Paper Citation Record · LEDGER

DictAS: A Framework for Class-Generalizable Few-Shot Anomaly Segmentation via Dictionary Lookup

As of 8 August 2026, this Paper Citation Record lists 44 of 44 outbound references and 1 inbound Pith citation observation for arXiv:2508.13560.

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

pith.paper-citation-record.v1
2508.13560 v2

Coverage vector

measured 44 of 44 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-05T18:59:10.064175Z

measured 45 of 45 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-08T06:32:00.761636+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-04T23:20:41.133891Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-04T23:20:42.851582Z

Reference resolution

44 of 44 outbound references displayed

verified exact1
verified fuzzy34
unresolved9
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 100c29bc-65ac-4514-a22f-9ec9ecc75e4e · outbound

This paper cites write newline.

DictAS: A Framework for Class-Generalizable Few-Shot Anomaly Segmentation via Dictionary Lookup write newline

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-05T18:59:07.082035Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7080a032-4d7e-469d-b3d7-5be7c5793225 · outbound

This paper cites Medical image analysis using convolutional neural networks: a review.

DictAS: A Framework for Class-Generalizable Few-Shot Anomaly Segmentation via Dictionary Lookup Medical image analysis using convolutional neural networks: a review

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T18:59:19.043941Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 78faa7b2-4d07-487c-9eb9-fe4074839513 · outbound

This paper cites Fewsome: One-class few shot anomaly detection with siamese networks.

DictAS: A Framework for Class-Generalizable Few-Shot Anomaly Segmentation via Dictionary Lookup Fewsome: One-class few shot anomaly detection with siamese networks

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T18:59:18.783347Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 7d91c7a2-e83a-402a-87e2-50f4f646f2bc · outbound

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

DictAS: A Framework for Class-Generalizable Few-Shot Anomaly Segmentation via Dictionary Lookup Mvtec ad--a comprehensive real-world dataset for unsupervised anomaly detection

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T18:59:18.526037Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 88b3d618-9c8f-4be4-86d9-19a6302793f7 · outbound

This paper cites The MVTec 3D-AD Dataset for Unsupervised 3D Anomaly Detection and Localization.

DictAS: A Framework for Class-Generalizable Few-Shot Anomaly Segmentation via Dictionary Lookup The MVTec 3D-AD Dataset for Unsupervised 3D Anomaly Detection and Localization

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-05T18:59:07.400067Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8c6a614e-840a-423f-b798-6e39db9bb7bc · outbound

This paper cites Adaclip: Adapting clip with hybrid learnable prompts for zero-shot anomaly detection.

DictAS: A Framework for Class-Generalizable Few-Shot Anomaly Segmentation via Dictionary Lookup Adaclip: Adapting clip with hybrid learnable prompts for zero-shot anomaly detection

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T18:59:18.273321Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation f982a6a1-44d4-403f-bd21-67778df62fca · outbound

This paper cites Center-aware residual anomaly synthesis for multiclass industrial anomaly detection.

DictAS: A Framework for Class-Generalizable Few-Shot Anomaly Segmentation via Dictionary Lookup Center-aware residual anomaly synthesis for multiclass industrial anomaly detection

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T18:59:18.038054Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation e2ce198e-f9d7-4d71-bde9-707db797270c · outbound

This paper cites APRIL-GAN: A Zero-/Few-Shot Anomaly Classification and Segmentation Method for CVPR 2023 VAND Workshop Challenge Tracks 1&2: 1st Place on Zero-shot AD and 4th Place on Few-shot AD.

DictAS: A Framework for Class-Generalizable Few-Shot Anomaly Segmentation via Dictionary Lookup APRIL-GAN: A Zero-/Few-Shot Anomaly Classification and Segmentation Method for CVPR 2023 VAND Workshop Challenge Tracks 1&2: 1st Place on Zero-shot AD and 4th Place on Few-shot AD

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-05T18:59:07.580832Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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

This paper cites Sub-Image Anomaly Detection with Deep Pyramid Correspondences.

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

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-05T18:59:07.630867Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6eb41b03-36c4-4879-b46e-f9fef3084d03 · outbound

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

DictAS: A Framework for Class-Generalizable Few-Shot Anomaly Segmentation via Dictionary Lookup Padim: a patch distribution modeling framework for anomaly detection and localization

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T18:59:17.791899Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 6bad250d-979c-4efb-b48e-ec82f7226df4 · outbound

This paper cites The pascal visual object classes (voc) challenge.

