Zobrazeno 1 - 10
of 435
pro vyhledávání: '"Kim, HyungMin"'
Autor:
Park, Seongmin, Kim, Hyungmin, Jeon, Wonseok, Yang, Juyoung, Jeon, Byeongwook, Oh, Yoonseon, Choi, Jungwook
Deep neural network (DNN)-based policy models like vision-language-action (VLA) models are transformative in automating complex decision-making across applications by interpreting multi-modal data. However, scaling these models greatly increases comp
Externí odkaz:
http://arxiv.org/abs/2412.01034
In this paper, we propose SPACE, a novel anomaly detection methodology that integrates a Feature Encoder (FE) into the structure of the Student-Teacher method. The proposed method has two key elements: Spatial Consistency regularization Loss (SCL) an
Externí odkaz:
http://arxiv.org/abs/2411.05822
Publikováno v:
JMIR Medical Informatics, Vol 9, Iss 3, p e25635 (2021)
BackgroundRenal cell carcinoma (RCC) has a high recurrence rate of 20% to 30% after nephrectomy for clinically localized disease, and more than 40% of patients eventually die of the disease, making regular monitoring and constant management of utmost
Externí odkaz:
https://doaj.org/article/1a0976e914dd4eb881b09448b3af5eff
Recent advances in deep learning have significantly improved the performance of various computer vision applications. However, discovering novel categories in an incremental learning scenario remains a challenging problem due to the lack of prior kno
Externí odkaz:
http://arxiv.org/abs/2307.10943
Autor:
Lee, Minjae, Park, Seongmin, Kim, Hyungmin, Yoon, Minyong, Lee, Janghwan, Choi, Jun Won, Kim, Nam Sung, Kang, Mingu, Choi, Jungwook
3D object detection using point cloud (PC) data is essential for perception pipelines of autonomous driving, where efficient encoding is key to meeting stringent resource and latency requirements. PointPillars, a widely adopted bird's-eye view (BEV)
Externí odkaz:
http://arxiv.org/abs/2305.07522
Autor:
Kim, Hyungmin, Suh, Sungho, Baek, Sunghyun, Kim, Daehwan, Jeong, Daun, Cho, Hansang, Kim, Junmo
We present a novel adversarial penalized self-knowledge distillation method, named adversarial learning and implicit regularization for self-knowledge distillation (AI-KD), which regularizes the training procedure by adversarial learning and implicit
Externí odkaz:
http://arxiv.org/abs/2211.10938
Publikováno v:
In Expert Systems With Applications 15 November 2024 254
Publikováno v:
In Sustainable Cities and Society 1 November 2024 114
Autor:
Sung, Dong-Jin, Kim, Keun-Tae, Jeong, Ji-Hyeok, Kim, Laehyun, Lee, Song Joo, Kim, Hyungmin, Kim, Seung-Jong
Publikováno v:
In Heliyon 15 September 2024 10(17)
Autor:
Choi, Seunghwan, Kum, Jeungeun, Hyun, Seon Young, Park, Tae Young, Kim, Hyungmin, Kim, Sun Kwang, Kim, Jaeho
Publikováno v:
In Brain Stimulation September-October 2024 17(5):1119-1130