-resolution industrial anomaly detection via memory efficient tiled ensemble,.

Towards High-Resolution Industrial Image Anomaly Detection Divide and conquer: High-resolution industrial anomaly detection via memory efficient tiled ensemble,

Reference 5

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Observation eaf62869-deea-4d1d-b647-b05d8970817b · outbound

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

Towards High-Resolution Industrial Image Anomaly Detection Mvtec ad–a comprehensive real-world dataset for unsupervised anomaly detection,

Reference 6

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Observation 4294b2a1-a740-448e-a753-de54e616000d · outbound

This paper cites Spot-the- difference self-supervised pre-training for anomaly detection and seg- mentation,.

Towards High-Resolution Industrial Image Anomaly Detection Spot-the- difference self-supervised pre-training for anomaly detection and seg- mentation,

Reference 7

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Observation b30dc894-c97b-4b61-b872-37e760c30822 · outbound

This paper cites Real-iad: A real-world multi-view dataset for benchmarking versatile industrial anomaly detection,.

Towards High-Resolution Industrial Image Anomaly Detection Real-iad: A real-world multi-view dataset for benchmarking versatile industrial anomaly detection,

Reference 8

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Observation 71db4804-eb93-4105-a828-8f6790d0e0a1 · outbound

This paper cites Towards total recall in industrial anomaly detection,.

Towards High-Resolution Industrial Image Anomaly Detection Towards total recall in industrial anomaly detection,

Reference 9

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Observation acbcd5f3-50e8-457f-bbe2-da243fec7803 · outbound

This paper cites Padim: a patch dis- tribution modeling framework for anomaly detection and localization,.

Towards High-Resolution Industrial Image Anomaly Detection Padim: a patch dis- tribution modeling framework for anomaly detection and localization,

Reference 10

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Observation 2ca23135-c44b-406d-aaeb-84857292f042 · outbound

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

Towards High-Resolution Industrial Image Anomaly Detection Anomaly detection via reverse distillation from one-class embedding,

Reference 11

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Observation 8e0f8334-ab17-4b6f-a47e-eb968a18a40f · outbound

This paper cites Draem-a discriminatively trained reconstruction embedding for surface anomaly detection,.

Towards High-Resolution Industrial Image Anomaly Detection Draem-a discriminatively trained reconstruction embedding for surface anomaly detection,

Reference 12

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Observation 4fe8f011-113d-42de-98ba-b6dd81f8fe7b · outbound

This paper cites Unsupervised anomaly segmentation via deep feature reconstruction,.

Towards High-Resolution Industrial Image Anomaly Detection Unsupervised anomaly segmentation via deep feature reconstruction,

Reference 13

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Observation 2e1307a0-2384-454c-87c6-72af04625dd0 · outbound

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

Towards High-Resolution Industrial Image Anomaly Detection Dsr-a dual subspace re- projection network for surface anomaly detection,

Reference 14

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Observation b2f52b6c-9378-449c-b7d0-9403f4455996 · outbound

This paper cites Omni-frequency channel-selection representations for unsupervised anomaly detection,.

Towards High-Resolution Industrial Image Anomaly Detection Omni-frequency channel-selection representations for unsupervised anomaly detection,

Reference 15

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Observation dbb6a443-3aee-4c9a-87d3-1db4eb956d9d · outbound

This paper cites Patch svdd: Patch-level svdd for anomaly detection and segmentation,.

Towards High-Resolution Industrial Image Anomaly Detection Patch svdd: Patch-level svdd for anomaly detection and segmentation,

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T17:21:44.631932Z

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Observation cfbfd6bf-9e88-4637-b227-8c523e504d9a · outbound

This paper cites Cflow-ad: Real-time unsu- pervised anomaly detection with localization via conditional normalizing flows,.

Towards High-Resolution Industrial Image Anomaly Detection Cflow-ad: Real-time unsu- pervised anomaly detection with localization via conditional normalizing flows,

Reference 17

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Observation 76a53b53-def8-4dac-8185-77afedd1f076 · outbound

This paper cites Pni: Industrial anomaly detection using position and neighborhood information,.

Towards High-Resolution Industrial Image Anomaly Detection Pni: Industrial anomaly detection using position and neighborhood information,

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T17:21:44.596690Z

Source-reported events for the cited work

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Observation ff96c198-6345-4438-a852-0f08cffd2e7d · outbound

This paper cites Cross-attention regression flow for defect detection,.

Towards High-Resolution Industrial Image Anomaly Detection Cross-attention regression flow for defect detection,

Reference 19

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Source-reported events for the cited work

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Observation 24a71766-76a2-448a-9a19-a460aa5cad6e · outbound

This paper cites Cutpaste: Self-supervised learning for anomaly detection and localization,.

Towards High-Resolution Industrial Image Anomaly Detection Cutpaste: Self-supervised learning for anomaly detection and localization,

Reference 20

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Observation 3a1943ec-7abb-402c-addf-e518291980bf · outbound

This paper cites Natural synthetic anomalies for self-supervised anomaly detection and localization,.

