用少量正常样本实现跨类别动作异常检测，提升模型泛化能力。
Few-shot Human Action Anomaly Detection via a Unified Contrastive Learning Framework
- 通过对比学习构建无类别依赖的表征空间，支持小样本测试。
- 在HumanAct12上达到新最好性能，未见类别下仍保持高准确率。
- 首次引入基于扩散模型的动作增强策略，提升训练多样性与鲁棒性。
人类动作异常检测（HAAD）旨在仅使用正常动作数据训练时识别异常动作。现有方法通常采用每类别一个模型的范式，需为每个动作类别单独训练且依赖大量正常样本，限制了可扩展性，难以适应真实场景中数据稀缺或新类别频繁出现的情况。为此，本文提出一种兼容少样本场景的统一框架。该方法通过对比学习构建无类别依赖的表征空间，使异常检测可通过将测试样本与少量正常样本（称为支持集）进行比较完成。为增强类别间泛化性和类别内鲁棒性，引入基于扩散模型的生成式运动增强策略，生成多样化且逼真的训练样本。据我们所知，这是首个专门针对动作异常检测设计的此类增强策略。在HumanAct12数据集上的大量实验表明，该方法在已见和未见类别设置下均表现优异，具备更高的训练效率与模型可扩展性。
原文摘要 · Abstract (English)
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.
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