Track Any Anomalous Object: A Granular Video Anomaly Detection Pipeline | Paper Citation Record

Observation b559cb40-bff1-4d95-b134-9d1842c82f98 · outbound

This paper cites VideoPatchCore: An Effective Method to Memorize Normality for Video Anomaly Detection.

Track Any Anomalous Object: A Granular Video Anomaly Detection Pipeline VideoPatchCore: An Effective Method to Memorize Normality for Video Anomaly Detection

Reference 1

Resolution: verified exact; local_arxiv, observed 2026-08-07T10:29:10.369717Z

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 77ecf19a-ecdd-4526-8738-508f87b2de9f · outbound

This paper cites Integrating View Conditions for Image Synthesis.

Track Any Anomalous Object: A Granular Video Anomaly Detection Pipeline Integrating View Conditions for Image Synthesis

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-07T10:29:03.881937Z

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Observation 3a048f84-747f-4159-85fa-b26e53ff10fd · outbound

This paper cites HumanEdit: A High-Quality Human-Rewarded Dataset for Instruction-based Image Editing.

Track Any Anomalous Object: A Granular Video Anomaly Detection Pipeline HumanEdit: A High-Quality Human-Rewarded Dataset for Instruction-based Image Editing

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-07T10:29:04.034900Z

Source-reported events for the cited work

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Observation 891edaf8-2d6e-4f54-8758-85fa1179e2a6 · outbound

This paper cites Meissonic: Revitalizing Masked Generative Transformers for Efficient High-Resolution Text-to-Image Synthesis.

Track Any Anomalous Object: A Granular Video Anomaly Detection Pipeline Meissonic: Revitalizing Masked Generative Transformers for Efficient High-Resolution Text-to-Image Synthesis

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-07T10:29:04.155666Z

Source-reported events for the cited work

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Observation 1c767bec-9fbe-470f-b511-2bb1dec661e2 · outbound

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

Track Any Anomalous Object: A Granular Video Anomaly Detection Pipeline Adaclip: Adapting clip with hybrid learnable prompts for zero-shot anomaly de- tection

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:29:44.035584Z

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 4de5d131-4b35-46aa-b2b3-379deb93a3e5 · outbound

This paper cites Unsupervised anomaly segmentation for brain lesions using dual semantic-manifold reconstruc- tion.

Track Any Anomalous Object: A Granular Video Anomaly Detection Pipeline Unsupervised anomaly segmentation for brain lesions using dual semantic-manifold reconstruc- tion

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:29:44.025918Z

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 1ccd65d6-d087-40a5-8f64-de6fc2212b72 · outbound

This paper cites Any-shot sequential anomaly detection in surveillance videos.

Track Any Anomalous Object: A Granular Video Anomaly Detection Pipeline Any-shot sequential anomaly detection in surveillance videos

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:29:44.015775Z

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 98f007e9-6f1d-4b7d-a2f2-a783740dfb17 · outbound

This paper cites Continual learning for anomaly detection in surveillance videos.

Track Any Anomalous Object: A Granular Video Anomaly Detection Pipeline Continual learning for anomaly detection in surveillance videos

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:29:44.005930Z

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 ceaaef94-20c2-479c-9f3b-76dcb612f5b4 · outbound

This paper cites Instantsplat: Sparse-view gaussian splatting in sec- onds, 2024.

Track Any Anomalous Object: A Granular Video Anomaly Detection Pipeline Instantsplat: Sparse-view gaussian splatting in sec- onds, 2024

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:29:43.995881Z

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 f02137cd-f9bb-47b5-9398-91c18e2d005b · outbound

This paper cites an unresolved cited work.

Track Any Anomalous Object: A Granular Video Anomaly Detection Pipeline Unresolved cited work

Reference 10

Resolution: unresolved; raw_fallback, observed 2026-08-07T10:29:43.985476Z

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 534d46c6-c898-47cf-bfa9-bb13c99da18e · outbound

This paper cites Anomaly detection in video via self- supervised and multi-task learning.

