3 papers
cs.CV2025
AnyAnomaly: Zero-Shot Customizable Video Anomaly Detection with LVLM
Sunghyun Ahn, Youngwan Jo, Kijung Lee +3
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 diff…
cs.CV2024
Advanced Knowledge Transfer: Refined Feature Distillation for Zero-Shot Quantization in Edge Computing
Inpyo Hong, Youngwan Jo, Hyojeong Lee +2
We introduce AKT (Advanced Knowledge Transfer), a novel method to enhance the training ability of low-bit quantized (Q) models in the field of zero-shot quantization (ZSQ). Existin…
cs.CV2024
VideoPatchCore: An Effective Method to Memorize Normality for Video Anomaly Detection
Sunghyun Ahn, Youngwan Jo, Kijung Lee +1
Video anomaly detection (VAD) is a crucial task in video analysis and surveillance within computer vision. Currently, VAD is gaining attention with memory techniques that store the…