DictAS: A Framework for Class-Generalizable Few-Shot Anomaly Segmentation via Dictionary Lookup The pascal visual object classes (voc) challenge

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-05T18:59:07.723092Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c6938484-bfc3-47ec-b7aa-8b1d2adee633 · outbound

This paper cites Fastrecon: Few-shot industrial anomaly detection via fast feature reconstruction.

DictAS: A Framework for Class-Generalizable Few-Shot Anomaly Segmentation via Dictionary Lookup Fastrecon: Few-shot industrial anomaly detection via fast feature reconstruction

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T18:59:17.546425Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation bbbecc70-c176-4cff-b898-c28694fab4f4 · outbound

This paper cites Metauas: Universal anomaly segmentation with one-prompt meta-learning.

DictAS: A Framework for Class-Generalizable Few-Shot Anomaly Segmentation via Dictionary Lookup Metauas: Universal anomaly segmentation with one-prompt meta-learning

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T18:59:17.236054Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 8b8ee526-ef15-4420-bc95-68c0e0386d3f · outbound

This paper cites Anomalygpt: Detecting industrial anomalies using large vision-language models.

DictAS: A Framework for Class-Generalizable Few-Shot Anomaly Segmentation via Dictionary Lookup Anomalygpt: Detecting industrial anomalies using large vision-language models

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T18:59:17.017586Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 449b1b85-8254-4bd9-a96b-48c1691882b2 · outbound

This paper cites Automated segmentation of macular edema in oct using deep neural networks.

DictAS: A Framework for Class-Generalizable Few-Shot Anomaly Segmentation via Dictionary Lookup Automated segmentation of macular edema in oct using deep neural networks

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T18:59:16.768298Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation adc261a3-1dad-491b-a459-f1bec371eff4 · outbound

This paper cites Registration based few-shot anomaly detection.

DictAS: A Framework for Class-Generalizable Few-Shot Anomaly Segmentation via Dictionary Lookup Registration based few-shot anomaly detection

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T18:59:16.456372Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 91e9256a-e1a3-4df0-a2d7-a7c6535b6c87 · outbound

This paper cites Winclip: Zero-/few-shot anomaly classification and segmentation.

DictAS: A Framework for Class-Generalizable Few-Shot Anomaly Segmentation via Dictionary Lookup Winclip: Zero-/few-shot anomaly classification and segmentation

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T18:59:16.123121Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 990b2a91-9a36-4ae8-931b-6f07a7a0c0ee · outbound

This paper cites Deep learning-based defect detection of metal parts: evaluating current methods in complex conditions.

DictAS: A Framework for Class-Generalizable Few-Shot Anomaly Segmentation via Dictionary Lookup Deep learning-based defect detection of metal parts: evaluating current methods in complex conditions

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T18:59:15.837919Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation a61db4ab-ca6f-4da6-9b13-d8b6d5dea54b · outbound

This paper cites Adam: A Method for Stochastic Optimization.

DictAS: A Framework for Class-Generalizable Few-Shot Anomaly Segmentation via Dictionary Lookup Adam: A Method for Stochastic Optimization

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-05T18:59:08.224096Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 293cff77-699b-450e-84d3-1a9fc4f2ec88 · outbound

This paper cites Deep learning.

DictAS: A Framework for Class-Generalizable Few-Shot Anomaly Segmentation via Dictionary Lookup Deep learning

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-05T18:59:08.297271Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3abea52b-0a4e-4edd-955a-d2fde4e40e9c · outbound

This paper cites Promptad: Learning prompts with only normal samples for few-shot anomaly detection.

DictAS: A Framework for Class-Generalizable Few-Shot Anomaly Segmentation via Dictionary Lookup Promptad: Learning prompts with only normal samples for few-shot anomaly detection

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T18:59:15.627768Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation aacabae7-a46f-4a0e-9444-032444009acc · outbound

This paper cites COFT-AD: COntrastive Fine-Tuning for Few-Shot Anomaly Detection.

DictAS: A Framework for Class-Generalizable Few-Shot Anomaly Segmentation via Dictionary Lookup COFT-AD: COntrastive Fine-Tuning for Few-Shot Anomaly Detection

Reference 22

Resolution: verified exact; local_arxiv, observed 2026-08-05T18:59:10.303460Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 91e9f8fe-05da-4faf-8e66-96c8d624c155 · outbound

This paper cites Medical image classification using generalized zero shot learning.