Track Any Anomalous Object: A Granular Video Anomaly Detection Pipeline Anomaly detection in video via self- supervised and multi-task learning

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:29:43.975313Z

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 15043935-5885-452d-a5e0-8604129c70fd · outbound

This paper cites Roy-Chowdhury, and Larry S.

Track Any Anomalous Object: A Granular Video Anomaly Detection Pipeline Roy-Chowdhury, and Larry S

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:29:43.965071Z

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 2fc3bdcb-3935-4084-b657-abafd5c8b32a · outbound

This paper cites Degradation-resistant unfolding network for heterogeneous image fusion.

Track Any Anomalous Object: A Granular Video Anomaly Detection Pipeline Degradation-resistant unfolding network for heterogeneous image fusion

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:29:43.952960Z

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 53aa1e3f-2c19-4d72-ba6f-577ab5a10004 · outbound

This paper cites Weakly- supervised concealed object segmentation with sam-based pseudo labeling and multi-scale feature grouping.NeurIPS, 36, 2024.

Track Any Anomalous Object: A Granular Video Anomaly Detection Pipeline Weakly- supervised concealed object segmentation with sam-based pseudo labeling and multi-scale feature grouping.NeurIPS, 36, 2024

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:29:43.941366Z

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 8dbed0cb-c58f-470f-b27a-0ba1e59b0d1d · outbound

This paper cites Strategic preys make acute predators: Enhancing camouflaged object detectors by generating camouflaged objects.ICLR, 2024.

Track Any Anomalous Object: A Granular Video Anomaly Detection Pipeline Strategic preys make acute predators: Enhancing camouflaged object detectors by generating camouflaged objects.ICLR, 2024

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:29:43.929341Z

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 78dc6d03-e576-49e4-9a4a-796f5a1d6f12 · outbound

This paper cites Reti-diff: Illumination degradation image restoration with retinex-based latent diffusion model.ICLR, 2025.

Track Any Anomalous Object: A Granular Video Anomaly Detection Pipeline Reti-diff: Illumination degradation image restoration with retinex-based latent diffusion model.ICLR, 2025

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:29:43.916417Z

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 ba3a45e9-ef63-4aff-80fd-d2b25a36172c · outbound

This paper cites Diffusion models in low-level vision: A survey.TPAMI, 2025.

Track Any Anomalous Object: A Granular Video Anomaly Detection Pipeline Diffusion models in low-level vision: A survey.TPAMI, 2025

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:29:43.904222Z

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 4ed58d5b-648d-4316-9d96-74bd34438225 · outbound

This paper cites RUN: Reversible Unfolding Network for Concealed Object Segmentation.

Track Any Anomalous Object: A Granular Video Anomaly Detection Pipeline RUN: Reversible Unfolding Network for Concealed Object Segmentation

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-07T10:29:05.615079Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation bd7f53e4-a479-4b46-b8af-c9fc76a71c78 · outbound

This paper cites Joint detection and recounting of abnormal events by learning deep generic knowledge.

Track Any Anomalous Object: A Granular Video Anomaly Detection Pipeline Joint detection and recounting of abnormal events by learning deep generic knowledge

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:29:43.893204Z

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 a998674b-6ff8-4dc4-9fb5-ac39d2dd19f7 · outbound

This paper cites Object-centric auto-encoders and dummy anomalies for abnormal event detection in video.

Track Any Anomalous Object: A Granular Video Anomaly Detection Pipeline Object-centric auto-encoders and dummy anomalies for abnormal event detection in video

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:29:43.881220Z

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 23f99e2a-08a1-440c-8bc9-16ccfba0199b · outbound

This paper cites Real-time weakly supervised video anomaly detection.

Track Any Anomalous Object: A Granular Video Anomaly Detection Pipeline Real-time weakly supervised video anomaly detection

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:29:43.864517Z

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 6d4ca4ab-16e8-403c-8cf5-aa2301016013 · outbound

This paper cites Segment any- thing.

Track Any Anomalous Object: A Granular Video Anomaly Detection Pipeline Segment any- thing

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-07T10:29:05.974650Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation daa92c75-9eca-4793-ba90-bb390533f7c0 · outbound

This paper cites Unsupervised Anomaly Segmentation using Image-Semantic Cycle Translation.

