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Paper Citation Record · LEDGER

CLIP-FSAC++: Few-Shot Anomaly Classification with Anomaly Descriptor Based on CLIP

As of 15 August 2026, this Paper Citation Record lists 64 of 64 outbound references and 0 inbound Pith citation observations for arXiv:2412.03829.

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

pith.paper-citation-record.v1
2412.03829 v1

Coverage vector

measured 64 of 64 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-11T22:07:34.787934Z

measured 64 of 64 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-15T06:32:42.880941+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

64 of 64 outbound references displayed

  • verified exact2
  • verified fuzzy53
  • unresolved9
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation a27001f3-92fa-4680-851b-1120cdc0ce94 · outbound

This paper cites A reconstruction-based feature adaptation for anomaly detection with self-supervised multi-scale aggregation,.

CLIP-FSAC++: Few-Shot Anomaly Classification with Anomaly Descriptor Based on CLIP A reconstruction-based feature adaptation for anomaly detection with self-supervised multi-scale aggregation,

Reference 1

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No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-11T22:07:34.532840Z digest=sha256:f649148a09ab689877a148c28f29ab850e75bce5bdca2d36cdb9f34fccbe2047

Observation acad7fd4-9499-4444-ac8e-5a974e17e3cd · outbound

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

CLIP-FSAC++: Few-Shot Anomaly Classification with Anomaly Descriptor Based on CLIP Simplenet: A simple network for image anomaly detection and localization,

Reference 2

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

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Observation 54df62d4-6d26-4de7-b6c5-221a2e19268f · outbound

This paper cites Omni-frequency channel-selection representations for unsupervised anomaly detection,.

CLIP-FSAC++: Few-Shot Anomaly Classification with Anomaly Descriptor Based on CLIP Omni-frequency channel-selection representations for unsupervised anomaly detection,

Reference 3

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

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Observation 4a1c222f-8ed5-4453-aa5e-24ee72149901 · outbound

This paper cites Target be- fore shooting: Accurate anomaly detection and localization under one millisecond via cascade patch retrieval,.

CLIP-FSAC++: Few-Shot Anomaly Classification with Anomaly Descriptor Based on CLIP Target be- fore shooting: Accurate anomaly detection and localization under one millisecond via cascade patch retrieval,

Reference 4

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-11T22:07:34.545842Z digest=sha256:9e6f3dc92cd177f17458bfdec89a0c7a2fc903b241d4be7ef96f2d1077cdff5e

Observation 79aaeaa5-ac31-4816-bd7b-5bcdbea220eb · outbound

This paper cites Deep one-class classification,.

CLIP-FSAC++: Few-Shot Anomaly Classification with Anomaly Descriptor Based on CLIP Deep one-class classification,

Reference 5

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-11T22:07:34.549834Z digest=sha256:59646705e5cf5c2bcdbd12a518fbee5eff417c3980fab79cc6646fc00a0f8d6b

Observation 8944d4b1-59ef-42c0-8985-dec0e8c6186f · outbound

This paper cites Learning deep features for one-class classification,.

CLIP-FSAC++: Few-Shot Anomaly Classification with Anomaly Descriptor Based on CLIP Learning deep features for one-class classification,

Reference 6

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-11T22:07:34.553803Z digest=sha256:a354dff02b40d0750323f37e69e913b97e936c6a23730f192528a179040a208e

Observation 27af588a-5dc0-4db1-a332-f9ab65e455c3 · outbound

This paper cites Coft-ad: Contrastive fine-tuning for few-shot anomaly detection,.

CLIP-FSAC++: Few-Shot Anomaly Classification with Anomaly Descriptor Based on CLIP Coft-ad: Contrastive fine-tuning for few-shot anomaly detection,

Reference 7

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-11T22:07:34.558134Z digest=sha256:aaac8d53023f516c6cb9c3202a9ac6fe16855098a84a50bf4b28e26d153bb3dc

Observation 1ba7adae-18f1-4426-a882-67a18011c256 · outbound

This paper cites Few-shot anomaly detection with adversarial loss for robust feature representations,.

