Training-free Anomaly Event Detection via LLM-guided Symbolic Pattern Discovery

Yuhui Zeng, Haoxiang Wu, Wenjie Nie, Guangyao Chen, Xiawu Zheng, Yunhang Shen, Guilin Li, Yixiong Zou, Yonghong Tian, Rongrong Ji

Abstract: Our key innovation lies in bridging the semantic gap between object detection and event understanding without requiring expensive task-specific training. The proposed plug-and-play framework interfaces with any open-vocabulary detector while extending their inherent capabilities across architectures. At its core, our approach combines (i) a symbolic regression mechanism exploring relationship patterns among detected entities and (ii) a LLM-guided strategically guiding the search toward meaningful expressions. These discovered symbolic rules transform low-level visual perception into interpretable event understanding, providing a transparent reasoning path from objects to events with strong transferability across domains.We compared our training-free framework against specialized event recognition systems across diverse application domains. Experiments demonstrate that our framework enhances multiple object detector architectures to recognize complex events such as illegal fishing activities (75% AUROC, +8.36% improvement), construction safety violations (+15.77%), and abnormal crowd behaviors (+23.16%). The code will be released soon.