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Paper Citation Record · LEDGER

VFAD: Variational Semantic Prompting Meets Frequency-Adaptive Representation Learning for Zero-Shot Anomaly Detection

As of 7 August 2026, this Paper Citation Record lists 52 of 52 outbound references and 0 inbound Pith citation observations for arXiv:2607.29370.

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

pith.paper-citation-record.v1
2607.29370 v1

Coverage vector

measured 52 of 52 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-03T08:25:36.012333Z

measured 52 of 52 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

52 of 52 outbound references displayed

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  • unresolved52
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 3442c5a1-59bb-41e8-b6b9-e5538e57cd7d · outbound

This paper cites Communication, Simulation, and Intelligent Agents: Implications of Personal Intelligent Machines for Medical Education.

VFAD: Variational Semantic Prompting Meets Frequency-Adaptive Representation Learning for Zero-Shot Anomaly Detection Communication, Simulation, and Intelligent Agents: Implications of Personal Intelligent Machines for Medical Education

Reference 1

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Source-reported events for the cited work

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source=arxiv_source observed=2026-08-03T08:25:29.977416Z digest=sha256:7061e6df530da524b73c6cf1a83b5e169e304805c2555812a8006cbf9c182f52

Observation 3a193bd3-8c3e-4f38-af13-114c6f62db31 · outbound

This paper cites Classification Problem Solving.

VFAD: Variational Semantic Prompting Meets Frequency-Adaptive Representation Learning for Zero-Shot Anomaly Detection Classification Problem Solving

Reference 2

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source=arxiv_source observed=2026-08-03T08:25:30.110454Z digest=sha256:e9489d9e4f961a0d84b52df719648197d5d3030b647b30b873f625bfe2101d8a

Observation 7431171c-64d3-4f1f-b3ff-3582d6ba124d · outbound

This paper cites , title =.

VFAD: Variational Semantic Prompting Meets Frequency-Adaptive Representation Learning for Zero-Shot Anomaly Detection , title =

Reference 3

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no resolver link, observed 2026-08-03T08:25:30.292554Z

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source=arxiv_source observed=2026-08-03T08:25:30.292554Z digest=sha256:9c22a4244dd7db93a5955a647fd50dd61e10782cecaaae1b3c5af6cf99b87bd2

Observation 590675da-ea69-4821-8eca-211cc4c8a132 · outbound

This paper cites New Ways to Make Microcircuits Smaller---Duplicate Entry.

VFAD: Variational Semantic Prompting Meets Frequency-Adaptive Representation Learning for Zero-Shot Anomaly Detection New Ways to Make Microcircuits Smaller---Duplicate Entry

Reference 4

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source=arxiv_source observed=2026-08-03T08:25:30.434692Z digest=sha256:cb2fd3bfce4a2b65f6a36c8da20692ca70175973b52580048719a5bcc5a1b121

Observation 9f6c02ed-a642-4d7a-9572-818f56b0b1f4 · outbound

This paper cites Clancey and Glenn Rennels , abstract =.

VFAD: Variational Semantic Prompting Meets Frequency-Adaptive Representation Learning for Zero-Shot Anomaly Detection Clancey and Glenn Rennels , abstract =

Reference 5

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source=arxiv_source observed=2026-08-03T08:25:30.586139Z digest=sha256:08f6f1a3338379e4d6f5a5693587355114e7fa9db403f38d3f42221a3bd47e1f

Observation 05f5b790-0def-4cf5-b005-91871437736f · outbound

This paper cites and Rennels, Glenn R.

VFAD: Variational Semantic Prompting Meets Frequency-Adaptive Representation Learning for Zero-Shot Anomaly Detection and Rennels, Glenn R

Reference 6

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-03T08:25:30.728430Z digest=sha256:30eb73b1a4b9a592bfc7a3b4442d156d2e0876aded166f3846e5fcd781a7d271

Observation 6721f500-b05c-4ed2-9546-aa60ce1caadf · outbound

This paper cites Poligon: A System for Parallel Problem Solving.

VFAD: Variational Semantic Prompting Meets Frequency-Adaptive Representation Learning for Zero-Shot Anomaly Detection Poligon: A System for Parallel Problem Solving

Reference 7

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source=arxiv_source observed=2026-08-03T08:25:30.922377Z digest=sha256:c0bb5aacafd40435aa068c70917fbabf4f258f0254042eaa78b50d30315098bf

Observation cfaab6cb-e9a0-4e9d-b30e-67940fa9bbcc · outbound

This paper cites Transfer of Rule-Based Expertise through a Tutorial Dialogue.

