mlsanityData to 7 Oct 2026

Towards Open Set Video Anomaly Detection

arXiv 2208.11113 ↗First posted 23 Aug 2022cs.CVcited by 26

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Abstract (arXiv)

Open Set Video Anomaly Detection (OpenVAD) aims to identify abnormal events from video data where both known anomalies and novel ones exist in testing. Unsupervised models learned solely from normal videos are applicable to any testing anomalies but suffer from a high false positive rate. In contrast, weakly supervised methods are effective in detecting known anomalies but could fail in an open world. We develop a novel weakly supervised method for the OpenVAD problem by integrating evidential deep learning (EDL) and normalizing flows (NFs) into a multiple instance learning (MIL) framework. Specifically, we propose to use graph neural networks and triplet loss to learn discriminative features for training the EDL classifier, where the EDL is capable of identifying the unknown anomalies by quantifying the uncertainty. Moreover, we develop an uncertainty-aware selection strategy to obtain clean anomaly instances and a NFs module to generate the pseudo anomalies. Our method is superior to existing approaches by inheriting the advantages of both the unsupervised NFs and the weakly-supervised MIL framework. Experimental results on multiple real-world video datasets show the effectiveness of our method.

Key facts. Cited by 26 papers in the map (data through 7 Oct 2026). Builds on 40 of its 62 references that are in the map. First posted on arXiv 23 Aug 2022.

Builds on

40 of 62 references.

References · 40

  1. 2016Semi-Supervised Classification with Graph Convolutional Networkscited by 11,282
  2. 2017Quo Vadis, Action Recognition? A New Model and the Kinetics Datasetcited by 3,259
  3. 2016Learning without Forgettingcited by 2,465
  4. 2016Density estimation using Real NVPcited by 2,029
  5. 2018Glow: Generative Flow with Invertible 1x1 Convolutionscited by 1,539
  6. 2017A Closer Look at Memorization in Deep Networkscited by 936
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  9. 2016Improving Variational Inference with Inverse Autoregressive Flowcited by 636
  10. 2018Real-world Anomaly Detection in Surveillance Videoscited by 570
  11. 2019Memorizing Normality to Detect Anomaly: Memory-augmented Deep Autoencoder for Unsupervised Anomaly Detectioncited by 560
  12. 2015Towards Open Set Deep Networkscited by 533
28 more
  1. 2019Likelihood Ratios for Out-of-Distribution Detectioncited by 451
  2. 2017Future Frame Prediction for Anomaly Detection -- A New Baselinecited by 391
  3. 2019How does Disagreement Help Generalization against Label Corruption?cited by 390
  4. 2018Recent Advances in Open Set Recognition: A Surveycited by 309
  5. 2022VOS: Learning What You Don't Know by Virtual Outlier Synthesiscited by 300
  6. 2016Learning Temporal Regularity in Video Sequencescited by 254
  7. 2020Not only Look, but also Listen: Learning Multimodal Violence Detection under Weak Supervisioncited by 235
  8. 2017Decoupling "when to update" from "how to update"cited by 230
  9. 2021Weakly-supervised Video Anomaly Detection with Robust Temporal Feature Magnitude Learningcited by 226
  10. 2019Graph Convolutional Label Noise Cleaner: Train a Plug-and-play Action Classifier for Anomaly Detectioncited by 171
  11. 2020Posterior Network: Uncertainty Estimation without OOD Samples via Density-Based Pseudo-Countscited by 163
  12. 2017Active Bias: Training More Accurate Neural Networks by Emphasizing High Variance Samplescited by 147
  13. 2021Learning Placeholders for Open-Set Recognitioncited by 121
  14. 2020Hybrid Models for Open Set Recognitioncited by 109
  15. 2021Evidential Deep Learning for Open Set Action Recognitioncited by 96
  16. 2020A Survey of Single-Scene Video Anomaly Detectioncited by 88
  17. 2021Sample Selection with Uncertainty of Losses for Learning with Noisy Labelscited by 79
  18. 2020Cloze Test Helps: Effective Video Anomaly Detection via Learning to Complete Video Eventscited by 70
  19. 2019GODS: Generalized One-class Discriminative Subspaces for Anomaly Detectioncited by 64
  20. 2021Weakly Supervised Video Anomaly Detection via Center-guided Discriminative Learningcited by 44
  21. 2020Driver Anomaly Detection: A Dataset and Contrastive Learning Approachcited by 41
  22. 2020Evaluating Robustness of Predictive Uncertainty Estimation: Are Dirichlet-based Models Reliable?cited by 39
  23. 2019Deep Transfer Learning for Multiple Class Novelty Detectioncited by 33
  24. 2018Subspace Support Vector Data Descriptioncited by 31
  25. 2015Unsupervised Extraction of Video Highlights Via Robust Recurrent Auto-encoderscited by 30
  26. 2020A Self-Reasoning Framework for Anomaly Detection Using Video-Level Labelscited by 23
  27. 2020Unsupervised Video Anomaly Detection via Normalizing Flows with Implicit Latent Featurescited by 23
  28. 2019NFAD: Fixing anomaly detection using normalizing flowscited by 6

