Paper Citation Record · LEDGER

Towards Accurate Unified Anomaly Segmentation

As of 20 August 2026, this Paper Citation Record lists 54 of 54 outbound references and 2 inbound Pith citation observations for arXiv:2501.12295.

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

pith.paper-citation-record.v1
2501.12295 v1

Coverage vector

measured 54 of 54 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-10T17:24:38.823409Z

measured 56 of 56 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-20T06:33:59.587034+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-10T17:24:38.823409Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-06T23:28:15.978230Z

Reference resolution

54 of 54 outbound references displayed

verified exact2
verified fuzzy35
unresolved16
parse uncertain0
malformed identifier1
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 6a69d24d-2626-42d4-b271-ca9070b6abde · outbound

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

Towards Accurate Unified Anomaly Segmentation Mvtec ad–a comprehensive real-world dataset for unsupervised anomaly detection

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T17:24:39.379371Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation a75a3934-b60d-4993-b835-6bd679a572bf · outbound

This paper cites Improving Unsupervised Defect Segmentation by Applying Structural Similarity to Autoencoders.

Towards Accurate Unified Anomaly Segmentation Improving Unsupervised Defect Segmentation by Applying Structural Similarity to Autoencoders

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-10T17:24:38.637912Z

Source-reported events for the cited work

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Observation 17299c14-d408-467d-86d7-85a7d55da50a · outbound

This paper cites AUPIMO: Redefining Visual Anomaly Detection Benchmarks with High Speed and Low Tolerance.

Towards Accurate Unified Anomaly Segmentation AUPIMO: Redefining Visual Anomaly Detection Benchmarks with High Speed and Low Tolerance

Reference 3

Resolution: verified exact; local_arxiv, observed 2026-08-10T17:24:38.951997Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 2da68990-70f0-490c-bbbe-e7ad5171aa89 · outbound

This paper cites Swin-unet: Unet-like pure transformer for medical image segmentation.

Towards Accurate Unified Anomaly Segmentation Swin-unet: Unet-like pure transformer for medical image segmentation

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T17:24:39.369120Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 65e00aa5-6874-44b1-bd76-1b9b7bf08885 · outbound

This paper cites End-to- end object detection with transformers.

Towards Accurate Unified Anomaly Segmentation End-to- end object detection with transformers

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T17:24:39.358626Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 71e9fe47-74f5-4b58-8fca-42ae4bcf2021 · outbound

This paper cites TransUNet: Transformers Make Strong Encoders for Medical Image Segmentation.

Towards Accurate Unified Anomaly Segmentation TransUNet: Transformers Make Strong Encoders for Medical Image Segmentation

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-10T17:24:38.653731Z

Source-reported events for the cited work

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Observation 7d9ad4f4-b4a0-4941-8ffc-578a256b7a4e · outbound

This paper cites Utrad: Anomaly detection and localization with u-transformer.

Towards Accurate Unified Anomaly Segmentation Utrad: Anomaly detection and localization with u-transformer

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T17:24:39.349122Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 6a438ede-d9c7-4e05-8e86-c30a4710be07 · outbound

This paper cites Masked-attention mask transformer for universal image segmentation.

Towards Accurate Unified Anomaly Segmentation Masked-attention mask transformer for universal image segmentation

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T17:24:39.338968Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation a42625ea-3841-4914-8445-783a74ea7623 · outbound

This paper cites Per- pixel classification is not all you need for semantic segmen- tation.

Towards Accurate Unified Anomaly Segmentation Per- pixel classification is not all you need for semantic segmen- tation

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T17:24:39.327696Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation a515ff42-da28-48d0-9947-661eb730237d · outbound

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

Towards Accurate Unified Anomaly Segmentation Padim: a patch distribution modeling framework for anomaly detection and localization

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T17:24:39.316155Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation a1a1ae33-5002-446e-abd8-d0e4d0f41d0a · outbound

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

Towards Accurate Unified Anomaly Segmentation Anomaly detection via reverse distillation from one-class embedding

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T17:24:39.304967Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 2c6c6d44-4743-44e7-9228-96b36f649777 · outbound

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

Towards Accurate Unified Anomaly Segmentation An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale

Reference 12

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Source-reported events for the cited work

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Observation 98ad82ae-d4b3-4c90-92a4-4f569839bf1d · outbound

This paper cites Deep learning for medical anomaly detection–a survey.

