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Generative Cooperative Learning for Unsupervised Video Anomaly Detection

arXiv 2203.03962 ↗First posted 8 Mar 2022cs.CVcited by 102

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Abstract (arXiv)

Video anomaly detection is well investigated in weakly-supervised and one-class classification (OCC) settings. However, unsupervised video anomaly detection methods are quite sparse, likely because anomalies are less frequent in occurrence and usually not well-defined, which when coupled with the absence of ground truth supervision, could adversely affect the performance of the learning algorithms. This problem is challenging yet rewarding as it can completely eradicate the costs of obtaining laborious annotations and enable such systems to be deployed without human intervention. To this end, we propose a novel unsupervised Generative Cooperative Learning (GCL) approach for video anomaly detection that exploits the low frequency of anomalies towards building a cross-supervision between a generator and a discriminator. In essence, both networks get trained in a cooperative fashion, thereby allowing unsupervised learning. We conduct extensive experiments on two large-scale video anomaly detection datasets, UCF crime, and ShanghaiTech. Consistent improvement over the existing state-of-the-art unsupervised and OCC methods corroborate the effectiveness of our approach.

Key facts. Cited by 102 papers in the map (data through 7 Oct 2026). Builds on 40 of its 73 references that are in the map. First posted on arXiv 8 Mar 2022.

Builds on

40 of 73 references.

References · 40

  1. 2015Distilling the Knowledge in a Neural Networkcited by 10,076
  2. 2017Deep Mutual Learningcited by 621
  3. 2018Real-world Anomaly Detection in Surveillance Videoscited by 570
  4. 2019Memorizing Normality to Detect Anomaly: Memory-augmented Deep Autoencoder for Unsupervised Anomaly Detectioncited by 560
  5. 2017Can Spatiotemporal 3D CNNs Retrace the History of 2D CNNs and ImageNet?cited by 551
  6. 2017Future Frame Prediction for Anomaly Detection -- A New Baselinecited by 391
  7. 2019Uninformed Students: Student-Teacher Anomaly Detection with Discriminative Latent Embeddingscited by 386
  8. 2019Deep Learning for Anomaly Detection: A Surveycited by 357
  9. 2020Learning Memory-guided Normality for Anomaly Detectioncited by 278
  10. 2016Learning Temporal Regularity in Video Sequencescited by 254
  11. 2020Not only Look, but also Listen: Learning Multimodal Violence Detection under Weak Supervisioncited by 235
  12. 2021Weakly-supervised Video Anomaly Detection with Robust Temporal Feature Magnitude Learningcited by 226
28 more
  1. 2014Learning Fine-grained Image Similarity with Deep Rankingcited by 189
  2. 2019Graph Convolutional Label Noise Cleaner: Train a Plug-and-play Action Classifier for Anomaly Detectioncited by 171
  3. 2016Dual Learning for Machine Translationcited by 146
  4. 2017UntrimmedNets for Weakly Supervised Action Recognition and Detectioncited by 129
  5. 2019Anomaly Detection in Video Sequence with Appearance-Motion Correspondencecited by 118
  6. 2020Old is Gold: Redefining the Adversarially Learned One-Class Classifier Training Paradigmcited by 115
  7. 2017Abnormal Event Detection in Videos using Generative Adversarial Netscited by 82
  8. 2020CLAWS: Clustering Assisted Weakly Supervised Learning with Normalcy Suppression for Anomalous Event Detectioncited by 79
  9. 2015Learning Deep Representations of Appearance and Motion for Anomalous Event Detectioncited by 75
  10. 2018AutoLoc: Weakly-supervised Temporal Action Localizationcited by 75
  11. 2019GODS: Generalized One-class Discriminative Subspaces for Anomaly Detectioncited by 64
  12. 2017Joint Detection and Recounting of Abnormal Events by Learning Deep Generic Knowledgecited by 55
  13. 20193C-Net: Category Count and Center Loss for Weakly-Supervised Action Localizationcited by 52
  14. 2019Motion-Aware Feature for Improved Video Anomaly Detectioncited by 48
  15. 2020Robust Unsupervised Video Anomaly Detection by Multi-Path Frame Predictioncited by 42
  16. 2016Plug-and-Play CNN for Crowd Motion Analysis: An Application in Abnormal Event Detectioncited by 32
  17. 2018Subspace Support Vector Data Descriptioncited by 31
  18. 2021Learning Not to Reconstruct Anomaliescited by 31
  19. 2021Semi-orthogonal Embedding for Efficient Unsupervised Anomaly Segmentationcited by 26
  20. 2021Synthetic Temporal Anomaly Guided End-to-End Video Anomaly Detectioncited by 26
  21. 2020A Self-Reasoning Framework for Anomaly Detection Using Video-Level Labelscited by 23
  22. 2020Unsupervised Video Anomaly Detection via Normalizing Flows with Implicit Latent Featurescited by 23
  23. 2017Limiting the Reconstruction Capability of Generative Neural Network using Negative Learningcited by 16
  24. 2021OLED: One-Class Learned Encoder-Decoder Network with Adversarial Context Masking for Novelty Detectioncited by 14
  25. 2021Cleaning Label Noise with Clusters for Minimally Supervised Anomaly Detectioncited by 13
  26. 2020A Survey on Deep Learning Techniques for Video Anomaly Detectioncited by 11
  27. 2017Cooperative Learning with Visual Attributescited by 8
  28. 2019Hybrid Deep Network for Anomaly Detectioncited by 5

