The Evolution of Video Anomaly Detection: A Unified Framework from DNN to MLLM | Paper Citation Record

Observation c45a6acb-ff9d-4c7b-9bd6-d6edd8cc887d · outbound

This paper cites Applications of outlier analysis,.

The Evolution of Video Anomaly Detection: A Unified Framework from DNN to MLLM Applications of outlier analysis,

Reference 2

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Observation 6f9a20fa-7c9a-490c-b928-338ca8bb7604 · outbound

This paper cites Weakly Supervised Anomaly Detection: A Survey.

The Evolution of Video Anomaly Detection: A Unified Framework from DNN to MLLM Weakly Supervised Anomaly Detection: A Survey

Reference 3

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Observation 4ba4aa76-f8f7-418b-9bb7-b2ff8fbb3f17 · outbound

This paper cites Deep learning for anomaly detection: A review,.

The Evolution of Video Anomaly Detection: A Unified Framework from DNN to MLLM Deep learning for anomaly detection: A review,

Reference 4

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Observation d3cd4329-4dc0-4c04-87e7-86368d8cf71e · outbound

This paper cites Dota: Unsupervised detection of traffic anomaly in driving videos,.

The Evolution of Video Anomaly Detection: A Unified Framework from DNN to MLLM Dota: Unsupervised detection of traffic anomaly in driving videos,

Reference 5

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This paper cites Sparse reconstruction cost for abnormal event detection,.

The Evolution of Video Anomaly Detection: A Unified Framework from DNN to MLLM Sparse reconstruction cost for abnormal event detection,

Reference 6

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Observation f9c0b77a-6b98-4db7-9823-dcde5747360f · outbound

This paper cites Learning temporal regularity in video sequences,.

The Evolution of Video Anomaly Detection: A Unified Framework from DNN to MLLM Learning temporal regularity in video sequences,

Reference 7

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This paper cites Future frame prediction for anomaly detection–a new baseline,.

The Evolution of Video Anomaly Detection: A Unified Framework from DNN to MLLM Future frame prediction for anomaly detection–a new baseline,

Reference 8

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Observation ec3946a4-db25-4d6a-8404-e50b8f3f59f1 · outbound

This paper cites Uncovering what why and how: A compre- hensive benchmark for causation understanding of video anomaly,.

The Evolution of Video Anomaly Detection: A Unified Framework from DNN to MLLM Uncovering what why and how: A compre- hensive benchmark for causation understanding of video anomaly,

Reference 10

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Observation a496e6cd-b3cb-4a29-b0d3-f4163cd7b07b · outbound

This paper cites Hawk: Learning to understand open-world video anomalies,.

The Evolution of Video Anomaly Detection: A Unified Framework from DNN to MLLM Hawk: Learning to understand open-world video anomalies,

Reference 11

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Observation 202293b4-14f8-46e8-896e-cf398c53cf27 · outbound

This paper cites Vera: Explainable video anomaly detection via verbalized learning of vision-language models,.

The Evolution of Video Anomaly Detection: A Unified Framework from DNN to MLLM Vera: Explainable video anomaly detection via verbalized learning of vision-language models,

Reference 12

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Observation 1287bfae-b620-43d0-8392-b49828ce3438 · outbound

This paper cites Holmes-vau: Towards long-term video anomaly understanding at any granularity,.

The Evolution of Video Anomaly Detection: A Unified Framework from DNN to MLLM Holmes-vau: Towards long-term video anomaly understanding at any granularity,

Reference 13

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Observation f058c997-81a4-4b30-af05-30ebf44ec2ea · outbound

This paper cites Abnormal event detection at 150 fps in matlab,.

The Evolution of Video Anomaly Detection: A Unified Framework from DNN to MLLM Abnormal event detection at 150 fps in matlab,

Reference 14

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Observation 4c95d730-5d89-4f3a-a5bc-2fcdd98aa500 · outbound

This paper cites Anomaly detection and localization in crowded scenes,.