CLIP-FSAC++: Few-Shot Anomaly Classification with Anomaly Descriptor Based on CLIP Few-shot anomaly detection with adversarial loss for robust feature representations,

Reference 8

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-11T22:07:34.561728Z digest=sha256:7ce3cdd2aae8f2ccae162e5ae10f6e70efc3b661e5aef5e666178ab636c5c721

Observation 40072f3c-2d60-4c4c-926f-89e2319fc9bc · outbound

This paper cites Zero-shot versus many-shot: Unsupervised texture anomaly detection,.

CLIP-FSAC++: Few-Shot Anomaly Classification with Anomaly Descriptor Based on CLIP Zero-shot versus many-shot: Unsupervised texture anomaly detection,

Reference 9

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-11T22:07:34.565635Z digest=sha256:7f50ead1d18b5a835715c75e3fb3d41eef165c0fca405c13f3f921d894af029d

Observation f86c455d-8940-4d98-8da7-9657696b3a15 · outbound

This paper cites Pushing the limits of fewshot anomaly detection in industry vision: Graphcore,.

CLIP-FSAC++: Few-Shot Anomaly Classification with Anomaly Descriptor Based on CLIP Pushing the limits of fewshot anomaly detection in industry vision: Graphcore,

Reference 10

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-11T22:07:34.569333Z digest=sha256:fa6a8a3408e58fd9d7afcd1bd2ac7a0143fe4929d23e008270d61e0a38af8223

Observation 94c316fe-21bf-436a-abec-7ffc31158e2a · outbound

This paper cites MiniGPT-4: Enhancing Vision-Language Understanding with Advanced Large Language Models.

CLIP-FSAC++: Few-Shot Anomaly Classification with Anomaly Descriptor Based on CLIP MiniGPT-4: Enhancing Vision-Language Understanding with Advanced Large Language Models

Reference 11

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-11T22:07:34.573225Z digest=sha256:e26ac2af1d5efc24417d24fafae743767ecb63fc5d638a00acbc5bc42fdae26e

Observation a5fa07da-c53b-486e-941f-6cc26d90e1d7 · outbound

This paper cites Flamingo: a visual language model for few-shot learning,.

CLIP-FSAC++: Few-Shot Anomaly Classification with Anomaly Descriptor Based on CLIP Flamingo: a visual language model for few-shot learning,

Reference 12

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-11T22:07:34.578329Z digest=sha256:1c7aab460cc2409b37f903d4a8978b4fc38e7796322f5dded38e421d029c4574

Observation c5580fee-00f2-4aa9-a8d4-5aef7c5f29a4 · outbound

This paper cites AnomalyCLIP: Object-agnostic prompt learning for zero-shot anomaly detection,.

CLIP-FSAC++: Few-Shot Anomaly Classification with Anomaly Descriptor Based on CLIP AnomalyCLIP: Object-agnostic prompt learning for zero-shot anomaly detection,

Reference 13

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-11T22:07:34.582029Z digest=sha256:f629ed5f8b8da9a5a21f3e395e8922012529339cf7cac66ac70511268020613c

Observation ee6f690a-d48e-48c9-8d00-def79c5b421f · outbound

This paper cites Learning transferable visual models from natural language supervi- sion,.

CLIP-FSAC++: Few-Shot Anomaly Classification with Anomaly Descriptor Based on CLIP Learning transferable visual models from natural language supervi- sion,

Reference 14

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raw_fallback, observed 2026-08-11T22:07:35.527832Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-11T22:07:34.585756Z digest=sha256:5d096c3d31eb3415ba49246aa8b6a94b69e2b6b648035ad8c7dc25a2ece42d82

Observation a962dbca-f15d-47bc-81fc-97536ebf034b · outbound

This paper cites Clip-fsac: Boosting clip for few-shot anomaly classification with synthetic anomalies,.

CLIP-FSAC++: Few-Shot Anomaly Classification with Anomaly Descriptor Based on CLIP Clip-fsac: Boosting clip for few-shot anomaly classification with synthetic anomalies,

Reference 15

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-11T22:07:34.589657Z digest=sha256:a2b1e6a9ffbe512d72b80ef9f6ba95913be12c12822c670911a62c633d37c928

Observation 40806851-1bf3-4169-bc6c-36e5a8a8431a · outbound

This paper cites CLIP-Adapter: Better Vision-Language Models with Feature Adapters.