VFAD: Variational Semantic Prompting Meets Frequency-Adaptive Representation Learning for Zero-Shot Anomaly Detection Transfer of Rule-Based Expertise through a Tutorial Dialogue

Reference 8

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source=arxiv_source observed=2026-08-03T08:25:31.030947Z digest=sha256:46014e5aee2432ae14407bcaa4bb2a7744d5b4ee091799ec14a0e1e93e9993e1

Observation 234890e8-be21-4247-8b8c-de333bca83b4 · outbound

This paper cites The Engineering of Qualitative Models.

VFAD: Variational Semantic Prompting Meets Frequency-Adaptive Representation Learning for Zero-Shot Anomaly Detection The Engineering of Qualitative Models

Reference 9

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source=arxiv_source observed=2026-08-03T08:25:31.150374Z digest=sha256:aaf11631c43f033a1d8997457f3ac6347607e552f125852adbfa4a3de9f357c0

Observation a3f77ebd-fd28-407d-bd5c-8bdb12c54091 · outbound

This paper cites 2023 , eprint=.

VFAD: Variational Semantic Prompting Meets Frequency-Adaptive Representation Learning for Zero-Shot Anomaly Detection 2023 , eprint=

Reference 10

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source=arxiv_source observed=2026-08-03T08:25:31.260716Z digest=sha256:bdfa16fc401843d9be6a5edf7897ca3d1269f925a43cc068db1d04ec0f38b786

Observation 2f843337-33cc-49ff-bd0d-9d8d42469160 · outbound

This paper cites Pluto: The 'Other' Red Planet.

VFAD: Variational Semantic Prompting Meets Frequency-Adaptive Representation Learning for Zero-Shot Anomaly Detection Pluto: The 'Other' Red Planet

Reference 11

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source=arxiv_source observed=2026-08-03T08:25:31.357841Z digest=sha256:381092f217dc5d1871723fb2255ef511006808a1ae596efbb64b4a6691752553

Observation 54ae4de5-aacb-444f-81a1-2e6bee3c5cd1 · outbound

This paper cites Pattern Recognition , pages=.

VFAD: Variational Semantic Prompting Meets Frequency-Adaptive Representation Learning for Zero-Shot Anomaly Detection Pattern Recognition , pages=

Reference 12

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no resolver link, observed 2026-08-03T08:25:31.461661Z

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source=arxiv_source observed=2026-08-03T08:25:31.461661Z digest=sha256:1a664999bb697d8d7ffbfcc93d15944992db112c5b15c5d060ba18040a89900e

Observation 3dcc7a1b-6d38-4d08-b174-4d2838a68ee6 · outbound

This paper cites Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , pages=.

VFAD: Variational Semantic Prompting Meets Frequency-Adaptive Representation Learning for Zero-Shot Anomaly Detection Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , pages=

Reference 13

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source=arxiv_source observed=2026-08-03T08:25:31.537369Z digest=sha256:f38deee3e920fe0a0412795130f3372f2d78deeaab9c93b7a68466010507e367

Observation 6f050212-b65b-40f4-9283-919a1ea9fe67 · outbound

This paper cites European Conference on Computer Vision , pages=.

VFAD: Variational Semantic Prompting Meets Frequency-Adaptive Representation Learning for Zero-Shot Anomaly Detection European Conference on Computer Vision , pages=

Reference 14

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source=arxiv_source observed=2026-08-03T08:25:31.631013Z digest=sha256:8d7ef222a89da62730a4dd7d6b85291e5d2833cca012f9c086eab3f847ed3047

Observation e1d6bda8-c0cd-4c6a-b872-ac33d97e22a3 · outbound

This paper cites Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , pages=.

VFAD: Variational Semantic Prompting Meets Frequency-Adaptive Representation Learning for Zero-Shot Anomaly Detection Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , pages=

Reference 15

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source=arxiv_source observed=2026-08-03T08:25:31.765250Z digest=sha256:81ca27f385afe90ca377efb6e2252afe701e77ea176ef483b36ef4547b834d05

Observation 813f18f8-c9e1-4a2e-8776-f7740112b74a · outbound

This paper cites Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , pages=.

