Youngwook Kim
I am an Assistant Professor at School of Artificial Intelligence, College of Computer Science, Kookmin University.
I received my Ph.D degree from Department of Eletrical and Computer Engineering, Seoul National University, in 2024, under advisor Jungwoo Lee. Also, I received my bachelor's degree from Department of Civil and Environmental Engineering, Seoul National University, in 2018.
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Resume
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News
(24/09) We are recruting 1~2 graduate students for Intelligent Computer Vision Lab at Kookmin University. If you are interested, please contact me via e-mail with your resume and a description of your experience in AI.
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Research
I'm interested in computer vision, learning from noisy labels, multi-label learning, weakly supervised learning, and remote sensing.
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Learning Dual Hierarchical Representation for 3D Surface Reconstruction
Jiyoon Shin, Youngwook Kim, Sangwoo Hong, Jungwoo Lee
Asian Conference on Computer Vision (ACCV), 2024
We introduce Dual Hierarchical Representation (DHR), which allows for faithful reconstructions under constrained computation by hierarchical encoding, decoding, and optimization procedures.
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Instance-Dependent Multi-Label Noise Generation for Multi-Label Remote Sensing Image Classification
Youngwook Kim, Sehwan Kim, Youngmin Ro, Jungwoo Lee
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing (JSTARS), 2024
[GitHub]
We introduce generating instance-dependent multi-label noise into multi-label remote sensing image datasets which is more feasible and challenging noise scenario.
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Bridging the Gap between Model Explanations in Partially Annotated Multi-label Classification
Youngwook Kim, Jae Myung Kim, Jieun Jeong, Cordelia Schmid, Zeynep Akata, Jungwoo Lee
IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2023
[GitHub] /
[arXiv] /
[Project page] /
[Poster] /
[Slide] /
[YouTube]
By investigating the difference in model explanation, we devise a simple but effective solution to compensate the damage by false negative labels.
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Large Loss Matters in Weakly Supervised Multi-Label Classification
Youngwook Kim*, Jae Myung Kim*, Zeynep Akata, Jungwoo Lee
IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2022
[GitHub] /
[arXiv] /
[Poster] /
[Slide]
By utilizing memorization effect, we can efficiently train multi-label classification model with small number of partially annotated labels.
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Invited Talk
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Smart city seminar, Seoul National University, Department of Civil and Environmental Engineering, September 2023
AIIS Spring retreat (Oral presenation), May 2023 [Presentation video] (in Korean)
University of Seoul, June 2022
Kakao enterprise, July 2022
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Teaching
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Artificial Intelligence, Fall 2024
Intelligent Mobility Services, Fall 2024
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Reviewer Service
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Conferences : CVPR, ICCV, ECCV
Journals : IEEE Transactions on Multimedia
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The format of this website is borrowed from Jon Barron.
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