DictAS: A Framework for Class-Generalizable Few-Shot Anomaly Segmentation via Dictionary Lookup Medical image classification using generalized zero shot learning

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T18:59:15.381222Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 6e76bfec-195c-4efe-8d1d-5dc74d371e75 · outbound

This paper cites From softmax to sparsemax: A sparse model of attention and multi-label classification.

DictAS: A Framework for Class-Generalizable Few-Shot Anomaly Segmentation via Dictionary Lookup From softmax to sparsemax: A sparse model of attention and multi-label classification

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T18:59:15.112120Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation b4354657-cee6-475d-8dba-806b593d9fa2 · outbound

This paper cites The multimodal brain tumor image segmentation benchmark (brats).

DictAS: A Framework for Class-Generalizable Few-Shot Anomaly Segmentation via Dictionary Lookup The multimodal brain tumor image segmentation benchmark (brats)

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T18:59:14.894216Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 0295532f-ec23-402f-a310-21df325710c5 · outbound

This paper cites Vt-adl: A vision transformer network for image anomaly detection and localization.

DictAS: A Framework for Class-Generalizable Few-Shot Anomaly Segmentation via Dictionary Lookup Vt-adl: A vision transformer network for image anomaly detection and localization

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T18:59:14.654603Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation e1534bb3-9b2f-4fb0-bfd6-840639e1c0cf · outbound

This paper cites Lscad: A large-small model collaboration framework for unsupervised industrial anomaly detection.

DictAS: A Framework for Class-Generalizable Few-Shot Anomaly Segmentation via Dictionary Lookup Lscad: A large-small model collaboration framework for unsupervised industrial anomaly detection

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T18:59:14.383140Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 0294bb8d-5b1a-4542-91f0-a6bc92d7ac25 · outbound

This paper cites Investigating shift equivalence of convolutional neural networks in industrial defect segmentation.

DictAS: A Framework for Class-Generalizable Few-Shot Anomaly Segmentation via Dictionary Lookup Investigating shift equivalence of convolutional neural networks in industrial defect segmentation

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T18:59:14.157033Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation fca53566-2a02-4d8c-925d-a986afc30f60 · outbound

This paper cites Vcp-clip: A visual context prompting model for zero-shot anomaly segmentation.

DictAS: A Framework for Class-Generalizable Few-Shot Anomaly Segmentation via Dictionary Lookup Vcp-clip: A visual context prompting model for zero-shot anomaly segmentation

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T18:59:13.902424Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 584a7644-09e6-4a05-8608-6655e8fef205 · outbound

This paper cites Bayesian prompt flow learning for zero-shot anomaly detection.

DictAS: A Framework for Class-Generalizable Few-Shot Anomaly Segmentation via Dictionary Lookup Bayesian prompt flow learning for zero-shot anomaly detection

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T18:59:13.669065Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 7459fcd7-ab6a-473b-845b-259b2296ab31 · outbound

This paper cites Learning transferable visual models from natural language supervision.

DictAS: A Framework for Class-Generalizable Few-Shot Anomaly Segmentation via Dictionary Lookup Learning transferable visual models from natural language supervision

Reference 31

Resolution: unresolved; no resolver link, observed 2026-08-05T18:59:09.037393Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f40d3f5c-823a-49ac-a1e8-4cc908b009d1 · outbound

This paper cites Towards total recall in industrial anomaly detection.

DictAS: A Framework for Class-Generalizable Few-Shot Anomaly Segmentation via Dictionary Lookup Towards total recall in industrial anomaly detection

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T18:59:13.411064Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 9939af31-fad2-494f-b89f-e219e4eae541 · outbound

This paper cites Same same but differnet: Semi-supervised defect detection with normalizing flows.

DictAS: A Framework for Class-Generalizable Few-Shot Anomaly Segmentation via Dictionary Lookup Same same but differnet: Semi-supervised defect detection with normalizing flows

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T18:59:13.124059Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation dc94d27e-33e3-47f6-b3f3-7c493c989e8e · outbound

This paper cites Maeday: Mae for few-and zero-shot anomaly-detection.