VFAD: Variational Semantic Prompting Meets Frequency-Adaptive Representation Learning for Zero-Shot Anomaly Detection Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , pages=

Reference 16

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no resolver link, observed 2026-08-03T08:25:31.873118Z

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source=arxiv_source observed=2026-08-03T08:25:31.873118Z digest=sha256:b0992dad5abe761fb0af437cda9d1adb162bd473c07ed5346d49d774dea4be20

Observation d0153262-b432-4da1-80d5-8413940e7aab · outbound

This paper cites European Conference on Computer Vision , pages=.

VFAD: Variational Semantic Prompting Meets Frequency-Adaptive Representation Learning for Zero-Shot Anomaly Detection European Conference on Computer Vision , pages=

Reference 17

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no resolver link, observed 2026-08-03T08:25:31.950035Z

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source=arxiv_source observed=2026-08-03T08:25:31.950035Z digest=sha256:59c9ec51c4e3a07d57baf1f4989e210e7d6fae82ba18881eed99f28f6bf394be

Observation 811a0965-ac22-4e92-a5ff-f21b6d8dfc9c · outbound

This paper cites 2021 IEEE 30th International Symposium on Industrial Electronics (ISIE) , pages=.

VFAD: Variational Semantic Prompting Meets Frequency-Adaptive Representation Learning for Zero-Shot Anomaly Detection 2021 IEEE 30th International Symposium on Industrial Electronics (ISIE) , pages=

Reference 18

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source=arxiv_source observed=2026-08-03T08:25:32.057371Z digest=sha256:13dde5f3a101ccf4f2b555c68658805fcfbd9ac5ad0f7a634b285d65cbc2004f

Observation d945bee3-2e02-41a6-944c-0b8167888902 · outbound

This paper cites DAGM symposium in , volume=.

VFAD: Variational Semantic Prompting Meets Frequency-Adaptive Representation Learning for Zero-Shot Anomaly Detection DAGM symposium in , volume=

Reference 19

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no resolver link, observed 2026-08-03T08:25:32.152803Z

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source=arxiv_source observed=2026-08-03T08:25:32.152803Z digest=sha256:f6ee98512d526904b3f8676848682e64f1b8b8e3ecdc46f5907b23d95997e027

Observation 5c85badf-b77b-4863-9e5b-f64b6be0fd53 · outbound

This paper cites Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision , pages=.

VFAD: Variational Semantic Prompting Meets Frequency-Adaptive Representation Learning for Zero-Shot Anomaly Detection Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision , pages=

Reference 20

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source=arxiv_source observed=2026-08-03T08:25:32.263954Z digest=sha256:d000c1b8ca597e9a260996fac6d8141d68e6b7613d32a764d168b37379fbedb4

Observation e6a76d57-c67a-4711-8bf7-9fd075c316cf · outbound

This paper cites Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , pages=.

VFAD: Variational Semantic Prompting Meets Frequency-Adaptive Representation Learning for Zero-Shot Anomaly Detection Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , pages=

Reference 21

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source=arxiv_source observed=2026-08-03T08:25:32.371186Z digest=sha256:1e4726e2dbc8e0d2eac47c679077a9433b74801cc8fe0638b5b7510cff8bba3b

Observation 90084e85-eba0-4238-8b12-addd8276cb98 · outbound

This paper cites Brain , volume=.

VFAD: Variational Semantic Prompting Meets Frequency-Adaptive Representation Learning for Zero-Shot Anomaly Detection Brain , volume=

Reference 22

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no resolver link, observed 2026-08-03T08:25:32.444308Z

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source=arxiv_source observed=2026-08-03T08:25:32.444308Z digest=sha256:9eaac79405edfd1f5da1858320ffc6073fd0085fa443a5bbcfaf5be45c208098

Observation a0fe916b-4c6b-44b3-abd1-52d3bee386f7 · outbound

This paper cites 2020 , howpublished =.

VFAD: Variational Semantic Prompting Meets Frequency-Adaptive Representation Learning for Zero-Shot Anomaly Detection 2020 , howpublished =

Reference 23

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source=arxiv_source observed=2026-08-03T08:25:32.503309Z digest=sha256:3b3a13d2ad8ea7506b4fcfd3338281b78c9090e236f43a19b1722dd55f5aaf54

Observation 2fa516db-d655-4aee-a6bb-426e644b6cf7 · outbound

This paper cites IEEE transactions on medical imaging , volume=.

