mlsanityData to 14 Oct 2026

Lightning Fast Video Anomaly Detection via Adversarial Knowledge Distillation

arXiv 2211.15597 ↗First posted 28 Nov 2022cs.CV cs.AI cs.LGcited by 3

On the map: Knowledge distillation → Detection and dense prediction distillation

Abstract (arXiv)

We propose a very fast frame-level model for anomaly detection in video, which learns to detect anomalies by distilling knowledge from multiple highly accurate object-level teacher models. To improve the fidelity of our student, we distill the low-resolution anomaly maps of the teachers by jointly applying standard and adversarial distillation, introducing an adversarial discriminator for each teacher to distinguish between target and generated anomaly maps. We conduct experiments on three benchmarks (Avenue, ShanghaiTech, UCSD Ped2), showing that our method is over 7 times faster than the fastest competing method, and between 28 and 62 times faster than object-centric models, while obtaining comparable results to recent methods. Our evaluation also indicates that our model achieves the best trade-off between speed and accuracy, due to its previously unheard-of speed of 1480 FPS. In addition, we carry out a comprehensive ablation study to justify our architectural design choices. Our code is freely available at: https://github.com/ristea/fast-aed.

Key facts. Cited by 3 papers in the map (data through 14 Oct 2026). Placed in Detection and dense prediction distillation, one of 12 directions in Knowledge distillation. Builds on 57 of its 91 references that are in the map. First posted on arXiv 28 Nov 2022.

Builds on

57 of 91 references.

Closest ancestors · 2

Same direction, newest first

  1. 2022SSMTL++: Revisiting Self-Supervised Multi-Task Learning for Video Anomaly Detection16 Jul
  2. 2022Anomaly Detection via Reverse Distillation from One-Class Embedding26 Jan