CLIP-FSAC++: Few-Shot Anomaly Classification with Anomaly Descriptor Based on CLIP CLIP-Adapter: Better Vision-Language Models with Feature Adapters

Reference 16

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-11T22:07:34.593415Z digest=sha256:6b4617af55695a8dc8b4ac5fe2625616e62d5c1f8a0f8219184e760e3db454fc

Observation a70e232e-cef7-474f-846a-4a9fdffbfb76 · outbound

This paper cites Pni: Industrial anomaly detection using position and neighborhood information,.

CLIP-FSAC++: Few-Shot Anomaly Classification with Anomaly Descriptor Based on CLIP Pni: Industrial anomaly detection using position and neighborhood information,

Reference 17

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-11T22:07:34.597684Z digest=sha256:280a15af37e3da8539800af885b350ba290d6bcb73ee5325f05de606774a1ca6

Observation 9e9af9c5-029a-4fef-b309-d41cfc23cd5e · outbound

This paper cites Multiresolution knowledge distillation for anomaly detection,.

CLIP-FSAC++: Few-Shot Anomaly Classification with Anomaly Descriptor Based on CLIP Multiresolution knowledge distillation for anomaly detection,

Reference 18

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No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-11T22:07:34.601391Z digest=sha256:b59b580ab3fe621c0edd0a184bf67e459ef168d4995799833a2a94eabb34c5ce

Observation 46383726-a7c1-4698-ab6c-b715bbc85c25 · outbound

This paper cites Filo: Zero-shot anomaly detection by fine-grained description and high-quality localization,.

CLIP-FSAC++: Few-Shot Anomaly Classification with Anomaly Descriptor Based on CLIP Filo: Zero-shot anomaly detection by fine-grained description and high-quality localization,

Reference 19

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No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-11T22:07:34.605375Z digest=sha256:700332bc8a4eb367e3ad648b614710931212299dd0a9796114475e0698bddf90

Observation f6f4abec-8070-47d4-a4c3-875f1beeeb19 · outbound

This paper cites Ganomaly: Semi- supervised anomaly detection via adversarial training,.

CLIP-FSAC++: Few-Shot Anomaly Classification with Anomaly Descriptor Based on CLIP Ganomaly: Semi- supervised anomaly detection via adversarial training,

Reference 20

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raw_fallback, observed 2026-08-11T22:07:35.469934Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-11T22:07:34.609213Z digest=sha256:03bb0fee37ea6047079e6761dd991a28e4a2ce281eb3ac7802dfc1e6417b605f

Observation 5a79b133-dbc9-46db-acbe-4ad261c93a24 · outbound

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

CLIP-FSAC++: Few-Shot Anomaly Classification with Anomaly Descriptor Based on CLIP Focus the discrepancy: Intra- and inter-correlation learning for image anomaly detection,

Reference 21

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raw_fallback, observed 2026-08-11T22:07:35.457993Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-11T22:07:34.613011Z digest=sha256:019c0f747a566894ac40ce873c240e900593b75f42711510b0b6950f51ed096c

Observation 3a46d2de-76a5-42ef-aed8-5d8152914c5d · outbound

This paper cites Unsupervised anomaly detection for surface defects with dual-siamese network,.

CLIP-FSAC++: Few-Shot Anomaly Classification with Anomaly Descriptor Based on CLIP Unsupervised anomaly detection for surface defects with dual-siamese network,

Reference 22

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-11T22:07:34.616763Z digest=sha256:2ff4943658cd5ada036f3f95d7dc46c2cbf78cd2e2d19612c2e2b52e24cc5489

Observation 0fe6fbaa-e6f2-4bf0-8b51-a679019c3d41 · outbound

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

CLIP-FSAC++: Few-Shot Anomaly Classification with Anomaly Descriptor Based on CLIP Cutpaste: Self-supervised learning for anomaly detection and localization,

Reference 23

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raw_fallback, observed 2026-08-11T22:07:35.435730Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-11T22:07:34.620415Z digest=sha256:8187f7253f59178ac23467e523630046c473560b43cc1abafd3f0e478d1ecc85

Observation 8bc3a575-65f1-4eb7-8535-5ea4058027bb · outbound

This paper cites Generative adversarial nets,.

CLIP-FSAC++: Few-Shot Anomaly Classification with Anomaly Descriptor Based on CLIP Generative adversarial nets,

Reference 24

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raw_fallback, observed 2026-08-11T22:07:35.423933Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-11T22:07:34.624322Z digest=sha256:aa1763dcbaff42e4bcc02fe24eff3acced54792788784c3705de9189d46a7470

Observation 92ebefa7-92e2-4539-a4d1-97a0f66b7102 · outbound

This paper cites Diad: A diffusion-based frame- work for multi-class anomaly detection,.