VFAD: Variational Semantic Prompting Meets Frequency-Adaptive Representation Learning for Zero-Shot Anomaly Detection IEEE transactions on medical imaging , volume=

Reference 24

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source=arxiv_source observed=2026-08-03T08:25:32.612360Z digest=sha256:f0ab8e999be2845c238d3e5524726806b0507ff28d42236c651d6ddf88ffad61

Observation 16986e20-a728-47f5-9882-8aa60ee232e4 · outbound

This paper cites saliency maps from physicians , author=.

VFAD: Variational Semantic Prompting Meets Frequency-Adaptive Representation Learning for Zero-Shot Anomaly Detection saliency maps from physicians , author=

Reference 25

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source=arxiv_source observed=2026-08-03T08:25:32.717938Z digest=sha256:52b0fedaace00f3efe81062ed5a130fb45851cf1b77eafc485c16100b5627e43

Observation e30d1601-00f7-429a-8397-fffb1d2d6ab2 · outbound

This paper cites Pattern Recognition.

VFAD: Variational Semantic Prompting Meets Frequency-Adaptive Representation Learning for Zero-Shot Anomaly Detection Pattern Recognition

Reference 26

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source=arxiv_source observed=2026-08-03T08:25:32.825227Z digest=sha256:e1ed82ef582a91726308e42e951a363f4302e27f0653f2902dc830156fabae5b

Observation 37c7b6ec-a309-4ee0-b915-8bf35a067702 · outbound

This paper cites International conference on multimedia modeling , pages=.

VFAD: Variational Semantic Prompting Meets Frequency-Adaptive Representation Learning for Zero-Shot Anomaly Detection International conference on multimedia modeling , pages=

Reference 27

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source=arxiv_source observed=2026-08-03T08:25:32.951120Z digest=sha256:396754f84cf0766000e9de58c6b5f6d1d104bd3014970d87d545734c894f04b8

Observation 809ea8bb-0883-4e70-a788-1e0ee46c683d · outbound

This paper cites 2018 IEEE 15th international symposium on biomedical imaging (ISBI 2018) , pages=.

VFAD: Variational Semantic Prompting Meets Frequency-Adaptive Representation Learning for Zero-Shot Anomaly Detection 2018 IEEE 15th international symposium on biomedical imaging (ISBI 2018) , pages=

Reference 28

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source=arxiv_source observed=2026-08-03T08:25:33.043078Z digest=sha256:db4c17524613abab2203f2c7ab9208199beca365fb04c33dabb7dbfb7c929427

Observation bbd705e6-4766-4031-87a0-0ba03c0d6aeb · outbound

This paper cites Computers in Industry , volume=.

VFAD: Variational Semantic Prompting Meets Frequency-Adaptive Representation Learning for Zero-Shot Anomaly Detection Computers in Industry , volume=

Reference 29

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no resolver link, observed 2026-08-03T08:25:33.206316Z

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source=arxiv_source observed=2026-08-03T08:25:33.206316Z digest=sha256:c5e6012d6308418a20471a2b09c2638915f55ef92f46e1b00771048ef8068fcb

Observation 8c4aa201-8826-4f09-8f45-f25b2ea7f01b · outbound

This paper cites Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , pages=.

VFAD: Variational Semantic Prompting Meets Frequency-Adaptive Representation Learning for Zero-Shot Anomaly Detection Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , pages=

Reference 30

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source=arxiv_source observed=2026-08-03T08:25:33.323318Z digest=sha256:b819be81916d52bb0e93d4f99ef13d9ceed496e96f75c7ec863d275fe9c497ae

Observation 4985bf58-9384-4507-98e1-86f01e5b0cd1 · outbound

This paper cites European conference on computer vision , pages=.

VFAD: Variational Semantic Prompting Meets Frequency-Adaptive Representation Learning for Zero-Shot Anomaly Detection European conference on computer vision , pages=

Reference 31

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no resolver link, observed 2026-08-03T08:25:33.485341Z

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source=arxiv_source observed=2026-08-03T08:25:33.485341Z digest=sha256:0cc043134ec17e450e3dc1ea81c0dfa91d0687a70725ca7119a1c326d2dc893b

Observation a1f92c9f-8de9-44d6-9bcb-126f000116cc · outbound

This paper cites International Conference on Learning Representations , volume=.