Track Any Anomalous Object: A Granular Video Anomaly Detection Pipeline Unsupervised Anomaly Segmentation using Image-Semantic Cycle Translation

Reference 23

Resolution: verified exact; local_arxiv, observed 2026-08-07T10:29:10.191368Z

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 34a13786-88bf-4d98-afe6-49781ac04716 · outbound

This paper cites Consistent posterior distributions under vessel-mixing: a regularization for cross-domain reti- nal artery/vein classification.

Track Any Anomalous Object: A Granular Video Anomaly Detection Pipeline Consistent posterior distributions under vessel-mixing: a regularization for cross-domain reti- nal artery/vein classification

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:29:43.838248Z

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 91b6c880-26c3-4c90-b554-53f57b183c5b · outbound

This paper cites Hierarchical deep network with uncertainty-aware semi-supervised learning 9 for vessel segmentation.Neural Computing and Applica- tions, pages 1–14, 2022.

Track Any Anomalous Object: A Granular Video Anomaly Detection Pipeline Hierarchical deep network with uncertainty-aware semi-supervised learning 9 for vessel segmentation.Neural Computing and Applica- tions, pages 1–14, 2022

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:29:43.820435Z

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 9f57e0bd-6528-42da-bde1-986c4429ce8a · outbound

This paper cites U-KAN Makes Strong Backbone for Medical Image Segmentation and Generation.

Track Any Anomalous Object: A Granular Video Anomaly Detection Pipeline U-KAN Makes Strong Backbone for Medical Image Segmentation and Generation

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-07T10:29:06.350117Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 493bb12e-876f-4de7-8497-bcb797fcddc5 · outbound

This paper cites Blip-2: Bootstrapping language-image pre-training with frozen image encoders and large language models.

Track Any Anomalous Object: A Granular Video Anomaly Detection Pipeline Blip-2: Bootstrapping language-image pre-training with frozen image encoders and large language models

Reference 27

Resolution: unresolved; no resolver link, observed 2026-08-07T10:29:06.439819Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 66973fc2-2542-4ad5-ab5b-c8e5a304f789 · outbound

This paper cites Fusion2void: Unsupervised multi-focus image fusion based on image in- painting.IEEE Transactions on Circuits and Systems for Video Technology, 2024.

Track Any Anomalous Object: A Granular Video Anomaly Detection Pipeline Fusion2void: Unsupervised multi-focus image fusion based on image in- painting.IEEE Transactions on Circuits and Systems for Video Technology, 2024

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:29:43.788273Z

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 bc92862c-0755-4f3a-8fdd-fcb40a6629e9 · outbound

This paper cites AGLLDiff: Guiding Diffusion Models Towards Unsupervised Training-free Real-world Low-light Image Enhancement.

Track Any Anomalous Object: A Granular Video Anomaly Detection Pipeline AGLLDiff: Guiding Diffusion Models Towards Unsupervised Training-free Real-world Low-light Image Enhancement

Reference 29

Resolution: unresolved; no resolver link, observed 2026-08-07T10:29:06.592566Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 69daba2c-474a-4d44-bfb7-39494f6433f7 · outbound

This paper cites Visual instruction tuning.Advances in neural information processing systems, 36, 2024.

Track Any Anomalous Object: A Granular Video Anomaly Detection Pipeline Visual instruction tuning.Advances in neural information processing systems, 36, 2024

Reference 30

Resolution: unresolved; no resolver link, observed 2026-08-07T10:29:06.685902Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f58136d6-5927-48cf-b648-39897bf0b7c9 · outbound

This paper cites Fu- ture frame prediction for anomaly detection–a new baseline.

Track Any Anomalous Object: A Granular Video Anomaly Detection Pipeline Fu- ture frame prediction for anomaly detection–a new baseline

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:29:43.761712Z

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 58b2cd86-0499-4ea7-bd4b-17c7fa3ac240 · outbound

This paper cites A hybrid video anomaly detection framework via memory-augmented flow reconstruction and flow-guided frame prediction.

