Minsu Cho Minsu Cho (조 민수)
Associate Professor
Department of Computer Science and Engineering
Graduate School of Artificial Intelligence
POSTECH (Pohang Univ. of Science and Technology, 포항공과대학교)
77 Cheongam-ro, Nam-gu, Pohang, Gyeongbuk, South Korea 37673

Email: mscho ~at~ postech dot ac dot kr

I am an associate professor in the Department of Computer Science and Engineering and Graduate School of Artificial Intelligence at POSTECH, South Korea, working as a faculty member of POSTECH Computer Vision Lab. My research lies in the areas of computer vision and machine learning, especially in the problems of visual semantic correspondence, symmetry analysis, object discovery, action recognition, and minimally-supervised learning. I am interested in the relationship between correspondence, symmetry, and supervision in visual learning.

Before joining POSTECH in the fall of 2016, I was a researcher (starting research position) in the Inria WILLOW team at École Normale Supérieure (ENS), Paris, France, where I have worked with Jean Ponce and Cordelia Schmid. I completed my Ph.D. in 2012, under the supervision of Kyoung Mu Lee at Seoul National University, Korea.

I am an editorial board member of the International Journal of Computer Vision (IJCV) and have been serving an area chair in prestigious conferences including CVPR, ICCV, WACV, AAAI, IJCAI, ACCV, BMVC. In 2020, I have been inducted into the Young Korean Academy of Science and Technology (Y-KAST).

POSTECH CV Lab Publications - Software - Students - Teaching - CV - Google Scholar


NEWS 
  • Nine papers are accepted at CVPR 2022.
  • I will serve as a Workshop Chair for ICCV 2023.
  • One paper is accepted at WACV 2022.
  • Three papers are accepted at NeurIPS 2021.
  • Six papers are accepted at ICCV 2021.
  • I am an Area Chair / Senior Program Committee member for CVPR 2022 and AAAI 2022.
  • I won the Samsung Future Technology grant for Advanced AI from Samsung Science & Technology Foundation.
  • Our paper, IntegralAction, is accepted at CVPR 2021 Workshop on Large Scale Holistic Video Understanding.
  • Two papers are accepted at CVPR 2021.
  • One paper is accepted at WACV 2021.
  • I am inducted into the Young Korean Academy of Science and Technology (Y-KAST).
  • I am serving as a Program Chair for MVA 2021.
  • Our paper, CircleGAN, is accepted at NeurIPS 2020.
  • Two papers are accepted at ECCV 2020.
  • Two papers are accepted at CVPR 2020.
  • I am an Area Chair / Senior Program Committee member for CVPR 2021, ICCV 2021, BMVC 2021, AAAI 2021, and IJCAI 2021. .
  • I won 2019 Proud POSTECHIAN Award in education.


    RECENT PUBLICATIONS (Click here for a full list)

    Jongmin Lee, Byungjin Kim, Minsu Cho
    Self-Supervised Equivariant Learning for Oriented Keypoint Detection   
    IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2022, New Orleans.

    Doyup Lee, Chiheon Kim, Saehoon Kim, Minsu Cho, Wook-Shin Han
    Autoregressive Image Generation using Residual Quantization   
    IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2022, New Orleans.

    Seungwook Kim, Juhong Min, Minsu Cho
    TransforMatcher: Match-to-Match Attention for Semantic Correspondence   
    IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2022, New Orleans.

    Ahyun Seo, Byungjin Kim, Suha Kwak, Minsu Cho
    Reflection and Rotation Symmetry Detection via Equivariant Learning   
    IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2022, New Orleans.

    Dayoung Gong, Joonseok Lee, Manjin Kim, Seongjong Ha, Minsu Cho
    Future Transformer for Long-term Action Anticipation   
    IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2022, New Orleans.

    Sungyeon Kim, Dongwon Kim, Minsu Cho, Suha Kwak
    Self-Taught Metric Learning without Labels   
    IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2022, New Orleans.

    Chunghyun Park, Yoonwoo Jeong, Minsu Cho, Jaesik Park
    Fast Point Transformer   
    IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2022, New Orleans.

    Dahyun Kang, Minsu Cho
    Integrative Few-Shot Learning for Classification and Segmentation   
    IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2022, New Orleans.

    Dongkeun Kim, Jinsung Lee, Minsu Cho, Suha Kwak
    Detector-Free Weakly Supervised Group Activity Recognition   
    IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2022, New Orleans.

