Junghoon Seo
Hi, I was the technical leader of the research center at SI Analytics, a satellite/aerial imagery analysis company in South Korea. At the same time, I also study for a Master’s degree at KAIST HCI Tech Lab (Prof. Sangho Yoon). My main scope of work is directing research projects on computer vision and machine learning for remote sensing. Besides, I am interested in computer graphics, parallel computing, and intelligent sensing technique for human-computer interaction. Don’t hesitate to contact me via Email or LinkedIn if you have any business!
Specialties
- Machine Learning, Computer Vision, Computer Graphics, and their Applications to Remote Sensing
- Parallel Computing on HPC and General-Purpose Computing on GPU
- Intelligent Sensing Techniques for Human-Computer Interaction
- Process Management and Policymaking for AI-driven Product-Business Alignment
Career
- Sep 2020 - Sep 2024, Technical Leader of Research Center & Co-founder @ SI Analytics
- Jul 2017 - Feb 2020, ML/CV Research Scientist @ Satrec Initiative
Education
- Feb 2023 - Current, KAIST, Daejeon, South Korea
- Studying for Master’s degree in Graduate School of Culture Technology
- Mar 2014 - Feb 2021, GIST, Gwangju, South Korea
- B.S degree, Major in Electrical Engineering and Computer Science
Publications (Google Scholar)
- Posture-Informed Muscular Force Learning for Robust Hand Pressure Estimation
- Junghoon Seo, Kyungjin Seo, Hanseok Jeong, Sangpil Kim, Sang Ho Yoon
- NeurIPS. 2024. To be Appeared.
- 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
- 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.
Under Review / On-going Scripts (Working Titles)
- Pressure Estimation in Hand-based Interaction (2 Projects)
- Pitfalls and Fixes in Attribution-based Explainer Evaluation (2 Projects)
- Improved Off-policy Evaluation from Multiple Logging Policies
- Improved Query-based Object Detection
Awards
- 2024, my lab’s demo, which included the project I was leading, had been selected as the Popular Choice Winner in CHI 2024.
- 2021, I graduated the college with Best Undergraduate Thesis Award.
- 2020, my team ranked the 5th place in xView2 Challange.
- 2018, my team ranked the 3rd-4th place in CVPR NTIRE Super-resolution Challange.
- 2018, my team ranked the 2nd place in DOTA Challange.
- 2016, my team got the grand prize at KISTI National Supercomputing Competition.
- 2016, I got the Qualcomm-GIST Innovation Award.
Talks
- 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
Review-serving
- IEEE Transactions on Pattern Analysis and Machine Intelligence
- IEEE Transactions on Neural Networks and Learning Systems
- IEEE Geoscience and Remote Sensing Letters
- Several Conferences including NeurIPS, CVPR, AISTATS, BMVC…
- Several Workshops on ICCV, ICLR, CVPR…
Patents
- Method for detecting object, granted, 10-2364882
- Method for detecting on-the-fly disaster damage based on image, granted, 10-2255998
- Method for predicting frame using deep learning, application, 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, application, 10-2023-0075347
- Method of training object prediction models using ambiguous labels, application, 10-2022-0000169
- Method for detecting object, application, 10-2021-0168545
- Method to detect object, application, 10-2021-0135451
- Method for data clustering, application, 10-2020-0107206
Note
Due to confidentiality provisions related to the field, I cannot disclose the outline of the project to the public.