The Evolution of Video Anomaly Detection: A Unified Framework from DNN to MLLM Anomaly detection and localization in crowded scenes,

Reference 15

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Observation 2f415b88-8f5d-4506-b33c-7dc72e2f1f25 · outbound

This paper cites Real-world anomaly detection in surveillance videos,.

The Evolution of Video Anomaly Detection: A Unified Framework from DNN to MLLM Real-world anomaly detection in surveillance videos,

Reference 16

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Observation 89ab1ca4-8cbb-4114-b20d-30ae0d149732 · outbound

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

The Evolution of Video Anomaly Detection: A Unified Framework from DNN to MLLM A survey of single- scene video anomaly detection,

Reference 19

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Observation d7e4ba7b-9ef2-468e-83d6-1dfba9dc4108 · outbound

This paper cites A comprehensive review on deep learning-based methods for video anomaly detection,.

The Evolution of Video Anomaly Detection: A Unified Framework from DNN to MLLM A comprehensive review on deep learning-based methods for video anomaly detection,

Reference 20

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Observation fe7a0fe2-ab32-4515-8ddf-e46c0926ff2a · outbound

This paper cites Anomaly analysis in images and videos: A comprehensive review,.

The Evolution of Video Anomaly Detection: A Unified Framework from DNN to MLLM Anomaly analysis in images and videos: A comprehensive review,

Reference 21

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Observation 6d5084c6-9a25-4687-bb6b-9c2cd9f2e8a6 · outbound

This paper cites Generalized video anomaly event detection: Systematic taxonomy and comparison of deep models,.

The Evolution of Video Anomaly Detection: A Unified Framework from DNN to MLLM Generalized video anomaly event detection: Systematic taxonomy and comparison of deep models,

Reference 22

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Observation c7ee42d3-2f38-4557-b071-e817ba8dab60 · outbound

This paper cites Deep Learning for Video Anomaly Detection: A Review.

The Evolution of Video Anomaly Detection: A Unified Framework from DNN to MLLM Deep Learning for Video Anomaly Detection: A Review

Reference 23

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Observation 41e0b66f-7409-4ccb-a4dc-bafcea50f6b4 · outbound

This paper cites Video Anomaly Detection in 10 Years: A Survey and Outlook.

The Evolution of Video Anomaly Detection: A Unified Framework from DNN to MLLM Video Anomaly Detection in 10 Years: A Survey and Outlook

Reference 24

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Observation c31c3a18-cde0-4b82-8042-fb614ab1f685 · outbound

This paper cites Quo Vadis, Anomaly Detection? LLMs and VLMs in the Spotlight.

The Evolution of Video Anomaly Detection: A Unified Framework from DNN to MLLM Quo Vadis, Anomaly Detection? LLMs and VLMs in the Spotlight

Reference 25

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Observation 7cdf79dd-fab1-4c23-8153-53a2e96c637d · outbound

This paper cites Networking systems for video anomaly detection: A tutorial and survey,.

The Evolution of Video Anomaly Detection: A Unified Framework from DNN to MLLM Networking systems for video anomaly detection: A tutorial and survey,

Reference 26

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Observation b4d1dd38-1086-4932-9d26-db6a5a3c2292 · outbound

This paper cites Learning Not to Reconstruct Anomalies.

The Evolution of Video Anomaly Detection: A Unified Framework from DNN to MLLM Learning Not to Reconstruct Anomalies

Reference 27

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Observation ee4a3715-1386-4207-892b-62b26cd2f159 · outbound

This paper cites Making reconstruction-based method great again for video anomaly detection,.

The Evolution of Video Anomaly Detection: A Unified Framework from DNN to MLLM Making reconstruction-based method great again for video anomaly detection,

Reference 28

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Observation 7472c54e-4265-408c-bfdf-ba356ed92c24 · outbound

This paper cites Pedestrian Spatio-Temporal Information Fusion For Video Anomaly Detection.

