Paper Citation Record · LEDGER

NexViTAD: Few-shot Unsupervised Cross-Domain Defect Detection via Vision Foundation Models and Multi-Task Learning

As of 7 August 2026, this Paper Citation Record lists 36 of 36 outbound references and 0 inbound Pith citation observations for arXiv:2507.07579.

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

pith.paper-citation-record.v1
2507.07579 v1

Coverage vector

measured 36 of 36 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-06T18:42:12.404751Z

measured 36 of 36 standing notices

One-hop event checks from named stored sources.

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

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

36 of 36 outbound references displayed

verified exact1
verified fuzzy29
unresolved6
parse uncertain0
malformed identifier0
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 7c538e24-0fa4-466b-bb51-251ca231de24 · outbound

This paper cites Learning to adapt structured output space for semantic segmentation.

NexViTAD: Few-shot Unsupervised Cross-Domain Defect Detection via Vision Foundation Models and Multi-Task Learning Learning to adapt structured output space for semantic segmentation

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:42:18.792330Z

Source-reported events for the cited work

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

Observation ee49b3a2-6e1f-4092-a7a9-665a22c63b20 · outbound

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

NexViTAD: Few-shot Unsupervised Cross-Domain Defect Detection via Vision Foundation Models and Multi-Task Learning Efficientad: Accurate visual anomaly detection at millisecond- level latencies

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:42:18.633995Z

Source-reported events for the cited work

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

Observation e5d66393-5bcc-42e0-bbc8-e9e7294e58ae · outbound

This paper cites an unresolved cited work.

NexViTAD: Few-shot Unsupervised Cross-Domain Defect Detection via Vision Foundation Models and Multi-Task Learning Unresolved cited work

Reference 3

Resolution: unresolved; raw_fallback, observed 2026-08-06T18:42:18.483733Z

Source-reported events for the cited work

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

Observation a5fb4e97-ee26-4771-8724-ad27b8e18f60 · outbound

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

NexViTAD: Few-shot Unsupervised Cross-Domain Defect Detection via Vision Foundation Models and Multi-Task Learning Mvtec ad — a comprehensive real-world dataset for unsupervised anomaly detection

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:42:18.367001Z

Source-reported events for the cited work

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

Observation eecf33c6-1e0b-46b9-861d-129c3c0d0722 · outbound

This paper cites DecoupleNet: Decoupled Network for Domain Adaptive Semantic Segmentation, page 369–387.

NexViTAD: Few-shot Unsupervised Cross-Domain Defect Detection via Vision Foundation Models and Multi-Task Learning DecoupleNet: Decoupled Network for Domain Adaptive Semantic Segmentation, page 369–387

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:42:18.241660Z

Source-reported events for the cited work

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

Observation 2ff69af7-e013-4a8d-b46c-c4117ae11461 · outbound

This paper cites AnomalyControl: Learning Cross-modal Semantic Features for Controllable Anomaly Synthesis.

NexViTAD: Few-shot Unsupervised Cross-Domain Defect Detection via Vision Foundation Models and Multi-Task Learning AnomalyControl: Learning Cross-modal Semantic Features for Controllable Anomaly Synthesis

Reference 6

Resolution: verified exact; local_arxiv, observed 2026-08-06T18:42:13.264332Z

Source-reported events for the cited work

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

Observation efb8a970-c4de-472a-b9f6-7d29026ab405 · outbound

This paper cites Advancing industrial object detection through domain adaptation: A solution for industry 5.0.

NexViTAD: Few-shot Unsupervised Cross-Domain Defect Detection via Vision Foundation Models and Multi-Task Learning Advancing industrial object detection through domain adaptation: A solution for industry 5.0

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:42:18.067958Z

Source-reported events for the cited work

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

Observation 4cdadd27-e573-45d4-89da-7a6e6cfa01d1 · outbound

This paper cites Test-time feature caching network for cross-domain multilayer ceramic capacitors defect detection.

