PLOVAD: Prompting Vision-Language Models for Open Vocabulary Video Anomaly Detection

Video anomaly detection (VAD) confronts significant challenges arising from data scarcity in real-world open scenarios, encompassing sparse annotations, labeling costs, and limitations on closed-set class definitions, particularly when scene diversity surpasses available training data. Although current weakly-supervised VAD methods offer partial alleviation, their inherent confinement to closed-set paradigms renders them inadequate in open-world contexts. Therefore, this paper explores open vocabulary video anomaly detection (OVVAD), leveraging abundant vision-related language data to detect and categorize both seen and unseen anomalies. To this end, we propose a robust framework, PLOVAD, designed to prompt tuning large-scale pretrained image-based vision-language models (I-VLMs) for the OVVAD task. PLOVAD consists of two main modules: the Prompting Module, featuring a learnable prompt to capture domain-specific knowledge and an anomaly-specific prompt crafted by a large language model (LLM) to capture semantic nuances and enhance generalization; and the Temporal Module, which integrates temporal information using graph attention network (GAT) stacking atop frame-wise visual features to address the transition from static images to videos. Extensive experiments on four benchmarks demonstrate the superior detection and categorization performance of our approach in the OVVAD task without bringing excessive parameters.