Few-shot Human Action Anomaly Detection via a Unified Contrastive Learning Framework | Paper Citation Record

Observation 93d4d899-86f1-4dff-a4ae-37fc6ef2bc00 · outbound

This paper cites Uninformed students: Student-teacher anomaly detection with discriminative latent embeddings.

Few-shot Human Action Anomaly Detection via a Unified Contrastive Learning Framework Uninformed students: Student-teacher anomaly detection with discriminative latent embeddings

Reference 1

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Observation 1eadf4ab-6233-4fd3-9629-0f256aa2b073 · outbound

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

Few-shot Human Action Anomaly Detection via a Unified Contrastive Learning Framework Mvtec ad – a comprehensive real-world dataset for unsupervised anomaly detection

Reference 2

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Observation fbedaee0-7a0d-414d-b6e9-9b9e248afa85 · outbound

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

Few-shot Human Action Anomaly Detection via a Unified Contrastive Learning Framework Padim: a patch distribution modeling framework for anomaly detection and localization

Reference 3

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This paper cites Anomaly detection for industrial surface inspection: applica- tion in maintenance of aircraft components.

Few-shot Human Action Anomaly Detection via a Unified Contrastive Learning Framework Anomaly detection for industrial surface inspection: applica- tion in maintenance of aircraft components

Reference 4

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Observation 1ce0d92a-4ad2-4636-b1b8-7bd42e4ed3ae · outbound

This paper cites Supervised anomaly detection for complex indus- trial images.

Few-shot Human Action Anomaly Detection via a Unified Contrastive Learning Framework Supervised anomaly detection for complex indus- trial images

Reference 5

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Observation 75aba50e-63d1-46fe-86d1-02a96ff3fb0b · outbound

This paper cites f-anogan: Fast unsupervised anomaly detection with generative adversarial networks.

Few-shot Human Action Anomaly Detection via a Unified Contrastive Learning Framework f-anogan: Fast unsupervised anomaly detection with generative adversarial networks

Reference 6

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Observation 1ca7543b-fdd0-456d-b05d-4c60f23b399b · outbound

This paper cites Madgan: Unsupervised medical anomaly detection gan us- ing multiple adjacent brain mri slice reconstruction.

Few-shot Human Action Anomaly Detection via a Unified Contrastive Learning Framework Madgan: Unsupervised medical anomaly detection gan us- ing multiple adjacent brain mri slice reconstruction

Reference 7

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Observation 62b6f9ad-e1db-4faf-9f30-92f978aea08f · outbound

This paper cites Unsupervised deep anomaly detection for medical images using an improved adversarial autoen- coder.

Few-shot Human Action Anomaly Detection via a Unified Contrastive Learning Framework Unsupervised deep anomaly detection for medical images using an improved adversarial autoen- coder

Reference 8

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Observation 980e6f5e-b2d2-4c19-8af9-f7fa27f74038 · outbound

This paper cites Unsu- pervised anomaly detection for posteroanterior chest x-rays using multiresolution patch-based self-supervised learning.

Few-shot Human Action Anomaly Detection via a Unified Contrastive Learning Framework Unsu- pervised anomaly detection for posteroanterior chest x-rays using multiresolution patch-based self-supervised learning

Reference 9

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Observation 2d98808f-e61b-447f-aca4-3e11789f113e · outbound

This paper cites When Every Millisecond Counts: Real-Time Anomaly Detection via the Multimodal Asynchronous Hybrid Network.

Few-shot Human Action Anomaly Detection via a Unified Contrastive Learning Framework When Every Millisecond Counts: Real-Time Anomaly Detection via the Multimodal Asynchronous Hybrid Network

Reference 10

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Observation b865dfb3-7e62-4ff5-8204-0e1a45444718 · outbound

This paper cites Scaling out-of- distribution detection for real-world settings.

Few-shot Human Action Anomaly Detection via a Unified Contrastive Learning Framework Scaling out-of- distribution detection for real-world settings

Reference 11

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Observation 2287c2c7-2932-4c91-99b2-59cb26b9840f · outbound

This paper cites Spotting the unexpected (stu): A 3d lidar dataset for anomaly segmenta- tion in autonomous driving.

Few-shot Human Action Anomaly Detection via a Unified Contrastive Learning Framework Spotting the unexpected (stu): A 3d lidar dataset for anomaly segmenta- tion in autonomous driving

Reference 12

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Observation d032ece4-9cc4-46e7-80ca-0bf16ab103bd · outbound

This paper cites Frequency-Guided Multi-Level Human Action Anomaly Detection with Normalizing Flows.