NexViTAD: Few-shot Unsupervised Cross-Domain Defect Detection via Vision Foundation Models and Multi-Task Learning Test-time feature caching network for cross-domain multilayer ceramic capacitors defect detection

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:42:17.823999Z

Source-reported events for the cited work

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

Observation cd8f631a-2f30-473f-814b-0d625c9bdcca · outbound

This paper cites Cross-domain graph level anomaly detection.

NexViTAD: Few-shot Unsupervised Cross-Domain Defect Detection via Vision Foundation Models and Multi-Task Learning Cross-domain graph level anomaly detection

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:42:17.585927Z

Source-reported events for the cited work

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

Observation 96d0f928-e48d-4c31-a09a-739f2b41d2b9 · outbound

This paper cites YOLO-Pdd: A Novel Multi-scale PCB Defect Detection Method Using Deep Representations with Sequential Images, page 297–313.

NexViTAD: Few-shot Unsupervised Cross-Domain Defect Detection via Vision Foundation Models and Multi-Task Learning YOLO-Pdd: A Novel Multi-scale PCB Defect Detection Method Using Deep Representations with Sequential Images, page 297–313

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:42:17.361705Z

Source-reported events for the cited work

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

Observation b378aa80-1f45-48c4-935d-84e9b2db30a1 · outbound

This paper cites Cross-domain few-shot anomaly detection for equipment in nuclear power plants.

NexViTAD: Few-shot Unsupervised Cross-Domain Defect Detection via Vision Foundation Models and Multi-Task Learning Cross-domain few-shot anomaly detection for equipment in nuclear power plants

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:42:17.148812Z

Source-reported events for the cited work

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

Observation d4472732-7f54-4f96-9c98-28e332d1a48b · outbound

This paper cites DINOv2: Learning Robust Visual Features without Supervision.

NexViTAD: Few-shot Unsupervised Cross-Domain Defect Detection via Vision Foundation Models and Multi-Task Learning DINOv2: Learning Robust Visual Features without Supervision

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-06T18:42:09.705925Z

Source-reported events for the cited work

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Observation 9243a817-73d6-402e-b58a-3f800cfa21c0 · outbound

This paper cites Hiera: A hierarchical vision transformer without the bells-and-whistles.

NexViTAD: Few-shot Unsupervised Cross-Domain Defect Detection via Vision Foundation Models and Multi-Task Learning Hiera: A hierarchical vision transformer without the bells-and-whistles

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:42:17.020164Z

Source-reported events for the cited work

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

Observation 0b54ab50-e684-4f35-8d09-9a907b5cc661 · outbound

This paper cites Adapting Vision Foundation Models for Real-time Ultrasound Image Segmentation.

NexViTAD: Few-shot Unsupervised Cross-Domain Defect Detection via Vision Foundation Models and Multi-Task Learning Adapting Vision Foundation Models for Real-time Ultrasound Image Segmentation

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-06T18:42:09.895795Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 20c57d7e-c193-4a9c-a9fd-a0740fedc4c4 · outbound

This paper cites U-Net: Convolutional Networks for Biomedical Image Segmentation, page 234–241.

NexViTAD: Few-shot Unsupervised Cross-Domain Defect Detection via Vision Foundation Models and Multi-Task Learning U-Net: Convolutional Networks for Biomedical Image Segmentation, page 234–241

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:42:16.889837Z

Source-reported events for the cited work

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

Observation b3f4ff08-4628-41bb-93db-40109296c8f8 · outbound

This paper cites Deep residual learning for image recognition.

NexViTAD: Few-shot Unsupervised Cross-Domain Defect Detection via Vision Foundation Models and Multi-Task Learning Deep residual learning for image recognition

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:42:16.679222Z

Source-reported events for the cited work

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

Observation b4629028-f474-471c-9339-70eab1758c08 · outbound

This paper cites A Survey on Foundation-Model-Based Industrial Defect Detection.

NexViTAD: Few-shot Unsupervised Cross-Domain Defect Detection via Vision Foundation Models and Multi-Task Learning A Survey on Foundation-Model-Based Industrial Defect Detection

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-06T18:42:10.254754Z

Source-reported events for the cited work

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Observation 790ccc7e-703f-4adb-ae0d-d0d2d14e20be · outbound

This paper cites Towards total recall in industrial anomaly detection.

