Ungsik Kim — accepted AI-conference papers

2026

Privacy-Preserving Argumentative Explanations (Student Abstract)

Ungsik Kim, Minjae Lee, Jiho Bae, Minje Kim, Sang-Min Choi, Suwon Lee

AAAI 2026technical

We propose a framework for privacy-preserving argumentative explanations using homomorphic encryption. This method applies the Cheon-Kim-Kim-Song scheme, along with a soft k-means adapted for encrypted computation, to generate explanations without exposing sensitive data. By leveraging GPU accelerat

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2026

SwiftMOS: A Fast and Lightweight Moving Object Segmentation via Feature Flowing Direct View Transformation

Minjae Lee, Ungsik Kim, Gun-Woo Kim, Suwon Lee

RA-L 2026

Autonomous vehicles must recognize their surroundings and distinguish between dynamic and static objects to avoid collisions. Most recent moving object segmentation (MOS) studies project LiDAR point-cloud streams into multiple views to capture spatio-temporal cues. When a single view proves insuffic

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