Junghoon Seo

Leading computer vision and machine learning research for AI robotics @ PIT IN Corp. Experienced in satellite/aerial imagery and HCI sensing, with strong interests in GPU parallel computing and computer graphics.

Computer Vision Machine Learning AI Robotics Remote Sensing GPGPU/HPC HCI Sensing
Junghoon Seo
Currently leading AI research @ PIT IN Corp.

Career

  • Dec 2024 - Current, Principal Researcher, AI Robot Team @ PIT IN Corp.
  • Sep 2020 - Sep 2024, Technical Leader of Research Center & Co-founder @ SI Analytics
  • Jul 2017 - Feb 2020, ML/CV Research Scientist @ Satrec Initiative

Education

  • Mar 2023 - Feb 2025, KAIST, Daejeon, South Korea
  • Mar 2014 - Feb 2021, GIST, Gwangju, South Korea
    • B.S degree, Major in Electrical Engineering and Computer Science

Specialties

  • Machine Learning, Computer Vision, Computer Graphics, and Remote Sensing Applications
  • Parallel Computing on HPC and GPGPU
  • Intelligent Sensing Techniques for Human-Computer Interaction

Current Focus

  • Reliable Perception for AI Robotics
  • High-precision Photogrammetry
  • AI Agent for User-friendly Automation
  • Parallel/Efficient Computing Methods for Robotics

Publications link

  • 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. link
  • 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.

Working Titles Under Review

  • Pressure Estimation for Hand-based Interaction
  • Off-policy Evaluation from Multiple Logging Policies
  • Monocular RGB Category-level Multi-object Pose Estimation
  • Efficient and Robust Camera Calibration

Invited 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. Slides (Korean)
  • Deep Perceptual Super-resolution: Going Beyond Distortion @ KARI. Jul 2018. Slides

Review Services

  • Journals: IEEE TPAMI, IEEE TNNLS, IEEE TGRS, IEEE GRSL, and more
  • Conferences: ICLR `26, CVPR `26, AISTATS `26, NeurIPS `25, ICML `25, and others

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