Other references · 55

  1. 2017Attention Is All You Needcited by 60,361
  2. 2014Adam: A Method for Stochastic Optimizationcited by 37,240
  3. 2020An Image is Worth 16x16 Words: Transformers for Image Recognition at Scalecited by 30,186
  4. 2015Distilling the Knowledge in a Neural Networkcited by 10,324
  5. 2020End-to-End Object Detection with Transformerscited by 7,414
  6. 2020Deformable DETR: Deformable Transformers for End-to-End Object Detectioncited by 3,422
  7. 2018YOLOv3: An Incremental Improvementcited by 2,659
  8. 2020Knowledge Distillation: A Surveycited by 1,603
  9. 2021TransUNet: Transformers Make Strong Encoders for Medical Image Segmentationcited by 1,558
  10. 2021CvT: Introducing Convolutions to Vision Transformerscited by 947
  11. 2019Relational Knowledge Distillationcited by 889
  12. 2018Image Transformercited by 772
38 more
  1. 2021Point-BERT: Pre-training 3D Point Cloud Transformers with Masked Point Modelingcited by 653
  2. 2018Real-world Anomaly Detection in Surveillance Videoscited by 577
  3. 2019Memorizing Normality to Detect Anomaly: Memory-augmented Deep Autoencoder for Unsupervised Anomaly Detectioncited by 564
  4. 2013Do Deep Nets Really Need to be Deep?cited by 474
  5. 2017Future Frame Prediction for Anomaly Detection -- A New Baselinecited by 391
  6. 2019Uninformed Students: Student-Teacher Anomaly Detection with Discriminative Latent Embeddingscited by 386
  7. 2020Multiresolution Knowledge Distillation for Anomaly Detectioncited by 285
  8. 2020Learning Memory-guided Normality for Anomaly Detectioncited by 278
  9. 2016Learning Temporal Regularity in Video Sequencescited by 254
  10. 2019Graph Convolutional Label Noise Cleaner: Train a Plug-and-play Action Classifier for Anomaly Detectioncited by 171
  11. 2020Deep Learning for Anomaly Detection: A Reviewcited by 159
  12. 2020Anomaly Detection in Video via Self-Supervised and Multi-Task Learningcited by 148
  13. 2021A Hybrid Video Anomaly Detection Framework via Memory-Augmented Flow Reconstruction and Flow-Guided Frame Predictioncited by 129
  14. 2021Self-Supervised Predictive Convolutional Attentive Block for Anomaly Detectioncited by 119
  15. 2019Anomaly Detection in Video Sequence with Appearance-Motion Correspondencecited by 118
  16. 2018Object-centric Auto-encoders and Dummy Anomalies for Abnormal Event Detection in Videocited by 111
  17. 2022Generative Cooperative Learning for Unsupervised Video Anomaly Detectioncited by 102
  18. 2019Street Scene: A new dataset and evaluation protocol for video anomaly detectioncited by 90
  19. 2020A Survey of Single-Scene Video Anomaly Detectioncited by 88
  20. 2020A Background-Agnostic Framework with Adversarial Training for Abnormal Event Detection in Videocited by 86
  21. 2020Self-trained Deep Ordinal Regression for End-to-End Video Anomaly Detectioncited by 83
  22. 2017Abnormal Event Detection in Videos using Generative Adversarial Netscited by 82
  23. 2020Cloze Test Helps: Effective Video Anomaly Detection via Learning to Complete Video Eventscited by 70
  24. 2020Feature-map-level Online Adversarial Knowledge Distillationcited by 62
  25. 2022Video Anomaly Detection by Solving Decoupled Spatio-Temporal Jigsaw Puzzlescited by 62
  26. 2017Joint Detection and Recounting of Abnormal Events by Learning Deep Generic Knowledgecited by 55
  27. 2016Deep-Anomaly: Fully Convolutional Neural Network for Fast Anomaly Detection in Crowded Scenescited by 50
  28. 2017Unmasking the abnormal events in videocited by 48
  29. 2019Learning Metrics from Teachers: Compact Networks for Image Embeddingcited by 39
  30. 2020Any-Shot Sequential Anomaly Detection in Surveillance Videoscited by 34
  31. 2022Self-Supervised Masked Convolutional Transformer Block for Anomaly Detectioncited by 34
  32. 2016Plug-and-Play CNN for Crowd Motion Analysis: An Application in Abnormal Event Detectioncited by 32
  33. 2020Learning a distance function with a Siamese network to localize anomalies in videoscited by 31
  34. 2021Learning Not to Reconstruct Anomaliescited by 31
  35. 2018Detecting abnormal events in video using Narrowed Normality Clusterscited by 30
  36. 2021Synthetic Temporal Anomaly Guided End-to-End Video Anomaly Detectioncited by 26
  37. 2016A Discriminative Framework for Anomaly Detection in Large Videoscited by 25
  38. 2019Triplet Distillation for Deep Face Recognitioncited by 17

50 most cited of 55 All 55 (JSON)

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2024: 1; 2025: 0; 2026: 2.

3 papers.

  1. 2026Benchmarking Compact VLMs for Clip-Level Surveillance Anomaly Detection Under Weak Supervisioncited by 1
  2. 2024Exploring the Magnitude-Shape Plot Framework for Anomaly Detection in Crowded Video Scenescited by 0
  3. 2026Forward Consistency Learning with Gated Context Aggregation for Video Anomaly Detectioncited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2025SlowFastVAD: Video Anomaly Detection via Integrating Simple Detector and RAG-Enhanced Vision-Language Model
  2. 2024Advancing Pre-trained Teacher: Towards Robust Feature Discrepancy for Anomaly Detection
  3. 2024Unlocking the Potential of Reverse Distillation for Anomaly Detection
  4. 2025Architectural Insights into Knowledge Distillation for Object Detection: A Comprehensive Review
  5. 2023MixedTeacher : Knowledge Distillation for fast inference textural anomaly detection
  6. 2023Knowledge Distillation for Anomaly Detection
  7. 2021New Perspective on Progressive GANs Distillation for One-class Novelty Detection
  8. 2023Prior Knowledge Guided Network for Video Anomaly Detection