CLIP-FSAC++: Few-Shot Anomaly Classification with Anomaly Descriptor Based on CLIP Diad: A diffusion-based frame- work for multi-class anomaly detection,

Reference 25

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raw_fallback, observed 2026-08-11T22:07:35.412217Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-11T22:07:34.628128Z digest=sha256:03d8806447961d1550c24f3d718dd421ec24fcf25ffab441733d36ab4d08ad68

Observation 6e816e44-4fa7-47b8-8168-51145311dacc · outbound

This paper cites DiffusionAD: Norm-guided One-step Denoising Diffusion for Anomaly Detection.

CLIP-FSAC++: Few-Shot Anomaly Classification with Anomaly Descriptor Based on CLIP DiffusionAD: Norm-guided One-step Denoising Diffusion for Anomaly Detection

Reference 26

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-11T22:07:34.631876Z digest=sha256:e43c3e2ca7b3f10c2e8930c5ec0601aed845974b15cc141e8b598d4a6ed9e7fb

Observation b994dc12-818e-4ba4-aaef-3faaf815c49b · outbound

This paper cites 12-in-1: Multi- task vision and language representation learning,.

CLIP-FSAC++: Few-Shot Anomaly Classification with Anomaly Descriptor Based on CLIP 12-in-1: Multi- task vision and language representation learning,

Reference 27

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raw_fallback, observed 2026-08-11T22:07:35.399627Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-11T22:07:34.636098Z digest=sha256:7189eeb9507622044f8d7d5da7c72d002c98243ea87bafc4cccdb17368cbe1c8

Observation acdbe881-0b25-4eb3-acd4-df21a5eb7484 · outbound

This paper cites Alpha-clip: A clip model focusing on wherever you want,.

CLIP-FSAC++: Few-Shot Anomaly Classification with Anomaly Descriptor Based on CLIP Alpha-clip: A clip model focusing on wherever you want,

Reference 28

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raw_fallback, observed 2026-08-11T22:07:35.388381Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-11T22:07:34.640815Z digest=sha256:81c5dff871c0b383b6d1c7aa67fc2cae4743c869856a58d0b11b0afb818fe5f7

Observation 74f76195-b50a-4001-8679-8de279a6c20c · outbound

This paper cites Interpreting CLIP’s image representation via text-based decomposition,.

CLIP-FSAC++: Few-Shot Anomaly Classification with Anomaly Descriptor Based on CLIP Interpreting CLIP’s image representation via text-based decomposition,

Reference 29

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raw_fallback, observed 2026-08-11T22:07:35.377481Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-11T22:07:34.644658Z digest=sha256:72e787df20fac7cb18b23e28c1c624b7ed88fa293ef127b6677343003e477399

Observation 38cc4454-6cc4-418e-841d-9ba302699cc8 · outbound

This paper cites Pointclip: Point cloud understanding by clip,.

CLIP-FSAC++: Few-Shot Anomaly Classification with Anomaly Descriptor Based on CLIP Pointclip: Point cloud understanding by clip,

Reference 30

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-11T22:07:34.648784Z digest=sha256:f3d40f76ad49ba204789a7493cb442f387883564e34bbf3794166b51ef53969f

Observation 4d812f91-e19f-4064-bcf2-a188526606f9 · outbound

This paper cites Actionclip: Adapting language-image pretrained models for video action recognition,.

CLIP-FSAC++: Few-Shot Anomaly Classification with Anomaly Descriptor Based on CLIP Actionclip: Adapting language-image pretrained models for video action recognition,

Reference 31

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raw_fallback, observed 2026-08-11T22:07:35.353612Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-11T22:07:34.652528Z digest=sha256:e2b2f2be3d10216814ffabe86505338b36bfa317e93b239eaaaad094be3fb83f

Observation a982f189-5240-4187-80db-194971adc53b · outbound

This paper cites Grounded language-image pre-training,.

