25 citations · 39 across the 5 of their papers we have counts for
5 papers
Active anomaly detection based on deep one-class classification
Minkyung Kim, Junsik Kim, Jongmin Yu +1
Active learning has been utilized as an efficient tool in building anomaly detection models by leveraging expert feedback. In an active learning framework, a model queries samples…
Focus or Not: A Baseline for Anomaly Event Detection On the Open Public Places with Satellite Images
Yongjin Jeon, Youngtack Oh, Doyoung Jeong +2
In recent years, monitoring the world wide area with satellite images has been emerged as an important issue. Site monitoring task can be divided into two independent tasks; 1) Cha…
Unsupervised Deep One-Class Classification with Adaptive Threshold based on Training Dynamics
Minkyung Kim, Junsik Kim, Jongmin Yu +1
One-class classification has been a prevailing method in building deep anomaly detection models under the assumption that a dataset consisting of normal samples is available. In pr…
Normality-Calibrated Autoencoder for Unsupervised Anomaly Detection on Data Contamination
Jongmin Yu, Hyeontaek Oh, Minkyung Kim +1
In this paper, we propose Normality-Calibrated Autoencoder (NCAE), which can boost anomaly detection performance on the contaminated datasets without any prior information or expli…
Camera-Tracklet-Aware Contrastive Learning for Unsupervised Vehicle Re-Identification
Jongmin Yu, Junsik Kim, Minkyung Kim +1
Recently, vehicle re-identification methods based on deep learning constitute remarkable achievement. However, this achievement requires large-scale and well-annotated datasets. In…