Efficient Video Anomaly Detection with Masked Auto-Encoders
The author proposes a lightweight masked auto-encoder model for efficient video anomaly detection, leveraging motion gradients and self-distillation to improve performance significantly.
Multiscale Log-Density Estimation for Video Anomaly Detection
Neural network models log-density function for video anomaly detection.
Spatio-Temporal Relation Prediction for Effective Video Anomaly Detection
A self-supervised learning approach for video anomaly detection that leverages spatio-temporal coherence within video frames by predicting the order of shuffled patches in a video.
Leveraging Local Patterns for Generalizable Video Anomaly Detection
The proposed framework leverages image-text contrastive learning to extract local patterns that generalize to novel anomalies, and models the dynamics of these local patterns to effectively detect both spatial and temporal anomalies.
Appearance Blur-driven AutoEncoder and Motion-guided Memory Module for Efficient Video Anomaly Detection
The proposed method transforms video anomaly detection into a deblurring process, focusing on motion features to achieve efficient anomaly detection and cross-domain generalization.
영상 이상 탐지를 위한 외관 블러 기반 오토인코더와 동작 기반 메모리 모듈
영상 이상 탐지를 위해 외관 이미지에 가우시안 블러를 적용하여 가짜 이상 데이터를 생성하고, 동작 특징을 기반으로 한 메모리 모듈을 활용하여 정상 동작 분포를 학습하고 이상 동작을 탐지한다.
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