Towards High-Resolution Industrial Image Anomaly Detection Natural synthetic anomalies for self-supervised anomaly detection and localization,

Reference 21

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Observation f64c289a-ecbf-4c22-ba76-2188c668ded4 · outbound

This paper cites Target be- fore shooting: Accurate anomaly detection and localization under one millisecond via cascade patch retrieval,.

Towards High-Resolution Industrial Image Anomaly Detection Target be- fore shooting: Accurate anomaly detection and localization under one millisecond via cascade patch retrieval,

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T17:21:44.529252Z

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Observation b5a40188-8bac-431e-ac76-46fb1e1e0217 · outbound

This paper cites Realnet: A feature selection network with realistic synthetic anomaly for anomaly detection,.

Towards High-Resolution Industrial Image Anomaly Detection Realnet: A feature selection network with realistic synthetic anomaly for anomaly detection,

Reference 23

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Observation 8c9838d0-f1e7-4062-a5a9-887d073aab22 · outbound

This paper cites Understand- ing important features of deep learning models for segmentation of high- resolution transmission electron microscopy images,.

Towards High-Resolution Industrial Image Anomaly Detection Understand- ing important features of deep learning models for segmentation of high- resolution transmission electron microscopy images,

Reference 24

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Observation 93f2ee60-e522-40b9-bdbd-85855adc2df7 · outbound

This paper cites Towards high-resolution specular highlight detection,.

Towards High-Resolution Industrial Image Anomaly Detection Towards high-resolution specular highlight detection,

Reference 25

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Observation 7caaf1bc-0f09-4d39-80bf-110b8ff9626b · outbound

This paper cites The cityscapes dataset for semantic urban scene understanding,.

Towards High-Resolution Industrial Image Anomaly Detection The cityscapes dataset for semantic urban scene understanding,

Reference 26

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Observation db349a86-f43b-4d68-a21b-0644962877e6 · outbound

This paper cites Deep inference networks for reliable vehicle lateral position estimation in congested urban environ- ments,.

Towards High-Resolution Industrial Image Anomaly Detection Deep inference networks for reliable vehicle lateral position estimation in congested urban environ- ments,

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T17:21:44.439956Z

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

Observation 2560a545-bafb-435f-9895-41d84204130d · outbound

This paper cites Hilm-d: Enhancing mllms with multi-scale high-resolution details for autonomous driving,.

Towards High-Resolution Industrial Image Anomaly Detection Hilm-d: Enhancing mllms with multi-scale high-resolution details for autonomous driving,

Reference 28

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Observation 01c2799a-a0aa-4cdd-8d70-0a86ecb6ef20 · outbound

This paper cites A survey on deep learning-based change detection from high-resolution remote sensing images,.

Towards High-Resolution Industrial Image Anomaly Detection A survey on deep learning-based change detection from high-resolution remote sensing images,

Reference 29

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Observation 3d9ea08d-836d-42e9-84ac-e8d68fea377c · outbound

This paper cites Anomaly segmenta- tion for high-resolution remote sensing images based on pixel descrip- tors,.

Towards High-Resolution Industrial Image Anomaly Detection Anomaly segmenta- tion for high-resolution remote sensing images based on pixel descrip- tors,

Reference 30

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Observation 14bd5a8c-ab5e-4ba5-aaf9-6862c39a8793 · outbound

This paper cites Single-temporal supervised learning for universal remote sensing change detection,.

Towards High-Resolution Industrial Image Anomaly Detection Single-temporal supervised learning for universal remote sensing change detection,

Reference 31

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Observation f7c85fd4-ca39-4e64-8805-417589662f69 · outbound

This paper cites Deep learning in histopathology: the path to the clinic,.

Towards High-Resolution Industrial Image Anomaly Detection Deep learning in histopathology: the path to the clinic,

Reference 32

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Observation b0367924-c767-456d-b7f8-3159a36faf74 · outbound

This paper cites Robust whole slide image analysis for cervical cancer screening using deep learning,.

Towards High-Resolution Industrial Image Anomaly Detection Robust whole slide image analysis for cervical cancer screening using deep learning,

Reference 33

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This paper cites Task-specific fine-tuning via variational information bottle- neck for weakly-supervised pathology whole slide image classification,.

Towards High-Resolution Industrial Image Anomaly Detection Task-specific fine-tuning via variational information bottle- neck for weakly-supervised pathology whole slide image classification,

Reference 34

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Observation a6a1b996-3de8-4e2e-83f5-01d493f9ba3d · outbound

This paper cites High-resolution image anomaly detec- tion via spatiotemporal consistency incorporated knowledge distillation,.