Track Any Anomalous Object: A Granular Video Anomaly Detection Pipeline A hybrid video anomaly detection framework via memory-augmented flow reconstruction and flow-guided frame prediction

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:29:43.742920Z

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 3f2b1b5b-1d77-4bee-888c-41bd05442862 · outbound

This paper cites A hybrid video anomaly detection framework via memory-augmented flow reconstruction and flow-guided frame prediction.

Track Any Anomalous Object: A Granular Video Anomaly Detection Pipeline A hybrid video anomaly detection framework via memory-augmented flow reconstruction and flow-guided frame prediction

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:29:43.720929Z

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 fd0a22ef-7ee4-46bc-a36f-6d9a8c01576b · outbound

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

Track Any Anomalous Object: A Granular Video Anomaly Detection Pipeline Simplenet: A simple network for image anomaly detection and localization

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:29:43.700540Z

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 07dea209-a33e-4213-90e5-27471ca3c754 · outbound

This paper cites an unresolved cited work.

Track Any Anomalous Object: A Granular Video Anomaly Detection Pipeline Unresolved cited work

Reference 35

Resolution: unresolved; raw_fallback, observed 2026-08-07T10:29:43.677506Z

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 97f24090-deb6-4931-a316-88a4244a6c62 · outbound

This paper cites Video-ChatGPT: Towards Detailed Video Understanding via Large Vision and Language Models.

Track Any Anomalous Object: A Granular Video Anomaly Detection Pipeline Video-ChatGPT: Towards Detailed Video Understanding via Large Vision and Language Models

Reference 36

Resolution: unresolved; no resolver link, observed 2026-08-07T10:29:07.193048Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation aca711e6-5bbb-4eb6-b97c-ad6280d65e08 · outbound

This paper cites MULDE: Multiscale Log- Density Estimation via Denoising Score Matching for Video Anomaly Detection.

Track Any Anomalous Object: A Granular Video Anomaly Detection Pipeline MULDE: Multiscale Log- Density Estimation via Denoising Score Matching for Video Anomaly Detection

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:29:43.655210Z

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 6b15d565-c6ae-4f82-b3da-547b3db8c6ea · outbound

This paper cites Anomaly Detection with Conditioned Denoising Diffusion Models.

Track Any Anomalous Object: A Granular Video Anomaly Detection Pipeline Anomaly Detection with Conditioned Denoising Diffusion Models

Reference 38

Resolution: verified exact; local_arxiv, observed 2026-08-07T10:29:10.038707Z

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 d47c15da-404e-4043-9336-3081806b5b1e · outbound

This paper cites Spatio-temporal predictive tasks for abnormal event detection in videos.

Track Any Anomalous Object: A Granular Video Anomaly Detection Pipeline Spatio-temporal predictive tasks for abnormal event detection in videos

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:29:43.633353Z

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 679bd011-1644-4c5a-ac40-d7956e3d5b18 · outbound

This paper cites Learn- ing memory-guided normality for anomaly detection.

Track Any Anomalous Object: A Granular Video Anomaly Detection Pipeline Learn- ing memory-guided normality for anomaly detection

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:29:43.614105Z

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 79f4ed2a-9bab-4215-82e2-862aa6387013 · outbound

This paper cites Street scene: A new dataset and evaluation protocol for video anomaly detection.

Track Any Anomalous Object: A Granular Video Anomaly Detection Pipeline Street scene: A new dataset and evaluation protocol for video anomaly detection

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:29:43.593533Z

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 a61188d8-e595-4967-aaab-af8b3e4556ca · outbound

This paper cites Jones, and Ranga Raju Vatsavai.

Track Any Anomalous Object: A Granular Video Anomaly Detection Pipeline Jones, and Ranga Raju Vatsavai

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:29:43.569635Z

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 d2ad5494-eb9a-4b69-9c5c-11ec1e1ad029 · outbound

This paper cites SAM 2: Segment Anything in Images and Videos.

Track Any Anomalous Object: A Granular Video Anomaly Detection Pipeline SAM 2: Segment Anything in Images and Videos

Reference 43

Resolution: unresolved; no resolver link, observed 2026-08-07T10:29:07.751230Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f7ca9159-6cdb-4162-b6c7-bb90aa9dd093 · outbound

This paper cites Attribute-based representa- tions for accurate and interpretable video anomaly detection.