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2022: 11; 2023: 22; 2024: 28; 2025: 24; 2026: 17.

102 papers.

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  1. 2024Real-IAD: A Real-World Multi-View Dataset for Benchmarking Versatile Industrial Anomaly Detectioncited by 138
  2. 2022MGFN: Magnitude-Contrastive Glance-and-Focus Network for Weakly-Supervised Video Anomaly Detectioncited by 87
  3. 2024Harnessing Large Language Models for Training-free Video Anomaly Detectioncited by 85
  4. 2022CLIP-TSA: CLIP-Assisted Temporal Self-Attention for Weakly-Supervised Video Anomaly Detectioncited by 67
  5. 2024Holmes-VAU: Towards Long-term Video Anomaly Understanding at Any Granularitycited by 66
  6. 2023Unbiased Multiple Instance Learning for Weakly Supervised Video Anomaly Detectioncited by 64
  7. 2024Holmes-VAD: Towards Unbiased and Explainable Video Anomaly Detection via Multi-modal LLMcited by 63
  8. 2022Normalizing Flows for Human Pose Anomaly Detectioncited by 59
  9. 2023Generalized Video Anomaly Event Detection: Systematic Taxonomy and Comparison of Deep Modelscited by 59
  10. 2024Follow the Rules: Reasoning for Video Anomaly Detection with Large Language Modelscited by 55
  11. 2024Text Prompt with Normality Guidance for Weakly Supervised Video Anomaly Detectioncited by 49
  12. 2022SSMTL++: Revisiting Self-Supervised Multi-Task Learning for Video Anomaly Detectioncited by 45
38 more
  1. 2023Hierarchical Semantic Contrast for Scene-aware Video Anomaly Detectioncited by 39
  2. 2023Self-Distilled Masked Auto-Encoders are Efficient Video Anomaly Detectorscited by 37
  3. 2024PREGO: online mistake detection in PRocedural EGOcentric videoscited by 36
  4. 2023Delving into CLIP latent space for Video Anomaly Recognitioncited by 27
  5. 2024Deep Learning for Video Anomaly Detection: A Reviewcited by 27
  6. 2023Anomaly Heterogeneity Learning for Open-set Supervised Anomaly Detectioncited by 25
  7. 2024VERA: Explainable Video Anomaly Detection via Verbalized Learning of Vision-Language Modelscited by 24
  8. 2023Exploring Diffusion Models for Unsupervised Video Anomaly Detectioncited by 23
  9. 2023BatchNorm-based Weakly Supervised Video Anomaly Detectioncited by 22
  10. 2024Generative Semi-supervised Graph Anomaly Detectioncited by 22
  11. 2023Prompt-Guided Zero-Shot Anomaly Action Recognition using Pretrained Deep Skeleton Featurescited by 21
  12. 2024MULDE: Multiscale Log-Density Estimation via Denoising Score Matching for Video Anomaly Detectioncited by 21
  13. 2022Cross-Domain Video Anomaly Detection without Target Domain Adaptationcited by 20
  14. 2023Unsupervised Video Anomaly Detection with Diffusion Models Conditioned on Compact Motion Representationscited by 18