    Jeongbeen Yoon, Dahyun Kang, Minsu Cho
    Semi-Supervised Domain Adaptation via Sample-to-Sample Self-Distillation   
    IEEE Winter Conference on Applications of Computer Vision (WACV), 2022, Waikoloa, HI.
    [pdf]

    Manjin Kim*, Heeseung Kwon*, Chunyu Wang, Suha Kwak, Minsu Cho (*equal contribution)
    Relational Self-Attention: What's Missing in Attention for Video Understanding   
    Annual Conference on Neural Information Processing Systems (NeurIPS), 2021, Online.
    [pdf]

    Hyunsoo Chung*, Jungtaek Kim*, Boris Knyazev, Jinhwi Lee, Graham W. Taylor, Jaesik Park, Minsu Cho (*equal contribution)
    Brick-by-Brick: Combinatorial Construction with Deep Reinforcement Learning   
    Annual Conference on Neural Information Processing Systems (NeurIPS), 2021, Online.
    [pdf]

    Minguk Kang, Woohyeon Joseph Shim, Minsu Cho, Jaesik Park
    Rebooting ACGAN: Auxiliary Classifier GANs with Stable Training   
    Annual Conference on Neural Information Processing Systems (NeurIPS), 2021, Online.
    [pdf]

    Heeseung Kwon*, Manjin Kim*, Suha Kwak, Minsu Cho
    Learning Self-Similarity in Space and Time as Generalized Motion for Video Action Recognition    
    International Conference on Computer Vision (ICCV), 2021, Online.
    [pdf] [project page]

    Juhong Min, Dahyun Kang, Minsu Cho
    Hypercorrelation Squeeze for Few-Shot Segmentation    
    International Conference on Computer Vision (ICCV), 2021, Online.
    [pdf] [project page]

    Dahyun Kang, Heeseung Kwon, Juhong Min, Minsu Cho
    Relational Embedding for Few-Shot Classification    
    International Conference on Computer Vision (ICCV), 2021, Online.
    [pdf] [project page]

    Ahyun Seo*, Woohyeon Shim*, Minsu Cho
    Learning to Discover Reflection Symmetry via Polar Matching Convolution    
    International Conference on Computer Vision (ICCV), 2021, Online.
    [pdf] [project page]

    Junha Lee, Seungwook Kim, Minsu Cho, Jaesik Park
    Deep Hough Voting for Robust Global Registration    
    International Conference on Computer Vision (ICCV), 2021, Online.
    [pdf] [project page]

    Yoonwoo Jeong, Seokjun Ahn, Christopher Choy, Animashree Anandkumar, Minsu Cho, Jaesik Park
    Self-Calibrating Neural Radiance Fields    
    International Conference on Computer Vision (ICCV), 2021, Online.

    Gyeongsik Moon*, Heeseung Kwon*, Kyoung Mu Lee, Minsu Cho
    IntegralAction: Pose-driven Feature Integration for Robust Human Action Recognition in Videos
    CVPR Workshop on Large Scale Holistic Video Understanding, 2021, Online.
    [pdf]

    Juhong Min, Minsu Cho
    Convolutional Hough Matching Networks
    IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2021. (oral presentation)
    [pdf] [project page]

    Sungyeon Kim, Dongwon Kim, Minsu Cho, Suha Kwak
    Embedding Transfer with Label Relaxation for Improved Metric Learning
    IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2021, Online.
    [pdf]

    Woohyeon Shim, Minsu Cho
    CircleGAN: Generative Adversarial Learning across Spherical Circles
    Annual Conference on Neural Information Processing Systems (NeurIPS), 2020, Online.
    [pdf]

    Juhong Min, Jongmin Lee, Jean Ponce, Minsu Cho
    Learning to Compose Hypercolumns for Visual Correspondence   
    European Conference on Computer Vision (ECCV), 2020, Glasgow, UK.
    [pdf][project page]

    Heeseung Kwon, Manjin Kim, Suha Kwak, Minsu Cho
    MotionSqueeze: Neural Motion Feature Learning for Video Understanding   
    European Conference on Computer Vision (ECCV), 2020, Glasgow, UK.
    [pdf][project page]

    Sungyeon Kim, Dongwon Kim, Minsu Cho, Suha Kwak
    Proxy Anchor Loss for Deep Metric Learning   
    IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2020, Seattle, WA, USA.
    [pdf][project page]

    Jonghwan Mun, Minsu Cho, Bohyung Han
    Local-Global Video-Text Interactions for Temporal Grounding   
    IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2020, Seattle, WA, USA.
    [pdf]

    Ilchae Jung, Kihyun You, Hyeonwoo Noh, Minsu Cho, Bohyung Han
    Real-Time Object Tracking via Meta-Learning: Efficient Model Adaptation and One-Shot Channel Pruning   
    AAAI Conference on Artificial Intelligence (AAAI), 2020, Hilton New York Midtown, NY, USA.
    [pdf]

    Sangwoo Mo, Chiheon Kim, Sungwoong Kim, Minsu Cho, Jinwoo Shin
    Mining GOLD Samples for Conditional GANs   
    Annual Conference on Neural Information Processing Systems (NeurIPS), 2019, Vancouver, Canada.
    [pdf]

    Juhong Min, Jongmin Lee, Jean Ponce, Minsu Cho
    Hyperpixel Flow: Semantic Correspondence with Multi-layer Neural Features    
    International Conference on Computer Vision (ICCV), 2019, Seoul, Korea.
    [pdf] [project page]

    Wonpyo Park, Dongju Kim, Yan Lu, Minsu Cho
    Relational Knowledge Distillation   
    IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2019, Long Beach, CA.
    [pdf] [project page]

