no code implementations • 29 Jan 2024 • Yun Young Choi, Minho Lee, Sun Woo Park, Seunghwan Lee, Joohwan Ko
The Cy2Mixer is composed of three blocks based on MLPs: A message-passing block for encapsulating spatial information, a cycle message-passing block for enriching topological information through cyclic subgraphs, and a temporal block for capturing temporal properties.
Ranked #2 on Traffic Prediction on PeMS04
no code implementations • 31 Aug 2023 • Tom Van Wouwe, Seunghwan Lee, Antoine Falisse, Scott Delp, C. Karen Liu
Unlike existing methods, our model grants users the flexibility to determine the number and arrangement of sensors tailored to the specific activity of interest, without the need for retraining.
1 code implementation • 8 Mar 2023 • Seunghwan Lee, Gwanmo Park, Hyewon Son, Jiwon Ryu, Han Joo Chae
We introduce FastSurf, an accelerated neural radiance field (NeRF) framework that incorporates depth information for 3D reconstruction.
1 code implementation • 31 Jan 2023 • Seunghwan Lee, Tae Hyun Kim
Although several real-world noisy datasets have been presented, the number of train datasets (i. e., pairs of clean and real noisy images) is limited, and acquiring more real noise datasets is laborious and expensive.
no code implementations • CVPR 2022 • Han Joo Chae, Seunghwan Lee, Hyewon Son, Seungyeob Han, Taebin Lim
We introduce AiD Regen, a novel system that generates 3D wound models combining 2D semantic segmentation with 3D reconstruction so that they can be printed via 3D bio-printers during the surgery to treat diabetic foot ulcers (DFUs).
no code implementations • 24 Jul 2021 • Jongseok Kim, Youngjae Yu, Seunghwan Lee, GunheeKim
Since this one-way mapping is highly under-constrained, we couple it with an inverse relation learning with the Correction Network and introduce a cycled relation for given Image We participate in Fashion IQ 2020 challenge and have won the first place with the ensemble of our model.
1 code implementation • 27 Mar 2020 • Youngjae Yu, Seunghwan Lee, Yuncheol Choi, Gunhee Kim
In order to learn an effective image-text composition for the data in the fashion domain, our model proposes two key components as follows.
Ranked #16 on Image Retrieval on Fashion IQ
no code implementations • 9 Mar 2020 • Seunghwan Lee, Dongkyu Lee, Donghyeon Cho, Jiwon Kim, Tae Hyun Kim
However, these methods have limitations in using internal information available in a given test image.
no code implementations • CVPR 2021 • Seunghwan Lee, Donghyeon Cho, Jiwon Kim, Tae Hyun Kim
We analyze the restoration performance of the fine-tuned video denoising networks with the proposed self-supervision-based learning algorithm, and demonstrate that the FCN can utilize recurring patches without requiring accurate registration among adjacent frames.
no code implementations • 9 Jan 2020 • Seunghwan Lee, Donghyeon Cho, Jiwon Kim, Tae Hyun Kim
Under certain statistical assumptions of noise, recent self-supervised approaches for denoising have been introduced to learn network parameters without true clean images, and these methods can restore an image by exploiting information available from the given input (i. e., internal statistics) at test time.
1 code implementation • SIGGRAPH 2019 • Seunghwan Lee, Kyoungmin Lee, Moonseok Park, Jehee Lee
Many anatomical factors, such as bone geometry and muscle condition, interact to affect human movements.