18 citations · 20 across the 3 of their papers we have counts for
7 papers · 1 filter
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…
Cleaning Label Noise with Clusters for Minimally Supervised Anomaly Detection
Muhammad Zaigham Zaheer, Jin-ha Lee, Marcella Astrid +2
Learning to detect real-world anomalous events using video-level annotations is a difficult task mainly because of the noise present in labels. An anomalous labelled video may actu…
CLAWS: Clustering Assisted Weakly Supervised Learning with Normalcy Suppression for Anomalous Event Detection
Muhammad Zaigham Zaheer, Arif Mahmood, Marcella Astrid +1
Learning to detect real-world anomalous events through video-level labels is a challenging task due to the rare occurrence of anomalies as well as noise in the labels. In this work…
Old is Gold: Redefining the Adversarially Learned One-Class Classifier Training Paradigm
Muhammad Zaigham Zaheer, Jin-ha Lee, Marcella Astrid +1
A popular method for anomaly detection is to use the generator of an adversarial network to formulate anomaly scores over reconstruction loss of input. Due to the rare occurrence o…