Paper Citation Record · LEDGER

Generative Model-Based Feature Attention Module for Video Action Analysis

As of 7 August 2026, this Paper Citation Record lists 43 of 43 outbound references and 0 inbound Pith citation observations for arXiv:2508.13565.

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

pith.paper-citation-record.v1
2508.13565 v1

Coverage vector

measured 43 of 43 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-05T19:03:32.569204Z

measured 43 of 43 standing notices

One-hop event checks from named stored sources.

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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

43 of 43 outbound references displayed

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verified fuzzy36
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation e0f8bb7d-554d-430b-a0c6-50d4b3f89459 · outbound

This paper cites Medical image analysis using convolu- tional neural networks: a review.Journal of medical systems, 42:1–13, 2018.

Generative Model-Based Feature Attention Module for Video Action Analysis Medical image analysis using convolu- tional neural networks: a review.Journal of medical systems, 42:1–13, 2018

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T19:03:41.314767Z

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Observation 031ae5cd-7576-43a8-b1ac-fae96d9f6f93 · outbound

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

Generative Model-Based Feature Attention Module for Video Action Analysis Fewsome: One-class few shot anomaly detection with siamese networks

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T19:03:41.123661Z

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Observation ede0c70d-b8ba-4e84-8b2f-431cd777f845 · outbound

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

Generative Model-Based Feature Attention Module for Video Action Analysis Mvtec ad–a comprehensive real-world dataset for unsupervised anomaly detection

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T19:03:40.922487Z

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Observation 057b082f-5c8f-4dfc-af06-0497ce761f34 · outbound

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

Generative Model-Based Feature Attention Module for Video Action Analysis The MVTec 3D-AD Dataset for Unsupervised 3D Anomaly Detection and Localization

Reference 4

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Source-reported events for the cited work

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Observation 3717cded-97af-41c6-a6f3-f60ac079dd3f · outbound

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

Generative Model-Based Feature Attention Module for Video Action Analysis Adaclip: Adapting clip with hybrid learnable prompts for zero-shot anomaly de- tection

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T19:03:40.725580Z

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

Observation 4080185f-9600-4619-ae26-515e2758bccb · outbound

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

Generative Model-Based Feature Attention Module for Video Action Analysis Center-aware residual anomaly synthesis for multiclass industrial anomaly detec- tion

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T19:03:40.545898Z

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

Observation 943bdf82-48d2-45c2-b7cb-52496241ed87 · outbound

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

Generative Model-Based Feature Attention Module for Video Action Analysis APRIL-GAN: A Zero-/Few-Shot Anomaly Classification and Segmentation Method for CVPR 2023 VAND Workshop Challenge Tracks 1&2: 1st Place on Zero-shot AD and 4th Place on Few-shot AD

Reference 7

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Observation 5fe230a9-48b9-4fe0-b46c-082b2c6cebd5 · outbound

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

Generative Model-Based Feature Attention Module for Video Action Analysis Sub-Image Anomaly Detection with Deep Pyramid Correspondences

Reference 8

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Source-reported events for the cited work

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Observation d2c3fd0d-28b9-4d09-b66a-a86c1348e165 · outbound

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

Generative Model-Based Feature Attention Module for Video Action Analysis Padim: a patch distribution modeling framework for anomaly detection and localization

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T19:03:40.399595Z

Source-reported events for the cited work

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Observation d4844ca3-a02c-466e-bbee-41c96940c54d · outbound

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

Generative Model-Based Feature Attention Module for Video Action Analysis The pascal visual object classes (voc) challenge

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T19:03:40.274459Z

Source-reported events for the cited work

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

Observation 83e9d306-87b2-404f-9904-3ed6d0096953 · outbound

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

Generative Model-Based Feature Attention Module for Video Action Analysis Fastrecon: Few-shot indus- trial anomaly detection via fast feature reconstruction

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T19:03:40.120865Z

Source-reported events for the cited work

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Observation 2727b713-9f84-4195-97bb-3c2ee9b34437 · outbound

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

Generative Model-Based Feature Attention Module for Video Action Analysis Metauas: Universal anomaly segmentation with one-prompt meta-learning

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T19:03:39.888905Z

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Observation fd884883-942d-4414-9823-e241f721dc8e · outbound

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

Generative Model-Based Feature Attention Module for Video Action Analysis Anomalygpt: Detecting in- dustrial anomalies using large vision-language models

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T19:03:39.735481Z

Source-reported events for the cited work

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Observation 245db5e2-9988-4f20-85e4-3e3c2cb34b26 · outbound

This paper cites Automated seg- mentation of macular edema in oct using deep neural net- works.

