Seoul National University · 情報科学
Professor Kyoung Mu Lee's research lab specializes in computer vision and deep learning, with a focus on advancing interactive and weakly supervised image segmentation techniques. The lab develops intelligent systems that leverage deep reinforcement learning to minimize human input while maximizing segmentation accuracy, enabling robust and consistent object extraction. Key research directions include few-shot and interactive learning, semantic understanding in medical and natural images, and the design of efficient, user-friendly AI tools for real-world applications.
Figures are computed from collected data and may differ slightly.
In this paper, we propose an automatic seed generation technique with deep reinforcement learning to solve the interactive segmentation problem. One of the main issues of the interactive segmentation problem is robust and consistent object extraction with less human effort. Most of the existing algorithms highly depend on the distribution of inputs, which differs from one user to another and hence need sequential user interactions to achieve adequate performance. In our system, when a user first
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