mlsanityData to 7 Oct 2026

Detecting Contextual Anomalies by Discovering Consistent Spatial Regions

arXiv 2501.08470 ↗First posted 14 Jan 2025cs.CV cs.AIcited by 3

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Abstract (arXiv)

We describe a method for modeling spatial context to enable video anomaly detection. The main idea is to discover regions that share similar object-level activities by clustering joint object attributes using Gaussian mixture models. We demonstrate that this straightforward approach, using orders of magnitude fewer parameters than competing models, achieves state-of-the-art performance in the challenging spatial-context-dependent Street Scene dataset. As a side benefit, the high-resolution discovered regions learned by the model also provide explainable normalcy maps for human operators without the need for any pre-trained segmentation model.

Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Builds on 27 of its 44 references that are in the map. First posted on arXiv 14 Jan 2025.

Builds on

27 of 44 references.

References · 27

  1. 2021Learning Transferable Visual Models From Natural Language Supervisioncited by 33,456
  2. 2018The Unreasonable Effectiveness of Deep Features as a Perceptual Metriccited by 11,029
  3. 2021SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformerscited by 2,923
  4. 2017Simple Online and Realtime Tracking with a Deep Association Metriccited by 908
  5. 2019Memorizing Normality to Detect Anomaly: Memory-augmented Deep Autoencoder for Unsupervised Anomaly Detectioncited by 560
  6. 2017Future Frame Prediction for Anomaly Detection -- A New Baselinecited by 391
  7. 2020Learning Memory-guided Normality for Anomaly Detectioncited by 278
  8. 2020Inductive Biases for Deep Learning of Higher-Level Cognitioncited by 273
  9. 2016Learning Temporal Regularity in Video Sequencescited by 254
  10. 2020Anomaly Detection in Video via Self-Supervised and Multi-Task Learningcited by 147
  11. 2021A Hybrid Video Anomaly Detection Framework via Memory-Augmented Flow Reconstruction and Flow-Guided Frame Predictioncited by 129
  12. 2018Object-centric Auto-encoders and Dummy Anomalies for Abnormal Event Detection in Videocited by 110
15 more
  1. 2019Street Scene: A new dataset and evaluation protocol for video anomaly detectioncited by 89
  2. 2020A Survey of Single-Scene Video Anomaly Detectioncited by 88
  3. 2020A Background-Agnostic Framework with Adversarial Training for Abnormal Event Detection in Videocited by 85
  4. 2021Learning Normal Dynamics in Videos with Meta Prototype Networkcited by 71
  5. 2024Holmes-VAD: Towards Unbiased and Explainable Video Anomaly Detection via Multi-modal LLMcited by 63
  6. 2022Normalizing Flows for Human Pose Anomaly Detectioncited by 59
  7. 2023Video Event Restoration Based on Keyframes for Video Anomaly Detectioncited by 54
  8. 2023Diversity-Measurable Anomaly Detectioncited by 41
  9. 2023Hierarchical Semantic Contrast for Scene-aware Video Anomaly Detectioncited by 39
  10. 2020Unsupervised Video Anomaly Detection via Normalizing Flows with Implicit Latent Featurescited by 23
  11. 2024MULDE: Multiscale Log-Density Estimation via Denoising Score Matching for Video Anomaly Detectioncited by 21
  12. 2024VANE-Bench: Video Anomaly Evaluation Benchmark for Conversational LMMscited by 20
  13. 2022EVAL: Explainable Video Anomaly Localizationcited by 19
  14. 2021Discrete neural representations for explainable anomaly detectioncited by 12
  15. 2024Context-aware Video Anomaly Detection in Long-Term Datasetscited by 11

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2025: 2; 2026: 1.

3 papers.

  1. 2025The Evolution of Video Anomaly Detection: A Unified Framework from DNN to MLLMcited by 4
  2. 2025Leveraging Multimodal LLM Descriptions of Activity for Explainable Semi-Supervised Video Anomaly Detectioncited by 4
  3. 2026Is Video Anomaly Detection Misframed? Evidence from LLM-Based and Multi-Scene Modelscited by 1