About Me

My name is Jaehoon, and I am a senior research engineer at Hyundai Motors in South Korea. I am passionate about solving real-world problems using AI. Autonomous driving is a compelling domain where AI can significantly impact society, and I take great pride in my work. I enjoy collaborating with experts from diverse backgrounds and believe in the power of teamwork to drive innovation. Additionally, I am highly interested in publishing research papers, filing patents, and engaging in technical discussions.

I commit 1 hour every week to provide guidance, suggestions, and/or mentorships for students from underrepresented groups or whoever is in need. Please fill in this form if you are interested.

Paper

Learning Confidence Measure with Transformer in Stereo Matching
Jini Yang, Minjung Yoo, Jaehoon Cho, Sunok Kim
Pattern Recognition (PR), Oct. 2024
[Paper]
A Prototype Unit for Image De-raining using Time-Lapse Data
Jaehoon Cho, Jini Yang, Minjung Yoo, Sunok Kim
British Machine Vision Conference (BMVC), Nov. 2024
[Paper] [Poster]
Improving Image de-raining using Reference-guided Transformers
Zihao Ye, Jaehoon Cho, Changjae Oh
IEEE International Conference on Image Processing (ICIP), Oct. 2024
[Project page] [Paper]
Multi-task Learning for Real-time Autonomous Driving Leveraging Task-adaptive Attention Generator
Wonhyeok Choi, Mingyu Shin, HYUKZAE LEE, Jaehoon Cho, Jaehyeon Park, Sunghoon Im
IEEE International Conference on Robotics and Automation (ICRA), May. 2024
[Paper]
Memory-Guided Image De-Raining Using Time-Lapse Data
Jaehoon Cho, Seungryong Kim, Kwanghoon Sohn
IEEE Transactions on Image Processing (TIP), vol. 31, pp. 4090 - 4103, Jun. 2022 (Impact factor: 10.856)
[Code] [Paper]
Wide and Narrow: Video Prediction from Context and Motion
Jaehoon Cho, Jiyoung Lee, Changjae Oh, Wonil Song, Kwanghoon Sohn
British Machine Vision Conference (BMVC), 2021
[Paper]
Deep Monocular Depth Estimation Leveraging a Large-scale Outdoor Stereo Dataset
Jaehoon Cho, Dongbo Min, Youngjung Kim, Kwanghoon Sohn
Expert Systems With Applications (ESWA), vol. 178, Mar. 2021 (Impact factor: 5.452)
[Dataset] [Paper]
Pyramid Inter-Attention for High Dynamic Range Imaging
Sungil Choi, Jaehoon Cho, Wonil Song, Jihwan Choe, Jisung Yoo, Kwanghoon Sohn
Sensors, vol. 20, pp. 5102, Jun. 2021 (Impact factor: 3.275)
Single Image Deraining Using Time-lapse Data
Jaehoon Cho, Seungryong Kim, Dongbo Min, Kwanghoon Sohn
IEEE Transactions on Image Processing (TIP), vol. 29, pp. 7274-7289, Jun. 2020 (Impact factor: 9.340)
[Dataset] [Paper]
Multi-Task Self-Supervised Visual Representation Learning for Monocular Road Segmentation
Jaehoon Cho, Youngjung Kim, Changjae Oh, Kwanghoon Sohn
IEEE International Conference on Multimedia and Expo (ICME), 2018
[Dataset] [Paper]

Technical Report

DIML/CVL RGB-D Dataset: 2M RGB-D Images of Natural Indoor and Outdoor Scenes
Jaehoon Cho, Dongbo Min, Youngjung Kim, Kwanghoon Sohn
arXiv, 2021
[Project page] [Paper]

Patents

3D object detection using spatial representative knowledge distillation
KR 1020230151997, Jan. 2024.
Ensemble learning data generation technique for semi-supervised multi-task learning
KR 1020230151996, Jan. 2024.
Multi-GAN based rain dataset generation technique
KR 1020230178074, Feb. 2024.
Computationally efficient Argmax, Argmin algorithm
KR 1020230155783, Jan. 2024.
Multi-task learning method based on the task grouping technique
KR 1020230166997, Jan. 2024.
GAN-based Night Dataset Generation Techniques Using Histogram Vector
KR 1020230300154, Aug. 2023.
Semi-supervised semantic segmentation technique using Multiple Teacher Knowledge Distillation
US 18/507,548, DE 102023211571.9, CN 202311628407.4, KR 1020230200237, Sep. 2023.
Reliable pseudo label generation technique for semi-supervised semantic segmentation
US 18/508,967, KR 1020230044302, Jun. 2023.
GAN-based dataset generation technique using dual-domain discriminator
KR 1020230100239, Apr. 2023.
A prototype network-based noise information extraction method
US 18/367,845, KR 1020230034680, May. 2023.
Multi-task learning techniques using heterogeneous task data
US 18/367,845, KR 1020230035346, May. 2023.
Method and apparatus for removing noise images with linearity based on deep learning
KR 10-2095444, Mar. 2020.
DEVICE AND METHOD FOR ESTIMATING ROAD AREA BY DEEP LEARNING USING SELF-SUPERVISED LEARNING
KR 10-2097869, Mar. 2020.

Invited Talks

CVLAB, “The process of embedded network optimization”, Sep. 2024.
HMG Developer Conference, “Multi-Task Learning Research Considering Embedded Performance”, 2023. Link
NAVER LABS, “A Study on Outdoor Scene Understanding in the Dynamic Outdoor Environment”, 2021.
42dot, “Deep Neural Network for Single Image De-raining using Real-world Time-lapse Data”, 2021.

Blogs

HMG DEVELOPERS, “Developing an AI Model for Embedded Board Performance Prediction: A Success Story in Workflow Optimization”, Feb. 2025 Link
HMG DEVELOPERS, “[Deep Learning Optimization] Lightweighting Deep Learning Networks with Pruning: Concepts and Real-World Applications”, Nov. 2024 Link
HMG DEVELOPERS, “CVPR 2024 from the Perspective of an Autonomous Driving Researcher”, Oct. 2024 Link
HMG DEVELOPERS, “[Deep Learning Optimization] Why is Quantization Necessary for Deep Learning Networks in autonomous driving?”, Sep. 2024 Link
HMG DEVELOPERS, “[Deep Learning Optimization] Spatial Feature Knowledge Distillation for Monocular 3D Object Detection (2)”, Jun. 2024 Link
HMG DEVELOPERS, “Why do we need to conduct quantization?”, Sep. 2024 Link
HMG DEVELOPERS, “[Deep Learning Optimization] Spatial Feature Knowledge Distillation for Monocular 3D Object Detection (1)”, May. 2024 Link)
HMG DEVELOPERS, “[Deep Learning Optimization] The compression/optimization technique of the deep learning model being applied in practice is?”, Apr. 2024 Link
HMG DEVELOPERS, “[Deep Learning Optimization] Hardware-Friendly Deep Learning Network: Understanding Reparameterization Techniques”, Feb. 2024 Link