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Seunghoon Hong

Assistant Professor

School of Computing, KAIST

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.

Contact

  • seunghoon.hong@kaist.ac.kr

  • Bldg E3-1, Rm 3429, 291 Daehak-ro, Yuseong-gu, Daejeon, Korea, 34141

  • (+82)-42-350-3579

Education

  • PhD in Computer Science and Engineering, 2017

    POSTECH, Korea

  • BS in Computer Science and Engineering, 2011

    POSTECH, Korea

News

  • [Mar 2022] One paper is accepted to CVPR 2022.
  • [Jan 2022] One paper is accepted to ICLR 2022.
  • [Jan 2022] One paper is accepted to ICASSP 2022.
  • [Dec 2021] One paper is accepted to AAAI 2022.
  • [Oct 2021] One paper is accepted to BMVC 2021.
  • [Oct 2021] Two papers are accepted to NeurIPS 2021.
  • [Mar 2021] Two papers are accepted to CVPR 2021.
  • [Dec 2020] One paper is accepted to ICLR 2021.
  • [Nov 2020] One paper is accepted to WACV 2021.
  • [Sept 2020] One paper is accepted to NeurIPS 2020.
  • [July 2020] One paper is accepted to ECCV 2020.

Publications

  • MetaDTA: Meta-learning-based Drug-Target Binding Affinity Prediction

    Eunjoo Lee, Jiho Yoo, Huisun Lee, Seunghoon Hong

    MLDD workshop @ ICLR 2022

    [ Paper ]

  • Part-based Pseudo Label Refinement for Unsupervised Person Re-identification

    Yoonki Cho, Woo Jae Kim, Seunghoon Hong, Sung-Eui Yoon

    CVPR 2022

    [ arXiv / Code ]

  • Multi-Task Neural Processes

    Donggyun Kim, Seongwoong Cho, Wonkwang Lee, Seunghoon Hong

    ICLR 2022

    [ Paper / Code ]

  • Learning Continuous Representation of Audio for Arbitrary Scale Super-Resolution

    Jaechang Kim, Yunjoo Lee, Seunghoon Hong, and Jungseul Ok

    ICASSP 2022

    [ arXiv / Code ]

  • Learning Economic Indicators by Aggregating Multi-level Geospatial Information

    Sungwon Park, Sungwon Han, Donghyun Ahn, Jaeyeon Kim, Jeasurk Yang,
    Susang Lee, Seunghoon Hong, Jihee Kim, Sangyoon Park, Hyunjoo Yang, Meeyoung Cha

    AAAI 2022

    [ arXiv ]

  • Learning to Generate Novel Classes for Deep Metric Learning

    Kyungmoon Lee, Sungyeon Kim, Seunghoon Hong, Suha Kwak

    BMVC 2021

    [ Paper ]

  • Transformers Generalize DeepSets and Can be Extended to Graphs and Hypergraphs

    Jinwoo Kim, Saeyoon Oh, Seunghoon Hong

    NeurIPS 2021

    [ arXiv / Code ]

  • Multi-View Representation Learning via Total Correlation Objective

    HyeongJoo Hwang, Geon-hyeong Kim, Seunghoon Hong, Kee-Eung Kim

    NeurIPS 2021

    [ Paper / Code ]

  • SetVAE: Learning Hierarchical Composition for Generative Modeling of Set-Structured Data

    Jinwoo Kim, Jaehoon Yoo, Juho Lee, Seunghoon Hong

    CVPR 2021

    [ arXiv / Code ]

  • Improving Unsupervised Image Clustering With Robust Learning

    Sungwon Park, Sungwon Han, Sungdong Kim, Danu Kim, Sangkyu Park,
    Seunghoon Hong, Meeyoung Cha

    CVPR 2021

    [ arXiv / Code ]

  • Revisiting Hierarchical Approach for Persistent Long-Term Video Prediction

    Wonkwang Lee, Whie Jung, Han Zhang, Ting Chen, Jing Yu Koh, Thomas Huang,
    Hyungsuk Yoon, Honglak Lee, Seunghoon Hong

    ICLR 2021

    [ Paper / Project / Code ]

  • Neural Contrast Enhancement of CT Image

    Minkyo Seo, Dongkeun Kim, Kyungmoon Lee, Seunghoon Hong,
    Jae Seok Bae, Jung Hoon Kim, Suha Kwak

    WACV 2021

    [ Paper ]

  • Variational Interaction Information Maximization for Cross-domain Disentanglement

    HyeongJoo Hwang, Geon-hyeong Kim, Seunghoon Hong, Kee-Eung Kim

    NeurIPS 2020

    [ Paper / Code ]

  • High-Fidelity Synthesis with Disentangled Representation

    Wonkwang Lee, Donggyun Kim, Seunghoon Hong, Honglak Lee

    ECCV 2020

    [ arXiv / Code ]

  • Residual Neural Processes

    Byung-Jun Lee, Seunghoon Hong, and Kee-Eung Kim

    AAAI 2020

    [ Paper ]

  • Adversarial Defense via Learning to Generate Diverse Attacks.

