no code implementations • CVPR 2025 • Andrea Maracani, Savas Ozkan, Sijun Cho, Hyowon Kim, Eunchung Noh, Jeongwon Min, Cho Jung Min, Dookun Park, Mete Ozay
Scaling architectures have been proven effective for improving Scene Text Recognition (STR), but the individual contribution of vision encoder and text decoder scaling remain under-explored.
no code implementations • 1 Mar 2021 • Ziming Li, Dookun Park, Julia Kiseleva, Young-Bum Kim, Sungjin Lee
Digital assistants are experiencing rapid growth due to their ability to assist users with day-to-day tasks where most dialogues are happening multi-turn.
no code implementations • 29 May 2020 • Dookun Park, Hao Yuan, Dongmin Kim, Yinglei Zhang, Matsoukas Spyros, Young-Bum Kim, Ruhi Sarikaya, Edward Guo, Yuan Ling, Kevin Quinn, Pham Hung, Benjamin Yao, Sungjin Lee
An widely used approach to tackle this is to collect human annotation data and use them for evaluation or modeling.
no code implementations • 28 Nov 2018 • Jaewon Yang, Dookun Park, Jae Ho Sohn, Zhen Jane Wang, Grant T. Gullberg, Youngho Seo
Deep convolutional neural networks (DCNN) have demonstrated its capability to convert MR image to pseudo CT for PET attenuation correction in PET/MRI.