Few-shot human action anomaly detection via a unified contrastive learning framework

Human Action Anomaly Detection (HAAD) aims to identify anomalous actions using only normal-action data during training. Most prior work follows a one-model-per-category paradigm, requiring separate training for each action category and numerous normal samples. These requirements hinder scalability and limit applicability in real-world settings, where data are often scarce or novel categories frequently appear. To address these limitations, we propose a unified HAAD framework that supports few-shot settings. Our method learns a category-agnostic representation via contrastive learning and detects anomalies by comparing a test sample with a small support set of normal examples. To improve inter-category generalization and intra-category robustness, we introduce diffusion-based generative motion augmentation to synthesize diverse, realistic training samples. To the best of our knowledge, this is the first work to tailor diffusion-driven motion augmentation specifically to contrastive learning for action anomaly detection. Our few-shot design is particularly useful for monitoring applications, where normality often varies across individuals and contexts. Experiments on HumanAct12 demonstrate state-of-the-art performance on both seen and unseen categories, while improving training efficiency and scalability for few-shot HAAD.