CLIP-FSAC++: Few-Shot Anomaly Classification with Anomaly Descriptor Based on CLIP Grounded language-image pre-training,

Reference 32

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-11T22:07:34.656230Z digest=sha256:228f332328d140e66f66640119bd000aa6ba838031aeb0f697f5d6f23ffe2429

Observation 99ee8c33-36a3-4614-8072-ec2ce201b6c9 · outbound

This paper cites Denseclip: Language-guided dense prediction with context-aware prompting,.

CLIP-FSAC++: Few-Shot Anomaly Classification with Anomaly Descriptor Based on CLIP Denseclip: Language-guided dense prediction with context-aware prompting,

Reference 33

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raw_fallback, observed 2026-08-11T22:07:35.331182Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-11T22:07:34.660223Z digest=sha256:070463f994a8c2e8e822ffa3504d11e86a627ca0fb9b659f0383e230d7cf921c

Observation 2bfe86db-c017-48c3-bce0-9f86b56b479b · outbound

This paper cites In defense of clip-based video relation detection,.

CLIP-FSAC++: Few-Shot Anomaly Classification with Anomaly Descriptor Based on CLIP In defense of clip-based video relation detection,

Reference 34

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T22:07:35.319725Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-11T22:07:34.664214Z digest=sha256:88d5b4232bd75fea1ec64cf1a616fbd21061ff680a0d723229ed6a4ad8218bbe

Observation c2953a91-b3ee-4bee-bf69-3eb959481e08 · outbound

This paper cites Clip-driven fine-grained text- image person re-identification,.

CLIP-FSAC++: Few-Shot Anomaly Classification with Anomaly Descriptor Based on CLIP Clip-driven fine-grained text- image person re-identification,

Reference 35

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T22:07:35.307397Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-11T22:07:34.668310Z digest=sha256:535155b7fb039ffe96e30311f5efe9a9b281c0b1ec626922eb5b595473fee0fe

Observation 4d9405a2-2f49-4532-b549-15ec6196459f · outbound

This paper cites Turning a clip model into a scene text spotter,.

CLIP-FSAC++: Few-Shot Anomaly Classification with Anomaly Descriptor Based on CLIP Turning a clip model into a scene text spotter,

Reference 36

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T22:07:35.295665Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-11T22:07:34.672552Z digest=sha256:73933a5e32e635971b3aeacc37c7c5ff0fac152005ea7c7dff70135f0a6c3b18

Observation 9dd3bdb0-96be-4ca3-b451-afd008e88137 · outbound

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

CLIP-FSAC++: Few-Shot Anomaly Classification with Anomaly Descriptor Based on CLIP Winclip: Zero-/few-shot anomaly classification and segmentation,

Reference 37

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T22:07:35.284114Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-11T22:07:34.676334Z digest=sha256:5b53eebe46b58e040c9c3497ea2b5b8d64374b0cc36b26a0e1ad45dab0aa376f

Observation 37ebd55a-01a9-44da-bb66-de7df66eb3dc · outbound

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

CLIP-FSAC++: Few-Shot Anomaly Classification with Anomaly Descriptor Based on CLIP Fastrecon: Few- shot industrial anomaly detection via fast feature reconstruction,

Reference 38

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T22:07:35.272031Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-11T22:07:34.680314Z digest=sha256:23bcc8bd2418cfe27c56f227557065542b6dfa0bf4ce162b11f2f174b754fcfe

Observation 2f857b9b-8596-4935-be2e-d47c7598b4b3 · outbound

This paper cites Few- shot fast-adaptive anomaly detection,.

CLIP-FSAC++: Few-Shot Anomaly Classification with Anomaly Descriptor Based on CLIP Few- shot fast-adaptive anomaly detection,

Reference 39

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T22:07:35.260631Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-11T22:07:34.684089Z digest=sha256:278404fedf862af11abf97f41818f5c1ba1f346acd654e41dad5cba4e7bb477a

Observation 345a0080-3720-4615-899d-69b53c1737a4 · outbound

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

CLIP-FSAC++: Few-Shot Anomaly Classification with Anomaly Descriptor Based on CLIP Toward generalist anomaly detection via in-context residual learning with few-shot sample prompts,

Reference 40

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T22:07:35.249073Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-11T22:07:34.688029Z digest=sha256:e5155f8cfb7a58fda77830198494aa8edcc9eff2ab4bafdffc372f0b20d18cb5

Observation 1ddbb263-9dac-4297-bc00-558bc98f47e3 · outbound

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

CLIP-FSAC++: Few-Shot Anomaly Classification with Anomaly Descriptor Based on CLIP Fewsome: One- class few shot anomaly detection with siamese networks,

Reference 41

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T22:07:35.237231Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-11T22:07:34.692053Z digest=sha256:1aa2988c6d8de79ef09f12332863789198d90d79b84bfbb93ba3ef95fc2476e2

Observation 6d287d6b-fc05-43fd-8f4b-f17ef56704f0 · outbound

This paper cites Registration based few-shot anomaly detection,.

