mlsanityData to 7 Oct 2026

Weakly Supervised Video Anomaly Detection via Center-guided Discriminative Learning

arXiv 2104.07268 ↗First posted 15 Apr 2021cs.CVcited by 44

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Abstract (arXiv)

Anomaly detection in surveillance videos is a challenging task due to the diversity of anomalous video content and duration. In this paper, we consider video anomaly detection as a regression problem with respect to anomaly scores of video clips under weak supervision. Hence, we propose an anomaly detection framework, called Anomaly Regression Net (AR-Net), which only requires video-level labels in training stage. Further, to learn discriminative features for anomaly detection, we design a dynamic multiple-instance learning loss and a center loss for the proposed AR-Net. The former is used to enlarge the inter-class distance between anomalous and normal instances, while the latter is proposed to reduce the intra-class distance of normal instances. Comprehensive experiments are performed on a challenging benchmark: ShanghaiTech. Our method yields a new state-of-the-art result for video anomaly detection on ShanghaiTech dataset

Key facts. Cited by 44 papers in the map (data through 7 Oct 2026). Builds on 11 of its 21 references that are in the map. First posted on arXiv 15 Apr 2021.

Builds on

11 of 21 references.

References · 11

  1. 2014Adam: A Method for Stochastic Optimizationcited by 36,960
  2. 2017Quo Vadis, Action Recognition? A New Model and the Kinetics Datasetcited by 3,259
  3. 2018Real-world Anomaly Detection in Surveillance Videoscited by 570
  4. 2017Future Frame Prediction for Anomaly Detection -- A New Baselinecited by 391
  5. 2016Learning Temporal Regularity in Video Sequencescited by 254
  6. 2019Graph Convolutional Label Noise Cleaner: Train a Plug-and-play Action Classifier for Anomaly Detectioncited by 171
  7. 2018Object-centric Auto-encoders and Dummy Anomalies for Abnormal Event Detection in Videocited by 110
  8. 2018W-TALC: Weakly-supervised Temporal Activity Localization and Classificationcited by 87
  9. 2019C-MIL: Continuation Multiple Instance Learning for Weakly Supervised Object Detectioncited by 71
  10. 20193C-Net: Category Count and Center Loss for Weakly-Supervised Action Localizationcited by 52
  11. 2019Motion-Aware Feature for Improved Video Anomaly Detectioncited by 48

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 2; 2022: 8; 2023: 9; 2024: 10; 2025: 10; 2026: 5.

44 papers.

