Supervised learning algorithms have shown limited use in the field of anomaly detection due
to the unpredictability and difficulty in acquiring abnormal samples. In recent years …

We present a novel framework, ie, Segment Any Anomaly+(SAA+), for zero-shot anomaly
segmentation with hybrid prompt regularization to improve the adaptability of modern …

Learning discriminative features for effectively separating abnormal events from normality is
crucial for weakly supervised video anomaly detection (WS-VAD) tasks. Existing …

Identifying anomalies from data has attracted increasing attention in recent years due to its
broad range of potential applications. Although many efforts have been made for anomaly …

Video anomaly detection and localization is one of the key components of the intelligent
video surveillance system. Video anomaly detection refers to the process of spatiotemporal …

Optimal motion planning involves obstacles avoidance whereas path planning is the key to
success in optimal motion planning. Due to the computational demands, most of the path …

With a focus on abnormal events contained within untrimmed videos, there is increasing
interest among researchers in video anomaly detection. Among different video anomaly …

Formulating learning systems for the detection of real-world anomalous events using only
video-level labels is a challenging task mainly due to the presence of noisy labels as well as …

Video anomaly detection in real-world scenarios is challenging due to the complex temporal
blending of long and short-length anomalies with normal ones. Further, it is more difficult to …

Convolutional neural networks raised the bar for machine learning and artificial intelligence
applications, mainly due to the abundance of data and computations. However, there is not …