mlsanityData to 7 Oct 2026

Learning Temporal Regularity in Video Sequences

arXiv 1604.04574 ↗First posted 15 Apr 2016cs.CVcited by 254

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Abstract (arXiv)

Perceiving meaningful activities in a long video sequence is a challenging problem due to ambiguous definition of 'meaningfulness' as well as clutters in the scene. We approach this problem by learning a generative model for regular motion patterns, termed as regularity, using multiple sources with very limited supervision. Specifically, we propose two methods that are built upon the autoencoders for their ability to work with little to no supervision. We first leverage the conventional handcrafted spatio-temporal local features and learn a fully connected autoencoder on them. Second, we build a fully convolutional feed-forward autoencoder to learn both the local features and the classifiers as an end-to-end learning framework. Our model can capture the regularities from multiple datasets. We evaluate our methods in both qualitative and quantitative ways - showing the learned regularity of videos in various aspects and demonstrating competitive performance on anomaly detection datasets as an application.

Key facts. Cited by 254 papers in the map (data through 7 Oct 2026). First posted on arXiv 15 Apr 2016.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 51; 2022: 49; 2023: 36; 2024: 43; 2025: 45; 2026: 30.

254 papers.

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  1. 2021Weakly-supervised Video Anomaly Detection with Robust Temporal Feature Magnitude Learningcited by 226
  2. 2021MIST: Multiple Instance Self-Training Framework for Video Anomaly Detectioncited by 137
  3. 2021A Hybrid Video Anomaly Detection Framework via Memory-Augmented Flow Reconstruction and Flow-Guided Frame Predictioncited by 129
  4. 2023VadCLIP: Adapting Vision-Language Models for Weakly Supervised Video Anomaly Detectioncited by 121
  5. 2021Self-Supervised Predictive Convolutional Attentive Block for Anomaly Detectioncited by 119
  6. 2022Generative Cooperative Learning for Unsupervised Video Anomaly Detectioncited by 102
  7. 2021UBnormal: New Benchmark for Supervised Open-Set Video Anomaly Detectioncited by 91
  8. 2022MGFN: Magnitude-Contrastive Glance-and-Focus Network for Weakly-Supervised Video Anomaly Detectioncited by 87
  9. 2024Harnessing Large Language Models for Training-free Video Anomaly Detectioncited by 85
  10. 2021Divide-and-Assemble: Learning Block-wise Memory for Unsupervised Anomaly Detectioncited by 83
  11. 2021Learning Normal Dynamics in Videos with Meta Prototype Networkcited by 71
  12. 2022CLIP-TSA: CLIP-Assisted Temporal Self-Attention for Weakly-Supervised Video Anomaly Detectioncited by 67
38 more
  1. 2024Holmes-VAU: Towards Long-term Video Anomaly Understanding at Any Granularitycited by 66
  2. 2023Unbiased Multiple Instance Learning for Weakly Supervised Video Anomaly Detectioncited by 64
  3. 2024Holmes-VAD: Towards Unbiased and Explainable Video Anomaly Detection via Multi-modal LLMcited by 63
  4. 2022Video Anomaly Detection by Solving Decoupled Spatio-Temporal Jigsaw Puzzlescited by 62
  5. 2023Generalized Video Anomaly Event Detection: Systematic Taxonomy and Comparison of Deep Modelscited by 59
  6. 2023Open-Vocabulary Video Anomaly Detectioncited by 57
  7. 2024Follow the Rules: Reasoning for Video Anomaly Detection with Large Language Modelscited by 55
  8. 2023Video Event Restoration Based on Keyframes for Video Anomaly Detectioncited by 54
  9. 2024Text Prompt with Normality Guidance for Weakly Supervised Video Anomaly Detectioncited by 49
  10. 2022Exploiting Completeness and Uncertainty of Pseudo Labels for Weakly Supervised Video Anomaly Detectioncited by 47
  11. 2022SSMTL++: Revisiting Self-Supervised Multi-Task Learning for Video Anomaly Detectioncited by 45
  12. 2021Weakly Supervised Video Anomaly Detection via Center-guided Discriminative Learningcited by 44
  13. 2023Learning Prompt-Enhanced Context Features for Weakly-Supervised Video Anomaly Detectioncited by 43
  14. 2023A New Comprehensive Benchmark for Semi-supervised Video Anomaly Detection and Anticipationcited by 42
  15. 2023Multimodal Motion Conditioned Diffusion Model for Skeleton-based Video Anomaly Detectioncited by 42
  16. 2021Unsupervised Deep Anomaly Detection for Multi-Sensor Time-Series Signalscited by 41
  17. 2023Diversity-Measurable Anomaly Detectioncited by 41
  18. 2024Weakly Supervised Video Anomaly Detection and Localization with Spatio-Temporal Promptscited by 40
  19. 2023Hierarchical Semantic Contrast for Scene-aware Video Anomaly Detectioncited by 39
  20. 2023Self-Distilled Masked Auto-Encoders are Efficient Video Anomaly Detectorscited by 37
  21. 2022Self-Supervised Masked Convolutional Transformer Block for Anomaly Detectioncited by 34
  22. 2021Weakly-Supervised Spatio-Temporal Anomaly Detection in Surveillance Videocited by 32
  23. 2021Convolutional Transformer based Dual Discriminator Generative Adversarial Networks for Video Anomaly Detectioncited by 31
  24. 2021Learning Not to Reconstruct Anomaliescited by 31
  25. 2021Corner Cases for Visual Perception in Automated Driving: Some Guidance on Detection Approachescited by 27
  26. 2022Dynamic Local Aggregation Network with Adaptive Clusterer for Anomaly Detectioncited by 27
  27. 2024Deep Learning for Video Anomaly Detection: A Reviewcited by 27
  28. 2021Synthetic Temporal Anomaly Guided End-to-End Video Anomaly Detectioncited by 26
  29. 2022Modality-Aware Contrastive Instance Learning with Self-Distillation for Weakly-Supervised Audio-Visual Violence Detectioncited by 26
  30. 2022Towards Open Set Video Anomaly Detectioncited by 26
  31. 2022DyAnNet: A Scene Dynamicity Guided Self-Trained Video Anomaly Detection Networkcited by 26
  32. 2024VERA: Explainable Video Anomaly Detection via Verbalized Learning of Vision-Language Modelscited by 24
  33. 2022Bayesian Nonparametric Submodular Video Partition for Robust Anomaly Detectioncited by 23
  34. 2021Regularity Learning via Explicit Distribution Modeling for Skeletal Video Anomaly Detectioncited by 22
  35. 2021Deep Anomaly Discovery From Unlabeled Videos via Normality Advantage and Self-Paced Refinementcited by 21
  36. 2024MULDE: Multiscale Log-Density Estimation via Denoising Score Matching for Video Anomaly Detectioncited by 21
  37. 2024Networking Systems for Video Anomaly Detection: A Tutorial and Surveycited by 21
  38. 2022Visual Anomaly Detection Via Partition Memory Bank Module and Error Estimationcited by 20

50 most cited of 254 All 254 (JSON)