most citedDrivers Drowsiness Detection using Condition-Adaptive Representation Learning Framework

125 citations · 129 across the 4 of their papers we have counts for

collaborators

5 papers

cs.CV2020

Context-Aware Multi-Task Learning for Traffic Scene Recognition in Autonomous Vehicles

Younkwan Lee, Jihyo Jeon, Jongmin Yu +1

Traffic scene recognition, which requires various visual classification tasks, is a critical ingredient in autonomous vehicles. However, most existing approaches treat each relevan…

cs.CV2020

Predictively Encoded Graph Convolutional Network for Noise-Robust Skeleton-based Action Recognition

Jongmin Yu, Yongsang Yoon, Moongu Jeon

In skeleton-based action recognition, graph convolutional networks (GCNs), which model human body skeletons using graphical components such as nodes and connections, have achieved…

cs.CV20204 cited

Unsupervised Pixel-level Road Defect Detection via Adversarial Image-to-Frequency Transform

Jongmin Yu, Duyong Kim, Younkwan Lee +1

In the past few years, the performance of road defect detection has been remarkably improved thanks to advancements on various studies on computer vision and deep learning. Althoug…

cs.CV2019125 cited

Drivers Drowsiness Detection using Condition-Adaptive Representation Learning Framework

Jongmin Yu, Sangwoo Park, Sangwook Lee +1

We propose a condition-adaptive representation learning framework for the driver drowsiness detection based on 3D-deep convolutional neural network. The proposed framework consists…

cs.LG2019

Boosting Mapping Functionality of Neural Networks via Latent Feature Generation based on Reversible Learning

Jongmin Yu

This paper addresses a boosting method for mapping functionality of neural networks in visual recognition such as image classification and face recognition. We present reversible l…