Uncertainty-Weighted Image-Event Multimodal Fusion for Video Anomaly
  Detection
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.
681c423f198e1dea5c26f2f4
Sungheon Jeong
extracted_confirmed
2025-05-08T09:38:37.142Z
6445e9bd1cfc9ae6bb40985c
/avatars/33f48c1c820e592cbf4ea4af479dd378.svg
Evan Jeong
user
Eavn
681c423f198e1dea5c26f2f5
Jihong Park
extracted_confirmed
2025-05-08T05:36:09.797Z
646b57c6e5abcbf6709fabf6
/avatars/e9749acf7866eeaf017f0a43351794fc.svg
Jihong Park
user
Paper9795
681c423f198e1dea5c26f2f6
Mohsen Imani
Uncertainty-Weighted Image-Event Multimodal Fusion for Video Anomaly
  Detection
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.
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.
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