The Evolution of Video Anomaly Detection: A Unified Framework from DNN to MLLM Pedestrian Spatio-Temporal Information Fusion For Video Anomaly Detection

Reference 30

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Observation c5f1f3ff-7597-44a3-be8a-9997abd02fcb · outbound

This paper cites Motion-Aware Feature for Improved Video Anomaly Detection.

The Evolution of Video Anomaly Detection: A Unified Framework from DNN to MLLM Motion-Aware Feature for Improved Video Anomaly Detection

Reference 31

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This paper cites Temporal convolutional network with complementary inner bag loss for weakly supervised anomaly detec- tion,.

The Evolution of Video Anomaly Detection: A Unified Framework from DNN to MLLM Temporal convolutional network with complementary inner bag loss for weakly supervised anomaly detec- tion,

Reference 32

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This paper cites Collaborative normality learning framework for weakly supervised video anomaly detection,.

The Evolution of Video Anomaly Detection: A Unified Framework from DNN to MLLM Collaborative normality learning framework for weakly supervised video anomaly detection,

Reference 33

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

The Evolution of Video Anomaly Detection: A Unified Framework from DNN to MLLM Vadclip: Adapting vision-language models for weakly supervised video anomaly detection,

Reference 34

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This paper cites Harnessing large language models for training-free video anomaly detection,.

The Evolution of Video Anomaly Detection: A Unified Framework from DNN to MLLM Harnessing large language models for training-free video anomaly detection,

Reference 35

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

The Evolution of Video Anomaly Detection: A Unified Framework from DNN to MLLM Follow the rules: reasoning for video anomaly detection with large language models,

Reference 36

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Observation c021adb2-078c-4d05-ae07-dd61fa5ca5f0 · outbound

This paper cites LLaMA: Open and Efficient Foundation Language Models.

The Evolution of Video Anomaly Detection: A Unified Framework from DNN to MLLM LLaMA: Open and Efficient Foundation Language Models

Reference 37

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This paper cites Internvl: Scaling up vision foundation models and aligning for generic visual-linguistic tasks,.

The Evolution of Video Anomaly Detection: A Unified Framework from DNN to MLLM Internvl: Scaling up vision foundation models and aligning for generic visual-linguistic tasks,

Reference 38

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This paper cites Suvad: Semantic understanding based video anomaly detection using mllm,.

The Evolution of Video Anomaly Detection: A Unified Framework from DNN to MLLM Suvad: Semantic understanding based video anomaly detection using mllm,

Reference 39

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This paper cites Robust real-time unusual event detection using multiple fixed-location monitors,.

The Evolution of Video Anomaly Detection: A Unified Framework from DNN to MLLM Robust real-time unusual event detection using multiple fixed-location monitors,

Reference 40

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This paper cites Abnormal crowd behavior detection using social force model,.

The Evolution of Video Anomaly Detection: A Unified Framework from DNN to MLLM Abnormal crowd behavior detection using social force model,

Reference 41

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

The Evolution of Video Anomaly Detection: A Unified Framework from DNN to MLLM Street scene: A new dataset and evaluation protocol for video anomaly detection,

Reference 42

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This paper cites A new comprehensive bench- mark for semi-supervised video anomaly detection and anticipation,.

The Evolution of Video Anomaly Detection: A Unified Framework from DNN to MLLM A new comprehensive bench- mark for semi-supervised video anomaly detection and anticipation,

Reference 43

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This paper cites A revisit of sparse coding based anomaly detection in stacked rnn framework,.

The Evolution of Video Anomaly Detection: A Unified Framework from DNN to MLLM A revisit of sparse coding based anomaly detection in stacked rnn framework,

Reference 44

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This paper cites Adnet: Temporal anomaly detection in surveillance videos,.

The Evolution of Video Anomaly Detection: A Unified Framework from DNN to MLLM Adnet: Temporal anomaly detection in surveillance videos,

Reference 45

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This paper cites Tad: A large-scale benchmark for traffic accidents detection from video surveillance,.