Few-shot Human Action Anomaly Detection via a Unified Contrastive Learning Framework Frequency-Guided Multi-Level Human Action Anomaly Detection with Normalizing Flows

Reference 13

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Observation ec49c918-1366-4ec6-84e5-9d570a0329c7 · outbound

This paper cites Multimodal motion con- ditioned diffusion model for skeleton-based video anomaly detection.

Few-shot Human Action Anomaly Detection via a Unified Contrastive Learning Framework Multimodal motion con- ditioned diffusion model for skeleton-based video anomaly detection

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T17:06:13.267665Z

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Observation 26daaf13-8948-472f-a0bd-dc184e1127cf · outbound

This paper cites Normalizing flows for human pose anomaly detection.

Few-shot Human Action Anomaly Detection via a Unified Contrastive Learning Framework Normalizing flows for human pose anomaly detection

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T17:06:13.256446Z

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Observation 37705a6b-2d9c-4de5-8589-ac72df3b2cf8 · outbound

This paper cites A unified model for multi-class anomaly detection.

Few-shot Human Action Anomaly Detection via a Unified Contrastive Learning Framework A unified model for multi-class anomaly detection

Reference 16

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Observation 215f719a-6559-4333-b00d-b2b02762ff0a · outbound

This paper cites Registration based few-shot anomaly detection.

Few-shot Human Action Anomaly Detection via a Unified Contrastive Learning Framework Registration based few-shot anomaly detection

Reference 17

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Observation d7b21d97-dbe6-4d61-b25e-a8c8d9aa6f11 · outbound

This paper cites Omnial: A unified cnn framework for unsuper- vised anomaly localization.

Few-shot Human Action Anomaly Detection via a Unified Contrastive Learning Framework Omnial: A unified cnn framework for unsuper- vised anomaly localization

Reference 18

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Observation de5f2a68-0976-4e14-a98d-e53ab7474f62 · outbound

This paper cites Hierarchical vector quantized transformer for multi-class unsupervised anomaly detection, 2023.

Few-shot Human Action Anomaly Detection via a Unified Contrastive Learning Framework Hierarchical vector quantized transformer for multi-class unsupervised anomaly detection, 2023

Reference 19

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Observation 3a889f3b-1d4a-4a47-863e-fc9a9a5a686f · outbound

This paper cites Karls- son, Biqing Huang, and Chin Yew Lin.

Few-shot Human Action Anomaly Detection via a Unified Contrastive Learning Framework Karls- son, Biqing Huang, and Chin Yew Lin

Reference 20

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Observation dbb23674-5423-4541-9774-0e9dd780082f · outbound

This paper cites Mambaad: Exploring state space models for multi-class unsupervised anomaly detec- tion.

Few-shot Human Action Anomaly Detection via a Unified Contrastive Learning Framework Mambaad: Exploring state space models for multi-class unsupervised anomaly detec- tion

Reference 21

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Observation 727bbf43-56bc-4041-a136-e47c87effa11 · outbound

This paper cites Learning to detect multi-class anomalies with just one normal image prompt.

Few-shot Human Action Anomaly Detection via a Unified Contrastive Learning Framework Learning to detect multi-class anomalies with just one normal image prompt

Reference 22

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Observation 5ebad5e7-015c-493f-8b2b-6dfbdded243f · outbound

This paper cites Dinomaly: The less is more philosophy in multi-class unsupervised anomaly detection.

Few-shot Human Action Anomaly Detection via a Unified Contrastive Learning Framework Dinomaly: The less is more philosophy in multi-class unsupervised anomaly detection

Reference 23

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Observation f5578d6d-11bd-47cb-82db-c1661a6a62d6 · outbound

This paper cites Correcting deviations from normality: A reformulated diffusion model for multi-class unsupervised anomaly detection.

Few-shot Human Action Anomaly Detection via a Unified Contrastive Learning Framework Correcting deviations from normality: A reformulated diffusion model for multi-class unsupervised anomaly detection

Reference 24

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Observation 52c6bcc2-9f2b-4c10-b1aa-7525b009af14 · outbound

This paper cites A simple framework for contrastive learning of visual representations.