CLIP-FSAC++: Few-Shot Anomaly Classification with Anomaly Descriptor Based on CLIP Registration based few-shot anomaly detection,

Reference 42

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T22:07:35.224689Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-11T22:07:34.695818Z digest=sha256:1595f67c85eea6afe2746923f3b278f64e21333a909f2fb69b8122d968e3c313

Observation 3851f3e4-ec8f-4f13-b466-3fd16a273282 · outbound

This paper cites ClipSAM: CLIP and SAM Collaboration for Zero-Shot Anomaly Segmentation.

CLIP-FSAC++: Few-Shot Anomaly Classification with Anomaly Descriptor Based on CLIP ClipSAM: CLIP and SAM Collaboration for Zero-Shot Anomaly Segmentation

Reference 43

Resolution
unresolved
no resolver link, observed 2026-08-11T22:07:34.699824Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-11T22:07:34.699824Z digest=sha256:f3382bcbe37047d4f7e10fba12c1db0394194c6fc987a53cb2427547ecab0968

Observation 628c1d1c-9b8d-43cc-bc1c-563439a634e8 · outbound

This paper cites CLIP-AD: A Language-Guided Staged Dual-Path Model for Zero-shot Anomaly Detection.

CLIP-FSAC++: Few-Shot Anomaly Classification with Anomaly Descriptor Based on CLIP CLIP-AD: A Language-Guided Staged Dual-Path Model for Zero-shot Anomaly Detection

Reference 44

Resolution
unresolved
no resolver link, observed 2026-08-11T22:07:34.704463Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-11T22:07:34.704463Z digest=sha256:846fd8b2c3025b491d78d31396b6a899dc379c24c3b71eef1bd8ca314ad2478f

Observation 53a00488-3356-4050-99ce-9c97a9874308 · outbound

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

CLIP-FSAC++: Few-Shot Anomaly Classification with Anomaly Descriptor Based on CLIP Anomalygpt: Detecting industrial anomalies using large vision- language models,

Reference 45

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T22:07:35.211496Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-11T22:07:34.708922Z digest=sha256:aea7161c52d0014c40a586aa41205f3f3838638e11a34e3121756540134d170c

Observation 7e4047c7-3004-47f6-895f-4d7985c83eaf · outbound

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

CLIP-FSAC++: Few-Shot Anomaly Classification with Anomaly Descriptor Based on CLIP Promptad: Learning prompts with only normal samples for few-shot anomaly detection,

Reference 46

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T22:07:35.199610Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-11T22:07:34.712816Z digest=sha256:1711fd3704d67e96909de01719f00c98080270f55b9b9573374091edbf43a81a

Observation 35db4451-b7c7-4cd7-8ba9-918e12b215ee · outbound

This paper cites Collaborative discrepancy optimization for reliable image anomaly localization,.

CLIP-FSAC++: Few-Shot Anomaly Classification with Anomaly Descriptor Based on CLIP Collaborative discrepancy optimization for reliable image anomaly localization,

Reference 47

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T22:07:35.187382Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-11T22:07:34.716560Z digest=sha256:06a72ea8b36b3c31ea4dad10b08873ea071eed66439825b23fb915c186b07dff

Observation 04bc81c6-69de-48c6-a018-08250301891e · outbound

This paper cites Natural synthetic anomalies for self-supervised anomaly detection and localization,.

CLIP-FSAC++: Few-Shot Anomaly Classification with Anomaly Descriptor Based on CLIP Natural synthetic anomalies for self-supervised anomaly detection and localization,

Reference 48

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T22:07:35.174973Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-11T22:07:34.720571Z digest=sha256:cba8130853bbdcbc01873236ca498f95984b824a9d73802978ddd673e22963d6

Observation 9d77c78b-5d9b-49b6-a85b-ab40b6a58f9f · outbound

This paper cites Momentum contrast for unsupervised visual representation learning,.