Towards Accurate Unified Anomaly Segmentation Deep learning for medical anomaly detection–a survey

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T17:24:39.293648Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 8ace9783-d8d0-4222-81ac-5080c6c6aaec · outbound

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

Towards Accurate Unified Anomaly Segmentation Cflow-ad: Real-time unsupervised anomaly detection with localization via conditional normalizing flows

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T17:24:39.282872Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation c522c58a-c8a3-435b-9a3a-057c33392637 · outbound

This paper cites DiAD: A Diffusion-based Framework for Multi-class Anomaly Detection.

Towards Accurate Unified Anomaly Segmentation DiAD: A Diffusion-based Framework for Multi-class Anomaly Detection

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-10T17:24:38.685531Z

Source-reported events for the cited work

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Observation 42f09846-15a2-4ac9-bb39-c848e1fdbe4d · outbound

This paper cites Gaussian Error Linear Units (GELUs).

Towards Accurate Unified Anomaly Segmentation Gaussian Error Linear Units (GELUs)

Reference 16

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Source-reported events for the cited work

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Observation 465cfdd8-c702-4f26-83bc-975fbcc122d1 · outbound

This paper cites Label-free liver tumor segmentation.

Towards Accurate Unified Anomaly Segmentation Label-free liver tumor segmentation

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T17:24:39.272642Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 0f3a69c5-0b2a-408d-b79d-ae177d4cd75a · outbound

This paper cites Oneformer: One transformer to rule universal image segmentation.

Towards Accurate Unified Anomaly Segmentation Oneformer: One transformer to rule universal image segmentation

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-10T17:24:38.696438Z

Source-reported events for the cited work

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Observation d6d7aa85-f4ff-4f97-b813-e0062da39357 · outbound

This paper cites Variational inference with normalizing flows.

Towards Accurate Unified Anomaly Segmentation Variational inference with normalizing flows

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T17:24:39.256879Z

Source-reported events for the cited work

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Observation b588de96-d64a-4f18-b5ad-0caedff7d9a4 · outbound

This paper cites Sanflow: Semantic-aware normalizing flow for anomaly detection.

Towards Accurate Unified Anomaly Segmentation Sanflow: Semantic-aware normalizing flow for anomaly detection

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T17:24:39.246373Z

Source-reported events for the cited work

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Observation c648e1fc-3f86-47c0-8c5a-9abe3cebc535 · outbound

This paper cites Panoptic segmentation.

Towards Accurate Unified Anomaly Segmentation Panoptic segmentation

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T17:24:39.235761Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 0e03a7c9-05a7-4ec2-9fbf-2442c93c5ee1 · outbound

This paper cites Cutpaste: Self-supervised learning for anomaly de- tection and localization.

Towards Accurate Unified Anomaly Segmentation Cutpaste: Self-supervised learning for anomaly de- tection and localization

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T17:24:39.225056Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 6e1d75f4-3330-4a8e-8431-ac99c97acfd8 · outbound

This paper cites Feature pyra- mid networks for object detection.

Towards Accurate Unified Anomaly Segmentation Feature pyra- mid networks for object detection

Reference 23

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Source-reported events for the cited work

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Observation e3978302-d31a-4a87-967d-62f8853e3dbe · outbound

This paper cites Swin transformer: Hierarchical vision transformer using shifted windows.

Towards Accurate Unified Anomaly Segmentation Swin transformer: Hierarchical vision transformer using shifted windows

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-10T17:24:38.716835Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 238c1007-5ad4-420f-b253-6baec4af64b9 · outbound

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

Towards Accurate Unified Anomaly Segmentation Simplenet: A simple network for image anomaly detection and localization

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T17:24:39.201034Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 768aa984-c740-4630-af88-8417eac7ed49 · outbound

This paper cites Decoupled Weight Decay Regularization.

Towards Accurate Unified Anomaly Segmentation Decoupled Weight Decay Regularization

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-10T17:24:38.724112Z

Source-reported events for the cited work

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Observation 5d307c15-b92d-40e3-8c7b-804693e3bbed · outbound

This paper cites Hierarchical vector quantized transformer for multi-class unsupervised anomaly detection.

Towards Accurate Unified Anomaly Segmentation Hierarchical vector quantized transformer for multi-class unsupervised anomaly detection

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T17:24:39.189644Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 52e48b81-651c-474b-94ff-7b2c42d9bd1b · outbound

This paper cites On Pixel-level Performance Assessment in Anomaly Detection.