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  1. 2022MGFN: Magnitude-Contrastive Glance-and-Focus Network for Weakly-Supervised Video Anomaly Detectioncited by 87
  2. 2022CLIP-TSA: CLIP-Assisted Temporal Self-Attention for Weakly-Supervised Video Anomaly Detectioncited by 67
  3. 2023Generalized Video Anomaly Event Detection: Systematic Taxonomy and Comparison of Deep Modelscited by 59
  4. 2023Learning Prompt-Enhanced Context Features for Weakly-Supervised Video Anomaly Detectioncited by 43
  5. 2024Deep Learning for Video Anomaly Detection: A Reviewcited by 27
  6. 2022Towards Open Set Video Anomaly Detectioncited by 26
  7. 2023Weakly Supervised Anomaly Detection: A Surveycited by 22
  8. 2022Contrastive Transformer-based Multiple Instance Learning for Weakly Supervised Polyp Frame Detectioncited by 20
  9. 2025UCF-Crime-DVS: A Novel Event-Based Dataset for Video Anomaly Detection with Spiking Neural Networkscited by 18
  10. 2021Anomaly Detection in Video Sequences: A Benchmark and Computational Modelcited by 10
  11. 2023Long-Short Temporal Co-Teaching for Weakly Supervised Video Anomaly Detectioncited by 10
  12. 2022Locality-aware Attention Network with Discriminative Dynamics Learning for Weakly Supervised Anomaly Detectioncited by 8
32 more
  1. 2022Adaptive Graph Convolutional Networks for Weakly Supervised Anomaly Detection in Videoscited by 7
  2. 2024Anomaly Detection for People with Visual Impairments Using an Egocentric 360-Degree Cameracited by 7
  3. 2025Aligning Effective Tokens with Video Anomaly in Large Language Modelscited by 7
  4. 2024Cross-Domain Learning for Video Anomaly Detection with Limited Supervisioncited by 6
  5. 2024Interleaving One-Class and Weakly-Supervised Models with Adaptive Thresholding for Unsupervised Video Anomaly Detectioncited by 5
  6. 2023Human-Scene Network: A Novel Baseline with Self-rectifying Loss for Weakly supervised Video Anomaly Detectioncited by 4
  7. 2025The Evolution of Video Anomaly Detection: A Unified Framework from DNN to MLLMcited by 4
  8. 2021Discriminative-Generative Dual Memory Video Anomaly Detectioncited by 2
  9. 2022Weakly Supervised Video Anomaly Detection Based on Cross-Batch Clustering Guidancecited by 2
  10. 2024Multiple Instance Learning for Cheating Detection and Localization in Online Examinationscited by 2
  11. 2024Multimodal Attention-Enhanced Feature Fusion-based Weekly Supervised Anomaly Violence Detectioncited by 2
  12. 2025RefineVAD: Semantic-Guided Feature Recalibration for Weakly Supervised Video Anomaly Detectioncited by 2
  13. 2023A Lightweight Video Anomaly Detection Model with Weak Supervision and Adaptive Instance Selectioncited by 1
  14. 2024Identifying Spatio-Temporal Drivers of Extreme Eventscited by 1
  15. 2025ProDisc-VAD: An Efficient System for Weakly-Supervised Anomaly Detection in Video Surveillance Applicationscited by 1
  16. 2025Just Dance with π! A Poly-modal Inductor for Weakly-supervised Video Anomaly Detectioncited by 1
  17. 2025TRACES: Temporal Recall with Contextual Embeddings for Real-Time Video Anomaly Detectioncited by 1
  18. 2025CADE: Continual Weakly-supervised Video Anomaly Detection with Ensemblescited by 1
  19. 2026Fire on Motion: Optimizing Video Pass-bands for Efficient Spiking Action Recognitioncited by 1
  20. 2026MLE-UVAD: Minimal Latent Entropy Autoencoder for Fully Unsupervised Video Anomaly Detectioncited by 1
  21. 2022Real-world Video Anomaly Detection by Extracting Salient Features in Videoscited by 0
  22. 2023Audio-Visual Dataset and Method for Anomaly Detection in Traffic Videoscited by 0
  23. 2023A MIL Approach for Anomaly Detection in Surveillance Videos from Multiple Camera Viewscited by 0
  24. 2023Dynamic Erasing Network Based on Multi-Scale Temporal Features for Weakly Supervised Video Anomaly Detectioncited by 0
  25. 2024Class-aware and Augmentation-free Contrastive Learning from Label Proportioncited by 0
  26. 2024Temporal Divide-and-Conquer Anomaly Actions Localization in Semi-Supervised Videos with Hierarchical Transformercited by 0
  27. 2024Weakly-Supervised Anomaly Detection in Surveillance Videos Based on Two-Stream I3D Convolution Networkcited by 0
  28. 2025Advancing Video Anomaly Detection: A Bi-Directional Hybrid Framework for Enhanced Single- and Multi-Task Approachescited by 0
  29. 2025DAMS:Dual-Branch Adaptive Multiscale Spatiotemporal Framework for Video Anomaly Detectioncited by 0
  30. 2026Rethinking Weak Supervision in Anomaly Detection: A Comprehensive Benchmarkcited by 0
  31. 2026Event Stream based Multi-Modal Video Anomaly Detection: A Benchmark Dataset and Algorithmscited by 0
  32. 2026Cog-VADU: A Training-Free Cognitive Reasoning Framework for Video Anomaly Detection and Understandingcited by 0