DictAS: A Framework for Class-Generalizable Few-Shot Anomaly Segmentation via Dictionary Lookup Maeday: Mae for few-and zero-shot anomaly-detection

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T18:59:12.884981Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 73a10ba9-ab4d-47b5-8df5-60bb6615a307 · outbound

This paper cites A hierarchical transformation-discriminating generative model for few shot anomaly detection.

DictAS: A Framework for Class-Generalizable Few-Shot Anomaly Segmentation via Dictionary Lookup A hierarchical transformation-discriminating generative model for few shot anomaly detection

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T18:59:12.571768Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 56538cc0-c65b-4bce-9bac-a5d9002eb6a4 · outbound

This paper cites Learning unsupervised metaformer for anomaly detection.

DictAS: A Framework for Class-Generalizable Few-Shot Anomaly Segmentation via Dictionary Lookup Learning unsupervised metaformer for anomaly detection

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T18:59:12.319704Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation d84bca5a-7b80-44e3-a99c-a180969bce30 · outbound

This paper cites Learning unsupervised metaformer for anomaly detection.

DictAS: A Framework for Class-Generalizable Few-Shot Anomaly Segmentation via Dictionary Lookup Learning unsupervised metaformer for anomaly detection

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T18:59:12.060729Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 07083c70-f381-433a-9e41-75e0eb3855a7 · outbound

This paper cites Pushing the Limits of Fewshot Anomaly Detection in Industry Vision: Graphcore.

DictAS: A Framework for Class-Generalizable Few-Shot Anomaly Segmentation via Dictionary Lookup Pushing the Limits of Fewshot Anomaly Detection in Industry Vision: Graphcore

Reference 38

Resolution: unresolved; no resolver link, observed 2026-08-05T18:59:09.587820Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 447af4ff-b596-4f24-ac9f-20bf7ee69007 · outbound

This paper cites Resad: A simple framework for class generalizable anomaly detection.

DictAS: A Framework for Class-Generalizable Few-Shot Anomaly Segmentation via Dictionary Lookup Resad: A simple framework for class generalizable anomaly detection

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T18:59:11.838874Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation c6b87188-a999-4314-9617-76f9dba5cb62 · outbound

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

DictAS: A Framework for Class-Generalizable Few-Shot Anomaly Segmentation via Dictionary Lookup Draem-a discriminatively trained reconstruction embedding for surface anomaly detection

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T18:59:11.620613Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation e66932c8-5389-4bf0-81da-8d92654aabd7 · outbound

This paper cites Scene parsing through ade20k dataset.

DictAS: A Framework for Class-Generalizable Few-Shot Anomaly Segmentation via Dictionary Lookup Scene parsing through ade20k dataset

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T18:59:11.356429Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation fa56e008-9d96-47f1-8bcd-d2c78fece8c3 · outbound

This paper cites Anomaly CLIP : Object-agnostic prompt learning for zero-shot anomaly detection.

DictAS: A Framework for Class-Generalizable Few-Shot Anomaly Segmentation via Dictionary Lookup Anomaly CLIP : Object-agnostic prompt learning for zero-shot anomaly detection

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T18:59:11.086422Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation cdd29a99-1dad-4ceb-a350-7bf51817f88c · outbound

This paper cites Toward generalist anomaly detection via in-context residual learning with few-shot sample prompts.

DictAS: A Framework for Class-Generalizable Few-Shot Anomaly Segmentation via Dictionary Lookup Toward generalist anomaly detection via in-context residual learning with few-shot sample prompts

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T18:59:10.818852Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation cd0cf16b-4b82-480d-9878-6266f984f30d · outbound

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

DictAS: A Framework for Class-Generalizable Few-Shot Anomaly Segmentation via Dictionary Lookup Spot-the-difference self-supervised pre-training for anomaly detection and segmentation

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T18:59:10.521673Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Pith citing papers

Observation ea951715-8674-4dad-aac8-39b3889113a4 · inbound

STAGE: Segmentation-oriented Industrial Anomaly Synthesis via Graded Diffusion with Explicit Mask Alignment cites this paper.

STAGE: Segmentation-oriented Industrial Anomaly Synthesis via Graded Diffusion with Explicit Mask Alignment DictAS: A Framework for Class-Generalizable Few-Shot Anomaly Segmentation via Dictionary Lookup

Reference 20

Resolution: verified exact; local_arxiv, observed 2026-08-04T23:20:42.957200Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.