title: AnyAnomaly: Zero-Shot Customizable Video Anomaly Detection with LVLM
summary: 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.
paper_authors._id: 67cb8e882cfa481bcee9455e
paper_authors.name: Sunghyun Ahn
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-03-10T08:02:12.798Z
paper_authors._id: 66a07c07b7f0bb64d3b35497
paper_authors.avatarUrl: /avatars/c38af1ddb7a5b625e26b7ff05957ff7c.svg
paper_authors.fullname: SunghyunAhn
paper_authors.type: user
paper_authors.user: SkiddieAhn
paper_authors._id: 67cb8e882cfa481bcee9455f
paper_authors.name: Youngwan Jo
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-03-10T08:02:08.710Z
paper_authors._id: 673d7b70713e4b8db2d5ca94
paper_authors.avatarUrl: /avatars/b9e89eba62eb939ddd93c1cb91744e93.svg
paper_authors.fullname: Youngwan Jo
paper_authors.type: user
paper_authors.user: jyy1551
paper_authors._id: 67cb8e882cfa481bcee94560
paper_authors.name: Kijung Lee
paper_authors._id: 67cb8e882cfa481bcee94561
paper_authors.name: Sein Kwon
paper_authors._id: 67cb8e882cfa481bcee94562
paper_authors.name: Inpyo Hong
paper_authors._id: 67cb8e882cfa481bcee94563
paper_authors.name: Sanghyun Park
paper_title: AnyAnomaly: Zero-Shot Customizable Video Anomaly Detection with LVLM
paper_summary: 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.
paper_ai_keywords: context-aware visual question answering large vision language model C-VAD datasets UBnormal dataset VAD benchmark datasets