CLIP-FSAC++: Few-Shot Anomaly Classification with Anomaly Descriptor Based on CLIP Momentum contrast for unsupervised visual representation learning,

Reference 49

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T22:07:35.163767Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-11T22:07:34.724840Z digest=sha256:810b44b0bb172143da6e262c7d93584a9590fa97adf6692c62a21fe2c4b61943

Observation f5212c28-9415-4a00-aedd-75130fd0bafa · outbound

This paper cites Padim: A patch distribution modeling framework for anomaly detection and localiza- tion,.

CLIP-FSAC++: Few-Shot Anomaly Classification with Anomaly Descriptor Based on CLIP Padim: A patch distribution modeling framework for anomaly detection and localiza- tion,

Reference 50

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T22:07:35.151276Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-11T22:07:34.729008Z digest=sha256:6441cedfe223fd709f501683e1eeafe5a8e79d02690f0bc8beb27526d8136aa0

Observation f6d6917d-b513-4c8f-930e-847ae11cfc6e · outbound

This paper cites Towards total recall in industrial anomaly detection,.

CLIP-FSAC++: Few-Shot Anomaly Classification with Anomaly Descriptor Based on CLIP Towards total recall in industrial anomaly detection,

Reference 51

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T22:07:35.138185Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-11T22:07:34.733080Z digest=sha256:47c8af50d2e5f57c52b413c1a00c836274194b40e0782de8121a7909cddb8e91

Observation 8854f8e0-31ee-4d97-82c1-143a247eca6a · outbound

This paper cites Sub-image anomaly detection with deep pyramid correspondences,.

CLIP-FSAC++: Few-Shot Anomaly Classification with Anomaly Descriptor Based on CLIP Sub-image anomaly detection with deep pyramid correspondences,

Reference 52

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T22:07:35.124965Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-11T22:07:34.737501Z digest=sha256:d28883a60a71871401d167e1f5dbe52012d74c7099170d7430abafadda0191db

Observation 5fe9b4e6-0641-45af-bd51-b750502085eb · outbound

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

CLIP-FSAC++: Few-Shot Anomaly Classification with Anomaly Descriptor Based on CLIP Mvtec ad — a comprehensive real-world dataset for unsupervised anomaly detection,

Reference 53

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T22:07:35.112869Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-11T22:07:34.741409Z digest=sha256:0796e587788bc3f4e2947aa1bc8e0770184c39f249574177042d075074f87b45

Observation 7bf89a99-66a5-4345-b31e-af401be60de1 · outbound

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

CLIP-FSAC++: Few-Shot Anomaly Classification with Anomaly Descriptor Based on CLIP Spot-the- difference self-supervised pre-training for anomaly detection and seg- mentation,

Reference 54

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T22:07:35.101426Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-11T22:07:34.745321Z digest=sha256:e66bebf32f1b6109727898c032c94ab4b94d1ce4324e6a889b9e82bf73a5dd38

Observation 346f7ee2-22d9-4be5-80e2-9662d2c8b5d6 · outbound

This paper cites an unresolved cited work.

CLIP-FSAC++: Few-Shot Anomaly Classification with Anomaly Descriptor Based on CLIP Unresolved cited work

Reference 55

Resolution
unresolved
raw_fallback, observed 2026-08-11T22:07:35.089859Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-11T22:07:34.749418Z digest=sha256:32cf547097f5e382f11b922a4d7fe3ed8abda49fa3af10ab178d94e98ff770cb

Observation c0ef731f-eac5-4c18-8847-15542adb8476 · outbound

This paper cites Anople: Few-shot anomaly detection via bi-directional prompt learning with only normal samples,.

CLIP-FSAC++: Few-Shot Anomaly Classification with Anomaly Descriptor Based on CLIP Anople: Few-shot anomaly detection via bi-directional prompt learning with only normal samples,

Reference 56

Resolution
verified exact
raw_fallback, observed 2026-08-11T22:07:34.961472Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-11T22:07:34.753578Z digest=sha256:e682062f1edf3d5bc6e92d2b9c41d5ce40a8c7c4df28619c18d0c4efb2986889

Observation 314a5873-4dd6-41a4-bae0-045e63fd27de · outbound

This paper cites Openclip,.

