mlsanityData to 7 Oct 2026

Dynamic Distinction Learning: Adaptive Pseudo Anomalies for Video Anomaly Detection

arXiv 2404.04986 ↗First posted 7 Apr 2024cs.CV cs.AIcited by 6

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Abstract (arXiv)

We introduce Dynamic Distinction Learning (DDL) for Video Anomaly Detection, a novel video anomaly detection methodology that combines pseudo-anomalies, dynamic anomaly weighting, and a distinction loss function to improve detection accuracy. By training on pseudo-anomalies, our approach adapts to the variability of normal and anomalous behaviors without fixed anomaly thresholds. Our model showcases superior performance on the Ped2, Avenue and ShanghaiTech datasets, where individual models are tailored for each scene. These achievements highlight DDL's effectiveness in advancing anomaly detection, offering a scalable and adaptable solution for video surveillance challenges.

Key facts. Cited by 6 papers in the map (data through 7 Oct 2026). Builds on 18 of its 42 references that are in the map. First posted on arXiv 7 Apr 2024.

Builds on

18 of 42 references.

References · 18

  1. 2015Fast R-CNNcited by 2,930
  2. 2019Memorizing Normality to Detect Anomaly: Memory-augmented Deep Autoencoder for Unsupervised Anomaly Detectioncited by 560
  3. 2017Future Frame Prediction for Anomaly Detection -- A New Baselinecited by 391
  4. 2022Observation-Centric SORT: Rethinking SORT for Robust Multi-Object Trackingcited by 345
  5. 2020Learning Memory-guided Normality for Anomaly Detectioncited by 278
  6. 2016Learning Temporal Regularity in Video Sequencescited by 254
  7. 2021A Hybrid Video Anomaly Detection Framework via Memory-Augmented Flow Reconstruction and Flow-Guided Frame Predictioncited by 129
  8. 2020Old is Gold: Redefining the Adversarially Learned One-Class Classifier Training Paradigmcited by 115
  9. 2016Deep-Anomaly: Fully Convolutional Neural Network for Fast Anomaly Detection in Crowded Scenescited by 50
  10. 2022SSMTL++: Revisiting Self-Supervised Multi-Task Learning for Video Anomaly Detectioncited by 45
  11. 2020Robust Unsupervised Video Anomaly Detection by Multi-Path Frame Predictioncited by 42
  12. 2023Hierarchical Semantic Contrast for Scene-aware Video Anomaly Detectioncited by 39
6 more
  1. 2020MOCCA: Multi-Layer One-Class ClassificAtion for Anomaly Detectioncited by 31
  2. 2020Online Anomaly Detection in Surveillance Videos with Asymptotic Bounds on False Alarm Ratecited by 22
  3. 2022Cross-Domain Video Anomaly Detection without Target Domain Adaptationcited by 20
  4. 2021Discrete neural representations for explainable anomaly detectioncited by 12
  5. 2023PseudoBound: Limiting the anomaly reconstruction capability of one-class classifiers using pseudo anomaliescited by 8
  6. 2021Predicting Next Local Appearance for Video Anomaly Detectioncited by 2

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 1; 2025: 1; 2026: 4.

6 papers.

  1. 2024Bidirectional skip-frame prediction for video anomaly detection with intra-domain disparity-driven attentioncited by 5
  2. 2025ALFred: An Active Learning Framework for Real-world Semi-supervised Anomaly Detection with Adaptive Thresholdscited by 3
  3. 2026From Offline to Periodic Adaptation for Pose-Based Shoplifting Detection in Real-world Retail Securitycited by 3
  4. 2026Multimodal Learning for Arcing Detection in Pantograph-Catenary Systemscited by 0
  5. 2026IMPACT: Influence Modeling for Open-Set Time Series Anomaly Detectioncited by 0
  6. 2026Look Inside Each Video: Rethinking How Video Anomaly Detection Is Evaluatedcited by 0