Track Any Anomalous Object: A Granular Video Anomaly Detection Pipeline Attribute-based representa- tions for accurate and interpretable video anomaly detection

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:29:43.541630Z

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 4ec9bd1c-5a4c-4271-96cb-302bd7660aa0 · outbound

This paper cites Video anomaly detection via sequentially learning multiple pretext tasks.

Track Any Anomalous Object: A Granular Video Anomaly Detection Pipeline Video anomaly detection via sequentially learning multiple pretext tasks

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:29:43.514563Z

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 bcbcee49-2785-4f14-b652-b675a4095fae · outbound

This paper cites Few- shot medical image segmentation using a global correlation network with discriminative embedding.Computers in biol- ogy and medicine, 140:105067, 2022.

Track Any Anomalous Object: A Granular Video Anomaly Detection Pipeline Few- shot medical image segmentation using a global correlation network with discriminative embedding.Computers in biol- ogy and medicine, 140:105067, 2022

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:29:43.490114Z

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 4efcc9f0-12fb-460b-af22-e7d9da38646a · outbound

This paper cites Weakly-supervised video anomaly detection with robust temporal feature magni- tude learning.

Track Any Anomalous Object: A Granular Video Anomaly Detection Pipeline Weakly-supervised video anomaly detection with robust temporal feature magni- tude learning

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:29:43.466349Z

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 80d309ff-578a-4d85-bb65-26c939552592 · outbound

This paper cites Anomaly detection in crowd scene.

Track Any Anomalous Object: A Granular Video Anomaly Detection Pipeline Anomaly detection in crowd scene

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:29:43.436549Z

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 cd7d15b5-cde4-4ea5-8cc2-6a332e9b97d6 · outbound

This paper cites Learning high-frequency feature enhancement and alignment for pan-sharpening.

Track Any Anomalous Object: A Granular Video Anomaly Detection Pipeline Learning high-frequency feature enhancement and alignment for pan-sharpening

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:29:43.409470Z

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 27669353-d6b8-49fd-aad4-98d3154c3d39 · outbound

This paper cites Learn- ing diffusion high-quality priors for pan-sharpening: A two- stage approach with time-aware adapter fine-tuning.IEEE Transactions on Geoscience and Remote Sensing, 2025.

Track Any Anomalous Object: A Granular Video Anomaly Detection Pipeline Learn- ing diffusion high-quality priors for pan-sharpening: A two- stage approach with time-aware adapter fine-tuning.IEEE Transactions on Geoscience and Remote Sensing, 2025

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:29:43.312008Z

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 f0ce8bc0-b7e8-4c2e-8422-f5be9a7420cd · outbound

This paper cites Self-supervised sparse representa- 10 tion for video anomaly detection.

Track Any Anomalous Object: A Granular Video Anomaly Detection Pipeline Self-supervised sparse representa- 10 tion for video anomaly detection

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:29:43.132410Z

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 bb83be16-7a90-434c-89c9-9517cccbdcbc · outbound

This paper cites Vadclip: Adapting vision-language models for weakly supervised video anomaly detection.

Track Any Anomalous Object: A Granular Video Anomaly Detection Pipeline Vadclip: Adapting vision-language models for weakly supervised video anomaly detection

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:29:43.058452Z

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 6f9b0d5e-6ef6-4694-9e2e-8ecf614e7567 · outbound

This paper cites A survey of camouflaged object detection and be- yond.CAAI AIR, 2024.

Track Any Anomalous Object: A Granular Video Anomaly Detection Pipeline A survey of camouflaged object detection and be- yond.CAAI AIR, 2024

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:29:42.915736Z

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 f0e27f2f-ba9c-4713-b880-78470e4fc527 · outbound

This paper cites Nestedformer: Nested modality-aware transformer for brain tumor segmentation.

Track Any Anomalous Object: A Granular Video Anomaly Detection Pipeline Nestedformer: Nested modality-aware transformer for brain tumor segmentation

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:29:42.791722Z

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 b7fb2aed-b9cb-4d1f-8702-0a825c13e990 · outbound

This paper cites Diff-UNet: A Diffusion Embedded Network for Volumetric Segmentation.

