mlsanityData to 7 Oct 2026

IPAD: Industrial Process Anomaly Detection Dataset

arXiv 2404.15033 ↗First posted 23 Apr 2024cs.CVcited by 3

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Abstract (arXiv)

Video anomaly detection (VAD) is a challenging task aiming to recognize anomalies in video frames, and existing large-scale VAD researches primarily focus on road traffic and human activity scenes. In industrial scenes, there are often a variety of unpredictable anomalies, and the VAD method can play a significant role in these scenarios. However, there is a lack of applicable datasets and methods specifically tailored for industrial production scenarios due to concerns regarding privacy and security. To bridge this gap, we propose a new dataset, IPAD, specifically designed for VAD in industrial scenarios. The industrial processes in our dataset are chosen through on-site factory research and discussions with engineers. This dataset covers 16 different industrial devices and contains over 6 hours of both synthetic and real-world video footage. Moreover, we annotate the key feature of the industrial process, ie, periodicity. Based on the proposed dataset, we introduce a period memory module and a sliding window inspection mechanism to effectively investigate the periodic information in a basic reconstruction model. Our framework leverages LoRA adapter to explore the effective migration of pretrained models, which are initially trained using synthetic data, into real-world scenarios. Our proposed dataset and method will fill the gap in the field of industrial video anomaly detection and drive the process of video understanding tasks as well as smart factory deployment.

Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Builds on 50 of its 77 references that are in the map. First posted on arXiv 23 Apr 2024.

Builds on

50 of 77 references.

References · 50

  1. 2021LoRA: Low-Rank Adaptation of Large Language Modelscited by 14,084
  2. 2017Quo Vadis, Action Recognition? A New Model and the Kinetics Datasetcited by 3,259
  3. 2017CARLA: An Open Urban Driving Simulatorcited by 2,431
  4. 2021Video Swin Transformercited by 1,090
  5. 2018Real-world Anomaly Detection in Surveillance Videoscited by 570
  6. 2017Unsupervised Anomaly Detection with Generative Adversarial Networks to Guide Marker Discoverycited by 566
  7. 2019Memorizing Normality to Detect Anomaly: Memory-augmented Deep Autoencoder for Unsupervised Anomaly Detectioncited by 560
  8. 2021CutPaste: Self-Supervised Learning for Anomaly Detection and Localizationcited by 529
  9. 2017Future Frame Prediction for Anomaly Detection -- A New Baselinecited by 391
  10. 2019Uninformed Students: Student-Teacher Anomaly Detection with Discriminative Latent Embeddingscited by 386
  11. 2016Virtual Worlds as Proxy for Multi-Object Tracking Analysiscited by 340
  12. 2021CFLOW-AD: Real-Time Unsupervised Anomaly Detection with Localization via Conditional Normalizing Flowscited by 320
38 more
  1. 2020Multiresolution Knowledge Distillation for Anomaly Detectioncited by 285
  2. 2016Learning Temporal Regularity in Video Sequencescited by 254
  3. 2021FastFlow: Unsupervised Anomaly Detection and Localization via 2D Normalizing Flowscited by 225
  4. 2021VT-ADL: A Vision Transformer Network for Image Anomaly Detection and Localizationcited by 222
  5. 2019Graph Convolutional Label Noise Cleaner: Train a Plug-and-play Action Classifier for Anomaly Detectioncited by 171
  6. 2024Real-IAD: A Real-World Multi-View Dataset for Benchmarking Versatile Industrial Anomaly Detectioncited by 138
  7. 2021A Hybrid Video Anomaly Detection Framework via Memory-Augmented Flow Reconstruction and Flow-Guided Frame Predictioncited by 129
  8. 2019Towards Visually Explaining Variational Autoencoderscited by 113
  9. 2021Inpainting Transformer for Anomaly Detectioncited by 91
  10. 2021UBnormal: New Benchmark for Supervised Open-Set Video Anomaly Detectioncited by 91
  11. 2019Street Scene: A new dataset and evaluation protocol for video anomaly detectioncited by 89
  12. 2020CLAWS: Clustering Assisted Weakly Supervised Learning with Normalcy Suppression for Anomalous Event Detectioncited by 79
  13. 2022Omni-frequency Channel-selection Representations for Unsupervised Anomaly Detectioncited by 79
  14. 2022Deep Learning for Unsupervised Anomaly Localization in Industrial Images: A Surveycited by 67
  15. 2020Few-shot Scene-adaptive Anomaly Detectioncited by 62
  16. 2022Video Anomaly Detection by Solving Decoupled Spatio-Temporal Jigsaw Puzzlescited by 62
  17. 2018Understanding Humans in Crowded Scenes: Deep Nested Adversarial Learning and A New Benchmark for Multi-Human Parsingcited by 60
  18. 2019Multi-timescale Trajectory Prediction for Abnormal Human Activity Detectioncited by 59
  19. 2022Normalizing Flows for Human Pose Anomaly Detectioncited by 59
  20. 2022SSMTL++: Revisiting Self-Supervised Multi-Task Learning for Video Anomaly Detectioncited by 45
  21. 2022Self-Supervised Masking for Unsupervised Anomaly Detection and Localizationcited by 43
  22. 2023A New Comprehensive Benchmark for Semi-supervised Video Anomaly Detection and Anticipationcited by 42
  23. 2023Diversity-Measurable Anomaly Detectioncited by 41
  24. 2023Hierarchical Semantic Contrast for Scene-aware Video Anomaly Detectioncited by 39
  25. 2021Progressive Temporal Feature Alignment Network for Video Inpaintingcited by 34
  26. 2021A Hierarchical Spatio-Temporal Graph Convolutional Neural Network for Anomaly Detection in Videoscited by 28
  27. 2021Semi-orthogonal Embedding for Efficient Unsupervised Anomaly Segmentationcited by 26
  28. 2023Towards Video Anomaly Retrieval from Video Anomaly Detection: New Benchmarks and Modelcited by 25
  29. 2023Industrial Anomaly Detection with Domain Shift: A Real-world Dataset and Masked Multi-scale Reconstructioncited by 22
  30. 2020TrustMAE: A Noise-Resilient Defect Classification Framework using Memory-Augmented Auto-Encoders with Trust Regionscited by 21
  31. 2023Learning Global-Local Correspondence with Semantic Bottleneck for Logical Anomaly Detectioncited by 21
  32. 2021Learnable Locality-Sensitive Hashing for Video Anomaly Detectioncited by 17
  33. 2023Learning Causality-inspired Representation Consistency for Video Anomaly Detectioncited by 17
  34. 2023Weakly-Supervised Video Anomaly Detection with Snippet Anomalous Attentioncited by 15
  35. 2024COFT-AD: COntrastive Fine-Tuning for Few-Shot Anomaly Detectioncited by 13
  36. 2021Unsupervised Two-Stage Anomaly Detectioncited by 12
  37. 2021Transfer Learning Gaussian Anomaly Detection by Fine-tuning Representationscited by 10
  38. 2023Improving Anomaly Segmentation with Multi-Granularity Cross-Domain Alignmentcited by 6

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2026: 3.

3 papers.

  1. 2026LLM-Guided Safety Agent for Edge Robotics with an ISO-Compliant Perception-Compute-Control Architecturecited by 0
  2. 2026O-VAD: Industrial Video Anomaly Detection through Object-Centric Tracking and Reasoningcited by 0
  3. 2026Adaptive Multi-Granularity Temporal Modeling for Weakly Supervised Video Anomaly Detectioncited by 0