CLIP-FSAC++: Few-Shot Anomaly Classification with Anomaly Descriptor Based on CLIP Openclip,

Reference 57

Resolution
unresolved
no resolver link, observed 2026-08-11T22:07:34.757985Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-11T22:07:34.757985Z digest=sha256:8d1fd7675b809f3a2b058bc7ef960fddb3f5f689bf50eaf2fe3ee8be70e53651

Observation dd8cd18e-f060-465a-aae4-68666a561bee · outbound

This paper cites Laion-400m: Open dataset of clip-filtered 400 million image-text pairs,.

CLIP-FSAC++: Few-Shot Anomaly Classification with Anomaly Descriptor Based on CLIP Laion-400m: Open dataset of clip-filtered 400 million image-text pairs,

Reference 58

Resolution
unresolved
no resolver link, observed 2026-08-11T22:07:34.762762Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-11T22:07:34.762762Z digest=sha256:6410ea03d5821a18043aa5ba7a16b973a6ce5566b7f6c1450d2cb74340d86ba3

Observation 158b76b9-7691-4e50-9f80-d903fe84954f · outbound

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

CLIP-FSAC++: Few-Shot Anomaly Classification with Anomaly Descriptor Based on CLIP Efficientad: Accurate visual anomaly detection at millisecond-level latencies,

Reference 59

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T22:07:35.071068Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-11T22:07:34.767013Z digest=sha256:162282297b5dbc46faae318489c1ce22d7fa12b6df95ec804524ad3763c72a96

Observation aeff1e8b-6a2f-43fd-9e55-fb401c941f88 · outbound

This paper cites FAIR: Frequency-aware Image Restoration for Industrial Visual Anomaly Detection.

CLIP-FSAC++: Few-Shot Anomaly Classification with Anomaly Descriptor Based on CLIP FAIR: Frequency-aware Image Restoration for Industrial Visual Anomaly Detection

Reference 60

Resolution
unresolved
no resolver link, observed 2026-08-11T22:07:34.771217Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-11T22:07:34.771217Z digest=sha256:158248b8bd6fd052e886de772639a03425d706d4de306db5d5c4ec65cc45c0df

Observation 482518ba-093f-433c-a06e-9b35837ead56 · outbound

This paper cites Reconstruction from edge image combined with color and gradient difference for industrial surface anomaly detection.

CLIP-FSAC++: Few-Shot Anomaly Classification with Anomaly Descriptor Based on CLIP Reconstruction from edge image combined with color and gradient difference for industrial surface anomaly detection

Reference 61

Resolution
verified exact
local_arxiv, observed 2026-08-11T22:07:34.837882Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-11T22:07:34.775962Z digest=sha256:0155c5ec44bd48ab64624339cb3b6f197f878fef67d652c23c888eefb833db4f

Observation 87a9afcc-bdc5-4913-b998-a8dae7e757a3 · outbound

This paper cites A unified anomaly synthesis strategy with gradient ascent for industrial anomaly detection and localization,.

CLIP-FSAC++: Few-Shot Anomaly Classification with Anomaly Descriptor Based on CLIP A unified anomaly synthesis strategy with gradient ascent for industrial anomaly detection and localization,

Reference 62

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T22:07:35.059058Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-11T22:07:34.780397Z digest=sha256:ecfc808bf18dc5ab6ea3abcd5dcf3ba1a6eb93aebf1f00637ac8e88fa0ed2df7

Observation 3d412d58-d923-44eb-9bb8-4d3af00aa95c · outbound

This paper cites Visualizing data using t-sne,.

CLIP-FSAC++: Few-Shot Anomaly Classification with Anomaly Descriptor Based on CLIP Visualizing data using t-sne,

Reference 63

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T22:07:35.046764Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-11T22:07:34.784132Z digest=sha256:6b85bd93b0400b401b92ebbcaaccf685858ecdac8201aa73493ecace755bfc5a

Observation 020a4a85-e657-4ffa-9ea9-9d448d9e864d · outbound

This paper cites Learning to prompt for vision- language models,.

CLIP-FSAC++: Few-Shot Anomaly Classification with Anomaly Descriptor Based on CLIP Learning to prompt for vision- language models,

Reference 64

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T22:07:35.034993Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-11T22:07:34.787934Z digest=sha256:6ee30cac6ccfc11efff81a7233f91d0f12d449282b849db8d3d0b2bbf691b509

Pith citing papers

No inbound Pith citation observations are available.