Towards Accurate Unified Anomaly Segmentation On Pixel-level Performance Assessment in Anomaly Detection

Reference 28

Resolution: verified exact; local_arxiv, observed 2026-08-10T17:24:38.893969Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation eac3038d-df56-4a1f-8df3-cd8008b2b525 · outbound

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

Towards Accurate Unified Anomaly Segmentation Towards to- tal recall in industrial anomaly detection

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T17:24:39.179009Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 70af3833-3c3e-4304-9812-82e1f8989e6b · outbound

This paper cites Fully convolutional cross-scale-flows for image- based defect detection.

Towards Accurate Unified Anomaly Segmentation Fully convolutional cross-scale-flows for image- based defect detection

Reference 30

Resolution: unresolved; no resolver link, observed 2026-08-10T17:24:38.741132Z

Source-reported events for the cited work

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Observation 622868bb-13fb-4f3a-9199-3d32cb851246 · outbound

This paper cites The precision-recall plot is more informative than the roc plot when evaluat- ing binary classifiers on imbalanced datasets.

Towards Accurate Unified Anomaly Segmentation The precision-recall plot is more informative than the roc plot when evaluat- ing binary classifiers on imbalanced datasets

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T17:24:39.163470Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 7e690c99-9578-42b4-9c0d-c6a33e108f22 · outbound

This paper cites Multiresolution knowledge distillation for anomaly detection.

Towards Accurate Unified Anomaly Segmentation Multiresolution knowledge distillation for anomaly detection

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T17:24:39.153037Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 47e39eb3-16b6-4c46-9c4f-cce261cd2a7d · outbound

This paper cites Unsupervised anomaly detection with generative adversarial networks to guide marker discovery.

Towards Accurate Unified Anomaly Segmentation Unsupervised anomaly detection with generative adversarial networks to guide marker discovery

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T17:24:39.142592Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 5863be86-3812-4ae8-bf41-c25c53b892b8 · outbound

This paper cites Efficientnet: Rethinking model scaling for convolutional neural networks.

Towards Accurate Unified Anomaly Segmentation Efficientnet: Rethinking model scaling for convolutional neural networks

Reference 34

Resolution: unresolved; no resolver link, observed 2026-08-10T17:24:38.755613Z

Source-reported events for the cited work

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Observation cfa52723-856c-4338-b909-2139de545b57 · outbound

This paper cites MobileUtr: Revisiting the relationship between light-weight CNN and Transformer for efficient medical image segmentation.

Towards Accurate Unified Anomaly Segmentation MobileUtr: Revisiting the relationship between light-weight CNN and Transformer for efficient medical image segmentation

Reference 35

Resolution: unresolved; no resolver link, observed 2026-08-10T17:24:38.759094Z

Source-reported events for the cited work

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Observation 295fb7f5-517f-4fba-a824-406f2b6e261a · outbound

This paper cites Attention is all you need.

Towards Accurate Unified Anomaly Segmentation Attention is all you need

Reference 36

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Source-reported events for the cited work

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Observation 8ddb4eab-a8e8-4287-a032-9557bc55d3ff · outbound

This paper cites Student-Teacher Feature Pyramid Matching for Anomaly Detection.

Towards Accurate Unified Anomaly Segmentation Student-Teacher Feature Pyramid Matching for Anomaly Detection

Reference 37

Resolution: unresolved; no resolver link, observed 2026-08-10T17:24:38.765765Z

Source-reported events for the cited work

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Observation 938b8e4a-7f02-4188-b4f1-90fd41a9c003 · outbound

This paper cites Uncertainty-inspired open set learning for retinal anomaly identification.

Towards Accurate Unified Anomaly Segmentation Uncertainty-inspired open set learning for retinal anomaly identification

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T17:24:39.118926Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 3e772873-1a01-4401-bd0e-d5b976fc9141 · outbound

This paper cites Cbam: Convolutional block attention module.

Towards Accurate Unified Anomaly Segmentation Cbam: Convolutional block attention module

Reference 39

Resolution: unresolved; no resolver link, observed 2026-08-10T17:24:38.772256Z

Source-reported events for the cited work

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Observation 24997946-9ba1-403e-8ccf-c90fa3b023c9 · outbound

This paper cites Squid: Deep feature in-painting for unsupervised anomaly detec- tion.