Track Any Anomalous Object: A Granular Video Anomaly Detection Pipeline Diff-UNet: A Diffusion Embedded Network for Volumetric Segmentation

Reference 55

Resolution: unresolved; no resolver link, observed 2026-08-07T10:29:08.635044Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 105ba694-a1fa-4bd6-a645-dcd5f54fa092 · outbound

This paper cites Cross-conditioned diffu- sion model for medical image to image translation.

Track Any Anomalous Object: A Granular Video Anomaly Detection Pipeline Cross-conditioned diffu- sion model for medical image to image translation

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:29:42.637102Z

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 f7bb2d0f-2b55-4de4-9098-77be5fc6d5f2 · outbound

This paper cites Segmamba: Long-range sequential modeling mamba for 3d medical image segmentation.

Track Any Anomalous Object: A Granular Video Anomaly Detection Pipeline Segmamba: Long-range sequential modeling mamba for 3d medical image segmentation

Reference 57

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:29:42.511039Z

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 54335ae8-0c8d-4210-a304-21dbf378166f · outbound

This paper cites Follow the rules: Reasoning for video anomaly detection with large language models.

Track Any Anomalous Object: A Granular Video Anomaly Detection Pipeline Follow the rules: Reasoning for video anomaly detection with large language models

Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:29:42.455612Z

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 e907db8c-e180-4e7e-be77-6001cb461bdb · outbound

This paper cites Cloze test helps: Effec- tive video anomaly detection via learning to complete video events.

Track Any Anomalous Object: A Granular Video Anomaly Detection Pipeline Cloze test helps: Effec- tive video anomaly detection via learning to complete video events

Reference 59

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:29:11.022990Z

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 30b07665-0913-48f0-9d55-d0242091101b · outbound

This paper cites Harnessing large language mod- els for training-free video anomaly detection.

Track Any Anomalous Object: A Granular Video Anomaly Detection Pipeline Harnessing large language mod- els for training-free video anomaly detection

Reference 60

Resolution: unresolved; no resolver link, observed 2026-08-07T10:29:09.352789Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 878f78b2-569d-4f1a-8931-8e99a702d243 · outbound

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

Track Any Anomalous Object: A Granular Video Anomaly Detection Pipeline Draem- a discriminatively trained reconstruction embedding for sur- face anomaly detection

Reference 61

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:29:10.881392Z

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 9273eca1-0e34-4915-bc28-ddc9b36558bd · outbound

This paper cites Video-LLaMA: An Instruction-tuned Audio-Visual Language Model for Video Understanding.

Track Any Anomalous Object: A Granular Video Anomaly Detection Pipeline Video-LLaMA: An Instruction-tuned Audio-Visual Language Model for Video Understanding

Reference 62

Resolution: unresolved; no resolver link, observed 2026-08-07T10:29:09.584649Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1e504662-f7c3-467e-b878-f46f6cf3b9a4 · outbound

This paper cites Generator versus segmentor: Pseudo-healthy synthesis.

Track Any Anomalous Object: A Granular Video Anomaly Detection Pipeline Generator versus segmentor: Pseudo-healthy synthesis

Reference 63

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:29:10.689625Z

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 fef2e04b-4f43-4c13-b6db-4dbc664da922 · outbound

This paper cites Anomalyclip: Object-agnostic prompt learn- ing for zero-shot anomaly detection.arXiv preprint arXiv:2310.18961, 2023.

Track Any Anomalous Object: A Granular Video Anomaly Detection Pipeline Anomalyclip: Object-agnostic prompt learn- ing for zero-shot anomaly detection.arXiv preprint arXiv:2310.18961, 2023

Reference 64

Resolution: unresolved; no resolver link, observed 2026-08-07T10:29:09.701204Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 43def9d6-0480-4bf8-94ca-6385ef266bde · outbound

This paper cites Segment everything everywhere all at once.Advances in Neural Information Processing Systems, 36, 2024.

Track Any Anomalous Object: A Granular Video Anomaly Detection Pipeline Segment everything everywhere all at once.Advances in Neural Information Processing Systems, 36, 2024

Reference 65

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:29:10.545950Z

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.

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