mlsanityData to 14 Oct 2026

ProDisc-VAD: An Efficient System for Weakly-Supervised Anomaly Detection in Video Surveillance Applications

arXiv 2505.02179 ↗First posted 4 May 2025cs.CVcited by 1

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Abstract (arXiv)

Weakly-supervised video anomaly detection (WS-VAD) using Multiple Instance Learning (MIL) suffers from label ambiguity, hindering discriminative feature learning. We propose ProDisc-VAD, an efficient framework tackling this via two synergistic components. The Prototype Interaction Layer (PIL) provides controlled normality modeling using a small set of learnable prototypes, establishing a robust baseline without being overwhelmed by dominant normal data. The Pseudo-Instance Discriminative Enhancement (PIDE) loss boosts separability by applying targeted contrastive learning exclusively to the most reliable extreme-scoring instances (highest/lowest scores). ProDisc-VAD achieves strong AUCs (97.98% ShanghaiTech, 87.12% UCF-Crime) using only 0.4M parameters, over 800x fewer than recent ViT-based methods like VadCLIP. Code is available at https://github.com/modadundun/ProDisc-VAD.

Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 17 of its 23 references that are in the map. First posted on arXiv 4 May 2025.

Builds on

17 of 23 references.

References · 17

  1. 2020A Simple Framework for Contrastive Learning of Visual Representationscited by 12,049
  2. 2019Momentum Contrast for Unsupervised Visual Representation Learningcited by 7,513
  3. 2017Prototypical Networks for Few-shot Learningcited by 3,015
  4. 2020Unsupervised Learning of Visual Features by Contrasting Cluster Assignmentscited by 2,857
  5. 2020Contrastive Learning with Hard Negative Samplescited by 628
  6. 2018Real-world Anomaly Detection in Surveillance Videoscited by 577
  7. 2021Weakly-supervised Video Anomaly Detection with Robust Temporal Feature Magnitude Learningcited by 230
  8. 2019Graph Convolutional Label Noise Cleaner: Train a Plug-and-play Action Classifier for Anomaly Detectioncited by 171
  9. 2021MIST: Multiple Instance Self-Training Framework for Video Anomaly Detectioncited by 138
  10. 2023VadCLIP: Adapting Vision-Language Models for Weakly Supervised Video Anomaly Detectioncited by 123
  11. 2022Generative Cooperative Learning for Unsupervised Video Anomaly Detectioncited by 102
  12. 2020CLAWS: Clustering Assisted Weakly Supervised Learning with Normalcy Suppression for Anomalous Event Detectioncited by 79
5 more
  1. 2023Unbiased Multiple Instance Learning for Weakly Supervised Video Anomaly Detectioncited by 64
  2. 2022Exploiting Completeness and Uncertainty of Pseudo Labels for Weakly Supervised Video Anomaly Detectioncited by 47
  3. 2021Weakly Supervised Video Anomaly Detection via Center-guided Discriminative Learningcited by 44
  4. 2021Deep Anomaly Discovery From Unlabeled Videos via Normality Advantage and Self-Paced Refinementcited by 21
  5. 2023Weakly-Supervised Video Anomaly Detection with Snippet Anomalous Attentioncited by 15

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 1.

1 paper.

  1. 2026Event Stream based Multi-Modal Video Anomaly Detection: A Benchmark Dataset and Algorithmscited by 0