I am a senior research engineer at Hyundai Motor Company, South Korea. I received Ph.D. from Yonsei University, advised by Prof. Kwanghoon Sohn.

I am a computer vision fanatic and like the researches that could directly be applied to real-world problems. I am interested in a wide array of topics, ranging from low-level vision to high-level vision, and their connections to autonomous driving assistance systems. Recently, I have been thinking more from the 3D perspective. Here’s my CV.



Research

Memory-guided Image Deraining using Time-laspe data
Jaehoon Cho, Seungryong Kim, Kwanghoon Sohn
IEEE Trans. on Image Processing (TIP), vol. 31, pp. 4090 - 4103, Jun. 2022
(Impact factor: 10.856)
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)
Project page | Paper | Dataset
Single Image Deraining using Time-laspe data
Jaehoon Cho, Seungryong Kim, Dongbo Min, Kwanghoon Sohn
IEEE Trans. on Image Processing (TIP), vol. 29, pp. 7274-7289, Jun. 2020
(Impact factor: 9.340)
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. 2020 (Impact factor: 3.275)
Paper
Multi-task Self-supervised Visual Representation Learning for Monocular Road Segmentation
Jaehoon Cho, Youngjung Kim, Hyungjoo Jung, Changjae Oh, Jaesung Youn, Kwanghoon Sohn Sohn
IEEE International Conference on Multimedia and Expo (ICME) 2018
(Oral Presentation)
Paper | Data



Technical Report

DIML/CVL RGB-D Dataset:
2M RGB-D Images of Natural Indoor and Outdoor Scenes
Report



Invited Talks

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.



Professional Service

Reviewer: IEEE TIP, IEEE TNNLS, IEEE TCSVT