AnyAnomaly: Zero-Shot Customizable Video Anomaly Detection with LVLM
Authors: name: Sunghyun Ahn, name: Youngwan Jo, name: Kijung Lee, name: Sein Kwon, name: Inpyo Hong, name: Sanghyun Park

Video anomaly detection (VAD) is crucial for video analysis and surveillance
in computer vision. However, existing VAD models rely on learned normal
patterns, which makes them difficult to apply to diverse environments.
Consequently, users should retrain models or develop separate AI models for new
environments, which requires expertise in machine learning, high-performance
hardware, and extensive data collection, limiting the practical usability of
VAD. To address these challenges, this study proposes customizable video
anomaly detection (C-VAD) technique and the AnyAnomaly model. C-VAD considers
user-defined text as an abnormal event and detects frames containing a
specified event in a video. We effectively implemented AnyAnomaly using a
context-aware visual question answering without fine-tuning the large vision
language model. To validate the effectiveness of the proposed model, we
constructed C-VAD datasets and demonstrated the superiority of AnyAnomaly.
Furthermore, our approach showed competitive performance on VAD benchmark
datasets, achieving state-of-the-art results on the UBnormal dataset and
outperforming other methods in generalization across all datasets. Our code is
available online at github.com/SkiddieAhn/Paper-AnyAnomaly.