VFAD: Variational Semantic Prompting Meets Frequency-Adaptive Representation Learning for Zero-Shot Anomaly Detection International Conference on Learning Representations , volume=

Reference 32

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source=arxiv_source observed=2026-08-03T08:25:33.649916Z digest=sha256:926fbd28fcef75b2264f5f9f595c59e93a6eb11af8fe9a5c13def38473b295ec

Observation 4dd46199-d982-4503-94d0-f5aa2a5223e2 · outbound

This paper cites Proceedings of the Computer Vision and Pattern Recognition Conference , pages=.

VFAD: Variational Semantic Prompting Meets Frequency-Adaptive Representation Learning for Zero-Shot Anomaly Detection Proceedings of the Computer Vision and Pattern Recognition Conference , pages=

Reference 33

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source=arxiv_source observed=2026-08-03T08:25:33.777213Z digest=sha256:3ac7c8068636a085e01d754dec45260612cc642d7b92e8b77106e0a4134c3948

Observation 65fd1888-239a-4d45-86d5-2167ca15777e · outbound

This paper cites ICASSP 2026-2026 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) , pages=.

VFAD: Variational Semantic Prompting Meets Frequency-Adaptive Representation Learning for Zero-Shot Anomaly Detection ICASSP 2026-2026 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) , pages=

Reference 34

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source=arxiv_source observed=2026-08-03T08:25:33.937031Z digest=sha256:1d2d4a093a54b0cac19fef24304386a0b226946e1657bb4a8f1ac6595c63e7e6

Observation dc5dd6a7-cf2a-44d9-860e-ad59e648b040 · outbound

This paper cites Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , pages=.

VFAD: Variational Semantic Prompting Meets Frequency-Adaptive Representation Learning for Zero-Shot Anomaly Detection Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , pages=

Reference 35

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no resolver link, observed 2026-08-03T08:25:34.061788Z

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source=arxiv_source observed=2026-08-03T08:25:34.061788Z digest=sha256:790ea721b7edccb266a80f06109737e6ce15132c3ff9f6c8e1b2328382319ca0

Observation 95865afd-83c3-4d07-acf3-04072d83a4ac · outbound

This paper cites International journal of computer vision , volume=.

VFAD: Variational Semantic Prompting Meets Frequency-Adaptive Representation Learning for Zero-Shot Anomaly Detection International journal of computer vision , volume=

Reference 36

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no resolver link, observed 2026-08-03T08:25:34.195542Z

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source=arxiv_source observed=2026-08-03T08:25:34.195542Z digest=sha256:f7c67771456a96321ae8f3d5cfac8e92938e15fa53c644fc75e3836e2d2fd5c8

Observation 2b99d2af-7afd-456d-ad59-64333ae05461 · outbound

This paper cites Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , pages=.

VFAD: Variational Semantic Prompting Meets Frequency-Adaptive Representation Learning for Zero-Shot Anomaly Detection Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , pages=

Reference 37

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no resolver link, observed 2026-08-03T08:25:34.380687Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-03T08:25:34.380687Z digest=sha256:e1bb6b52e7a5fc3b1f5384d1fbfbda38671175a7456d6887a611e3026b2755b6

Observation 0f78e2e2-ccd1-43ac-b91b-8e4b702afc5f · outbound

This paper cites Proceedings of the IEEE/CVF international conference on computer vision , pages=.

VFAD: Variational Semantic Prompting Meets Frequency-Adaptive Representation Learning for Zero-Shot Anomaly Detection Proceedings of the IEEE/CVF international conference on computer vision , pages=

Reference 38

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no resolver link, observed 2026-08-03T08:25:34.506901Z

Source-reported events for the cited work

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source=arxiv_source observed=2026-08-03T08:25:34.506901Z digest=sha256:b72f6d7fd54f0334240fe52d5a333d09bfa333e5f53c5be872535435c748004b

Observation c52d475e-0323-4f17-9c5a-68512fcc33a9 · outbound

This paper cites Proceedings of the IEEE/CVF International Conference on Computer Vision , pages=.

