I received my BS in 2011 and MS in 2013 at Electrical Engineering, KAIST. As of 2013, I am a PhD student at KAIST, working on visual representation learning with Prof. In So Kweon, but I decided to take some time off to join Lunit in 2017. As a full-time research scientist at Lunit, I am devoted to developing advanced AI for medical image analysis and interpretation via cutting-edge deep learning technology. During my internship experience at Adobe Research in the US, I worked on large-scale video representation learning. My research interests include visual recognition problems with deep learning approaches.
Representation learning, unsupervised learning, large-scale learning, for visual recognition.
ImageNet Large Scale Visual Recognition Challenge (ILSVRC)
Grand Prize in KAIST Undergraduate Research Program.
D. Yoo, N. Kim, S. Park, A. S. Paek, I. S. Kweon,
Pixel-Level Domain Transfer,
European Conference on Computer Vision (ECCV), 2016.
Paper | Supp | LookBook dataset | Code written by Fei Xia
(This paper was also invited to ECCV'16 workshop on TASK-CV)
D. Yoo, S. Park, J.-Y. Lee, A. S. Paek, I. S. Kweon,
AttentionNet: Aggregating Weak Directions for Accurate Object Detection,
IEEE International Conference on Computer Vision (ICCV), 2015.
Paper | Supp
Y. Yoon, H.-G. Jeon, D. Yoo, J.-Y. Lee, I. S. Kweon,
Learning a Deep Convolutional Network for Light-Field Image Super-Resolution,
IEEE International Conference on Computer Vision (ICCV) - CPCV Workshop, 2015.
D. Yoo, S. Park, J.-Y. Lee, I. S. Kweon,
Multi-scale Pyramid Pooling for Deep Convolutional Representation,
International Conference on Computer Vision and Pattern Recognition (CVPR) - DeepVision Workshop, 2014.
Paper | Supp
D. Yoo, K. Paeng, S. Park,J. Lee, S. Paek, S.-E. Yoon, I. S. Kweon,
PRISM: A System for Weighted Multi-Color Browsing of Fashion Products,
International World Wide Web Conference Companion (WWW), 2014.