Search Results for author:
Found 6 papers, 3 papers with code
Video Anomaly Detection and Explanation via Large Language Models
Video Anomaly Detection (VAD) aims to localize abnormal events on the timeline of long-range surveillance videos.
Unbiased Multiple Instance Learning for Weakly Supervised Video Anomaly Detection
At each MIL training iteration, we use the current detector to divide the samples into two groups with different context biases: the most confident abnormal/normal snippets and the rest ambiguous ones.
Spatio-Temporal Relation Learning for Video Anomaly Detection
Anomaly identification is highly dependent on the relationship between the object and the scene, as different/same object actions in same/different scenes may lead to various degrees of normality and anomaly.
Global Information Guided Video Anomaly Detection
Video anomaly detection (VAD) is currently a challenging task due to the complexity of anomaly as well as the lack of labor-intensive temporal annotations.
Learning Normal Dynamics in Videos with Meta Prototype Network
Frame reconstruction (current or future frame) based on Auto-Encoder (AE) is a popular method for video anomaly detection.
Localizing Anomalies from Weakly-Labeled Videos
In addition, in order to fully utilize the spatial context information, the immediate semantics are directly derived from the segment representations.
Anomaly Detection In Surveillance Videos
Video Anomaly Detection