VFAD: Variational Semantic Prompting Meets Frequency-Adaptive Representation Learning for Zero-Shot Anomaly Detection Proceedings of the IEEE/CVF International Conference on Computer Vision , pages=

Reference 39

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no resolver link, observed 2026-08-03T08:25:34.638250Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-03T08:25:34.638250Z digest=sha256:942048194c3c40c9e507031e1c4f3ef6a0c424cd0f1c80c95c920bfd8c35bec8

Observation dd261150-d5b5-4c66-8e66-a5bee4c441e7 · outbound

This paper cites European Conference on Computer Vision , pages=.

VFAD: Variational Semantic Prompting Meets Frequency-Adaptive Representation Learning for Zero-Shot Anomaly Detection European Conference on Computer Vision , pages=

Reference 40

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no resolver link, observed 2026-08-03T08:25:34.733028Z

Source-reported events for the cited work

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source=arxiv_source observed=2026-08-03T08:25:34.733028Z digest=sha256:0de664388ef1a6aaf995e2fc4aa7cadf914fed2e4d11dc264016b1dcc0fcc053

Observation e65f9461-a905-4f76-9376-5859c3b35b19 · outbound

This paper cites Proceedings of the AAAI Conference on Artificial Intelligence , volume=.

VFAD: Variational Semantic Prompting Meets Frequency-Adaptive Representation Learning for Zero-Shot Anomaly Detection Proceedings of the AAAI Conference on Artificial Intelligence , volume=

Reference 41

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no resolver link, observed 2026-08-03T08:25:34.882774Z

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source=arxiv_source observed=2026-08-03T08:25:34.882774Z digest=sha256:97743bb56f2c7114e20c7d39ff99335191cb7abe5955af6ba685b3370bd8f144

Observation 2e1f10bc-248c-4d2b-ad56-b7e43524b3f8 · outbound

This paper cites Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , pages=.

VFAD: Variational Semantic Prompting Meets Frequency-Adaptive Representation Learning for Zero-Shot Anomaly Detection Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , pages=

Reference 42

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no resolver link, observed 2026-08-03T08:25:35.001596Z

Source-reported events for the cited work

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source=arxiv_source observed=2026-08-03T08:25:35.001596Z digest=sha256:46df40d3246e73375cc9401a65284cd619b1bf1cfa38d0bcc7a3ea32163d4bf6

Observation 4d47d23a-acc1-422a-b361-845fa9c8f128 · outbound

This paper cites Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , pages=.

VFAD: Variational Semantic Prompting Meets Frequency-Adaptive Representation Learning for Zero-Shot Anomaly Detection Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , pages=

Reference 43

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no resolver link, observed 2026-08-03T08:25:35.170812Z

Source-reported events for the cited work

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source=arxiv_source observed=2026-08-03T08:25:35.170812Z digest=sha256:d96e294408aaafbaeed50006cc064a06472c995ccf4b7a8d5fb41d0bb18e9ffe

Observation 7a7f40b4-20c3-4b8e-afb7-fc08ba851a86 · outbound

This paper cites Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , pages=.

VFAD: Variational Semantic Prompting Meets Frequency-Adaptive Representation Learning for Zero-Shot Anomaly Detection Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , pages=

Reference 44

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no resolver link, observed 2026-08-03T08:25:35.258730Z

Source-reported events for the cited work

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source=arxiv_source observed=2026-08-03T08:25:35.258730Z digest=sha256:cc34db9aaba04d338b9abea138c95aae7a3a6bb7853240c8b9681a38f8554e93

Observation 9a5c0bed-8a56-4cef-9240-8512a0c9115a · outbound

This paper cites International conference on machine learning , pages=.

VFAD: Variational Semantic Prompting Meets Frequency-Adaptive Representation Learning for Zero-Shot Anomaly Detection International conference on machine learning , pages=

Reference 45

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no resolver link, observed 2026-08-03T08:25:35.380264Z

Source-reported events for the cited work

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source=arxiv_source observed=2026-08-03T08:25:35.380264Z digest=sha256:109cda195196930247fe0db5c532bb0473b312a9a99c2769e097c11f8783b7ea

Observation d42ff013-65a7-4af3-bcc1-b9d7e5d0c23d · outbound

This paper cites Proceedings of the Computer Vision and Pattern Recognition Conference , pages=.

