mlsanityData to 7 Oct 2026

Synthetic Temporal Anomaly Guided End-to-End Video Anomaly Detection

arXiv 2110.09768 ↗First posted 19 Oct 2021cs.CVcited by 26

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Abstract (arXiv)

Due to the limited availability of anomaly examples, video anomaly detection is often seen as one-class classification (OCC) problem. A popular way to tackle this problem is by utilizing an autoencoder (AE) trained only on normal data. At test time, the AE is then expected to reconstruct the normal input well while reconstructing the anomalies poorly. However, several studies show that, even with normal data only training, AEs can often start reconstructing anomalies as well which depletes their anomaly detection performance. To mitigate this, we propose a temporal pseudo anomaly synthesizer that generates fake-anomalies using only normal data. An AE is then trained to maximize the reconstruction loss on pseudo anomalies while minimizing this loss on normal data. This way, the AE is encouraged to produce distinguishable reconstructions for normal and anomalous frames. Extensive experiments and analysis on three challenging video anomaly datasets demonstrate the effectiveness of our approach to improve the basic AEs in achieving superiority against several existing state-of-the-art models.

Key facts. Cited by 26 papers in the map (data through 7 Oct 2026). Builds on 20 of its 47 references that are in the map. First posted on arXiv 19 Oct 2021.

Builds on

20 of 47 references.

References · 20

  1. 2014Adam: A Method for Stochastic Optimizationcited by 36,960
  2. 2019PyTorch: An Imperative Style, High-Performance Deep Learning Librarycited by 20,160
  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. 2017Future Frame Prediction for Anomaly Detection -- A New Baselinecited by 391
  6. 2015Deep multi-scale video prediction beyond mean square errorcited by 297
  7. 2020Learning Memory-guided Normality for Anomaly Detectioncited by 278
  8. 2016Learning Temporal Regularity in Video Sequencescited by 254
  9. 2018Adversarially Learned One-Class Classifier for Novelty Detectioncited by 202
  10. 2019Graph Convolutional Label Noise Cleaner: Train a Plug-and-play Action Classifier for Anomaly Detectioncited by 171
  11. 2018Latent Space Autoregression for Novelty Detectioncited by 149
  12. 2019Anomaly Detection in Video Sequence with Appearance-Motion Correspondencecited by 118
8 more
  1. 2020Old is Gold: Redefining the Adversarially Learned One-Class Classifier Training Paradigmcited by 115
  2. 2019Street Scene: A new dataset and evaluation protocol for video anomaly detectioncited by 89
  3. 2017Abnormal Event Detection in Videos using Generative Adversarial Netscited by 82
  4. 2020CLAWS: Clustering Assisted Weakly Supervised Learning with Normalcy Suppression for Anomalous Event Detectioncited by 79
  5. 2020A Self-Reasoning Framework for Anomaly Detection Using Video-Level Labelscited by 23
  6. 2019Autoencoding Binary Classifiers for Supervised Anomaly Detectioncited by 17
  7. 2017Limiting the Reconstruction Capability of Generative Neural Network using Negative Learningcited by 16
  8. 2021Cleaning Label Noise with Clusters for Minimally Supervised Anomaly Detectioncited by 13

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 2; 2022: 9; 2023: 4; 2024: 6; 2025: 3; 2026: 2.

26 papers.

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  1. 2021Self-Supervised Predictive Convolutional Attentive Block for Anomaly Detectioncited by 119
  2. 2022Generative Cooperative Learning for Unsupervised Video Anomaly Detectioncited by 102
  3. 2021UBnormal: New Benchmark for Supervised Open-Set Video Anomaly Detectioncited by 91
  4. 2024Advancing Video Anomaly Detection: A Concise Review and a New Datasetcited by 48
  5. 2022SSMTL++: Revisiting Self-Supervised Multi-Task Learning for Video Anomaly Detectioncited by 45
  6. 2023Self-Distilled Masked Auto-Encoders are Efficient Video Anomaly Detectorscited by 37
  7. 2022Self-Supervised Masked Convolutional Transformer Block for Anomaly Detectioncited by 34
  8. 2022Dynamic Local Aggregation Network with Adaptive Clusterer for Anomaly Detectioncited by 27
  9. 2022Cross-Domain Video Anomaly Detection without Target Domain Adaptationcited by 20
  10. 2022EVAL: Explainable Video Anomaly Localizationcited by 19
  11. 2025AnyAnomaly: Zero-Shot Customizable Video Anomaly Detection with LVLMcited by 15
  12. 2022Clustering Aided Weakly Supervised Training to Detect Anomalous Events in Surveillance Videoscited by 12
14 more
  1. 2023Video Anomaly Detection via Spatio-Temporal Pseudo-Anomaly Generation : A Unified Approachcited by 10
  2. 2023PseudoBound: Limiting the anomaly reconstruction capability of one-class classifiers using pseudo anomaliescited by 8
  3. 2024Vision-Language Models Assisted Unsupervised Video Anomaly Detectioncited by 6
  4. 2024Frequency-Guided Diffusion Model with Perturbation Training for Skeleton-Based Video Anomaly Detectioncited by 6
  5. 2025VAGU & GtS: LLM-Based Benchmark and Framework for Joint Video Anomaly Grounding and Understandingcited by 6
  6. 2024Exploiting Autoencoder's Weakness to Generate Pseudo Anomaliescited by 4
  7. 2022Lightning Fast Video Anomaly Detection via Adversarial Knowledge Distillationcited by 3
  8. 2022Contextual Information Based Anomaly Detection for a Multi-Scene UAV Aerial Videoscited by 1
  9. 2024Constricting Normal Latent Space for Anomaly Detection with Normal-only Training Datacited by 1
  10. 2024Identifying Spatio-Temporal Drivers of Extreme Eventscited by 1
  11. 2025Uncertainty-Weighted Fusion of Image and Synthetic Event for Video Anomaly Detectioncited by 1
  12. 2023Synthetic Pseudo Anomalies for Unsupervised Video Anomaly Detection: A Simple yet Efficient Framework based on Masked Autoencodercited by 0
  13. 2026Look Inside Each Video: Rethinking How Video Anomaly Detection Is Evaluatedcited by 0
  14. 2026Multi-Modal Anomaly Detection: A Surveycited by 0