Jaehoon Choi

kevchoi@umd.edu | jaehoonc44@gmail.com

I am a Ph.D. student at University of Maryland, College Park (UMD).

At UMD I've worked on computer vision and autonomous navigation systems. I did my MS at KAIST, where I was advised by Changick Kim. I did my bachelors at the KAIST.

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I'm interested in computer vision, robotics, and computer graphics.

SelfTune: Metrically Scaled Monocular Depth Estimation through Self-Supervised Learning
Jaehoon Choi*, Dongki Jung*, Yonghan Lee, Deokhwa Kim, Dinesh Manocha, Donghwan Lee
ICRA, 2022

We have developed a fine-tuning method for metrically accurate depth estimation in a self-supervised way.

DnD: Dense Depth Estimation in Crowded Dynamic Indoor Scenes
Dongki Jung*, Jaehoon Choi*, Yonghan Lee, Deokhwa Kim, Changick Kim, Dinesh Manocha, Donghwan Lee
ICCV, 2021

We present a novel approach for estimating depth from a monocular camera as it moves through complex and crowded indoor environments.

Just a Few Points are All You Need for Multi-view Stereo: A Novel Semi-supervised Learning Method for Multi-view Stereo
Taekyung Kim, Jaehoon Choi, Seokeon Choi, Dongki Jung, Changick Kim
ICCV, 2021

We first introduce a novel semi-supervised multi-view stereo framework.

SelfDeco: Self-Supervised Monocular Depth Completion in Challenging Indoor Environments
Jaehoon Choi, Dongki Jung, Yonghan Lee, Deokhwa Kim, Dinesh Manocha, Donghwan Lee
ICRA, 2021

We present a novel algorithm for self-supervised monocular depth completion in challenging indoor environments.

SAFENet: Self-Supervised Monocular Depth Estimation with Semantic-Aware Feature Extraction
Jaehoon Choi*, Dongki Jung*, Donghwan Lee, Changick Kim
NeurIPS Workshop on Machine Learning for Autonomous Driving, 2020

We propose SAFENet that is designed to leverage semantic information to overcome the limitations of the photometric loss.

Arbitrary Style Transfer Using Graph Instance Normalization
Dongki Jung, Seunghan Yang, Jaehoon Choi, Changick Kim
ICIP, 2020

We present a novel learnable normalization technique for style transfer using graph convolutional networks

Self-Ensembling with GAN-based Data Augmentation for Domain Adaptation in Semantic Segmentation
Jaehoon Choi, Taekyung Kim, Changick Kim
ICCV, 2019

We propose a novel framework consisting of two components: Target-Guided and Cycle-Free Data Augmentation and Self-Ensembling algorithm

Self-Training and Adversarial Background Regularization for Unsupervised Domain Adaptive One-Stage Object Detection
Seunghyeon Kim, Jaehoon Choi, Taekyung Kim, Changick Kim
ICCV, 2019   (Oral)

We introduce a weak self-training (WST) method and adversarial background score regularization (BSR) for domain adaptive one-stage object detection.

Pseudo-Labeling Curriculum for Unsueprvised Domain Adaptation
Jaehoon Choi, Minki Jeong, Taekyung Kim, Changick Kim
BMVC, 2019

We propose a pseudo-labeling curriculum based on a density-based clustering algorithm.


Conference Reviewer CVPR 2020, WACV 2021, ACCV 2020, AAAI 2021, ICRA 2021

Chosen as one of 66 outstanding reviewers of ACCV 2020
Teaching Assistant, CMSC250: Discrete Structure Fall 2021

Teaching Assistant, CMSC426: Computer Vision Spring 2021
Teaching Assistant, EE838–Special Topics in Image Engineering Optimization for Computer Vision Spring 2019

Student Tutor for Foreign Students: Introduction to Programming, CS101 2019

Thanks to Jon Barron! this guy's website is awesome,