mlsanityData to 7 Oct 2026

A Survey of Visual Sensory Anomaly Detection

arXiv 2202.07006 ↗First posted 14 Feb 2022cs.CVcited by 11

Not on the map. Why?

Abstract (arXiv)

Visual sensory anomaly detection (AD) is an essential problem in computer vision, which is gaining momentum recently thanks to the development of AI for good. Compared with semantic anomaly detection which detects anomaly at the label level (semantic shift), visual sensory AD detects the abnormal part of the sample (covariate shift). However, no thorough review has been provided to summarize this area for the computer vision community. In this survey, we are the first one to provide a comprehensive review of visual sensory AD and category into three levels according to the form of anomalies. Furthermore, we classify each kind of anomaly according to the level of supervision. Finally, we summarize the challenges and provide open directions for this community. All resources are available at https://github.com/M-3LAB/awesome-visual-sensory-anomaly-detection.

Key facts. Cited by 11 papers in the map (data through 7 Oct 2026). Builds on 36 of its 70 references that are in the map. First posted on arXiv 14 Feb 2022.

Builds on

36 of 70 references.

References · 36

  1. 2014ImageNet Large Scale Visual Recognition Challengecited by 8,441
  2. 2016Identity Mappings in Deep Residual Networkscited by 1,960
  3. 2021Generalized Out-of-Distribution Detection: A Surveycited by 796
  4. 2019Memorizing Normality to Detect Anomaly: Memory-augmented Deep Autoencoder for Unsupervised Anomaly Detectioncited by 560
  5. 2021CutPaste: Self-Supervised Learning for Anomaly Detection and Localizationcited by 529
  6. 2021DRAEM -- A discriminatively trained reconstruction embedding for surface anomaly detectioncited by 510
  7. 2020A Unifying Review of Deep and Shallow Anomaly Detectioncited by 423
  8. 2017Future Frame Prediction for Anomaly Detection -- A New Baselinecited by 391
  9. 2019Uninformed Students: Student-Teacher Anomaly Detection with Discriminative Latent Embeddingscited by 386
  10. 2019Deep Learning for Anomaly Detection: A Surveycited by 357
  11. 2021Weakly-supervised Video Anomaly Detection with Robust Temporal Feature Magnitude Learningcited by 226
  12. 2020Same Same But DifferNet: Semi-Supervised Defect Detection with Normalizing Flowscited by 212
24 more
  1. 2020PANDA: Adapting Pretrained Features for Anomaly Detection and Segmentationcited by 175
  2. 2021The MVTec 3D-AD Dataset for Unsupervised 3D Anomaly Detection and Localizationcited by 174
  3. 2019Detecting the Unexpected via Image Resynthesiscited by 137
  4. 2020Explainable Deep One-Class Classificationcited by 126
  5. 2016Lost and Found: Detecting Small Road Hazards for Self-Driving Vehiclescited by 117
  6. 2021Pixel-wise Anomaly Detection in Complex Driving Scenescited by 109
  7. 2020A Background-Agnostic Framework with Adversarial Training for Abnormal Event Detection in Videocited by 85
  8. 2021Divide-and-Assemble: Learning Block-wise Memory for Unsupervised Anomaly Detectioncited by 83
  9. 2020CLAWS: Clustering Assisted Weakly Supervised Learning with Normalcy Suppression for Anomalous Event Detectioncited by 79
  10. 2021A Hierarchical Transformation-Discriminating Generative Model for Few Shot Anomaly Detectioncited by 60
  11. 2020Localizing Anomalies from Weakly-Labeled Videoscited by 52
  12. 2020Iterative energy-based projection on a normal data manifold for anomaly localizationcited by 47
  13. 2019Learning Fixed Points in Generative Adversarial Networks: From Image-to-Image Translation to Disease Detection and Localizationcited by 38
  14. 2020Real-time Fusion Network for RGB-D Semantic Segmentation Incorporating Unexpected Obstacle Detection for Road-driving Imagescited by 36
  15. 2021Weakly-Supervised Spatio-Temporal Anomaly Detection in Surveillance Videocited by 32
  16. 2022Training Vision Transformers with Only 2040 Imagescited by 22
  17. 2020Detection and Retrieval of Out-of-Distribution Objects in Semantic Segmentationcited by 20
  18. 2018Detecting Regions of Maximal Divergence for Spatio-Temporal Anomaly Detectioncited by 18
  19. 2019A Novel Multi-layer Framework for Tiny Obstacle Discoverycited by 14
  20. 2018MergeNet: A Deep Net Architecture for Small Obstacle Discoverycited by 11
  21. 2021Visual Anomaly Detection for Images: A Surveycited by 11
  22. 2021Reference-based Defect Detection Networkcited by 10
  23. 2020OneFlow: One-class flow for anomaly detection based on a minimal volume regioncited by 9
  24. 2020Fabric Defect Detection Using Vision-Based Tactile Sensorcited by 3

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 4; 2024: 5; 2025: 0; 2026: 2.

11 papers.

  1. 2024MMAD: A Comprehensive Benchmark for Multimodal Large Language Models in Industrial Anomaly Detectioncited by 46
  2. 2023Industrial Anomaly Detection with Domain Shift: A Real-world Dataset and Masked Multi-scale Reconstructioncited by 22
  3. 2024"Don't forget to put the milk back!" Dataset for Enabling Embodied Agents to Detect Anomalous Situationscited by 9
  4. 2023Unveiling the Unseen: A Comprehensive Survey on Explainable Anomaly Detection in Images and Videoscited by 7
  5. 2024Toward Multi-class Anomaly Detection: Exploring Class-aware Unified Model against Inter-class Interferencecited by 7
  6. 2026AD-Copilot: A Vision-Language Assistant for Industrial Anomaly Detection via Visual In-context Comparisoncited by 5
  7. 2024UMAD: University of Macau Anomaly Detection Benchmark Datasetcited by 4
  8. 2023Point Cloud Video Anomaly Detection Based on Point Spatio-Temporal Auto-Encodercited by 1
  9. 2024Leveraging Unsupervised Learning for Cost-Effective Visual Anomaly Detectioncited by 1
  10. 2023Don't Miss Out on Novelty: Importance of Novel Features for Deep Anomaly Detectioncited by 0
  11. 2026AnomalyClaw: A Universal Visual Anomaly Detection Agent via Tool-Grounded Refutationcited by 0