mlsanityData to 7 Oct 2026

Learning a distance function with a Siamese network to localize anomalies in videos

arXiv 2001.09189 ↗First posted 24 Jan 2020cs.CVcited by 31

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Abstract (arXiv)

This work introduces a new approach to localize anomalies in surveillance video. The main novelty is the idea of using a Siamese convolutional neural network (CNN) to learn a distance function between a pair of video patches (spatio-temporal regions of video). The learned distance function, which is not specific to the target video, is used to measure the distance between each video patch in the testing video and the video patches found in normal training video. If a testing video patch is not similar to any normal video patch then it must be anomalous. We compare our approach to previously published algorithms using 4 evaluation measures and 3 challenging target benchmark datasets. Experiments show that our approach either surpasses or performs comparably to current state-of-the-art methods.

Key facts. Cited by 31 papers in the map (data through 7 Oct 2026). First posted on arXiv 24 Jan 2020.

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Citing papers per year: 2021: 7; 2022: 10; 2023: 7; 2024: 3; 2025: 4.

31 papers.

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  1. 2021Self-Supervised Predictive Convolutional Attentive Block for Anomaly Detectioncited by 119
  2. 2021UBnormal: New Benchmark for Supervised Open-Set Video Anomaly Detectioncited by 91
  3. 2022CLIP-TSA: CLIP-Assisted Temporal Self-Attention for Weakly-Supervised Video Anomaly Detectioncited by 67
  4. 2023Generalized Video Anomaly Event Detection: Systematic Taxonomy and Comparison of Deep Modelscited by 59
  5. 2024Advancing Video Anomaly Detection: A Concise Review and a New Datasetcited by 48
  6. 2022SSMTL++: Revisiting Self-Supervised Multi-Task Learning for Video Anomaly Detectioncited by 45
  7. 2023Hierarchical Semantic Contrast for Scene-aware Video Anomaly Detectioncited by 39
  8. 2023Self-Distilled Masked Auto-Encoders are Efficient Video Anomaly Detectorscited by 37
  9. 2022Self-Supervised Masked Convolutional Transformer Block for Anomaly Detectioncited by 34
  10. 2021Learning Not to Reconstruct Anomaliescited by 31
  11. 2024Deep Learning for Video Anomaly Detection: A Reviewcited by 27
  12. 2022DyAnNet: A Scene Dynamicity Guided Self-Trained Video Anomaly Detection Networkcited by 26
19 more
  1. 2023Exploring Diffusion Models for Unsupervised Video Anomaly Detectioncited by 23
  2. 2021Deep Anomaly Discovery From Unlabeled Videos via Normality Advantage and Self-Paced Refinementcited by 21
  3. 2024MULDE: Multiscale Log-Density Estimation via Denoising Score Matching for Video Anomaly Detectioncited by 21
  4. 2022EVAL: Explainable Video Anomaly Localizationcited by 19
  5. 2022Context Recovery and Knowledge Retrieval: A Novel Two-Stream Framework for Video Anomaly Detectioncited by 18
  6. 2021Learnable Locality-Sensitive Hashing for Video Anomaly Detectioncited by 17
  7. 2023Video Anomaly Detection via Spatio-Temporal Pseudo-Anomaly Generation : A Unified Approachcited by 10
  8. 2023PseudoBound: Limiting the anomaly reconstruction capability of one-class classifiers using pseudo anomaliescited by 8
  9. 2025Track Any Anomalous Object: A Granular Video Anomaly Detection Pipelinecited by 8
  10. 2025Aligning Effective Tokens with Video Anomaly in Large Language Modelscited by 7
  11. 2021Ano-Graph: Learning Normal Scene Contextual Graphs to Detect Video Anomaliescited by 4
  12. 2025The Evolution of Video Anomaly Detection: A Unified Framework from DNN to MLLMcited by 4
  13. 2025Leveraging Multimodal LLM Descriptions of Activity for Explainable Semi-Supervised Video Anomaly Detectioncited by 4
  14. 2022Spatio-temporal predictive tasks for abnormal event detection in videoscited by 3
  15. 2022Lightning Fast Video Anomaly Detection via Adversarial Knowledge Distillationcited by 3
  16. 2022Object-centric and memory-guided normality reconstruction for video anomaly detectioncited by 2
  17. 2021Anomaly Detection with Prototype-Guided Discriminative Latent Embeddingscited by 1
  18. 2022Contextual Information Based Anomaly Detection for a Multi-Scene UAV Aerial Videoscited by 1
  19. 2023Optimization- and AI-based approaches to academic quality quantification for transparent academic recruitment: part 1-model developmentcited by 0