NexViTAD: Few-shot Unsupervised Cross-Domain Defect Detection via Vision Foundation Models and Multi-Task Learning Towards total recall in industrial anomaly detection

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:42:16.549516Z

Source-reported events for the cited work

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

Observation 64e7bd1d-e671-41af-88a4-8f58b8021ce0 · outbound

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

NexViTAD: Few-shot Unsupervised Cross-Domain Defect Detection via Vision Foundation Models and Multi-Task Learning FastFlow: Unsupervised Anomaly Detection and Localization via 2D Normalizing Flows

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-06T18:42:10.464748Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 35516f23-55cc-42b0-8299-f8de33bfcc03 · outbound

This paper cites Efficientad: A deep learning approach for multi-stage ad classification using transfer learning.

NexViTAD: Few-shot Unsupervised Cross-Domain Defect Detection via Vision Foundation Models and Multi-Task Learning Efficientad: A deep learning approach for multi-stage ad classification using transfer learning

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:42:16.386505Z

Source-reported events for the cited work

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

Observation fccd3d36-19ce-4046-aad3-de1e8a1c04c5 · outbound

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

NexViTAD: Few-shot Unsupervised Cross-Domain Defect Detection via Vision Foundation Models and Multi-Task Learning Simplenet: A simple network for image anomaly detection and localization

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:42:16.274330Z

Source-reported events for the cited work

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

Observation e83a3da6-5c15-4fc6-90f9-d7f53d5771df · outbound

This paper cites SuperSimpleNet: Unifying Unsupervised and Supervised Learning for Fast and Reliable Surface Defect Detection, page 47–65.

NexViTAD: Few-shot Unsupervised Cross-Domain Defect Detection via Vision Foundation Models and Multi-Task Learning SuperSimpleNet: Unifying Unsupervised and Supervised Learning for Fast and Reliable Surface Defect Detection, page 47–65

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:42:16.172834Z

Source-reported events for the cited work

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

Observation 25697dee-6c7b-42dd-a8bf-ca51560aab7d · outbound

This paper cites Negative selection algorithm with constant detectors for anomaly detection.

NexViTAD: Few-shot Unsupervised Cross-Domain Defect Detection via Vision Foundation Models and Multi-Task Learning Negative selection algorithm with constant detectors for anomaly detection

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:42:15.999930Z

Source-reported events for the cited work

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

Observation 37968784-e3e4-4a9b-8451-d2fc733449bb · outbound

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

NexViTAD: Few-shot Unsupervised Cross-Domain Defect Detection via Vision Foundation Models and Multi-Task Learning Cutpaste: Self-supervised learning for anomaly detection and localization

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:42:15.842324Z

Source-reported events for the cited work

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

Observation 4f9c4c86-5750-4b87-80db-c7ec9195f790 · outbound

This paper cites DrÆm – a discriminatively trained reconstruction embedding for surface anomaly detection.

NexViTAD: Few-shot Unsupervised Cross-Domain Defect Detection via Vision Foundation Models and Multi-Task Learning DrÆm – a discriminatively trained reconstruction embedding for surface anomaly detection

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:42:15.594749Z

Source-reported events for the cited work

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

Observation 7bf8c17a-229c-4a97-a373-3f0187db1c91 · outbound

This paper cites Anomaly Detection with Conditioned Denoising Diffusion Models, page 181–195.

NexViTAD: Few-shot Unsupervised Cross-Domain Defect Detection via Vision Foundation Models and Multi-Task Learning Anomaly Detection with Conditioned Denoising Diffusion Models, page 181–195

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:42:15.385286Z

Source-reported events for the cited work

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

Observation 3851bae1-a570-4da7-8f0d-4bef1123095f · outbound

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

NexViTAD: Few-shot Unsupervised Cross-Domain Defect Detection via Vision Foundation Models and Multi-Task Learning Realnet: A feature selection network with realistic synthetic anomaly for anomaly detection

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:42:15.154750Z

Source-reported events for the cited work

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

Observation 577c0b51-27cd-4000-890e-dddb2859d7dc · outbound

This paper cites Springer Nature Switzerland, November 2024.

