Papers › ProDisc-VAD: An Efficient System for Weakly-Supervised Anomaly Detection in Video...

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

4 May 2025arXiv:2505.02179archive 2025-07-28

Tao Zhu, Qi Yu, Xinru Dong, Shiyu Li, Yue Liu, Jinlong Jiang, Lei Shu

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, demonstrating exceptional efficiency alongside state-of-the-art performance. Code is available at https://github.com/modadundun/ProDisc-VAD.

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Code

modadundun/ProDisc-VAD mentioned in papermentioned on GitHubpytorch report

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Tasks

Anomaly Detection In Surveillance Videos Contrastive Learning Multiple Instance Learning Supervised Anomaly Detection Video Anomaly Detection Weakly-supervised Anomaly Detection Weakly-supervised Video Anomaly Detection

Results from the paper archive 2025-07-28

Task	Dataset	Model	Metric	Value	Rank at snapshot	Leaderboard	Report
Anomaly Detection In Surveillance Videos	ShanghaiTech Weakly Supervised	ProDisc-VAD	AUC-ROC	97.98	#2 of 12	Archive leaderboard	report
Anomaly Detection In Surveillance Videos	UCF-Crime	ProDisc-VAD	ROC AUC	87.12	#6 of 21	Archive leaderboard	report

Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.

Methods

Contrastive Learning SET

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