AnyAnomaly: Zero-Shot Customizable Video Anomaly Detection with LVLM
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.
67cb8e882cfa481bcee9455e
Sunghyun Ahn
claimed_verified
2025-03-10T08:02:12.798Z
66a07c07b7f0bb64d3b35497
/avatars/c38af1ddb7a5b625e26b7ff05957ff7c.svg
SunghyunAhn
user
SkiddieAhn
67cb8e882cfa481bcee9455f
Youngwan Jo
claimed_verified
2025-03-10T08:02:08.710Z
673d7b70713e4b8db2d5ca94
/avatars/b9e89eba62eb939ddd93c1cb91744e93.svg
Youngwan Jo
user
jyy1551
67cb8e882cfa481bcee94560
Kijung Lee
67cb8e882cfa481bcee94561
Sein Kwon
67cb8e882cfa481bcee94562
Inpyo Hong
67cb8e882cfa481bcee94563
Sanghyun Park
AnyAnomaly: Zero-Shot Customizable Video Anomaly Detection with LVLM
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.
The AnyAnomaly model uses context-aware visual question answering for customizable video anomaly detection without fine-tuning, achieving state-of-the-art results across benchmark datasets.
video anomaly detection C-VAD AnyAnomaly context-aware visual question answering large vision language model VAD benchmark datasets UBnormal dataset