Generative Model-Based Feature Attention Module for Video Action Analysis Automated seg- mentation of macular edema in oct using deep neural net- works

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T19:03:39.557765Z

Source-reported events for the cited work

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Observation 962966cf-f0dc-4e99-9cc3-b5e040d81796 · outbound

This paper cites Registration based few-shot anomaly detection.

Generative Model-Based Feature Attention Module for Video Action Analysis Registration based few-shot anomaly detection

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T19:03:39.437794Z

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Observation 4abc3ca6-c5e6-47be-81f9-25e8d422e5d9 · outbound

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

Generative Model-Based Feature Attention Module for Video Action Analysis Winclip: Zero- /few-shot anomaly classification and segmentation

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T19:03:39.275451Z

Source-reported events for the cited work

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Observation 7d8b2ee9-a4a7-4dfc-8b21-a779d2fa53a7 · outbound

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

Generative Model-Based Feature Attention Module for Video Action Analysis Deep learning-based defect detection of metal parts: evaluating current methods in complex condi- tions

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T19:03:39.158693Z

Source-reported events for the cited work

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Observation df4334b8-bc54-4898-be11-c88db17ea438 · outbound

This paper cites Adam: A Method for Stochastic Optimization.

Generative Model-Based Feature Attention Module for Video Action Analysis Adam: A Method for Stochastic Optimization

Reference 18

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Observation 929b69e6-9deb-4ee5-a864-47c907e12bae · outbound

This paper cites Deep learning.

Generative Model-Based Feature Attention Module for Video Action Analysis Deep learning

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T19:03:39.013938Z

Source-reported events for the cited work

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Observation 0325a91e-c89e-4783-8ac9-2645c9ff2e7f · outbound

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

Generative Model-Based Feature Attention Module for Video Action Analysis Promptad: Learn- ing prompts with only normal samples for few-shot anomaly detection

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T19:03:38.895794Z

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

Observation 7511df04-42a8-46ba-b4f3-55365d741126 · outbound

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

Generative Model-Based Feature Attention Module for Video Action Analysis COFT-AD: COntrastive Fine-Tuning for Few-Shot Anomaly Detection

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-05T19:03:29.476048Z

Source-reported events for the cited work

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Observation 6acd9b36-c4d0-4c05-8aba-63d69025634d · outbound

This paper cites Medical image classification using gen- eralized zero shot learning.

Generative Model-Based Feature Attention Module for Video Action Analysis Medical image classification using gen- eralized zero shot learning

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T19:03:38.623832Z

Source-reported events for the cited work

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Observation ec906711-1e9a-48d8-9085-5e8a34c284d9 · outbound

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

Generative Model-Based Feature Attention Module for Video Action Analysis From softmax to sparsemax: A sparse model of attention and multi-label clas- sification

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T19:03:38.336048Z

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Observation fa115870-d329-4afe-a6fc-2e943ec12085 · outbound

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

Generative Model-Based Feature Attention Module for Video Action Analysis The multimodal brain tumor image segmentation benchmark (brats)

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T19:03:38.031339Z

Source-reported events for the cited work

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Observation 2ca012ff-6db7-4280-beea-2cc316a6e821 · outbound

This paper cites Vt-adl: A vision trans- former network for image anomaly detection and localiza- tion.

Generative Model-Based Feature Attention Module for Video Action Analysis Vt-adl: A vision trans- former network for image anomaly detection and localiza- tion

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T19:03:37.774093Z

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Observation 53825b62-693a-4356-9bc9-2f3186f142c4 · outbound

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

Generative Model-Based Feature Attention Module for Video Action Analysis Lscad: A large-small model collaboration framework for un- supervised industrial anomaly detection

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T19:03:37.412043Z

Source-reported events for the cited work

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Observation 3a0bca10-72f7-499f-9ba2-58ccfaf370aa · outbound

This paper cites Investigating shift equivalence of convolutional neural net- works in industrial defect segmentation.

