Welcome to Computer Vision Lab
We’re looking for strong and motivated graduate students and undergraduate interns.
If you are interested, please apply ►►► HERE.
The lab challenges unsolved real-life problems by inventing novel artificial intelligence for computer vision. To do so, our research focuses on developing innovative computer vision systems through deep learning algorithms and heterogeneous visual sensors. We also adopt machine learning to innovate visual machine intelligence.
News!
✦ 2024.03. One paper got accepted in Neural Networks(JCR IF Top 10%).
✦ 2024.02. Three papers got accepted in CVPR 2024. Congratulations to all authors!
✦ 2024.02. One paper got accepted in Computational Visual Media(JCR IF Top 10%). Congratulations to all authors!
✦ 2023.12. One paper got accepted in AAAI 2024. Congratulations to all authors!
✦ 2023.10. One paper got accepted in Information Sciences(JCR IF Top 10%). Congratulations to all authors!
2023 Publications
✦ One paper published in Neural Networks(JCR IF Top 10%).
✦ One paper published in Virtual Reality(Q1 Journal).
✦ One paper published in ICCV 2023.
✦ One paper published in WWW 2023.
✦ One paper published in BMVC 2023(Oral).
✦ Two papers published in ICML 2023 Workshop.
2022 Publications
✦ One paper published in CVPR 2022.
✦ One paper published in ECCV 2022.
✦ One paper published in BMVC 2022.
★ 2023.08. We received GCP Credit Award ($32,600) from Google Research.
★ 2023.05. Seunghyun Lee got an internship offer from Google Research, CA, USA.
★ 2023.05. Gyusam Chang got an internship offer from Samsung AIT(종합기술원).
★ 2022.06. We received Research Funding from Google Research USA.
★ 2023.01. We initiated a collaboration project with Google Research USA.
★ 2022.08. We initiated a collaboration project with Samsung Advanced Institute of Technology.
★ 2022.05. We received GCP Credit Award ($30,000) from Google Research.
★ 2021.09. We initiated a collaboration project with NVIDIA Research USA.
In the narrow scope of research, the lab is interested in the following:
– Multi-modal Generative Model
– Multi-modal Representation Learning
– Neural watermarking to protect the copyright of AI-generated content
– Domain Generalization and Unsupervised Domain Adaptation
– Computer Vision for Autonomous Driving
– 3D Computer Vision and Inverse Graphics
– Hand Pose Estimation
– Human Computer Interaction
– Diffusion Model
Computer Vision Lab
Department of Artificial Intelligence, Korea University
603, Woojung Hall of Informatics, Korea University, 145 Anam-ro, Seongbuk-gu, Seoul, 02841