1 code implementation • 17 Apr 2024 • Yeonguk Yu, Sungho Shin, Seunghyeok Back, Minhwan Ko, Sangjun Noh, Kyoobin Lee
After blocks are adjusted for current test domain, we generate pseudo-labels by averaging given test images and corresponding flipped counterparts.
1 code implementation • 28 Jun 2023 • Seunghyeok Back, Sangbeom Lee, KangMin Kim, Joosoon Lee, Sungho Shin, Jemo Maeng, Kyoobin Lee
Moreover, a real-world robotic experiment demonstrated the practical applicability of our method in improving the performance of target object grasping tasks in cluttered environments.
1 code implementation • 20 Sep 2022 • Seongju Lee, Yeonguk Yu, Seunghyeok Back, Hogeon Seo, Kyoobin Lee
Conventionally, learning-based automatic sleep scoring on single-channel electroencephalogram (EEG) is actively studied because obtaining multi-channel signals during sleep is difficult.
no code implementations • 22 Oct 2021 • Junseok Lee, Jongwon Kim, Jumi Park, Seunghyeok Back, Seongho Bak, Kyoobin Lee
This paper proposes a method to automatically detect the key feature parts in a CAD of commercial TV and monitor using a deep neural network.
1 code implementation • 23 Sep 2021 • Seunghyeok Back, Joosoon Lee, Taewon Kim, Sangjun Noh, Raeyoung Kang, Seongho Bak, Kyoobin Lee
Instance-aware segmentation of unseen objects is essential for a robotic system in an unstructured environment.
no code implementations • 7 Jan 2021 • Joosoon Lee, Seongju Lee, Seunghyeok Back, Sungho Shin, Kyoobin Lee
Understanding assembly instruction has the potential to enhance the robot s task planning ability and enables advanced robotic applications.
1 code implementation • 10 Feb 2020 • Seunghyeok Back, Jongwon Kim, Raeyoung Kang, Seungjun Choi, Kyoobin Lee
Segmentation of unseen industrial parts is essential for autonomous industrial systems.
1 code implementation • 18 Feb 2019 • Hogeon Seo, Seunghyeok Back, Seongju Lee, Deokhwan Park, Tae Kim, Kyoobin Lee
A deep learning model, named IITNet, is proposed to learn intra- and inter-epoch temporal contexts from raw single-channel EEG for automatic sleep scoring.
Ranked #1 on Sleep Stage Detection on MASS SS2