Towards High-Resolution Industrial Image Anomaly Detection High-resolution image anomaly detec- tion via spatiotemporal consistency incorporated knowledge distillation,

Reference 35

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Observation d4e6bd9b-f60f-4cc8-9204-94a78cfcd571 · outbound

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Towards High-Resolution Industrial Image Anomaly Detection Mamba: Linear-time sequence modeling with selective state spaces,

Reference 36

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Observation 75598d04-2df8-4f12-8d04-e95ae8caed8f · outbound

This paper cites A unified model for multi-class anomaly detection,.

Towards High-Resolution Industrial Image Anomaly Detection A unified model for multi-class anomaly detection,

Reference 37

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Observation b2e6a587-e209-4a3b-a82d-1e2707a084a5 · outbound

This paper cites Exploring plain vit features for multi-class unsupervised visual anomaly detection,.

Towards High-Resolution Industrial Image Anomaly Detection Exploring plain vit features for multi-class unsupervised visual anomaly detection,

Reference 38

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Observation 99ed619a-8bfe-464f-86e9-bd938a671d5a · outbound

This paper cites Deep residual learning for image recognition,.

Towards High-Resolution Industrial Image Anomaly Detection Deep residual learning for image recognition,

Reference 39

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This paper cites An image is worth 16x16 words: Transformers for image recognition at scale,.

Towards High-Resolution Industrial Image Anomaly Detection An image is worth 16x16 words: Transformers for image recognition at scale,

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T17:21:44.192432Z

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Observation 2598ea87-8bb9-4f2c-9d07-bfd928c4ea4d · outbound

This paper cites Adaptive mixtures of local experts,.

Towards High-Resolution Industrial Image Anomaly Detection Adaptive mixtures of local experts,

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T17:21:44.172473Z

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Observation edf8a466-1bbe-435b-ab5f-ef82a02187bb · outbound

This paper cites Outrageously large neural networks: The sparsely-gated mixture-of-experts layer,.

Towards High-Resolution Industrial Image Anomaly Detection Outrageously large neural networks: The sparsely-gated mixture-of-experts layer,

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T17:21:44.155655Z

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Observation cdf77cf1-65ef-45f2-bb64-a4a369445940 · outbound

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Towards High-Resolution Industrial Image Anomaly Detection Exploiting diffusion prior for real-world image super-resolution,

Reference 43

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Towards High-Resolution Industrial Image Anomaly Detection Poisson image editing,

Reference 44

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Observation 7cb6850b-397d-4c4c-a161-029fb2fe793e · outbound

This paper cites Ad3: Introducing a score for anomaly detection dataset difficulty assessment using viaduct dataset,.

Towards High-Resolution Industrial Image Anomaly Detection Ad3: Introducing a score for anomaly detection dataset difficulty assessment using viaduct dataset,

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T17:21:44.085086Z

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

Observation 4c8db302-02d2-4707-aa36-46ad27562123 · outbound

This paper cites Destseg: Segmentation guided denoising student-teacher for anomaly detection,.

Towards High-Resolution Industrial Image Anomaly Detection Destseg: Segmentation guided denoising student-teacher for anomaly detection,

Reference 46

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Observation b04bdc02-bfb3-4cce-998e-9f578aa54bcb · outbound

This paper cites FastFlow: Unsupervised Anomaly Detection and Localization via 2D Normalizing Flows.

Towards High-Resolution Industrial Image Anomaly Detection FastFlow: Unsupervised Anomaly Detection and Localization via 2D Normalizing Flows

Reference 47

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Observation e4649e00-d6bf-4bdd-94f0-92aaaad4d5cd · outbound

This paper cites Revisiting reverse distillation for anomaly detection,.

Towards High-Resolution Industrial Image Anomaly Detection Revisiting reverse distillation for anomaly detection,

Reference 48

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This paper cites Some methods for classification and analysis of multivariate observations,.

Towards High-Resolution Industrial Image Anomaly Detection Some methods for classification and analysis of multivariate observations,

Reference 49

Resolution: unresolved; no resolver link, observed 2026-08-15T17:21:43.940414Z

Source-reported events for the cited work

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Observation 66bad939-efb7-4929-9543-2c29efa0291f · outbound

This paper cites Efficientad: Accurate visual anomaly detection at millisecond-level latencies,.

Towards High-Resolution Industrial Image Anomaly Detection Efficientad: Accurate visual anomaly detection at millisecond-level latencies,

Reference 50

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Pith citing papers

Observation 570c79bd-b718-4cf1-8867-38c29f957c70 · inbound

UniADC: A Unified Framework for Anomaly Detection and Classification cites this paper.

UniADC: A Unified Framework for Anomaly Detection and Classification Towards High-Resolution Industrial Image Anomaly Detection

Reference 21

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Observation 906ddda9-cd96-43cb-9547-c7c5679ac8bc · inbound

LogiCo: A Unified Framework for Logical and Structural Anomaly Detection cites this paper.

LogiCo: A Unified Framework for Logical and Structural Anomaly Detection Towards High-Resolution Industrial Image Anomaly Detection

Reference 53

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Source-reported events for the cited work

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