Search Results for author: Dongwook Lee

Found 9 papers, 3 papers with code

CMDA: Cross-Modal and Domain Adversarial Adaptation for LiDAR-Based 3D Object Detection

no code implementations6 Mar 2024 Gyusam Chang, Wonseok Roh, Sujin Jang, Dongwook Lee, Daehyun Ji, Gyeongrok Oh, Jinsun Park, Jinkyu Kim, Sangpil Kim

Recent LiDAR-based 3D Object Detection (3DOD) methods show promising results, but they often do not generalize well to target domains outside the source (or training) data distribution.

3D Object Detection object-detection +1

BackTrack: Robust template update via Backward Tracking of candidate template

no code implementations21 Aug 2023 Dongwook Lee, Wonjun Choi, Seohyung Lee, ByungIn Yoo, Eunho Yang, Seongju Hwang

An effective method to tackle these challenges is template update, which updates the template to reflect the change of appearance in the target object during tracking.

Visual Object Tracking

RaScaNet: Learning Tiny Models by Raster-Scanning Images

no code implementations CVPR 2021 Jaehyoung Yoo, Dongwook Lee, Changyong Son, Sangil Jung, ByungIn Yoo, Changkyu Choi, Jae-Joon Han, Bohyung Han

RaScaNet reads only a few rows of pixels at a time using a convolutional neural network and then sequentially learns the representation of the whole image using a recurrent neural network.

Binary Classification

CollaGAN: Collaborative GAN for Missing Image Data Imputation

no code implementations CVPR 2019 Dongwook Lee, Junyoung Kim, Won-Jin Moon, Jong Chul Ye

In many applications requiring multiple inputs to obtain a desired output, if any of the input data is missing, it often introduces large amounts of bias.

Generative Adversarial Network Image Imputation +2

Which Contrast Does Matter? Towards a Deep Understanding of MR Contrast using Collaborative GAN

2 code implementations10 May 2019 Dongwook Lee, Won-Jin Moon, Jong Chul Ye

Thanks to the recent success of generative adversarial network (GAN) for image synthesis, there are many exciting GAN approaches that successfully synthesize MR image contrast from other images with different contrasts.

Generative Adversarial Network Image Generation +3

CollaGAN : Collaborative GAN for Missing Image Data Imputation

1 code implementation28 Jan 2019 Dongwook Lee, Junyoung Kim, Won-Jin Moon, Jong Chul Ye

In many applications requiring multiple inputs to obtain a desired output, if any of the input data is missing, it often introduces large amounts of bias.

Generative Adversarial Network Image Imputation +2

Deep Residual Learning for Accelerated MRI using Magnitude and Phase Networks

no code implementations2 Apr 2018 Dongwook Lee, Jaejun Yoo, Sungho Tak, Jong Chul Ye

The proposed deep residual learning networks are composed of magnitude and phase networks that are separately trained.

Deep artifact learning for compressed sensing and parallel MRI

no code implementations3 Mar 2017 Dongwook Lee, Jaejun Yoo, Jong Chul Ye

Furthermore, the computational time is by order of magnitude faster.

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