Few-shot Human Action Anomaly Detection via a Unified Contrastive Learning Framework A simple framework for contrastive learning of visual representations

Reference 25

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Observation 0b422442-399f-40a1-bff3-20c88471d003 · outbound

This paper cites Exploring simple siamese rep- resentation learning.

Few-shot Human Action Anomaly Detection via a Unified Contrastive Learning Framework Exploring simple siamese rep- resentation learning

Reference 26

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Observation ae9801cf-175e-4e4e-b16d-d7c1528754cd · outbound

This paper cites Boosting contrastive self- supervised learning with false negative cancellation.

Few-shot Human Action Anomaly Detection via a Unified Contrastive Learning Framework Boosting contrastive self- supervised learning with false negative cancellation

Reference 27

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Observation 0f31cf31-57c6-46cf-bc1a-ed5d3d49fb7c · outbound

This paper cites Humanmac: Masked motion completion for human motion prediction.

Few-shot Human Action Anomaly Detection via a Unified Contrastive Learning Framework Humanmac: Masked motion completion for human motion prediction

Reference 28

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Observation 7ad1b4b0-5459-4d0a-af71-ee6ba6147d8f · outbound

This paper cites Ac- tion2motion: Conditioned generation of 3d human motions.

Few-shot Human Action Anomaly Detection via a Unified Contrastive Learning Framework Ac- tion2motion: Conditioned generation of 3d human motions

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T17:06:13.113880Z

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Observation eff2e890-7b7e-4ce2-99ad-9f78486359d6 · outbound

This paper cites Anomaly detection and localization in crowded scenes.IEEE transactions on pattern analysis and machine intelligence , 36(1):18–32, 2013.

Few-shot Human Action Anomaly Detection via a Unified Contrastive Learning Framework Anomaly detection and localization in crowded scenes.IEEE transactions on pattern analysis and machine intelligence , 36(1):18–32, 2013

Reference 30

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Observation a39efe11-acbe-433e-8800-23882e4eed7f · outbound

This paper cites Deep-anomaly: Fully convolutional neural network for fast anomaly detection in crowded scenes.

Few-shot Human Action Anomaly Detection via a Unified Contrastive Learning Framework Deep-anomaly: Fully convolutional neural network for fast anomaly detection in crowded scenes

Reference 31

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Observation 11abdcce-59dc-43ca-942e-88c5bce23ca3 · outbound

This paper cites Video anomaly detection and localization by local motion based joint video representation and ocelm.

Few-shot Human Action Anomaly Detection via a Unified Contrastive Learning Framework Video anomaly detection and localization by local motion based joint video representation and ocelm

Reference 32

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Observation c50dea84-b8cc-45c4-a349-1bc306ef03e1 · outbound

This paper cites Learning regular- ity in skeleton trajectories for anomaly detection in videos.

Few-shot Human Action Anomaly Detection via a Unified Contrastive Learning Framework Learning regular- ity in skeleton trajectories for anomaly detection in videos

Reference 33

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Observation 3dfd7c3c-9eaa-435a-8b4d-0b5538cfb21f · outbound

This paper cites A survey of single-scene video anomaly detection.

Few-shot Human Action Anomaly Detection via a Unified Contrastive Learning Framework A survey of single-scene video anomaly detection

Reference 34

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Observation 3db237f3-94d8-4b10-b575-44879c2737ae · outbound

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

Few-shot Human Action Anomaly Detection via a Unified Contrastive Learning Framework Anomaly detection in video via self- supervised and multi-task learning

Reference 35

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Observation e5dea515-3eba-4f77-bcfe-f4d2569a3146 · outbound

This paper cites A background-agnostic framework with adversarial training for abnormal event detection in video.

Few-shot Human Action Anomaly Detection via a Unified Contrastive Learning Framework A background-agnostic framework with adversarial training for abnormal event detection in video

Reference 36

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Observation 03948e12-fca5-40ea-9d64-1c842ecad803 · outbound

This paper cites Hierarchical recur- rent neural network for skeleton based action recognition.

Few-shot Human Action Anomaly Detection via a Unified Contrastive Learning Framework Hierarchical recur- rent neural network for skeleton based action recognition

Reference 37

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Observation dbc6b70d-45bb-40ef-be27-6f0ea68db312 · outbound

This paper cites Spatial tempo- ral graph convolutional networks for skeleton-based action recognition.