The Evolution of Video Anomaly Detection: A Unified Framework from DNN to MLLM Tad: A large-scale benchmark for traffic accidents detection from video surveillance,

Reference 46

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This paper cites People detection and pose classification inside a moving train using computer vision,.

The Evolution of Video Anomaly Detection: A Unified Framework from DNN to MLLM People detection and pose classification inside a moving train using computer vision,

Reference 47

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This paper cites Camnuvem: A robbery dataset for video anomaly detection,.

The Evolution of Video Anomaly Detection: A Unified Framework from DNN to MLLM Camnuvem: A robbery dataset for video anomaly detection,

Reference 48

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Observation 5c9d601a-255d-4600-98bf-4cd34de9293f · outbound

This paper cites A Benchmark for Crime Surveillance Video Analysis with Large Models.

The Evolution of Video Anomaly Detection: A Unified Framework from DNN to MLLM A Benchmark for Crime Surveillance Video Analysis with Large Models

Reference 49

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Observation 3aa377e1-dcd3-4d29-baa8-615c4ea38524 · outbound

This paper cites When, Where, and What? A New Dataset for Anomaly Detection in Driving Videos.

The Evolution of Video Anomaly Detection: A Unified Framework from DNN to MLLM When, Where, and What? A New Dataset for Anomaly Detection in Driving Videos

Reference 50

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Observation d568bbd0-719e-4000-ae53-92347cb46694 · outbound

This paper cites Ubnormal: New benchmark for supervised open-set video anomaly detection,.

The Evolution of Video Anomaly Detection: A Unified Framework from DNN to MLLM Ubnormal: New benchmark for supervised open-set video anomaly detection,

Reference 51

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Observation 430a411e-5753-4982-8be5-58ca4ecc59f0 · outbound

This paper cites Exploring What Why and How: A Multifaceted Benchmark for Causation Understanding of Video Anomaly.

The Evolution of Video Anomaly Detection: A Unified Framework from DNN to MLLM Exploring What Why and How: A Multifaceted Benchmark for Causation Understanding of Video Anomaly

Reference 52

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Observation 452d992d-51e3-4788-b382-d3920acd75fe · outbound

This paper cites VANE-Bench: Video Anomaly Evaluation Benchmark for Conversational LMMs.

The Evolution of Video Anomaly Detection: A Unified Framework from DNN to MLLM VANE-Bench: Video Anomaly Evaluation Benchmark for Conversational LMMs

Reference 53

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This paper cites Towards surveillance video-and-language understanding: New dataset baselines and challenges,.

The Evolution of Video Anomaly Detection: A Unified Framework from DNN to MLLM Towards surveillance video-and-language understanding: New dataset baselines and challenges,

Reference 55

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This paper cites Two-person interaction detection using body-pose features and mul- tiple instance learning,.

The Evolution of Video Anomaly Detection: A Unified Framework from DNN to MLLM Two-person interaction detection using body-pose features and mul- tiple instance learning,

Reference 56

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This paper cites Bleu: a method for automatic evaluation of machine translation,.

The Evolution of Video Anomaly Detection: A Unified Framework from DNN to MLLM Bleu: a method for automatic evaluation of machine translation,

Reference 57

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The Evolution of Video Anomaly Detection: A Unified Framework from DNN to MLLM Rouge: A package for automatic evaluation of summaries,

Reference 58

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This paper cites Meteor: An automatic metric for mt evalua- tion with improved correlation with human judgments,.

The Evolution of Video Anomaly Detection: A Unified Framework from DNN to MLLM Meteor: An automatic metric for mt evalua- tion with improved correlation with human judgments,

Reference 59

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This paper cites Self- supervised sparse representation for video anomaly detection,.

The Evolution of Video Anomaly Detection: A Unified Framework from DNN to MLLM Self- supervised sparse representation for video anomaly detection,

Reference 60

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This paper cites Self-training multi-sequence learning with transformer for weakly supervised video anomaly detection,.

