AI Robot Team · PIT IN Corp.
서정훈
자율주행과 로보틱스를 위한 컴퓨터 비전·머신러닝 연구를 이끄는 수석연구원입니다. 위성/항공 이미지와 HCI 센싱까지 폭넓게 다루며, GPU 병렬 컴퓨팅과 컴퓨터 그래픽스에도 관심이 많습니다.
컴퓨터 비전
머신러닝
원격탐사
GPGPU/병렬컴퓨팅
HCI 센싱
대전·서울 기반 · 협업 환영
#### 전문 분야
- 머신러닝, 컴퓨터 비전, 컴퓨터 그래픽스, 그리고 원격 탐사 응용
- 고성능컴퓨팅에서의 병렬 컴퓨팅 및 GPGPU
- 인간-컴퓨터 상호작용을 위한 지능형 센싱 기술
#### 경력
- 2024년 12월 - 현재, AI Robot Team 수석연구원 @ PIT IN Corp.
- 2020년 9월 - 2024년 9월, 인공지능연구소 기술 리더 & 공동 창업자 @ SI Analytics
- 2017년 7월 - 2020년 2월, 머신러닝 및 컴퓨터 비전 연구원 @ Satrec Initiative
#### 학위
- 2023년 3월 - 2025년 2월, KAIST, 대전, 대한민국
- 문화기술대학원 석사 학위
- HCI Tech Lab, 지도교수: 윤상호 교수
- 2014년 3월 - 2021년 2월, GIST, 광주, 대한민국
- 학사 학위, 전기전자 및 컴퓨터 공학 전공
출판 이력 (Google Scholar)
- ForceCtrl: Hand-Raycasting with User-Defined Pinch Force for Control-Display Gain Application
Seo Young Oh, Junghoon Seo, Juyoung Lee, Boram Yoon, Woontack Woo, Sang Ho Yoon
IEEE TVCG. 2025. Link
- Thumb Force Estimation with Egocentric Vision
Hanseok Jeong, Junghoon Seo, Sang Ho Yoon
ACM UIST Demo. 2025. Link
- Guided Super Resolution of Land Surface Temperature Using Multi-Satellite Imageries
Sunju Lee, Yeji Choi, Beomkyu Choi, Junghoon Seo, Minki Song, Eunha Sohn, Sewoongg Ahn
IEEE TGRS. 2025. Link
- Hausdorff Distance Matching with Adaptive Query Denoising for Rotated Detection Transformer
Hakjin Lee, Minki Song, Jamyoung Koo, Junghoon Seo
IEEE WACV. 2025. Link
- Posture-Informed Muscular Force Learning for Robust Hand Pressure Estimation
Junghoon Seo, Kyungjin Seo, Hanseok Jeong, Sangpil Kim, Sang Ho Yoon
NeurIPS. 2024. Link
- A Billion-scale Foundation Model for Remote Sensing Images
Keumgang Cha, Junghoon Seo, and Taekyung Lee
IEEE J-STARS. 2024. Link
- Self-Pair: Synthesizing Changes from Single Source for Object Change Detection in Remote Sensing Imagery
Minseok Seo, Hakjin Lee, Yongjin Jeon, and Junghoon Seo
IEEE WACV. 2023. Link
- Geometric Remove-and-Retrain (GOAR): Coordinate-Invariant eXplainable AI Assessment
Yonghyun Park, Junghoon Seo, Bomseok Park, Seongsu Lee, and Junghyo Jo
NeurIPS Workshop. 2023.