Towards Accurate Unified Anomaly Segmentation Squid: Deep feature in-painting for unsupervised anomaly detec- tion

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T17:24:39.100796Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 2c18f5cd-ab29-42d0-b982-e24a3460c74b · outbound

This paper cites Adversarial medical im- age with hierarchical feature hiding.

Towards Accurate Unified Anomaly Segmentation Adversarial medical im- age with hierarchical feature hiding

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T17:24:39.090428Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 34437143-cce9-42ca-9762-1693307780e3 · outbound

This paper cites Label-free segmentation of covid-19 lesions in lung ct.IEEE transactions on medical imaging , 40(10):2808–2819, 2021.

Towards Accurate Unified Anomaly Segmentation Label-free segmentation of covid-19 lesions in lung ct.IEEE transactions on medical imaging , 40(10):2808–2819, 2021

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T17:24:39.080395Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation e73a4bf8-8a8f-43be-a845-a2960ddb1b27 · outbound

This paper cites Focus the discrepancy: Intra-and inter- correlation learning for image anomaly detection.

Towards Accurate Unified Anomaly Segmentation Focus the discrepancy: Intra-and inter- correlation learning for image anomaly detection

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T17:24:39.070910Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 9fce18ce-c049-4a90-abbe-20428ea8d6e5 · outbound

This paper cites One-for-all: Proposal masked cross-class anomaly detection.

Towards Accurate Unified Anomaly Segmentation One-for-all: Proposal masked cross-class anomaly detection

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T17:24:39.060946Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 7b09db44-2b02-4a9e-a270-cd6b167aa52f · outbound

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

Towards Accurate Unified Anomaly Segmentation A unified model for multi-class anomaly detection

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T17:24:39.049710Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 96d17f12-e154-4f84-9e98-9aea8048ceed · outbound

This paper cites Adtr: Anomaly detection transformer with feature reconstruction.

Towards Accurate Unified Anomaly Segmentation Adtr: Anomaly detection transformer with feature reconstruction

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T17:24:39.038725Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 08e64971-cbe6-4a18-afc2-b5172e7bb366 · outbound

This paper cites Draem- a discriminatively trained reconstruction embedding for sur- face anomaly detection.

Towards Accurate Unified Anomaly Segmentation Draem- a discriminatively trained reconstruction embedding for sur- face anomaly detection

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T17:24:39.027851Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 25679e51-fe5f-4389-bca3-9b90cb61b3bb · outbound

This paper cites Defect-gan: High-fidelity defect synthesis for automated defect inspection.

Towards Accurate Unified Anomaly Segmentation Defect-gan: High-fidelity defect synthesis for automated defect inspection

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T17:24:39.016675Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 2b446eec-06ff-4b90-9618-6db78f3091de · outbound

This paper cites Prototypical residual networks for anomaly detection and localization.

Towards Accurate Unified Anomaly Segmentation Prototypical residual networks for anomaly detection and localization

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T17:24:39.004976Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation bb3dec8f-110f-4e74-b5dd-a439ae829a0f · outbound

This paper cites Exploring Plain ViT Reconstruction for Multi-class Unsupervised Anomaly Detection.

Towards Accurate Unified Anomaly Segmentation Exploring Plain ViT Reconstruction for Multi-class Unsupervised Anomaly Detection

Reference 50

Resolution: unresolved; no resolver link, observed 2026-08-10T17:24:38.809225Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ea07efe7-e00e-42b3-9d73-262f9a1e3cec · outbound

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

Towards Accurate Unified Anomaly Segmentation Destseg: Segmentation guided denoising student-teacher for anomaly detection

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T17:24:38.993595Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 0eefd13c-50c5-4e85-8f9e-f61fd5b3dcfd · outbound

This paper cites Omnial: A unified cnn framework for unsuper- vised anomaly localization.

Towards Accurate Unified Anomaly Segmentation Omnial: A unified cnn framework for unsuper- vised anomaly localization

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T17:24:38.981751Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 90fb6f9a-402e-4173-b8af-8d63ec547921 · outbound

This paper cites Deep autoen- coding gaussian mixture model for unsupervised anomaly detection.

Towards Accurate Unified Anomaly Segmentation Deep autoen- coding gaussian mixture model for unsupervised anomaly detection

Reference 53

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This paper cites Towards Accurate Unified Anomaly Segmentation.

Towards Accurate Unified Anomaly Segmentation Towards Accurate Unified Anomaly Segmentation

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