The Evolution of Video Anomaly Detection: A Unified Framework from DNN to MLLM Self-training multi-sequence learning with transformer for weakly supervised video anomaly detection,

Reference 61

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This paper cites Exploiting completeness and uncertainty of pseudo labels for weakly supervised video anomaly detection,.

The Evolution of Video Anomaly Detection: A Unified Framework from DNN to MLLM Exploiting completeness and uncertainty of pseudo labels for weakly supervised video anomaly detection,

Reference 62

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Observation 1946af3a-5fd8-4f30-8cb7-f7a0dba68334 · outbound

This paper cites Learning causal temporal relation and feature discrimination for anomaly detection,.

The Evolution of Video Anomaly Detection: A Unified Framework from DNN to MLLM Learning causal temporal relation and feature discrimination for anomaly detection,

Reference 63

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Observation 49e64bcf-3e1d-4096-96b3-1a3f6d47d511 · outbound

This paper cites Weakly-supervised video anomaly detection with robust temporal feature magnitude learning,.

The Evolution of Video Anomaly Detection: A Unified Framework from DNN to MLLM Weakly-supervised video anomaly detection with robust temporal feature magnitude learning,

Reference 64

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This paper cites Look around for anomalies: Weakly-supervised anomaly detection via context- motion relational learning,.

The Evolution of Video Anomaly Detection: A Unified Framework from DNN to MLLM Look around for anomalies: Weakly-supervised anomaly detection via context- motion relational learning,

Reference 65

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This paper cites Mgfn: Magnitude-contrastive glance-and-focus network for weakly- supervised video anomaly detection,.

The Evolution of Video Anomaly Detection: A Unified Framework from DNN to MLLM Mgfn: Magnitude-contrastive glance-and-focus network for weakly- supervised video anomaly detection,

Reference 66

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Observation c63cc95d-7ed8-4e69-8f2b-1ac7940ac97b · outbound

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The Evolution of Video Anomaly Detection: A Unified Framework from DNN to MLLM Quo vadis, action recognition? a new model and the kinetics dataset,

Reference 67

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Observation 2272b5b4-4195-4d4a-8811-21b7d737ce25 · outbound

This paper cites Video swin transformer,.

The Evolution of Video Anomaly Detection: A Unified Framework from DNN to MLLM Video swin transformer,

Reference 68

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Observation cb18144a-76b6-4654-a077-055158463cbe · outbound

This paper cites Text prompt with normality guidance for weakly supervised video anomaly detection,.

The Evolution of Video Anomaly Detection: A Unified Framework from DNN to MLLM Text prompt with normality guidance for weakly supervised video anomaly detection,

Reference 69

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Observation 381f0e1a-75d4-4166-8321-a9d7885d15dd · outbound

This paper cites Unbiased multi- ple instance learning for weakly supervised video anomaly detection,.

The Evolution of Video Anomaly Detection: A Unified Framework from DNN to MLLM Unbiased multi- ple instance learning for weakly supervised video anomaly detection,

Reference 70

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This paper cites Weakly supervised video anomaly detection and localization with spatio-temporal prompts,.

The Evolution of Video Anomaly Detection: A Unified Framework from DNN to MLLM Weakly supervised video anomaly detection and localization with spatio-temporal prompts,

Reference 71

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Observation 1a8ccc85-663e-4be8-a1c6-0d471cfab4e7 · outbound

This paper cites Vision-Language Models Assisted Unsupervised Video Anomaly Detection.

The Evolution of Video Anomaly Detection: A Unified Framework from DNN to MLLM Vision-Language Models Assisted Unsupervised Video Anomaly Detection

Reference 72

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Observation 73ca25d3-75a5-496e-adec-9ab50552daa1 · outbound

This paper cites Video Anomaly Detection and Explanation via Large Language Models.

The Evolution of Video Anomaly Detection: A Unified Framework from DNN to MLLM Video Anomaly Detection and Explanation via Large Language Models

Reference 73

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Observation ea21770b-bf58-4bb9-946b-d5b07d57dc38 · outbound

This paper cites Mist: Multi-modal iterative spatial-temporal transformer for long-form video question answering,.

