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Paper Citation Record · LEDGER

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

As of 9 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 2026-08-09T06:31:02.800959+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

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-03T08:25:29.977416Z digest=sha256:382f67fb73f1b7dd7b6b8c54eba4d7377bd69d64021c9e0193731907e20033a4

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:b0eb2ca558da5658bba151bc97f8c0722ef07bbea0f5e188044d7e9b6da430c2

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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source=arxiv_source observed=2026-08-03T08:25:30.292554Z digest=sha256:54667ff00ab4f2ff58e4c3f09aa0a08c6fcff1aadfdcfb62fd2f96f5fb775f9b

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:d70c36d66d25b391fe72c2b1bd409f3a376c0ec3ed48e451f6798f76526a4f76

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=sha256:00c0ddd028358c023b6558b8a6c4019a72397f9856c7243058405377dbd91365

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

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source=arxiv_source observed=2026-08-03T08:25:30.728430Z digest=sha256:11721f2585bc9efcfc24130f920f64e5ed73c64864b1a6f0ffb8d17a00ca667d

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:df21ae196805292ee460225115154939744d73f3b925c792d67739f8d6b2b367

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=sha256:3a9544ac5bda96796e33e069d53d3a7b11f2d0f98e42d804d94823a545402b01

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=sha256:69be0945f5f5163b0b84d9c67d06626bb2a11131250beb3fd9ee6390c5428f2d

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:a70f3cc02ac91c3934236a87043b20ee126bcebb83ef60b5f153ea620408f72b

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:d38dc28658c2160f8c42e37a117e98a53e21cf42af0cc2275a071b1df99b636e

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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source=arxiv_source observed=2026-08-03T08:25:31.461661Z digest=sha256:18cc423137b5be3da3f8823e23a71bcf7f95c356c597f539c37aae563749354b

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=sha256:03422490293ed590100ae914fd2e6fbcc4c94c94c1880eb4b44cf83166901193

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=sha256:126d03c2e01114bbd40fa641b1cc3681437039d2250492f09f58946a52970205

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=sha256:1b33e7de4c311f86e83cd992f65010e8937b2e3e88df5e297f3264908f1d1d7f

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:dce1fdc952735505f9ac044b0700f70f07d0934bc1ad9ba89b4b0f95481ba11f

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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source=arxiv_source observed=2026-08-03T08:25:31.950035Z digest=sha256:68ec2dc0cf4f64c07c38ecc10c3ef618bf469c5a3de052a168ae3010323d16e7

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=sha256:1645e7a4466f651f3c0cd04da19b011e5bfd2a35237da0620ce96347b18f1dbd

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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source=arxiv_source observed=2026-08-03T08:25:32.152803Z digest=sha256:bb2aa9de72d965aeb99e49ac2359971d6ffe89e28cc6bb35477f23df6579dce6

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:dac1b9a3d5c4170b858911b36b06ea4fcd4e6f7c030c07f414922fcbb5f22ee7

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=sha256:802ccc4e274d8d3315a41cce18b428bd2de419691cf07c3164e54662d5a7c7ec

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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source=arxiv_source observed=2026-08-03T08:25:32.444308Z digest=sha256:b59593c0482d2b258f2fb92979a79d819e6b86a3ed700c6f90888d91fc6200a1

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:c548423be195eaf529ff06123d95aafb7fdc21ed2055c9a4713ffc69c7f18fe0

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:c6cd3d36780bc72145efedd115e82c713fb456d3e74ae62926d480dba73da592

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=sha256:10c8ec6fb0e4458ff050d3b986996f91ddbc423418bf18ee749dccd0d65a19b9

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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no resolver link, observed 2026-08-03T08:25:32.825227Z

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source=arxiv_source observed=2026-08-03T08:25:32.825227Z digest=sha256:12926703b5db550d8db412a51e735b1e59558b147462517b50fcde82760a57af

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=sha256:47c247bfaf9572ae10ebed00dce75232d2f75bd1ec02ae110df5b5fa9e73c8b1