VFAD: Variational Semantic Prompting Meets Frequency-Adaptive Representation Learning for Zero-Shot Anomaly Detection Proceedings of the Computer Vision and Pattern Recognition Conference , pages=

Reference 46

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no resolver link, observed 2026-08-03T08:25:35.447989Z

Source-reported events for the cited work

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source=arxiv_source observed=2026-08-03T08:25:35.447989Z digest=sha256:e1c7c3fc8959e57f96e0467beeff18766b9400dd3124cf89bbf860785c7b1882

Observation 1378ca3c-aabd-403d-982f-4c00af143430 · outbound

This paper cites The Thirteenth International Conference on Learning Representations , year=.

VFAD: Variational Semantic Prompting Meets Frequency-Adaptive Representation Learning for Zero-Shot Anomaly Detection The Thirteenth International Conference on Learning Representations , year=

Reference 47

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no resolver link, observed 2026-08-03T08:25:35.518996Z

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Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-03T08:25:35.518996Z digest=sha256:ede5baa1639f5ea8c9d8f99056e0b5e6392a4aafa18bce201908663d22b8d9a7

Observation f8c551ea-a1de-4ba0-9ec2-fec00f604433 · outbound

This paper cites Proceedings of the IEEE/CVF International Conference on Computer Vision , pages=.

VFAD: Variational Semantic Prompting Meets Frequency-Adaptive Representation Learning for Zero-Shot Anomaly Detection Proceedings of the IEEE/CVF International Conference on Computer Vision , pages=

Reference 48

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no resolver link, observed 2026-08-03T08:25:35.601110Z

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source=arxiv_source observed=2026-08-03T08:25:35.601110Z digest=sha256:bdcbb1eaad4dbe3028ba14ca0e2df6d6cc418409db373d5a0adea1e75f65c70c

Observation ee7d1ead-5f4b-4c2f-946d-27dbe76e6882 · outbound

This paper cites Proceedings of the AAAI Conference on Artificial Intelligence , volume=.

VFAD: Variational Semantic Prompting Meets Frequency-Adaptive Representation Learning for Zero-Shot Anomaly Detection Proceedings of the AAAI Conference on Artificial Intelligence , volume=

Reference 49

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no resolver link, observed 2026-08-03T08:25:35.728283Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-03T08:25:35.728283Z digest=sha256:68c1e2e47339704540a6e1dc9a5708c9c1dd653078501f15c74640360e8a1562

Observation 25a482cc-5840-4339-a6e4-842db7e1450f · outbound

This paper cites Proceedings of the AAAI Conference on Artificial Intelligence , volume=.

VFAD: Variational Semantic Prompting Meets Frequency-Adaptive Representation Learning for Zero-Shot Anomaly Detection Proceedings of the AAAI Conference on Artificial Intelligence , volume=

Reference 50

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no resolver link, observed 2026-08-03T08:25:35.859110Z

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source=arxiv_source observed=2026-08-03T08:25:35.859110Z digest=sha256:faa4b760cf8ab3ff8be3874ba9ff1e06719c4be46f5dae76daec7662b0ea563e

Observation 945c31eb-bc1e-4b04-8110-eb5e46030a3a · outbound

This paper cites Proceedings of the IEEE international conference on computer vision , pages=.

VFAD: Variational Semantic Prompting Meets Frequency-Adaptive Representation Learning for Zero-Shot Anomaly Detection Proceedings of the IEEE international conference on computer vision , pages=

Reference 51

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no resolver link, observed 2026-08-03T08:25:35.968716Z

Source-reported events for the cited work

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source=arxiv_source observed=2026-08-03T08:25:35.968716Z digest=sha256:9f3bfc1319f652cd56af056326fc47f597d82d0c0f08872210d96baf4c5ac1ef

Observation e35ed05b-238c-4e6b-84c8-628b93e02472 · outbound

This paper cites Dice Loss for Data-imbalanced NLP Tasks.

VFAD: Variational Semantic Prompting Meets Frequency-Adaptive Representation Learning for Zero-Shot Anomaly Detection Dice Loss for Data-imbalanced NLP Tasks

Reference 52

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no resolver link, observed 2026-08-03T08:25:36.012333Z

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source=arxiv_source observed=2026-08-03T08:25:36.012333Z digest=sha256:7562a2bd3443f7d6de0ad2af5a91e3cff5e1c6b6e4fe05d7d43836c08c150ec5

Pith citing papers

No inbound Pith citation observations are available.