Generative Model-Based Feature Attention Module for Video Action Analysis Investigating shift equivalence of convolutional neural net- works in industrial defect segmentation

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T19:03:37.112863Z

Source-reported events for the cited work

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Observation 0cd708c6-5a43-4f21-b2bb-76740aefff28 · outbound

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

Generative Model-Based Feature Attention Module for Video Action Analysis Vcp-clip: A visual context prompting model for zero-shot anomaly segmenta- tion

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T19:03:36.873665Z

Source-reported events for the cited work

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

Observation 60ba8a89-100f-495b-a959-ac9d00749f68 · outbound

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

Generative Model-Based Feature Attention Module for Video Action Analysis Bayesian prompt flow learning for zero-shot anomaly detec- tion

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T19:03:36.485417Z

Source-reported events for the cited work

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

Observation a4c10f83-4ef1-480b-b635-5841b33d66cc · outbound

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

Generative Model-Based Feature Attention Module for Video Action Analysis Learning transferable visual models from natural language supervi- sion

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T19:03:36.293641Z

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Observation 85a9e10f-6c50-447e-9eff-294dc1660e1d · outbound

This paper cites Towards to- tal recall in industrial anomaly detection.

Generative Model-Based Feature Attention Module for Video Action Analysis Towards to- tal recall in industrial anomaly detection

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T19:03:36.014416Z

Source-reported events for the cited work

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

Observation 47af037b-b642-480c-8620-8ec05d03dbf9 · outbound

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

Generative Model-Based Feature Attention Module for Video Action Analysis Same same but differnet: Semi-supervised defect detection with normalizing flows

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T19:03:35.725971Z

Source-reported events for the cited work

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

Observation 633d38c0-41e5-4dc8-b156-4a563ecf84fb · outbound

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

Generative Model-Based Feature Attention Module for Video Action Analysis Maeday: Mae for few-and zero-shot anomaly-detection

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T19:03:35.423042Z

Source-reported events for the cited work

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Observation 456c7152-656f-425d-8c37-a16b13bcf8db · outbound

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

Generative Model-Based Feature Attention Module for Video Action Analysis A hierarchical transformation-discriminating generative model for few shot anomaly detection

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T19:03:35.214727Z

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

Observation 3a57e2e8-e4be-426a-9577-62f5e5f37ff6 · outbound

This paper cites Learning unsupervised metaformer for anomaly de- tection.

Generative Model-Based Feature Attention Module for Video Action Analysis Learning unsupervised metaformer for anomaly de- tection

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T19:03:34.963910Z

Source-reported events for the cited work

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Observation 967a6f61-fe3a-4fe2-bc35-3866498e31bc · outbound

This paper cites Learning unsupervised metaformer for anomaly detection.

Generative Model-Based Feature Attention Module for Video Action Analysis Learning unsupervised metaformer for anomaly detection

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T19:03:34.702194Z

Source-reported events for the cited work

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Observation 81010138-53d2-4c2c-85f3-65005d3c6847 · outbound

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

Generative Model-Based Feature Attention Module for Video Action Analysis Pushing the Limits of Fewshot Anomaly Detection in Industry Vision: Graphcore

Reference 37

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Source-reported events for the cited work

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Observation 93f2d09e-4200-4b65-b3a4-c750a3623556 · outbound

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

Generative Model-Based Feature Attention Module for Video Action Analysis Resad: A simple framework for class generalizable anomaly detection

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T19:03:34.291044Z

Source-reported events for the cited work

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Observation f4642dc1-9f41-4948-aa4b-3f6e125d006d · outbound

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

Generative Model-Based Feature Attention Module for Video Action Analysis Draem- a discriminatively trained reconstruction embedding for sur- face anomaly detection

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T19:03:34.059231Z

Source-reported events for the cited work

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

Observation fbbd9073-09b7-4f83-b33f-cc8883289d8d · outbound

This paper cites Scene parsing through ade20k dataset.

Generative Model-Based Feature Attention Module for Video Action Analysis Scene parsing through ade20k dataset

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T19:03:33.759119Z

Source-reported events for the cited work

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Observation da26d2f6-889d-4b44-a1ab-ab442c9fc988 · outbound

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

Generative Model-Based Feature Attention Module for Video Action Analysis AnomalyCLIP: Object-agnostic prompt learning for zero-shot anomaly detection

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T19:03:33.451792Z

Source-reported events for the cited work

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

Observation 4d0cfb48-c79f-4383-a624-b77d0ec80030 · outbound

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

Generative Model-Based Feature Attention Module for Video Action Analysis Toward generalist anomaly detection via in-context residual learning with few-shot sam- ple prompts

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T19:03:33.242705Z

Source-reported events for the cited work

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

Observation 7e98b6e3-8920-40bf-a074-09732335899d · outbound

This paper cites raw_img_path.

Generative Model-Based Feature Attention Module for Video Action Analysis raw_img_path

Reference 43

Resolution: malformed identifier; raw_fallback, observed 2026-08-05T19:03:32.950335Z

Source-reported events for the cited work

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

Pith citing papers

No inbound Pith citation observations are available.