    Huy V. Vo, Francis Bach, Minsu Cho, Kai Han, Yann LeCun, Patrick Pérez, Jean Ponce
    Unsupervised Image Matching and Object Discovery as Optimization   
    IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2019, Long Beach, CA.
    [pdf]




    SOFTWARE CODES & DATASETS
  • SPair-71k Semantic Correspondence Benchmark Dataset (Paper)   
  • Hyperpixel Flow (Paper)   
  • Relational Knowledge Distillation (Paper)   
  • SCNet: Learning Semantic Correspondence (Paper)
  • ContextLocNet: ConvNet for Weakly-Supervised Object Detection (Paper)
  • Region Proposal Matching + Semantic Correspondence Benchmark (Paper, Data)
  • Unsupervised Object Discovery and Localization (Paper)
  • Graph Learning for Matching (Paper, Data)
  • Outlier-Robust Graph Matching (MPM) (Paper)
  • Progressive framework for Boosting Graph Matching Performance (Paper, Code)
  • Robust Graph Matching (RRWM) (Paper) & Hyper-Graph Matching (RRWHM), (Paper)
  • Mode Seeking on Graphs (Paper)
  • Hierarchical Authority-Shift Clustering (Paper)
  • Deformable Object Matching via Correspondence Clustering (Paper)
  • SNU Dataset for Cosegmentation (Paper, Data)


    STUDENTS
    PhD:
  • Deunsol Jung, 2016-
  • Woohyeon Shim, 2016-
  • Ahyun Seo, 2018-
  • Jeongbeen Yoon, 2018-
  • Jongmin Lee, 2018-
  • Juhong Min, 2018-
  • Manjin Kim, 2019-
  • Sanghyun Kim, 2019-
  • Dahyun Kang, 2019-
  • Seungwook Kim, 2020-
  • Jinhwi Lee (POSCO), 2019-
  • Yoonwoo Jeong, 2020-
  • Dayoung Kong, 2020-

  • Masters:
  • Seungho Lee, 2019-
  • Joonseok Lee, 2021-
  • Byungjin Kim (SAMSUNG), 2021-
  • Yunseon Choi, 2022-
  • Jaejun Hwang, 2022-

  • Interns:
  • .

  • Alumni:
  • Jeany Son (GIST), 2018, PhD (co-advised with B. Han)
  • Paul Hongsuck Seo (Google) , 2020, PhD (co-advised with B. Han)
  • Jonghwan Mun (KakaoBrain), 2020, PhD (co-advised with B. Han)
  • Dongju Kim (Qualcomm), 2019, Master
  • Wonpyo Park (Standigm), 2019, Master
  • Kihyun You (Lunit), 2020 at POSTECH
  • Heeseung Kwon (Inria), 2021, PhD
  • Seungkwan Lee (Deeping Source), 2021, Master
  • Hyunsoo Chung (Spacewalk), 2021 at POSTECH
  • Ilchae Jung (NAVER Clova), 2022, PhD (co-advised with B. Han)
  • Jungtaek Kim, 2022, PhD (co-advised with S. Choi)

  • Hector Dang-Nhu (ENS Paris), 2019, Intern at POSTECH
  • Pierre Jobic (ENS Cachan), 2018, Intern at POSTECH
  • Matthieu Toulemont (Ecole des Ponts Paristech), 2018, Intern at POSTECH
  • Idil Kanpolat (co-advised with E. Konukoglu, ETH Zurich), 2017, Intern at POSTECH
  • Kai Han (co-advised with J. Ponce, ENS), 2016, Intern at Inria Paris
  • Sergiu Irimie (co-advised with J. Ponce, ENS), 2016, Intern at Inria Paris
  • Yumin Suh (co-advised with J. Ponce, ENS), 2015, Intern at Inria Paris

  • TEACHING
    2022 Spring: Automata and Formal Languages (at POSTECH CSE)
    2022 Spring: Arficial Intelligence and Data Science (at POSTECH CSE/AIGS)
    2021 Fall: Automata and Formal Languages (at POSTECH CSE)
    2021 Spring: Arficial Intelligence and Data Science (at POSTECH CSE/AIGS)
    2021 Spring: Language and Vision (at POSTECH CSE/AIGS)
    2020 Fall: Automata and Formal Languages (at POSTECH CSE)
    2020 Spring: Deep Learning (at POSTECH CSE/AIGS)
    2020 Spring: Arficial Intelligence and Data Science (at POSTECH CSE/AIGS)
    2019 Fall: Introduction to Deep Learning (at POSTECH CSE)
    2019 Spring: Language and Vision (at POSTECH CSE)
    2018 Fall: Automata and Formal Languages (at POSTECH CSE)
    2018 Spring: Language and Vision (at POSTECH CSE)
    2017 Fall: Automata and Formal Languages (at POSTECH CSE)
    2017 Spring: Language and Vision (at POSTECH CSE)
    2016 Fall: Introduction to Computer Vision (at POSTECH CSE)