- Prototype-oriented Unsupervised Change Detection for Disaster Management
Youngtack Oh, Minseok Seo, Kim Doyi, and Junghoon Seo
NeurIPS Workshop. 2023. Link
- Semi-Implicit Hybrid Gradient Methods with Application to Adversarial Robustness
Beomsu Kim and Junghoon Seo
AISTATS. 2022. Link
- Quantile Autoencoder with Abnormality Accumulation for Anomaly Detection of Multi-variate Sensor Data
Seunghyoung Ryu, Jiyeon Yim, Junghoon Seo, Yonggyun Yu, and Hogeon Seo
IEEE Access. 2022. Link
- Contrastive Multiview Coding With Electro-Optics for SAR Semantic Segmentation
Keumgang Cha, Junghoon Seo, and Yeji Choi
IEEE GRSL. 2021. Link
- Training Domain-invariant Object Detector Faster with Feature Replay and Slow Learner
Chaehyeon Lee, Junghoon Seo, and Heechul Jung
CVPR Workshop. 2021. Link
- On the Power of Deep but Naive Partial Label Learning
Junghoon Seo and Joon Suk Huh
IEEE ICASSP. 2021. Link
- NL-LinkNet: Toward Lighter but More Accurate Road Extraction with Non-Local Operations
Yooseung Wang, Junghoon Seo, and Taegyun Jeon
IEEE GRSL. 2021. Link
- Revisiting Classical Bagging with Modern Transfer Learning for On-the-fly Disaster Damage Detector
Junghoon Seo, Seungwon Lee, Beomsu Kim, and Taegyun Jeon
NeurIPS Workshop. 2019. Link
- Deep Closed-Form Subspace Clustering
Junghoon Seo, Jamyoung Koo, and Taegyun Jeon
ICCV Workshop. 2019. Link
- Why are Saliency Maps Noisy? Cause of and Solution to Noisy Saliency Maps
Beomsu Kim, Junghoon Seo, Jeongyeol Choe, Jamyoung Koo, Seunghyeon Jeon, and Taegyun Jeon
ICCV Workshop. 2019. Link
- Bridging Adversarial Robustness and Gradient Interpretability
Beomsu Kim, Junghoon Seo, and Taegyun Jeon
ICLR Workshop. 2019. Link
- RBox-CNN: Rotated Bounding Box based CNN for Ship Detection in Remote Sensing Image
Jamyoung Koo, Junghoon Seo, Seunghyun Jeon, Jeongyeol Choe, and Taegyun Jeon
ACM SIGSPATIAL. 2018. Link
- Noise-adding Methods of Saliency Map as Series of Higher Order Partial Derivative
Junghoon Seo, Jeongyeol Choe, Jamyoung Koo, SeungHyun Jeon, Beomsu Kim, and Taegyun Jeon
ICML Workshop. 2018. Link
- Domain Adaptive Generation of Aircraft on Satellite Imagery via Simulated and Unsupervised Learning
Junghoon Seo, Seunghyun Jeon, and Taegyun Jeon
ACML Workshop. 2017. Link
- Multi-task Learning for Fine-grained Visual Classification of Aircraft
Seunghyun Jeon, Junghoon Seo and Taegyun Jeon
ACML Workshop. 2017.
#### 리뷰 중인 작업 타이틀
- Pressure Estimation for Hand-based Interaction
- Hand-Raycasting with Hand Force
- Off-policy Evaluation from Multiple Logging Policies
- Monocular RGB Category-level Multi-object Pose Estimation
- Efficient and Robust Camera Calibration
#### 수상 경력
- 2024년, 진행한 프로젝트를 포함한 랩 데모가 CHI 2024 Popular Choice Winner 선정
- 2021년, GIST에서 최우수학부논문상으로 학부 졸업
- 2020년, xView2 Challenge 5위
- 2018년, CVPR NTIRE Super-resolution Challenge 3-4위
- 2018년, DOTA Challenge 2위
- 2016년, KISTI National Supercomputing Competition 최우수상
- 2016년, Qualcomm-GIST Innovation Award 수상
#### 강연 및 발표
- Query as Representation: A Paradigm Shift in Computer Vision Caused by DETR @ Online. Nov 2022. YouTube (Korean)
- Recent XAI Trends in Deep Learning Era: (Under-)specification and Approaches @ KAERI. Feb 2020.
- Back to the Representation Learning with focusing on Visual Self-supervision @ ETRI. Jul 2019. Presentation (Korean)
- Deep Perceptual Super-resolution: Going Beyond Distortion @ KARI. Jul 2018. Presentation
#### 리뷰어 활동
- 저널: IEEE TPAMI, IEEE TNNLS, IEEE TGRS, IEEE GRSL 등
- 학회: ICLR `26, CVPR `26, AISTATS `26, NeurIPS `25, ICML `25 등
특허
- Method for detecting object, 등록, 10-2364882
- Method for detecting on-the-fly disaster damage based on image, 등록, 10-2255998
- Method for predicting frame using deep learning, 출원, 10-2023-0115699
- Method, system, and computing device for generating an alternative image of a satellite or aerial image in which an image of an area of interest is replaced, 출원, 10-2023-0075347
- Method of training object prediction models using ambiguous labels, 출원, 10-2022-0000169
- Method for detecting object, 출원, 10-2021-0168545
- Method to detect object, 출원, 10-2021-0135451
- Method for data clustering, 출원, 10-2020-0107206
참고
업무 분야의 보안 규정으로 인해 일부 진행 중인 프로젝트의 상세 내용은 공개할 수 없습니다.