ECCV 2024: Zero-shot Video Anomaly Detection: Leveraging Large Language Models for Rule-Based Reasoning

Video Anomaly Detection (VAD) is critical for applications such as surveillance and autonomous driving. However, existing methods lack transparent reasoning, limiting public trust in real-world deployments. We introduce a rule-based reasoning framework that leverages Large Language Models (LLMs) to induce detection rules from few-shot normal samples and apply them to identify anomalies, incorporating strategies such as rule aggregation and perception smoothing to enhance robustness. The abstract nature of language enables rapid adaptation to diverse VAD scenarios, ensuring flexibility and broad applicability.

ECCV 2024 Paper: is a a Ph.D. Candidate in the Department of Computer Science at Johns Hopkins University. Her research aims to deliver functional, trustworthy solutions for machine learning and AI systems.