Few-shot Human Action Anomaly Detection via a Unified Contrastive Learning Framework

• URL: http://arxiv.org/abs/2508.17726v1
• Date: Mon, 25 Aug 2025 07:07:35 GMT
• Title: Few-shot Human Action Anomaly Detection via a Unified Contrastive Learning Framework
• Authors: Koichiro Kamide, Shunsuke Sakai, Shun Maeda, Chunzhi Gu, Chao Zhang,
• Abstract summary: Human Action Anomaly Detection (HAAD) aims to identify anomalous actions given only normal action data during training.<n>Existing methods typically follow a one-model-per-category paradigm, requiring separate training for each action category and a large number of normal samples.<n>We propose a unified framework for HAAD that is compatible with few-shot scenarios.
• Score: 5.2816633000124975
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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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