The Evolution of Video Anomaly Detection: A Unified Framework from DNN to MLLM Mist: Multi-modal iterative spatial-temporal transformer for long-form video question answering,

Reference 74

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Observation 4e87cc5c-1929-4ec5-88e8-950c07881f3f · outbound

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

The Evolution of Video Anomaly Detection: A Unified Framework from DNN to MLLM Object- centric auto-encoders and dummy anomalies for abnormal event detec- tion in video,

Reference 75

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

The Evolution of Video Anomaly Detection: A Unified Framework from DNN to MLLM A background-agnostic framework with adversarial training for abnor- mal event detection in video,

Reference 77

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Observation a96cba92-696f-4b8d-ad0b-43257906707c · outbound

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The Evolution of Video Anomaly Detection: A Unified Framework from DNN to MLLM Anomaly detection in video se- quence with appearance-motion correspondence,

Reference 78

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Observation f2670b79-0e67-4ac3-ad11-51aa864e5d3d · outbound

This paper cites Generative neural networks for anomaly detection in crowded scenes,.

The Evolution of Video Anomaly Detection: A Unified Framework from DNN to MLLM Generative neural networks for anomaly detection in crowded scenes,

Reference 79

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Observation 817f4573-5a0e-48a7-ab39-93ad27169470 · outbound

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The Evolution of Video Anomaly Detection: A Unified Framework from DNN to MLLM Regularity learning via explicit distribution modeling for skeletal video anomaly detection,

Reference 80

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Observation 6f6b62eb-187f-4732-888a-8c30c3955322 · outbound

This paper cites Clustering driven deep autoencoder for video anomaly detection,.

The Evolution of Video Anomaly Detection: A Unified Framework from DNN to MLLM Clustering driven deep autoencoder for video anomaly detection,

Reference 81

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The Evolution of Video Anomaly Detection: A Unified Framework from DNN to MLLM Anomaly detection with bidirectional consistency in videos,

Reference 82

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Observation dd10a135-ae1c-4b16-b585-d0e4b17cbea2 · outbound

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The Evolution of Video Anomaly Detection: A Unified Framework from DNN to MLLM Self-supervision-augmented deep autoencoder for unsupervised visual anomaly detection,

Reference 83

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The Evolution of Video Anomaly Detection: A Unified Framework from DNN to MLLM Remembering history with convolutional lstm for anomaly detection,

Reference 84

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

The Evolution of Video Anomaly Detection: A Unified Framework from DNN to MLLM A hybrid video anomaly detection framework via memory-augmented flow reconstruction and flow-guided frame prediction,

Reference 85

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The Evolution of Video Anomaly Detection: A Unified Framework from DNN to MLLM Appearance-motion memory consistency network for video anomaly detection,

Reference 86

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This paper cites Hierarchical graph embedded pose regularity learning via spatio- temporal transformer for abnormal behavior detection,.

The Evolution of Video Anomaly Detection: A Unified Framework from DNN to MLLM Hierarchical graph embedded pose regularity learning via spatio- temporal transformer for abnormal behavior detection,

Reference 87

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Observation 6870c4a5-d9ea-4c79-8c25-c990d921f832 · outbound

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The Evolution of Video Anomaly Detection: A Unified Framework from DNN to MLLM Attention- driven loss for anomaly detection in video surveillance,

Reference 88

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Observation 5bfffa8e-ac57-4b79-9b84-fbbfbf491392 · outbound

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The Evolution of Video Anomaly Detection: A Unified Framework from DNN to MLLM Normality learning in multispace for video anomaly detection,

Reference 89

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Observation 7da9a2c6-ad91-4cf5-b761-70d3f4dcf126 · outbound

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The Evolution of Video Anomaly Detection: A Unified Framework from DNN to MLLM Robust unsupervised video anomaly detection by multipath frame prediction,

Reference 90

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Observation b0c8f3be-23f4-4f48-b828-de3ffa4112c9 · outbound

