title: Uncertainty-Weighted Image-Event Multimodal Fusion for Video Anomaly
    Detection
summary: Most existing video anomaly detectors rely solely on RGB frames, which lack
  the temporal resolution needed to capture abrupt or transient motion cues, key
  indicators of anomalous events. To address this limitation, we propose
  Image-Event Fusion for Video Anomaly Detection (IEF-VAD), a framework that
  synthesizes event representations directly from RGB videos and fuses them with
  image features through a principled, uncertainty-aware process. The system (i)
  models heavy-tailed sensor noise with a Student`s-t likelihood, deriving
  value-level inverse-variance weights via a Laplace approximation; (ii) applies
  Kalman-style frame-wise updates to balance modalities over time; and (iii)
  iteratively refines the fused latent state to erase residual cross-modal noise.
  Without any dedicated event sensor or frame-level labels, IEF-VAD sets a new
  state of the art across multiple real-world anomaly detection benchmarks. These
  findings highlight the utility of synthetic event representations in
  emphasizing motion cues that are often underrepresented in RGB frames, enabling
  accurate and robust video understanding across diverse applications without
  requiring dedicated event sensors. Code and models are available at
  https://github.com/EavnJeong/IEF-VAD.
paper_authors._id: 681c423f198e1dea5c26f2f4
paper_authors.name: Sungheon Jeong
paper_authors.status: extracted_confirmed
paper_authors.statusLastChangedAt: 2025-05-08T09:38:37.142Z
paper_authors._id: 6445e9bd1cfc9ae6bb40985c
paper_authors.avatarUrl: /avatars/33f48c1c820e592cbf4ea4af479dd378.svg
paper_authors.fullname: Evan Jeong
paper_authors.type: user
paper_authors.user: Eavn
paper_authors._id: 681c423f198e1dea5c26f2f5
paper_authors.name: Jihong Park
paper_authors.status: extracted_confirmed
paper_authors.statusLastChangedAt: 2025-05-08T05:36:09.797Z
paper_authors._id: 646b57c6e5abcbf6709fabf6
paper_authors.avatarUrl: /avatars/e9749acf7866eeaf017f0a43351794fc.svg
paper_authors.fullname: Jihong Park
paper_authors.type: user
paper_authors.user: Paper9795
paper_authors._id: 681c423f198e1dea5c26f2f6
paper_authors.name: Mohsen Imani
paper_title: Uncertainty-Weighted Image-Event Multimodal Fusion for Video Anomaly
    Detection
paper_summary: Most existing video anomaly detectors rely solely on RGB frames, which lack
  the temporal resolution needed to capture abrupt or transient motion cues, key
  indicators of anomalous events. To address this limitation, we propose
  Image-Event Fusion for Video Anomaly Detection (IEF-VAD), a framework that
  synthesizes event representations directly from RGB videos and fuses them with
  image features through a principled, uncertainty-aware process. The system (i)
  models heavy-tailed sensor noise with a Student`s-t likelihood, deriving
  value-level inverse-variance weights via a Laplace approximation; (ii) applies
  Kalman-style frame-wise updates to balance modalities over time; and (iii)
  iteratively refines the fused latent state to erase residual cross-modal noise.
  Without any dedicated event sensor or frame-level labels, IEF-VAD sets a new
  state of the art across multiple real-world anomaly detection benchmarks. These
  findings highlight the utility of synthetic event representations in
  emphasizing motion cues that are often underrepresented in RGB frames, enabling
  accurate and robust video understanding across diverse applications without
  requiring dedicated event sensors. Code and models are available at
  https://github.com/EavnJeong/IEF-VAD.
paper_ai_summary: The IEF-VAD framework improves video anomaly detection by fusing synthetic event representations with image features, emphasizing motion cues and setting a new state of the art in benchmarks.
paper_ai_keywords: event representations video anomaly detection heavy-tailed sensor noise Student's-t likelihood Laplace approximation Kalman-style frame-wise updates fused latent state synthetic event representations