mlsanityData to 7 Oct 2026

CLAWS: Clustering Assisted Weakly Supervised Learning with Normalcy Suppression for Anomalous Event Detection

arXiv 2011.12077 ↗First posted 24 Nov 2020cs.CV cs.AIcited by 79

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Abstract (arXiv)

Learning to detect real-world anomalous events through video-level labels is a challenging task due to the rare occurrence of anomalies as well as noise in the labels. In this work, we propose a weakly supervised anomaly detection method which has manifold contributions including1) a random batch based training procedure to reduce inter-batch correlation, 2) a normalcy suppression mechanism to minimize anomaly scores of the normal regions of a video by taking into account the overall information available in one training batch, and 3) a clustering distance based loss to contribute towards mitigating the label noise and to produce better anomaly representations by encouraging our model to generate distinct normal and anomalous clusters. The proposed method obtains83.03% and 89.67% frame-level AUC performance on the UCF Crime and ShanghaiTech datasets respectively, demonstrating its superiority over the existing state-of-the-art algorithms.

Key facts. Cited by 79 papers in the map (data through 7 Oct 2026). First posted on arXiv 24 Nov 2020.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 8; 2022: 16; 2023: 17; 2024: 15; 2025: 13; 2026: 10.

79 papers.

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  1. 2021Weakly-supervised Video Anomaly Detection with Robust Temporal Feature Magnitude Learningcited by 226
  2. 2023VadCLIP: Adapting Vision-Language Models for Weakly Supervised Video Anomaly Detectioncited by 121
  3. 2022Catching Both Gray and Black Swans: Open-set Supervised Anomaly Detectioncited by 104
  4. 2022Generative Cooperative Learning for Unsupervised Video Anomaly Detectioncited by 102
  5. 2021UBnormal: New Benchmark for Supervised Open-Set Video Anomaly Detectioncited by 91
  6. 2023Dual Memory Units with Uncertainty Regulation for Weakly Supervised Video Anomaly Detectioncited by 88
  7. 2022MGFN: Magnitude-Contrastive Glance-and-Focus Network for Weakly-Supervised Video Anomaly Detectioncited by 87
  8. 2024Harnessing Large Language Models for Training-free Video Anomaly Detectioncited by 85
  9. 2022Unknown-Aware Object Detection: Learning What You Don't Know from Videos in the Wildcited by 70
  10. 2022CLIP-TSA: CLIP-Assisted Temporal Self-Attention for Weakly-Supervised Video Anomaly Detectioncited by 67
  11. 2023Generalized Video Anomaly Event Detection: Systematic Taxonomy and Comparison of Deep Modelscited by 59
  12. 2023Open-Vocabulary Video Anomaly Detectioncited by 57
38 more
  1. 2024Text Prompt with Normality Guidance for Weakly Supervised Video Anomaly Detectioncited by 49
  2. 2022Exploiting Completeness and Uncertainty of Pseudo Labels for Weakly Supervised Video Anomaly Detectioncited by 47
  3. 2024Hawk: Learning to Understand Open-World Video Anomaliescited by 47
  4. 2022SSMTL++: Revisiting Self-Supervised Multi-Task Learning for Video Anomaly Detectioncited by 45
  5. 2024Video Anomaly Detection and Explanation via Large Language Modelscited by 45
  6. 2023Learning Prompt-Enhanced Context Features for Weakly-Supervised Video Anomaly Detectioncited by 43
  7. 2024Weakly Supervised Video Anomaly Detection and Localization with Spatio-Temporal Promptscited by 40
  8. 2023Self-Distilled Masked Auto-Encoders are Efficient Video Anomaly Detectorscited by 37
  9. 2021Learning Not to Reconstruct Anomaliescited by 31
  10. 2023Delving into CLIP latent space for Video Anomaly Recognitioncited by 27
  11. 2024Deep Learning for Video Anomaly Detection: A Reviewcited by 27
  12. 2021Synthetic Temporal Anomaly Guided End-to-End Video Anomaly Detectioncited by 26
  13. 2024VERA: Explainable Video Anomaly Detection via Verbalized Learning of Vision-Language Modelscited by 24
  14. 2021Regularity Learning via Explicit Distribution Modeling for Skeletal Video Anomaly Detectioncited by 22
  15. 2023BatchNorm-based Weakly Supervised Video Anomaly Detectioncited by 22
  16. 2021Deep Anomaly Discovery From Unlabeled Videos via Normality Advantage and Self-Paced Refinementcited by 21
  17. 2022Contrastive Transformer-based Multiple Instance Learning for Weakly Supervised Polyp Frame Detectioncited by 20
  18. 2024Collaborative Learning of Anomalies with Privacy (CLAP) for Unsupervised Video Anomaly Detection: A New Baselinecited by 17
  19. 2023Weakly-Supervised Video Anomaly Detection with Snippet Anomalous Attentioncited by 15
  20. 2025VADTree: Explainable Training-Free Video Anomaly Detection via Hierarchical Granularity-Aware Treecited by 15
  21. 2022Stabilizing Adversarially Learned One-Class Novelty Detection Using Pseudo Anomaliescited by 14
  22. 2023A Coarse-to-Fine Pseudo-Labeling (C2FPL) Framework for Unsupervised Video Anomaly Detectioncited by 13
  23. 2025Anomize: Better Open Vocabulary Video Anomaly Detectioncited by 13
  24. 2022Clustering Aided Weakly Supervised Training to Detect Anomalous Events in Surveillance Videoscited by 12
  25. 2023Learning Weakly Supervised Audio-Visual Violence Detection in Hyperbolic Spacecited by 12
  26. 2022A Survey of Visual Sensory Anomaly Detectioncited by 11
  27. 2023Long-Short Temporal Co-Teaching for Weakly Supervised Video Anomaly Detectioncited by 10
  28. 2023Survey on video anomaly detection in dynamic scenes with moving camerascited by 10
  29. 2023Video Anomaly Detection via Spatio-Temporal Pseudo-Anomaly Generation : A Unified Approachcited by 10
  30. 2023PseudoBound: Limiting the anomaly reconstruction capability of one-class classifiers using pseudo anomaliescited by 8
  31. 2022Adaptive Graph Convolutional Networks for Weakly Supervised Anomaly Detection in Videoscited by 7
  32. 2025Aligning Effective Tokens with Video Anomaly in Large Language Modelscited by 7
  33. 2024Vision-Language Models Assisted Unsupervised Video Anomaly Detectioncited by 6
  34. 2025AVadCLIP: Audio-Visual Collaboration for Robust Video Anomaly Detectioncited by 6
  35. 2026No Need For Real Anomaly: MLLM Empowered Zero-Shot Video Anomaly Detectioncited by 6
  36. 2022Hierarchical Semi-Supervised Contrastive Learning for Contamination-Resistant Anomaly Detectioncited by 5
  37. 2024Interleaving One-Class and Weakly-Supervised Models with Adaptive Thresholding for Unsupervised Video Anomaly Detectioncited by 5
  38. 2024Exploiting Autoencoder's Weakness to Generate Pseudo Anomaliescited by 4

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