29 citations · 73 across the 9 of their papers we have counts for
12 papers
PseudoBound: Limiting the anomaly reconstruction capability of one-class classifiers using pseudo anomalies
Marcella Astrid, Muhammad Zaigham Zaheer, Seung-Ik Lee
Due to the rarity of anomalous events, video anomaly detection is typically approached as one-class classification (OCC) problem. Typically in OCC, an autoencoder (AE) is trained t…
Stabilizing Adversarially Learned One-Class Novelty Detection Using Pseudo Anomalies
Muhammad Zaigham Zaheer, Jin Ha Lee, Arif Mahmood +2
Recently, anomaly scores have been formulated using reconstruction loss of the adversarially learned generators and/or classification loss of discriminators. Unavailability of anom…
Clustering Aided Weakly Supervised Training to Detect Anomalous Events in Surveillance Videos
Muhammad Zaigham Zaheer, Arif Mahmood, Marcella Astrid +1
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…
Learning Not to Reconstruct Anomalies
Marcella Astrid, Muhammad Zaigham Zaheer, Jae-Yeong Lee +1
Video anomaly detection is often seen as one-class classification (OCC) problem due to the limited availability of anomaly examples. Typically, to tackle this problem, an autoencod…
Synthetic Temporal Anomaly Guided End-to-End Video Anomaly Detection
Marcella Astrid, Muhammad Zaigham Zaheer, Seung-Ik Lee
Due to the limited availability of anomaly examples, video anomaly detection is often seen as one-class classification (OCC) problem. A popular way to tackle this problem is by uti…
Deep Visual Anomaly detection with Negative Learning
Jin-Ha Lee, Marcella Astrid, Muhammad Zaigham Zaheer +1
With the increase in the learning capability of deep convolution-based architectures, various applications of such models have been proposed over time. In the field of anomaly dete…