mlsanityData to 7 Oct 2026

Image/Video Deep Anomaly Detection: A Survey

arXiv 2103.01739 ↗First posted 2 Mar 2021cs.CVcited by 10

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Abstract (arXiv)

The considerable significance of Anomaly Detection (AD) problem has recently drawn the attention of many researchers. Consequently, the number of proposed methods in this research field has been increased steadily. AD strongly correlates with the important computer vision and image processing tasks such as image/video anomaly, irregularity and sudden event detection. More recently, Deep Neural Networks (DNNs) offer a high performance set of solutions, but at the expense of a heavy computational cost. However, there is a noticeable gap between the previously proposed methods and an applicable real-word approach. Regarding the raised concerns about AD as an ongoing challenging problem, notably in images and videos, the time has come to argue over the pitfalls and prospects of methods have attempted to deal with visual AD tasks. Hereupon, in this survey we intend to conduct an in-depth investigation into the images/videos deep learning based AD methods. We also discuss current challenges and future research directions thoroughly.

Key facts. Cited by 10 papers in the map (data through 7 Oct 2026). Builds on 28 of its 75 references that are in the map. First posted on arXiv 2 Mar 2021.

Builds on

28 of 75 references.

References · 28

  1. 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,604
  2. 2014Explaining and Harnessing Adversarial Examplescited by 7,287
  3. 2016Man is to Computer Programmer as Woman is to Homemaker? Debiasing Word Embeddingscited by 1,304
  4. 2018Real-world Anomaly Detection in Surveillance Videoscited by 570
  5. 2017Unsupervised Anomaly Detection with Generative Adversarial Networks to Guide Marker Discoverycited by 566
  6. 2019Memorizing Normality to Detect Anomaly: Memory-augmented Deep Autoencoder for Unsupervised Anomaly Detectioncited by 560
  7. 2017Future Frame Prediction for Anomaly Detection -- A New Baselinecited by 391
  8. 2019Deep Learning for Anomaly Detection: A Surveycited by 357
  9. 2019Deep Semi-Supervised Anomaly Detectioncited by 290
  10. 2020Learning Memory-guided Normality for Anomaly Detectioncited by 278
  11. 2018Deep Anomaly Detection Using Geometric Transformationscited by 269
  12. 2016Learning Temporal Regularity in Video Sequencescited by 254
16 more
  1. 2018Unsupervised Anomaly Detection via Variational Auto-Encoder for Seasonal KPIs in Web Applicationscited by 223
  2. 2018Adversarially Learned One-Class Classifier for Novelty Detectioncited by 202
  3. 2020Autoencoders for Unsupervised Anomaly Segmentation in Brain MR Images: A Comparative Studycited by 146
  4. 2019Anomaly Detection in Video Sequence with Appearance-Motion Correspondencecited by 118
  5. 2020Old is Gold: Redefining the Adversarially Learned One-Class Classifier Training Paradigmcited by 115
  6. 2016Deep Structured Energy Based Models for Anomaly Detectioncited by 113
  7. 2019Street Scene: A new dataset and evaluation protocol for video anomaly detectioncited by 89
  8. 2017Abnormal Event Detection in Videos using Generative Adversarial Netscited by 82
  9. 2020Few-shot Scene-adaptive Anomaly Detectioncited by 62
  10. 2016Deep-Anomaly: Fully Convolutional Neural Network for Fast Anomaly Detection in Crowded Scenescited by 50
  11. 2020Continual Learning for Anomaly Detection in Surveillance Videoscited by 49
  12. 2020G2D: Generate to Detect Anomalycited by 42
  13. 2020Towards Fair Deep Anomaly Detectioncited by 24
  14. 2015Real-Time Anomaly Detection and Localization in Crowded Scenescited by 20
  15. 2020A Survey on Deep Learning Techniques for Video Anomaly Detectioncited by 11
  16. 2019Self-Supervised Representation Learning via Neighborhood-Relational Encodingcited by 10

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Cited by year

Citing papers per year: 2021: 2; 2022: 4; 2023: 4.

10 papers.

  1. 2022Deep Learning for Unsupervised Anomaly Localization in Industrial Images: A Surveycited by 67
  2. 2022AnoViT: Unsupervised Anomaly Detection and Localization with Vision Transformer-based Encoder-Decodercited by 29
  3. 2021A Hierarchical Spatio-Temporal Graph Convolutional Neural Network for Anomaly Detection in Videoscited by 28
  4. 2023Exploring Diffusion Models for Unsupervised Video Anomaly Detectioncited by 23
  5. 2023Unsupervised Video Anomaly Detection with Diffusion Models Conditioned on Compact Motion Representationscited by 18
  6. 2021Deep Video Anomaly Detection: Opportunities and Challengescited by 14
  7. 2023Unveiling the Unseen: A Comprehensive Survey on Explainable Anomaly Detection in Images and Videoscited by 7
  8. 2022Deep Crowd Anomaly Detection: State-of-the-Art, Challenges, and Future Research Directionscited by 3
  9. 2022Look at Adjacent Frames: Video Anomaly Detection without Offline Trainingcited by 1
  10. 2023Audio-Visual Dataset and Method for Anomaly Detection in Traffic Videoscited by 0