    Yunseok Jang, Tianchen Zhao, Seunghoon Hong, Honglak Lee

    ICCV 2019

    [ Paper / Code ]

  • Interpretable Text-to-Image Synthesis with Hierarchical Semantic Layout Generation.

    Seunghoon Hong, Dingdong Yang, Jongwook Choi, Honglak Lee

    Bookchapter of "Explainable AI; Interpreting, Explaining and Visualizing Deep Learning" 2019

    [ Paper ]

  • Diversity-Sensitive Conditional Generative Adversarial Networks

    Dingdong Yang, Seunghoon Hong, Yunseok Jang, Tiangchen Zhao, Honglak Lee

    ICLR 2019

    [ arXiv / Code / Project ]

  • Learning Hierarchical Semantic Image Manipulation
    through Structured Representations

    Seunghoon Hong, Xinchen Yan, Thomas Huang, Honglak Lee

    NeurIPS 2018

    [ arXiv / Code ]

  • Inferring Semantic Layout for Hierarchical Text-to-Image Synthesis

    Seunghoon Hong, Dingdong Yang, Jongwook Choi, Honglak Lee

    CVPR 2018

    [ arXiv ]

  • Weakly Supervised Learning with Deep Convolutional Neural Networks
    for Semantic Segmentation

    Seunghoon Hong, Suha Kwak, Bohyung Han

    Signal Processing Magazine 2017

    [ Paper ]

  • Weakly Supervised Semantic Segmentation using Web-Crawled Videos

    Seunghoon Hong, Donghun Yeo, Suha Kwak, Honglak Lee, Bohyung Han

    CVPR 2017 Spotlight presentation

    [ arXiv ]

  • Decomposing Motion and Content for Natural Video Sequence Prediction

    Ruben Villegas, Jimei Yang, Seunghoon Hong, Xunyu Lin, Honglak Lee

    ICLR 2017

    [ arXiv / Code / Project ]

  • Personalized Image Aesthetic Quality Assessment
    by Joint Regression and Ranking

    Kayoung Park, Seunghoon Hong, Mooyeol Baek, Bohyung Han

    WACV 2017

    [ Paper ]

  • Weakly Supervised Semantic Segmentation
    using Superpixel Pooling Network

    Suha Kwak, Seunghoon Hong, Bohyung Han

    AAAI 2017

    [ Paper ]

  • Learning Transferrable Knowledge for Semantic Segmentation
    with Deep Convolutional Neural Network

    Seunghoon Hong, Junhyuk Oh, Bohyung Han, Honglak Lee

    CVPR 2016 Spotlight presentation

    [ arXiv / Code / Project ]

  • Decoupled Deep Neural Network for Semi-supervised Semantic Segmentation

    Seunghoon Hong, Hyeonwoo Noh, Bohyung Han

    NIPS 2015 Spotlight presentation

    [ arXiv / Code / Project ]

  • Learning Deconvolution Network for Semantic Segmentation

    Hyeonwoo Noh, Seunghoon Hong, Bohyung Han

    ICCV 2015

    [ arXiv / Code / Project ]

  • Joint Image Clustering and Labeling by Matrix Factorization

    Seunghoon Hong, Jonghyun Choi, Jan Feyereisl, Bohyung Han, Larry S. Davis

    TPAMI 2015

    [ Paper ]

  • Online Tracking by Learning Discriminative Saliency Map
    with Convolutional Neural Network

    Seunghoon Hong, Tackgeun You, Suha Kwak, Bohyung Han

    ICML 2015

    [ arXiv ]

  • Visual Tracking by Sampling Tree-Structured Graphical Models

    Seunghoon Hong, Bohyung Han

    ECCV 2014 Oral presentation

    [ Paper / Project ]

  • Online Graph-Based Tracking

    Hyeonseob Nam, Seunghoon Hong, Bohyung Han

    ECCV 2014

    [ Paper / Project ]

  • Orderless Tracking through Model-Averaged Posterior Estimation

    Seunghoon Hong, Suha Kwak, Bohyung Han

    ICCV 2013 Oral presentation

    [ Paper / Project ]

  • Joint Segmentation and Pose Tracking of Human in Natural Videos

    Taegyu Lim, Seunghoon Hong, Bohyung Han, Joon Hee Han

    ICCV 2013

    [ Paper ]

Students

Ph.D. Students

MS Students

Undergraduate Students

Alumni