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Junsik Kim

Postdoctoral Researcher
KAIST

About Me

I am a postdoctoral researcher in the School of Electrical Engineering at KAIST. I received my B.S., M.S. and Ph.D. from KAIST, advised by Prof. In So Kweon. My research background is computer vision and machine learning. Recently, I am working on machine learning problems related to data issues, including audio-visual learning, few-shot learning, and active learning.

CV   |   Google Scholar   |   GitHub

International Journal

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Learning to Localize Sound Source in Visual Scenes: Analysis and Applications
Arda Senocak, Tae-Hyun Oh, Junsik Kim, Ming-Hsuan Yang, In So Kweon
IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), to appear.
Paper | Project
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Robust and Globally Optimal Manhattan Frame Estimation in Near Real Time
Kyungdon Joo, Tae-Hyun Oh, Junsik Kim, In So Kweon
IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 2019
Paper | Project

International Conference

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Motion-blurred Video Interpolation and Extrapolation
Dawit Mureja Argaw, Junsik Kim, Francois Rameau, In So Kweon
AAAI Conference on Artificial Intelligence (AAAI), Feb 2021
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Optical Flow Estimation from a Single Motion-Blurred Image
Dawit Mureja Argaw, Junsik Kim, Francois Rameau, JaeWon Cho, In So Kweon
AAAI Conference on Artificial Intelligence (AAAI), Feb 2021
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ResNet or DenseNet? Introducing Dense Short-cuts to ResNet
Chaoning Zhang*, Philipp Benz*, Dawit Mureja Argaw, Seokju Lee, Junsik Kim, Francois Rameau, Jean-Charles Bazin, In So Kweon (* equal conribution)
IEEE Winter Conference on Applications of Computer Vision (WACV), Mar 2021.
Paper | GitHub
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DeepPTZ: Deep Self-Calibration for PTZ Cameras
Chaoning Zhang, Francois Rameau, Junsik Kim, Dawit Mureja Argaw, Jean-Charles Bazin, In So Kweon
IEEE Winter Conference on Applications of Computer Vision (WACV), Mar 2020.
Paper | GitHub
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Revisiting Residual Networks with Nonlinear Shortcuts
Chaoning Zhang, Francois Rameau, Seokju Lee, Junsik Kim, Philipp Benz, Dawit Mureja Argaw, Jean-Charles Bazin, In So Kweon
British Machine Vision Conference (BMVC), Sep 2019 (spotlight)
Paper | Project
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Visuomotor Understanding for Representation Learning of Driving Scenes
Seokju Lee, Junsik Kim, Tae-Hun Oh, Yongseop Jeong, Donggeun Yoo, Stephen Lin, In So Kweon
British Machine Vision Conference (BMVC), Sep 2019
Paper | Project | GitHub
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Variational Prototyping-Encoder: One-Shot Learning with Prototypical Images
Junsik Kim, Tae-Hyun Oh, Seokju Lee, Fei Pan, In So Kweon
IEEE International Conference on Computer Vision and Pattern Recognition (CVPR), Jun 2019
Paper | GitHub
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Part-based Player Identification using Deep Convolutional Representation and Multi-scale Pooling
Arda Senocak, Tae-Hyun Oh, Junsik Kim, In So Kweon
In CVSports workshop in conjunction with CVPR, Jun 2018 (oral)
Paper
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Learning to Localize Sound Source in Visual Scenes
Arda Senocak, Tae-Hyun Oh, Junsik Kim, Ming-Hsuan Yang, In So Kweon
IEEE International Conference on Computer Vision and Pattern Recognition (CVPR), Jun 2018
Paper | GitHub
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Co-domain Embedding using Deep Quadruplet Networks for Unseen Traffic Sign Recognition
Junsik Kim, Seokju Lee, Tae-Hyun Oh, In So Kweon
AAAI Conference on Artificial Intelligence (AAAI), Feb 2018
Paper
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VPGNet: Vanishing Point Guided Network for Lane and Road Marking Detection and Recognition
Seokju Lee, Junsik Kim, Jae Shin Yoon, Seunghak Shin, Oleksandr Bailo, Namil Kim, Tae-Hee Lee, Hyun Seok Hong, Seung-Hoon Han, In So Kweon
IEEE International Conference on Computer Vision (ICCV), Oct 2017
Paper | Project | GitHub
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Pixel-Level Matching for Video Object Segmentation Using Convolutional Neural Networks
Jae Shin Yoon, Francois Rameau, Junsik Kim, Seokju Lee, Seunghak Shin, In So Kweon
IEEE International Conference on Computer Vision (ICCV), Oct 2017
Paper | Project
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Globally Optimal Manhattan Frame Estimation in Real-time
Kyungdon Joo, Tae-Hyun Oh, Junsik Kim, In So Kweon
IEEE International Conference on Computer Vision and Pattern Recognition (CVPR), Jun 2016
Paper | Project | GitHub