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=sha256:48e40162188d390aeb581e7e3ef70dcab32f3350081493bb126b5e917ab06090

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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source=arxiv_source observed=2026-08-03T08:25:33.206316Z digest=sha256:91149237bba87d72f5b9142f96e1363cfcae7ed47130be077ac2b507c8c189c8

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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no resolver link, observed 2026-08-03T08:25:33.323318Z

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source=arxiv_source observed=2026-08-03T08:25:33.323318Z digest=sha256:1436d12e16f09902a7e58210d7bf1ea6c6791a4e4f33ec3d0c315ae2b5cc7a80

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=sha256:7bbd23b994a1af3913084d6edf5bb73fda89c239e0741eb5e155a9624205078c

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:fe5f35302654885a0f1b9726f02678723d89773ca82cd4426d225483bff527a7

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=sha256:21049c791fee254cbf0c17f3cc492cb5b856c5aef68d96b82d705ef1284ee817

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:df2bb16458f603a3484f3b4800f9cf2324cb218392deb4c62d3de60e51564f5b

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=sha256:9b73011b9bdea1942dd05a19f9aa0d47aa2960c6a35ed2c836e8efa34819b921

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=sha256:018ed7d26afd7856346654042f56bfd3abb5eb983f8bf40400bdb76399b90581

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:f1aac0e42e937678950eb4575f87036d723cc740eb8bb3905f19b49f8b248ddc

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

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source=arxiv_source observed=2026-08-03T08:25:34.506901Z digest=sha256:d2aebcb2ac0170c8c9f84964aa35ab4518cab5c92db9f7964b41944e785b6292

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

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Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-03T08:25:34.638250Z digest=sha256:335930495e9f32eb1b3315800cbef7e78f3cb0d7a4eeb1caf9f15e655f8fde87

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=sha256:30c1d30120a8b7fcc20dcf7d6a31291736eb26a464909fbf7d632c3bce3ed0e9

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

Source-reported events for the cited work

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source=arxiv_source observed=2026-08-03T08:25:34.882774Z digest=sha256:398d6a5320d26f3f01b3456de8b8e03b7e03cc6104496807749e9b39273f9d8c

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=sha256:6229a4a98feab4520fb358cb7c2d6cb06129546038258138c118ee5a68bdffec

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

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source=arxiv_source observed=2026-08-03T08:25:35.170812Z digest=sha256:3b54deade56ab79db2163023974e2770ff3a86bf4a54c507a81ead8250eb23c4

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:d0a3c5dcba961d2ccaa526ed73441b9353094d740ed003c75c7237123e62c066

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:aa943eda980c0bfece2754fecdcf1b58f9ee2b764791ee5c03d71595deb094f6

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

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source=arxiv_source observed=2026-08-03T08:25:35.447989Z digest=sha256:038f8815d3880309cda46832b99e2e71c84442d58bde1850e115047bc9d2193a

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

Source-reported events for the cited work

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source=arxiv_source observed=2026-08-03T08:25:35.518996Z digest=sha256:b92a5e1467b8eda26c4e2e821c4a9dfa6e86f3f642b0df4bdf93c28a6a512b14

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=sha256:4f097d4a9b83a90896e72f248a18d5cc0e1381ba940abaa92dcf23053976eb72

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

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source=arxiv_source observed=2026-08-03T08:25:35.728283Z digest=sha256:b2d3d73610283d413e9d2dff8a3af469e1c088cb3ed61b9dcac6001256addfab

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=sha256:4fee979f4382db5600707d2bbb6391374422661cc39030cd168ed759d7fb68a4

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

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-03T08:25:35.968716Z digest=sha256:348eac85b15c2007792a73673804c368163ed1eb0c695138b9a3baf82532141d

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=sha256:3ead2739da63285328a61a29d958f7810295e0931dcd14d7b19cdee8e099db28

Pith citing papers

No inbound Pith citation observations are available.