  15. 2024Video Anomaly Detection in 10 Years: A Survey and Outlookcited by 18
  16. 2024Collaborative Learning of Anomalies with Privacy (CLAP) for Unsupervised Video Anomaly Detection: A New Baselinecited by 17
  17. 2023The Devil is in the Wrongly-classified Samples: Towards Unified Open-set Recognitioncited by 16
  18. 2023Weakly-Supervised Video Anomaly Detection with Snippet Anomalous Attentioncited by 15
  19. 2025VADTree: Explainable Training-Free Video Anomaly Detection via Hierarchical Granularity-Aware Treecited by 15
  20. 2023Understanding the Challenges and Opportunities of Pose-based Anomaly Detectioncited by 14
  21. 2024MissionGNN: Hierarchical Multimodal GNN-based Weakly Supervised Video Anomaly Recognition with Mission-Specific Knowledge Graph Generationcited by 14
  22. 2023A Coarse-to-Fine Pseudo-Labeling (C2FPL) Framework for Unsupervised Video Anomaly Detectioncited by 13
  23. 2022Clustering Aided Weakly Supervised Training to Detect Anomalous Events in Surveillance Videoscited by 12
  24. 2025Flashback: Memory-Driven Zero-shot, Real-time Video Anomaly Detectioncited by 12
  25. 2024Privacy-Preserving Video Anomaly Detection: A Surveycited by 11
  26. 2024Beyond Walking: A Large-Scale Image-Text Benchmark for Text-based Person Anomaly Searchcited by 11
  27. 2023Survey on video anomaly detection in dynamic scenes with moving camerascited by 10
  28. 2023Video Anomaly Detection via Spatio-Temporal Pseudo-Anomaly Generation : A Unified Approachcited by 10
  29. 2024TI-PREGO: Chain of Thought and In-Context Learning for Online Mistake Detection in PRocedural EGOcentric Videoscited by 10
  30. 2025MoniTor: Exploiting Large Language Models with Instruction for Online Video Anomaly Detectioncited by 10
  31. 2025Towards Visual Discrimination and Reasoning of Real-World Physical Dynamics: Physics-Grounded Anomaly Detectioncited by 9
  32. 2024Cross-Modal Learning for Anomaly Detection in Complex Industrial Process: Methodology and Benchmarkcited by 8
  33. 2024FUN-AD: Fully Unsupervised Learning for Anomaly Detection with Noisy Training Datacited by 8
  34. 2025HiProbe-VAD: Video Anomaly Detection via Hidden States Probing in Tuning-Free Multimodal LLMscited by 8
  35. 2025Unlocking Vision-Language Models for Video Anomaly Detection via Fine-Grained Promptingcited by 8
  36. 2024Evaluating the Effectiveness of Video Anomaly Detection in the Wild: Online Learning and Inference for Real-world Deploymentcited by 7
  37. 2025Sequential keypoint density estimator: an overlooked baseline of skeleton-based video anomaly detectioncited by 7
  38. 2025Aligning Effective Tokens with Video Anomaly in Large Language Modelscited by 7

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