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 4; 2024: 7; 2025: 12; 2026: 3.

26 papers.

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  1. 2023HoloAssist: an Egocentric Human Interaction Dataset for Interactive AI Assistants in the Real Worldcited by 159
  2. 2023Open-Vocabulary Video Anomaly Detectioncited by 57
  3. 2024Deep Learning for Video Anomaly Detection: A Reviewcited by 27
  4. 2023Anomaly Heterogeneity Learning for Open-set Supervised Anomaly Detectioncited by 25
  5. 2024A Comprehensive Survey on Evidential Deep Learning and Its Applicationscited by 25
  6. 2024Revisiting Essential and Nonessential Settings of Evidential Deep Learningcited by 15
  7. 2025Anomize: Better Open Vocabulary Video Anomaly Detectioncited by 13
  8. 2023Video Anomaly Detection via Spatio-Temporal Pseudo-Anomaly Generation : A Unified Approachcited by 10
  9. 2025Distribution Prototype Diffusion Learning for Open-set Supervised Anomaly Detectioncited by 10
  10. 2025Language-guided Open-world Video Anomaly Detection under Weak Supervisioncited by 8
  11. 2024Cross-Domain Learning for Video Anomaly Detection with Limited Supervisioncited by 6
  12. 2024Human-Centric Video Anomaly Detection Through Spatio-Temporal Pose Tokenization and Transformercited by 6
14 more
  1. 2024Vision-Language Models Assisted Unsupervised Video Anomaly Detectioncited by 6
  2. 2025VAGU & GtS: LLM-Based Benchmark and Framework for Joint Video Anomaly Grounding and Understandingcited by 6
  3. 2026No Need For Real Anomaly: MLLM Empowered Zero-Shot Video Anomaly Detectioncited by 6
  4. 2024Are you Struggling? Dataset and Baselines for Struggle Determination in Assembly Videoscited by 5
  5. 2025The Evolution of Video Anomaly Detection: A Unified Framework from DNN to MLLMcited by 4
  6. 2025A Survey on Video Anomaly Detection via Deep Learning: Human, Vehicle, and Environmentcited by 4
  7. 2025A Survey on Video Analytics in Cloud-Edge-Terminal Collaborative Systemscited by 2
  8. 2026Rethinking Open-World Video Anomaly Detection: Diagnosing Definition Blindnesscited by 2
  9. 2025Mistake Attribution: Fine-Grained Mistake Understanding in Egocentric Videoscited by 1
  10. 2025CADE: Continual Weakly-supervised Video Anomaly Detection with Ensemblescited by 1
  11. 2026ESOM: Efficiently Understanding Streaming Video Anomalies with Open-world Dynamic Definitionscited by 1
  12. 2025Advancing Video Anomaly Detection: A Bi-Directional Hybrid Framework for Enhanced Single- and Multi-Task Approachescited by 0
  13. 2025What Can We Learn from Harry Potter? An Exploratory Study of Visual Representation Learning from Atypical Videoscited by 0
  14. 2025PA-VAD: Diffusion-Based Pseudo-Only Video Anomaly Detection via Domain-Aligned Memory Updatescited by 0