Few-shot Human Action Anomaly Detection via a Unified Contrastive Learning Framework Spatial tempo- ral graph convolutional networks for skeleton-based action recognition

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T17:06:13.032737Z

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Observation 528d6e6c-3eb2-4171-933d-b77dd8bd6cab · outbound

This paper cites Skeleton-based action recognition with directed graph neu- ral networks.

Few-shot Human Action Anomaly Detection via a Unified Contrastive Learning Framework Skeleton-based action recognition with directed graph neu- ral networks

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T17:06:13.021616Z

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Observation 08c4b890-d46e-4066-88bd-eb9ecf0f74d3 · outbound

This paper cites Revisiting skeleton-based action recognition.

Few-shot Human Action Anomaly Detection via a Unified Contrastive Learning Framework Revisiting skeleton-based action recognition

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T17:06:13.009454Z

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Observation fe2a57fe-df46-477a-ac20-ad5836900cba · outbound

This paper cites A survey on 3d skeleton-based action recognition using learn- ing method.

Few-shot Human Action Anomaly Detection via a Unified Contrastive Learning Framework A survey on 3d skeleton-based action recognition using learn- ing method

Reference 41

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Observation 78b0232f-4531-46ed-a533-e2cc1bec8ab5 · outbound

This paper cites Explainable Deep One-Class Classification.

Few-shot Human Action Anomaly Detection via a Unified Contrastive Learning Framework Explainable Deep One-Class Classification

Reference 42

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Observation d17f75a0-7036-4f69-aff1-7257e384abde · outbound

This paper cites Adtr: Anomaly detection transformer with feature reconstruction.

Few-shot Human Action Anomaly Detection via a Unified Contrastive Learning Framework Adtr: Anomaly detection transformer with feature reconstruction

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T17:06:12.991197Z

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Observation b2f4a2f0-9b75-4dd9-9252-e5647a7eaaf6 · outbound

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

Few-shot Human Action Anomaly Detection via a Unified Contrastive Learning Framework Draem- a discriminatively trained reconstruction embedding for sur- face anomaly detection

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T17:06:12.978946Z

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Observation cfe126d9-86ca-4b56-8c55-d2fe7ff05afb · outbound

This paper cites Learning trajectory dependencies for human motion pre- diction.

Few-shot Human Action Anomaly Detection via a Unified Contrastive Learning Framework Learning trajectory dependencies for human motion pre- diction

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T17:06:12.967583Z

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Observation 14929d61-6270-4a44-b02a-9e888c9d3e38 · outbound

This paper cites Nonrigid structure from motion in trajectory space.

Few-shot Human Action Anomaly Detection via a Unified Contrastive Learning Framework Nonrigid structure from motion in trajectory space

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T17:06:12.956740Z

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Observation b79c114c-3f13-4f58-8bcf-7b980a822623 · outbound

This paper cites Semi-Supervised Classification with Graph Convolutional Networks.

Few-shot Human Action Anomaly Detection via a Unified Contrastive Learning Framework Semi-Supervised Classification with Graph Convolutional Networks

Reference 47

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Observation b9418301-73ba-47cc-aadb-dedc5378ac6f · outbound

This paper cites Denoising dif- fusion probabilistic models.

Few-shot Human Action Anomaly Detection via a Unified Contrastive Learning Framework Denoising dif- fusion probabilistic models

Reference 48

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Observation 8b7eaaab-4f10-4166-ac1e-0e738ce79adf · outbound

This paper cites Adam: A Method for Stochastic Optimization.

Few-shot Human Action Anomaly Detection via a Unified Contrastive Learning Framework Adam: A Method for Stochastic Optimization

Reference 49

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Observation d614ccd2-7c5e-4630-a86d-863297a32569 · outbound

This paper cites Denoising Diffusion Implicit Models.

Few-shot Human Action Anomaly Detection via a Unified Contrastive Learning Framework Denoising Diffusion Implicit Models

Reference 50

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Observation dca373a7-83cf-428f-b636-a5868075483c · outbound

This paper cites Improved denoising diffusion probabilistic models.

Few-shot Human Action Anomaly Detection via a Unified Contrastive Learning Framework Improved denoising diffusion probabilistic models

Reference 51

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Observation fc6f2fe0-b71c-4dc5-b0c6-179eae6a0bda · outbound

This paper cites Visualiz- ing data using t-sne.

Few-shot Human Action Anomaly Detection via a Unified Contrastive Learning Framework Visualiz- ing data using t-sne

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T17:06:12.933211Z

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