Minsu Cho Minsu Cho (조 민수)
Assistant Professor
Dept. of Computer Science and Engineering
POSTECH (Pohang Univ. of Science and Technology)
77 Cheongam-ro, Nam-gu, Pohang, Gyeongbuk, South Korea 37673

Email: mscho at postech dot ac dot kr
Tel: +82 054 279 2385
Fax: +82 054 279 2299

I am an assistant professor at the department of Computer Science and Engineering 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 object discovery, weakly-supervised learning, semantic correspondence, and graph matching.

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 closely collaborate with Cordelia Schmid in the Inria LEAR team. I completed my Ph.D. in 2012, under the supervision of Kyoung Mu Lee at Seoul National University, Korea.

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


NEWS 
  • `Multi-Object Tracking with Quadruplet Convolutional Neural Networks'  has been accepted in CVPR 2017.
  • `Nonconvex Potential Guided Image Filtering'  paper has been accepted in TPAMI.
  • `Text-guided attention model for image captioning'  paper has been accepted in AAAI 2017.
  • I joined POSTECH as an assistant professor at the department of Computer Science and Engineering.
  • `ContextLocNet'  paper has been accepted in ECCV 2016.
  • Two papers, `Thin-Slicing for Pose'  and `Proposal Flow'  have been accepted in CVPR 2016.
  • The code for `Unsupervised Object Discovery' (CVPR 2015 oral) has been available here.


    RECENT & SELECTED PUBLICATIONS (Click here for a full list)

    Jeany Son, Mooyeol Baek, Minsu Cho, Bohyung Han
    Multi-Object Tracking with Quadruplet Convolutional Neural Networks   
    The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2017, Honolulu, HI, US.
    To appear

    Bumsub Ham, Minsu Cho, Jean Ponce
    Robust Guided Image Filtering Using Nonconvex Potentials   
    IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), Feb 2017.
    [link]

    Jonghwan Mun, Minsu Cho, Bohyung Han
    Text-Guided Attention Model for Image Captioning   
    AAAI Conference on Artificial Intelligence (AAAI), 2017, San Francisco, CA, US.
    [pdf]

    Vadim Kantorov, Maxime Oquab, Minsu Cho, Ivan Laptev
    ContextLocNet: Context-aware Deep Network Models for Weakly Supervised Localization   
    The European Conference on Computer Vision (ECCV), 2016, Amsterdam, Netherlands.
    [pdf] [project page]

    Suha Kwak, Minsu Cho, Ivan Laptev
    Thin-Slicing for Pose: Learning to Understand Pose without Explicit Pose Estimation   
    The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016, Las Vegas, Nevada.
    [pdf] [project page]

    Bumsub Ham, Minsu Cho, Cordelia Schmid, Jean Ponce
    Proposal Flow   
    The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016, Las Vegas, Nevada.
    [pdf] [project page]

    Suha Kwak, Minsu Cho, Ivan Laptev, Jean Ponce, Cordelia Schmid
    Unsupervised Object Discovery and Tracking in Video Collections
    the International Conference on Computer Vision (ICCV), 2015, Santiago, Chile.
    [pdf] [project page]

    Junchi Yan, Minsu Cho, Hongyuan Zha, Xiaokang Yang, Stephen Chu
    Multi-Graph Matching via Affinity Optimization with Graduated Consistency Regularization
    IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), Sept 2015.
    [link]

    Minsu Cho, Suha Kwak, Cordelia Schmid, Jean Ponce
    Unsupervised Object Discovery and Localization in the Wild: Part-based Matching with Bottom-up Region Proposals
    The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2015, Boston, MA (oral).
    [pdf] [project page]

    Minsu Cho, Karteek Alahari, Jean Ponce
    Learning Graphs to Match
    The IEEE International Conference on Computer Vision (ICCV), 2013, Sydney, Australia (oral).
    [pdf] [project page]

    Minsu Cho, Kyoung Mu Lee
    Progressive Graph Matching: Making a Move of Graphs via Probabilistic Voting
    The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2012, Providence, RI (oral).
    [pdf] [project page]

    Minsu Cho, Young Min Shin, Kyoung Mu Lee
    Unsupervised Detection and Segmentation of Identical Objects
    The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2010, San Francisco, CA (oral).
    [pdf] [project page]

    Minsu Cho, Jungmin Lee, Kyoung Mu Lee
    Reweighted Random Walks for Graph Matching
    The European Conference on Computer Vision (ECCV), 2010, Crete, Greece.
    [pdf] [project page]

    Minsu Cho, Jungmin Lee, Kyoung Mu Lee
    Feature Correspondence and Deformable Object Matching via Agglomerative Correspondence Clustering
    The IEEE International Conference on Computer Vision (ICCV), 2009, Kyoto, Japan.
    [pdf] [project page]


    SOFTWARE CODES & DATASETS
  • 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)
  • 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 & POSTDOCS
    PhD:
  • Deunsol Jung, 2016- (co-advised with B. Han) at POSTECH
  • Vadim Kantorov, 2015- (co-advised with I. Laptev) at Inria
  • Interns:
  • Kai Han, 2016- (co-advised with J. Ponce) at Inria
  • Sergiu Irimie, 2016 (co-advised with J. Ponce) at Inria
  • Yumin Suh, 2015 (co-advised with J. Ponce) at Inria

  • TEACHING
    2016 Fall: Introduction to Computer Vision (at POSTECH)