Video anomaly detection is well investigated in weakly supervised and one-class classification
(OCC) settings. However, unsupervised video anomaly detection is quite sparse, likely …

A popular method for anomaly detection is to use the generator of an adversarial network to
formulate anomaly score over reconstruction loss of input. Due to the rare occurrence of …

There are many limitations applying object detection algorithm on various environments.
Specifically, detecting small objects is still challenging because they have low-resolution and …

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, we …

… Seung-Ik Lee received his MS and PhD in computer science from Yonsei University,
Seoul, Korea, in 1997 and 2001, respectively. He is currently working for ETRI, Korea. He has …

Anomalous event detection in surveillance videos is a challenging and practical research
problem among image and video processing community. Compared to the frame-level …

Convolutional Neural Networks (CNNs) has shown a great success in many areas including
complex image classification tasks. However, they need a lot of memory and computational …

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 utilizing …

OBJECTIVE: The purpose of this study is to describe the high-resolution CT findings of
varicella-zoster pneumonia in three immunocompetent patients. CONCLUSION: High-resolution …

Data augmentation has been proven effective which, by preventing overfitting, can not only
enhances the performance of a deep neural network but also leads to a better generalization …