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Few-shot Scene-adaptive Anomaly Detection

arXiv 2007.07843 ↗First posted 15 Jul 2020cs.CV cs.LGcited by 62

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Abstract (arXiv)

We address the problem of anomaly detection in videos. The goal is to identify unusual behaviours automatically by learning exclusively from normal videos. Most existing approaches are usually data-hungry and have limited generalization abilities. They usually need to be trained on a large number of videos from a target scene to achieve good results in that scene. In this paper, we propose a novel few-shot scene-adaptive anomaly detection problem to address the limitations of previous approaches. Our goal is to learn to detect anomalies in a previously unseen scene with only a few frames. A reliable solution for this new problem will have huge potential in real-world applications since it is expensive to collect a massive amount of data for each target scene. We propose a meta-learning based approach for solving this new problem; extensive experimental results demonstrate the effectiveness of our proposed method.

Key facts. Cited by 62 papers in the map (data through 7 Oct 2026). First posted on arXiv 15 Jul 2020.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 16; 2022: 11; 2023: 11; 2024: 13; 2025: 8; 2026: 3.

62 papers.

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  1. 2021A Unified Survey on Anomaly, Novelty, Open-Set, and Out-of-Distribution Detection: Solutions and Future Challengescited by 154
  2. 2021Self-Supervised Predictive Convolutional Attentive Block for Anomaly Detectioncited by 119
  3. 2021UBnormal: New Benchmark for Supervised Open-Set Video Anomaly Detectioncited by 91
  4. 2024Toward Generalist Anomaly Detection via In-context Residual Learning with Few-shot Sample Promptscited by 88
  5. 2022MGFN: Magnitude-Contrastive Glance-and-Focus Network for Weakly-Supervised Video Anomaly Detectioncited by 87
  6. 2021Learning Normal Dynamics in Videos with Meta Prototype Networkcited by 71
  7. 2021Explainable Deep Few-shot Anomaly Detection with Deviation Networkscited by 69
  8. 2023Generalized Video Anomaly Event Detection: Systematic Taxonomy and Comparison of Deep Modelscited by 59
  9. 2024Follow the Rules: Reasoning for Video Anomaly Detection with Large Language Modelscited by 55
  10. 2024Advancing Video Anomaly Detection: A Concise Review and a New Datasetcited by 48
  11. 2024ARC: A Generalist Graph Anomaly Detector with In-Context Learningcited by 46
  12. 2022SSMTL++: Revisiting Self-Supervised Multi-Task Learning for Video Anomaly Detectioncited by 45
38 more
  1. 2023MetaGCD: Learning to Continually Learn in Generalized Category Discoverycited by 40
  2. 2023Hierarchical Semantic Contrast for Scene-aware Video Anomaly Detectioncited by 39
  3. 2023Self-Distilled Masked Auto-Encoders are Efficient Video Anomaly Detectorscited by 37
  4. 2023Anomaly Detection under Distribution Shiftcited by 35
  5. 2021Learning Not to Reconstruct Anomaliescited by 31
  6. 2024Deep Learning for Video Anomaly Detection: A Reviewcited by 27
  7. 2023Anomaly Heterogeneity Learning for Open-set Supervised Anomaly Detectioncited by 25
  8. 2025VAU-R1: Advancing Video Anomaly Understanding via Reinforcement Fine-Tuningcited by 24
  9. 2021Deep Anomaly Discovery From Unlabeled Videos via Normality Advantage and Self-Paced Refinementcited by 21
  10. 2022Cross-Domain Video Anomaly Detection without Target Domain Adaptationcited by 20
  11. 2021Progressive Attention on Multi-Level Dense Difference Maps for Generic Event Boundary Detectioncited by 19
  12. 2022EVAL: Explainable Video Anomaly Localizationcited by 19
  13. 2022Context Recovery and Knowledge Retrieval: A Novel Two-Stream Framework for Video Anomaly Detectioncited by 18
  14. 2021FastAno: Fast Anomaly Detection via Spatio-temporal Patch Transformationcited by 15
  15. 2021Anomaly Detection in Multi-Agent Trajectories for Automated Drivingcited by 15
  16. 2025AnyAnomaly: Zero-Shot Customizable Video Anomaly Detection with LVLMcited by 15
  17. 2021Image/Video Deep Anomaly Detection: A Surveycited by 10
  18. 2023Video Anomaly Detection via Spatio-Temporal Pseudo-Anomaly Generation : A Unified Approachcited by 10
  19. 2023PseudoBound: Limiting the anomaly reconstruction capability of one-class classifiers using pseudo anomaliescited by 8
  20. 2024Towards Zero-shot 3D Anomaly Localizationcited by 8
  21. 2023Unveiling the Unseen: A Comprehensive Survey on Explainable Anomaly Detection in Images and Videoscited by 7
  22. 2024A Survey of Few-Shot Learning for Biomedical Time Seriescited by 7
  23. 2025ComplexVAD: Detecting Interaction Anomalies in Videocited by 7
  24. 2025Aligning Effective Tokens with Video Anomaly in Large Language Modelscited by 7
  25. 2021A Modular and Unified Framework for Detecting and Localizing Video Anomaliescited by 6
  26. 2024Cross-Domain Learning for Video Anomaly Detection with Limited Supervisioncited by 6
  27. 2024Vision-Language Models Assisted Unsupervised Video Anomaly Detectioncited by 6
  28. 2025VAGU & GtS: LLM-Based Benchmark and Framework for Joint Video Anomaly Grounding and Understandingcited by 6
  29. 2021Ano-Graph: Learning Normal Scene Contextual Graphs to Detect Video Anomaliescited by 4
  30. 2021Dual-Modality Vehicle Anomaly Detection via Bilateral Trajectory Tracingcited by 4
  31. 2022Enhancing Few-shot Image Classification with Cosine Transformercited by 4
  32. 2024Exploiting Autoencoder's Weakness to Generate Pseudo Anomaliescited by 4
  33. 2025The Evolution of Video Anomaly Detection: A Unified Framework from DNN to MLLMcited by 4
  34. 2025A Survey on Video Anomaly Detection via Deep Learning: Human, Vehicle, and Environmentcited by 4
  35. 2024IPAD: Industrial Process Anomaly Detection Datasetcited by 3
  36. 2024Appearance Blur-driven AutoEncoder and Motion-guided Memory Module for Video Anomaly Detectioncited by 3
  37. 2021Predicting Next Local Appearance for Video Anomaly Detectioncited by 2
  38. 2022Learning to Adapt to Unseen Abnormal Activities under Weak Supervisioncited by 2

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