NexViTAD: Few-shot Unsupervised Cross-Domain Defect Detection via Vision Foundation Models and Multi-Task Learning Springer Nature Switzerland, November 2024

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:42:14.906213Z

Source-reported events for the cited work

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

Observation 5ae80257-52e7-487f-abb9-2596728e267d · outbound

This paper cites Xdnet: A few-shot meta-learning approach for cross-domain visual inspection.

NexViTAD: Few-shot Unsupervised Cross-Domain Defect Detection via Vision Foundation Models and Multi-Task Learning Xdnet: A few-shot meta-learning approach for cross-domain visual inspection

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:42:14.671201Z

Source-reported events for the cited work

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

Observation 21efc6b6-0b29-4e50-bb40-c0da31f82a08 · outbound

This paper cites Detect Everything with Few Examples.

NexViTAD: Few-shot Unsupervised Cross-Domain Defect Detection via Vision Foundation Models and Multi-Task Learning Detect Everything with Few Examples

Reference 30

Resolution: unresolved; no resolver link, observed 2026-08-06T18:42:11.735023Z

Source-reported events for the cited work

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Observation 147c0918-9510-47e0-8e43-1835bc8d9e83 · outbound

This paper cites Stones from other hills can polish the jade: facile mgo-templated synthesis of co-doped nimn ldh hollow nanotubes for high-performance asymmetric supercapacitor.

NexViTAD: Few-shot Unsupervised Cross-Domain Defect Detection via Vision Foundation Models and Multi-Task Learning Stones from other hills can polish the jade: facile mgo-templated synthesis of co-doped nimn ldh hollow nanotubes for high-performance asymmetric supercapacitor

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:42:14.484749Z

Source-reported events for the cited work

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

Observation fe37a00c-6ccb-4f91-84e9-983de9132251 · outbound

This paper cites Anomalydino: Boosting patch-based few-shot anomaly detection with dinov2.

NexViTAD: Few-shot Unsupervised Cross-Domain Defect Detection via Vision Foundation Models and Multi-Task Learning Anomalydino: Boosting patch-based few-shot anomaly detection with dinov2

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:42:14.264761Z

Source-reported events for the cited work

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

Observation 4391731b-5e83-462a-9a1d-85a56a7ff6b6 · outbound

This paper cites Joint forecasting of source-load-price for integrated energy system based on multi-task learning and hybrid attention mechanism.

NexViTAD: Few-shot Unsupervised Cross-Domain Defect Detection via Vision Foundation Models and Multi-Task Learning Joint forecasting of source-load-price for integrated energy system based on multi-task learning and hybrid attention mechanism

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:42:14.107261Z

Source-reported events for the cited work

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

Observation 7397175b-dda9-440d-a0c6-e136ec0ea425 · outbound

This paper cites Multitask learning.

NexViTAD: Few-shot Unsupervised Cross-Domain Defect Detection via Vision Foundation Models and Multi-Task Learning Multitask learning

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:42:13.884941Z

Source-reported events for the cited work

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

Observation 864dd2bd-7e1b-4b9d-b917-f574bbe91b78 · outbound

This paper cites Multi-task learning for thyroid nodule segmentation with thyroid region prior.

NexViTAD: Few-shot Unsupervised Cross-Domain Defect Detection via Vision Foundation Models and Multi-Task Learning Multi-task learning for thyroid nodule segmentation with thyroid region prior

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:42:13.685047Z

Source-reported events for the cited work

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

Observation 22568478-f5cf-4f87-ae25-9067ad2c2df5 · outbound

This paper cites Lightspeed computation of optimal transportation distances.Advances in Neural Information Processing Systems, 26(2):2292–2300, 2013.

NexViTAD: Few-shot Unsupervised Cross-Domain Defect Detection via Vision Foundation Models and Multi-Task Learning Lightspeed computation of optimal transportation distances.Advances in Neural Information Processing Systems, 26(2):2292–2300, 2013

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:42:13.426573Z

Source-reported events for the cited work

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

Pith citing papers

No inbound Pith citation observations are available.