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The Evolution of Video Anomaly Detection: A Unified Framework from DNN to MLLM Abnormal event detection and localization via adversarial event prediction,

Reference 91

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Observation a849242d-b4aa-4cb3-91d0-da7f0f0e1a17 · outbound

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The Evolution of Video Anomaly Detection: A Unified Framework from DNN to MLLM Object-guided and motion-refined atten- tion network for video anomaly detection,

Reference 92

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Observation f1f01806-f4cc-49e9-9031-de373a7f3181 · outbound

This paper cites Spatial- temporal graph convolutional network boosted flow-frame prediction for video anomaly detection,.

The Evolution of Video Anomaly Detection: A Unified Framework from DNN to MLLM Spatial- temporal graph convolutional network boosted flow-frame prediction for video anomaly detection,

Reference 93

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This paper cites Amp-net: Appearance-motion prototype network assisted automatic video anomaly detection system,.

The Evolution of Video Anomaly Detection: A Unified Framework from DNN to MLLM Amp-net: Appearance-motion prototype network assisted automatic video anomaly detection system,

Reference 94

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Observation b47c9610-b361-4b17-a49e-9d045cdc64d1 · outbound

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The Evolution of Video Anomaly Detection: A Unified Framework from DNN to MLLM Cloze test helps: Effective video anomaly detection via learning to complete video events,

Reference 95

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The Evolution of Video Anomaly Detection: A Unified Framework from DNN to MLLM Video event restoration based on keyframes for video anomaly detection,

Reference 96

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The Evolution of Video Anomaly Detection: A Unified Framework from DNN to MLLM Video anomaly detection via visual cloze tests,

Reference 97

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The Evolution of Video Anomaly Detection: A Unified Framework from DNN to MLLM Video anomaly detection by solving decoupled spatio-temporal jigsaw puzzles,

Reference 98

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The Evolution of Video Anomaly Detection: A Unified Framework from DNN to MLLM Video anomaly detection via sequentially learning multiple pretext tasks,

Reference 99

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Observation 9df77423-1fde-4440-8cc4-f9bbbcfce8f2 · outbound

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The Evolution of Video Anomaly Detection: A Unified Framework from DNN to MLLM Ssmtl++: Revisiting self-supervised multi-task learning for video anomaly detection,

Reference 100

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The Evolution of Video Anomaly Detection: A Unified Framework from DNN to MLLM Abnormal event detection using deep contrastive learning for intelligent video surveillance system,

Reference 101

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Observation c7ce6d6a-ceb6-4634-99f5-9369142f5edc · outbound

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The Evolution of Video Anomaly Detection: A Unified Framework from DNN to MLLM Cluster attention contrast for video anomaly detection,

Reference 102

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The Evolution of Video Anomaly Detection: A Unified Framework from DNN to MLLM Learnable locality-sensitive hashing for video anomaly detection,

Reference 103

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Observation c6b67398-571b-4f45-8aa3-381cffc78031 · outbound

This paper cites Multimodal motion conditioned diffusion model for skeleton-based video anomaly detection,.

The Evolution of Video Anomaly Detection: A Unified Framework from DNN to MLLM Multimodal motion conditioned diffusion model for skeleton-based video anomaly detection,

Reference 104

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Observation 66a08337-be92-4fa9-887b-9cc1f6389b62 · outbound

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The Evolution of Video Anomaly Detection: A Unified Framework from DNN to MLLM Adversarial 3d convolutional auto- encoder for abnormal event detection in videos,

Reference 105

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The Evolution of Video Anomaly Detection: A Unified Framework from DNN to MLLM Nm-gan: Noise- modulated generative adversarial network for video anomaly detec- tion,

Reference 106

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Observation d9e904ff-55f5-47ab-8276-69218ef69135 · outbound

This paper cites Spatio- temporal autoencoder for video anomaly detection,.

The Evolution of Video Anomaly Detection: A Unified Framework from DNN to MLLM Spatio- temporal autoencoder for video anomaly detection,

Reference 107

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