Výsledky vyhledávání - "Min-Kook Choi"

Development of Fast Refinement Detectors on AI Edge Platforms

Autor: Min-Kook Choi, Heechul Jung

Publikováno v: Pattern Recognition. ICPR International Workshops and Challenges ISBN: 9783030687984
ICPR Workshops (4)

Refinement detector (RefineDet) is a state-of-the-art model in object detection that has been developed and refined based on high-end GPU systems. In this study, we discovered that the speed of models developed in high-end GPU systems is inconsistent

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::4183431cfaa881fcf6ad2dcd90fadf38
https://doi.org/10.1007/978-3-030-68799-1_43

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hSDB-instrument: Instrument Localization Database for Laparoscopic and Robotic Surgeries

Autor: Sunghwan Heo, Jiwon Lee, Jayeon Lim, Min-Kook Choi, Seungbum Hong, Jihun Yoon, Bokyung Park, Sung Hyun Park, Chi Hyun H. Song, Seul Gi Hong, Hayeong Yu, Woo Jin Hyung

Publikováno v: Medical Image Computing and Computer Assisted Intervention – MICCAI 2021 ISBN: 9783030872014

Automated surgical instrument localization is an important technology to understand the surgical process and in order to analyze them to provide meaningful guidance during surgery or surgical index after surgery to the surgeon. We introduce a new dat

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::d92e64b4b787b21339dee45a41b102f6
https://doi.org/10.1007/978-3-030-87202-1_38

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Memory-Efficient Models for Scene Text Recognition via Neural Architecture Search

Autor: Min-Kook Choi, SeulGi Hong, DongHyun Kim

Publikováno v: WACV Workshops

Meta-learning techniques based on neural architecture search (NAS) show excellent performance in the design of learning models used in deep neural networks. In particular, when NAS is applied to design a convolutional neural network (CNN) for image r

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::10d2324f5df13ab68ec2e1ca176e05de
https://doi.org/10.1109/wacvw50321.2020.9096928

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Semi-Supervised Object Detection with Sparsely Annotated Dataset

Autor: Seungbum Hong, Sanha Jeong, Jihun Yoon, Min-Kook Choi

In training object detector based on convolutional neural networks, selection of effective positive examples for training is an important factor. However, when training an anchor-based detectors with sparse annotations on an image, effort to find eff

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::67abda456acf9d5d64e96cca8f376563

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Semi-supervised Learning for Instrument Detection with a Class Imbalanced Dataset

Autor: Jihun Yoon, Sung Hyun Park, Woo Jin Hyung, Min-Kook Choi, Jiwon Lee

Publikováno v: Interpretable and Annotation-Efficient Learning for Medical Image Computing ISBN: 9783030611651
iMIMIC/MIL3iD/LABELS@MICCAI

The automated recognition of surgical instruments in surgical videos is an essential factor for the evaluation and analysis of surgery. The analysis of surgical instrument localization information can help in analyses related to surgical evaluation a

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::0e346a32a73674abb47048cf25b20c18
https://doi.org/10.1007/978-3-030-61166-8_28

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