I'm a Research Engineer at NAVER Clova. My current research interest includes following tasks:
Visual recognition on image/video
Object detection, Instance/panoptic segmentation, Person keypoints estimation.
3D vision
Mono/stereo depth estimation, Neural scene representation (e.g., NeRF)
Publication
Self-Supervised Monocular Depth Estimation with Isometric-Self-Sample-Based Learning
Geonho Cha, Ho-Deok Jang, Dongyoon Wee
IEEE Robotics and Automation Letters (RA-L), 2022.
[pdf]
Propose-and-Attend Single Shot Detector
Ho-Deok Jang, Sanghyun Woo, Philipp Benz, Jinsun Park, In So Kweon
IEEE Winter Conference on Applications of Computer Vision (WACV), 2020. (Oral)
[pdf], [video oral], [video demo]
Gated Bidirectional Feature Pyramid Network for Accurate One-shot Detection
Sanghyun Woo, Soonmin Hwang, Ho-Deok Jang, In So Kweon
Machine Vision and Applications (MVA), 2019.
[pdf]
Project
Visual AI system for NAVER map
Member : NAVER CLOVA Video Team (w/ Min-Young Chang as intern), GLACE Team
Task : Developing a visual recognition system for parsing road view information under purpose of enhancing NAVER map services.
Contribution: object detection/tracking dev codebase, modeling of object detector w/ class imbalance, modeling of lane detector
Software : Python, PyTorch
Link : [video]
Visual recognition algorithm for robot manipulation
Member : Ho-Deok Jang and Philipp Benz
Task : Developing a visual recognition system for robot manipulation. Specifically, the work is to design a 2d/3d object detection algorithms based on an RGB-D sensor and build a custom dataset for the task. Refer to a demo video below for the results.
Advisor : Prof. Jun-Ho Oh and Prof.In So Kweon
Software : Python, PyTorch, ROS
Paper Reproduce
Implemented and reproduced "Zhu et al. Feature Selective Anchor-Free Module for Single-Shot Object Detection. CVPR, 2019." in PyTorch (Sep. 2019).
Task: object detection
Note: To my best knowledge, this is the "first" open source code that implements and reproduces this paper on github at the time of upload.