Inkyu Shin
About Inkyu Shin
Inkyu Shin is a researcher at KAIST, specializing in domain adaptive recognition in computer vision. He holds a Bachelor's degree from Hanyang University and a Master's degree from KAIST, where he has been conducting research since 2019.
Work at KAIST
Inkyu Shin has been a researcher at KAIST since 2019. His work is primarily focused on domain adaptive recognition in computer vision, addressing challenges related to data requirements in deep learning. He conducts his research in the Robotics and Computer Vision Lab, under the supervision of Professor In So Kweon. His role involves exploring innovative solutions to improve recognition systems, which are essential in various applications of artificial intelligence.
Education and Expertise
Inkyu Shin earned his Bachelor's degree in the Division of Future Vehicle from 한양대학교, completing his studies from 2013 to 2019. He furthered his education at 한국과학기술원 (KAIST), where he obtained a Master's degree in the same division from 2019 to 2021. His academic background provides a strong foundation in vehicle technology and computer vision, enabling him to contribute effectively to his research focus.
Background
Inkyu Shin's academic journey began at 한양대학교, where he studied for six years in the Division of Future Vehicle. Following his undergraduate studies, he transitioned to KAIST for his Master's degree, further specializing in the field. Prior to his current role at KAIST, he gained practical experience as a research intern at 고려대학교 in 2018, where he worked for three months. This experience helped shape his research interests and skills in the field of computer vision.
Research Focus
Inkyu Shin's research primarily addresses the data hungry problem in deep learning through domain adaptive recognition in computer vision. This area of study is critical as it seeks to enhance the performance of machine learning models in real-world scenarios where labeled data may be scarce. His work aims to develop methods that improve the adaptability and efficiency of recognition systems, contributing to advancements in robotics and artificial intelligence.