I am an assistant professor at the School of Computing, KAIST, leading KAIST Vision and Learning Lab. Before joining KAIST, I was a visiting faculty researcher at Google Brain, and a postdoctoral fellow at University of Michigan collaborated with Professor Honglak Lee on topics related to deep learning and its application to computer vision. I received my Ph.D. degree at POSTECH, Korea under the supervision of Professor Bohyung Han.
My research interests include machine learning and computer vision. Particularly, I am interested in scaling up machine learning algorithms for visual perception by minimizing human supervision for training. I am also interested in making such algorithms interpretable to humans, allowing users to more easily understand and get involved in the decision making process in ML systems.
seunghoon.hong@kaist.ac.kr
Bldg E3-1, Rm 3429, 291 Daehak-ro, Yuseong-gu, Daejeon, Korea, 34141
(+82)-42-350-3579
PhD in Computer Science and Engineering, 2017
POSTECH, Korea
BS in Computer Science and Engineering, 2011
POSTECH, Korea
Meta-Controller: Few-Shot Imitation of Unseen Embodiments and Tasks in Continuous Control
NeurIPS 2024
[ Comming soon ]
Simulation-Free Training of Neural ODEs on Paired Data
NeurIPS 2024
[ Comming soon ]
Learning to Merge Tokens via Decoupled Embedding for Efficient Vision Transformers
NeurIPS 2024
[ Comming soon ]
Chameleon: A Data-Efficient Generalist for Dense Visual Prediction in the Wild
ECCV 2024 Oral presentation
[ arXiv ]
Learning to Compose: Improving Object Centric Learning by Injecting Compositionality
ICLR 2024
[ Paper ]
3D Denoisers are Good 2D Teachers: Molecular Pretraining via Denoising and Cross-Modal Distillation
arXiv 2023
[ arXiv ]
Information-Theoretic State Space Model for Multi-View Reinforcement Learning
ICML 2023 Oral presentation
[ Paper ]
MetaDTA: Meta-learning-based Drug-Target Binding Affinity Prediction
MLDD workshop @ ICLR 2022
[ Paper ]
Interpretable Text-to-Image Synthesis with Hierarchical Semantic Layout Generation.
Bookchapter of "Explainable AI; Interpreting, Explaining and Visualizing Deep Learning" 2019
[ Paper ]
Weakly Supervised Learning with Deep Convolutional Neural Networks
for Semantic Segmentation
Signal Processing Magazine 2017
[ Paper ]
Weakly Supervised Semantic Segmentation using Web-Crawled Videos
CVPR 2017 Spotlight presentation
[ arXiv ]
Online Tracking by Learning Discriminative Saliency Map
with Convolutional Neural Network
ICML 2015
[ arXiv ]
Hyeongjoo Hwang (co-advised with Prof. Kee-Eung Kim)
Yeonwoo Cha
Wonkwang Lee (M.S. -> Phd. @ Seoul National University)
Sunghyun Myung (M.S.)