Few-shot Human Action Anomaly Detection via a Unified Contrastive Learning Framework
This addresses scalability and data scarcity issues in real-world human action anomaly detection, though it is incremental as it builds on existing contrastive learning and diffusion models.
The paper tackles the problem of human action anomaly detection in few-shot scenarios by proposing a unified contrastive learning framework with generative motion augmentation, achieving state-of-the-art results on the HumanAct12 dataset in terms of training efficiency and scalability.
Human Action Anomaly Detection (HAAD) aims to identify anomalous actions given only normal action data during training. Existing methods typically follow a one-model-per-category paradigm, requiring separate training for each action category and a large number of normal samples. These constraints hinder scalability and limit applicability in real-world scenarios, where data is often scarce or novel categories frequently appear. To address these limitations, we propose a unified framework for HAAD that is compatible with few-shot scenarios. Our method constructs a category-agnostic representation space via contrastive learning, enabling AD by comparing test samples with a given small set of normal examples (referred to as the support set). To improve inter-category generalization and intra-category robustness, we introduce a generative motion augmentation strategy harnessing a diffusion-based foundation model for creating diverse and realistic training samples. Notably, to the best of our knowledge, our work is the first to introduce such a strategy specifically tailored to enhance contrastive learning for action AD. Extensive experiments on the HumanAct12 dataset demonstrate the state-of-the-art effectiveness of our approach under both seen and unseen category settings, regarding training efficiency and model scalability for few-shot HAAD.