Dongwon Kim

POSTECH Computer vision lab. kdwon@postech.ac.kr

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I am a Ph.D student at POSTECH Computer Vision Lab., supervised by Prof. Suha Kwak. I completed my B.S. in computer science and engineering also at POSTECH.

I am currently engaged in research that intersects visual perception with language understanding, focusing on object-centric representation. My aspiration is to delve into the intricacies of how visual objects can be mapped as the lexicon of a unique language, reflecting the compositional and symbolic essence of language where objects are the primary semantic units.

In line with this research interest, I have recently worked on developing effective representation schemes for cross-modal retrieval (DivE) and referring image segmentation with weak supervision (Shatter-Gather).

I am looking for the oppertunities of interesting collaborations and internship! If you are interested in my works or have any questions, please feel free to contact me.

News

Mar 14, 2024 :loudspeaker: Our paper about referring image segmentation has been accepted at NAACL 2024 main track.
Dec 7, 2023 :loudspeaker: I am really grateful to have been granted the POSTECHIAN fellowship!
Jul 15, 2023 :loudspeaker: A paper about weakly-supervised referring image segmentation is accepted at ICCV 2023!
Mar 21, 2023 :loudspeaker: A paper about cross-modal retrieval is selected as a highlight at CVPR 2023!

Education

Mar, 2015 - Aug, 2019 B.S. in Computer Science & Engineering
POSTECH, Pohang, South Korea
Sep, 2019 - Present Integrated M.S & Ph.D in Computer Science & Engineering
POSTECH, Pohang, South Korea
Advisor: Prof. Suha Kwak


Selected Publications

  1. risclip.jpg
    Extending CLIP’s Image-Text Alignment to Referring Image Segmentation
    Seoyeon Kim, Minguk Kang, Dongwon Kim, Jaesik Park, and Suha Kwak
    In Annual Conference on the North American Chapter of the Association for Computational Linguistics (NAACL), Jun 2024
    Code
  2. sag.png
    Shatter and Gather: Learning Referring Image Segmentation with Text Supervision
    Dongwon Kim*, Namyup Kim*, Cuiling Lan, and Suha Kwak
    In IEEE/CVF International Conference on Computer Vision (ICCV), Oct 2023
    arXiv Code Website
  3. dive.png
    Improving Cross-Modal Retrieval With Set of Diverse Embeddings
    Dongwon Kim, Namyup Kim, and Suha Kwak
    In IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Jun 2023
    (Highlight, 235/9155 = 2.5%)
    arXiv Code Website
  4. restr.png
    ReSTR: Convolution-Free Referring Image Segmentation Using Transformers
    Namyup Kim, Dongwon Kim, Cuiling Lan, Wenjun Zeng, and Suha Kwak
    In IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Jun 2022
    arXiv Website
  5. stml.png
    Self-Taught Metric Learning Without Labels
    Sungyeon Kim, Dongwon Kim, Minsu Cho, and Suha Kwak
    In IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Jun 2022
    arXiv Code Website
  6. et.png
    Embedding Transfer With Label Relaxation for Improved Metric Learning
    Sungyeon Kim, Dongwon Kim, Minsu Cho, and Suha Kwak
    In IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Jun 2021
    arXiv Website
  7. pa.gif
    Proxy Anchor Loss for Deep Metric Learning
    Sungyeon Kim, Dongwon Kim, Minsu Cho, and Suha Kwak
    In IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Jun 2020
    arXiv Code Website

Professional Services

Reviewer
  • IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI)
  • IEEE/CVF International Conference on Computer Vision (ICCV), 2023
  • IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2022-2023
  • European Conference on Computer Vision (ECCV), 2022
  • Winter Conference on Applications of Computer Vision (WACV), 2023
  • Asian Conference on Computer Vision (ACCV), 2022

Honors and Awards

POSTECHIAN Fellowship, POSTECH, 2023
  • $5,000 grant
BK21 Best Paper Award, POSTECH GSAI, 2023
  • Self-Taught Metric Learning without Labels (CVPR 2022)
Qualcomm Innovation Fellowship Winner, Qualcomm Korea Corp., 2022
  • Self-Taught Metric Learning without Labels (CVPR 2022)
  • ReSTR: Convolution?free Referring Image Segmentation Using Transformers (CVPR 2022)
NAVER x POSTECH AI DAY The 2nd and 3rd Prize, 2022
  • ReSTR: Convolution-free Referring Image Segmentation Using Transformers (CVPR 2022)
Qualcomm Innovation Fellowship Winner, Qualcomm Korea Corp., 2021
  • Embedding Transfer with Label Relaxation for Improved Metric Learning (CVPR 2021)
IPIU Best Paper Award, 2021
  • Embedding Transfer with Label Relaxation for Improved Metric Learning (CVPR 2021)