mlsanityData to 14 Oct 2026

Graph Convolutional Label Noise Cleaner: Train a Plug-and-play Action Classifier for Anomaly Detection

arXiv 1903.07256 ↗First posted 18 Mar 2019cs.CVcited by 171

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Abstract (arXiv)

Video anomaly detection under weak labels is formulated as a typical multiple-instance learning problem in previous works. In this paper, we provide a new perspective, i.e., a supervised learning task under noisy labels. In such a viewpoint, as long as cleaning away label noise, we can directly apply fully supervised action classifiers to weakly supervised anomaly detection, and take maximum advantage of these well-developed classifiers. For this purpose, we devise a graph convolutional network to correct noisy labels. Based upon feature similarity and temporal consistency, our network propagates supervisory signals from high-confidence snippets to low-confidence ones. In this manner, the network is capable of providing cleaned supervision for action classifiers. During the test phase, we only need to obtain snippet-wise predictions from the action classifier without any extra post-processing. Extensive experiments on 3 datasets at different scales with 2 types of action classifiers demonstrate the efficacy of our method. Remarkably, we obtain the frame-level AUC score of 82.12% on UCF-Crime.

Key facts. Cited by 171 papers in the map (data through 14 Oct 2026). First posted on arXiv 18 Mar 2019.

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Citing papers per year: 2021: 26; 2022: 41; 2023: 28; 2024: 34; 2025: 27; 2026: 15.

171 papers.

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  1. 2021Generalized Out-of-Distribution Detection: A Surveycited by 802
  2. 2021Weakly-supervised Video Anomaly Detection with Robust Temporal Feature Magnitude Learningcited by 230
  3. 2021MIST: Multiple Instance Self-Training Framework for Video Anomaly Detectioncited by 138
  4. 2023VadCLIP: Adapting Vision-Language Models for Weakly Supervised Video Anomaly Detectioncited by 123
  5. 2022Generative Cooperative Learning for Unsupervised Video Anomaly Detectioncited by 102
  6. 2021UBnormal: New Benchmark for Supervised Open-Set Video Anomaly Detectioncited by 93
  7. 2023Dual Memory Units with Uncertainty Regulation for Weakly Supervised Video Anomaly Detectioncited by 90
  8. 2022MGFN: Magnitude-Contrastive Glance-and-Focus Network for Weakly-Supervised Video Anomaly Detectioncited by 89
  9. 2024Harnessing Large Language Models for Training-free Video Anomaly Detectioncited by 87
  10. 2021Learning Normal Dynamics in Videos with Meta Prototype Networkcited by 71
  11. 2024Holmes-VAU: Towards Long-term Video Anomaly Understanding at Any Granularitycited by 70
  12. 2022CLIP-TSA: CLIP-Assisted Temporal Self-Attention for Weakly-Supervised Video Anomaly Detectioncited by 67
38 more
  1. 2023Unbiased Multiple Instance Learning for Weakly Supervised Video Anomaly Detectioncited by 64
  2. 2024Holmes-VAD: Towards Unbiased and Explainable Video Anomaly Detection via Multi-modal LLMcited by 64
  3. 2023Generalized Video Anomaly Event Detection: Systematic Taxonomy and Comparison of Deep Modelscited by 59
  4. 2023Open-Vocabulary Video Anomaly Detectioncited by 57
  5. 2024Text Prompt with Normality Guidance for Weakly Supervised Video Anomaly Detectioncited by 49
  6. 2022Exploiting Completeness and Uncertainty of Pseudo Labels for Weakly Supervised Video Anomaly Detectioncited by 47
  7. 2024Video Anomaly Detection and Explanation via Large Language Modelscited by 47
  8. 2022SSMTL++: Revisiting Self-Supervised Multi-Task Learning for Video Anomaly Detectioncited by 45
  9. 2021Weakly Supervised Video Anomaly Detection via Center-guided Discriminative Learningcited by 44
  10. 2023Learning Prompt-Enhanced Context Features for Weakly-Supervised Video Anomaly Detectioncited by 43
  11. 2024Weakly Supervised Video Anomaly Detection and Localization with Spatio-Temporal Promptscited by 40
  12. 2023Hierarchical Semantic Contrast for Scene-aware Video Anomaly Detectioncited by 39
  13. 2021Multiscale Spatio-Temporal Graph Neural Networks for 3D Skeleton-Based Motion Predictioncited by 37
  14. 2023Self-Distilled Masked Auto-Encoders are Efficient Video Anomaly Detectorscited by 37
  15. 2021Weakly-Supervised Spatio-Temporal Anomaly Detection in Surveillance Videocited by 32
  16. 2021Learning Not to Reconstruct Anomaliescited by 31
  17. 2021Unsupervised Multi-Source Domain Adaptation for Person Re-Identificationcited by 30
  18. 2023Delving into CLIP latent space for Video Anomaly Recognitioncited by 27
  19. 2024Deep Learning for Video Anomaly Detection: A Reviewcited by 27
  20. 2021Synthetic Temporal Anomaly Guided End-to-End Video Anomaly Detectioncited by 26
  21. 2022Towards Open Set Video Anomaly Detectioncited by 26
  22. 2022DyAnNet: A Scene Dynamicity Guided Self-Trained Video Anomaly Detection Networkcited by 26
  23. 2024VERA: Explainable Video Anomaly Detection via Verbalized Learning of Vision-Language Modelscited by 24
  24. 2022Bayesian Nonparametric Submodular Video Partition for Robust Anomaly Detectioncited by 23
  25. 2023TeD-SPAD: Temporal Distinctiveness for Self-supervised Privacy-preservation for video Anomaly Detectioncited by 23
  26. 2022Multiple Instance Learning via Iterative Self-Paced Supervised Contrastive Learningcited by 22
  27. 2023Weakly Supervised Anomaly Detection: A Surveycited by 22
  28. 2023Learning on Graphs under Label Noisecited by 22
  29. 2023Data-centric Graph Learning: A Surveycited by 22
  30. 2023BatchNorm-based Weakly Supervised Video Anomaly Detectioncited by 22
  31. 2021Deep Anomaly Discovery From Unlabeled Videos via Normality Advantage and Self-Paced Refinementcited by 21
  32. 2022Contrastive Transformer-based Multiple Instance Learning for Weakly Supervised Polyp Frame Detectioncited by 20
  33. 2022A Survey of Graph-based Deep Learning for Anomaly Detection in Distributed Systemscited by 20
  34. 2023Graph Anomaly Detection in Time Series: A Surveycited by 18
  35. 2024Video Anomaly Detection in 10 Years: A Survey and Outlookcited by 18
  36. 2025VADTree: Explainable Training-Free Video Anomaly Detection via Hierarchical Granularity-Aware Treecited by 18
  37. 2021Learning from Pixel-Level Label Noise: A New Perspective for Semi-Supervised Semantic Segmentationcited by 16
  38. 2021FastAno: Fast Anomaly Detection via Spatio-temporal Patch Transformationcited by 15

50 most cited of 171 All 171 (JSON)