ProfessorsProfessors of Johns Hopkins UniversityTae Soo Kim

Tae Soo Kim

Johns Hopkins University

H-index: 7

North America-United States

About Tae Soo Kim

Tae Soo Kim, With an exceptional h-index of 7 and a recent h-index of 7 (since 2020), a distinguished researcher at Johns Hopkins University, specializes in the field of Computer Vision, Machine Learning, Fine-grained Recognition, Semi-supervised Learning.

His recent articles reflect a diverse array of research interests and contributions to the field:

Enhancing breast cancer risk prediction by incorporating prior images

ELVIS: Empowering Locality of Vision Language Pre-training with Intra-modal Similarity

Did you get what you paid for? rethinking annotation cost of deep learning based computer aided detection in chest radiographs

Granular Analysis of Pretrained Object Detectors

Learning from synthetic vehicles

Video-based assessment of intraoperative surgical skill

OOOE: Only-one-object-exists assumption to find very small objects in chest radiographs

Learning Human Vehicle Interactions using Simulated Data

Tae Soo Kim Information

University	Johns Hopkins University
Position	PhD Student
Citations(all)	1146
Citations(since 2020)	1067
Cited By	477
hIndex(all)	7
hIndex(since 2020)	7
i10Index(all)	6
i10Index(since 2020)	6
Email	Access Email
University Profile Page	Johns Hopkins University
Google Scholar	View Google Scholar Profile

Tae Soo Kim Skills & Research Interests

Computer Vision

Machine Learning

Fine-grained Recognition

Semi-supervised Learning

Top articles of Tae Soo Kim

Title	Journal	Author(s)	Publication Date
Enhancing breast cancer risk prediction by incorporating prior images	arXiv preprint arXiv:2303.15699	Hyeonsoo Lee Junha Kim Eunkyung Park Minjeong Kim Taesoo Kim ...	2023/3/28
ELVIS: Empowering Locality of Vision Language Pre-training with Intra-modal Similarity	arXiv preprint arXiv:2304.05303	Sumin Seo JaeWoong Shin Jaewoo Kang Tae Soo Kim Thijs Kooi	2023/4/11
Did you get what you paid for? rethinking annotation cost of deep learning based computer aided detection in chest radiographs		Tae Soo Kim Geonwoon Jang Sanghyup Lee Thijs Kooi	2022/9/16
Granular Analysis of Pretrained Object Detectors		Eric Xue Tae Soo Kim	2022/2/21
Learning from synthetic vehicles		Tae Soo Kim Bohoon Shim Michael Peven Weichao Qiu Alan Yuille ...	2022
Video-based assessment of intraoperative surgical skill	International journal of computer assisted radiology and surgery	Sanchit Hira Digvijay Singh Tae Soo Kim Shobhit Gupta Gregory Hager ...	2022/10
OOOE: Only-one-object-exists assumption to find very small objects in chest radiographs		Gunhee Nam Taesoo Kim Sanghyup Lee Thijs Kooi	2022/9/18
Learning Human Vehicle Interactions using Simulated Data		Chuyu Liu Tae Soo Kim	2021/4/13
Learning to recognize masked faces by data synthesis		Ziyan Wang Tae Soo Kim	2021/4/13
Motion guided attention fusion to recognize interactions from videos		Tae Soo Kim Jonathan Jones Gregory D Hager	2021
Model-driven and Data-driven Methods for Recognizing Compositional Interactions from Videos		Tae Soo Kim	2021/6/30
Daszl: Dynamic action signatures for zero-shot learning	Proceedings of the AAAI conference on artificial intelligence	Tae Soo Kim Jonathan Jones Michael Peven Zihao Xiao Jin Bai ...	2021/5/18
Safcar: Structured attention fusion for compositional action recognition	arXiv preprint arXiv:2012.02109	Tae Soo Kim Gregory D Hager	2020/12/3