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Jonghwi Kim

7 accepted papers

2025

MiLQ: Benchmarking IR Models for Bilingual Web Search with Mixed Language Queries

EMNLP 2025

Despite bilingual speakers frequently using mixed-language queries in web searches, Information Retrieval (IR) research on them remains scarce. To address this, we introduce ***MiLQ***, ***Mi***xed-***L***anguage ***Q***uery test set, the first public benchmark of mixed-language queries, qualified a

2025

Safeguarding RAG Pipelines with GMTP: A Gradient-based Masked Token Probability Method for Poisoned Document Detection

ACL 2025finding

Retrieval-Augmented Generation (RAG) enhances Large Language Models (LLMs) by providing external knowledge for accurate and up-to-date responses. However, this reliance on external sources exposes a security risk; attackers can inject poisoned documents into the knowledge base to steer the generatio…

Cited by 0SourcePDFScholar
2023

Navigable Area Detection and Perception-Guided Model Predictive Control for Autonomous Navigation in Narrow Waterways

RA-L 2023

This letter presents an integrated navigation and control strategy for an autonomous surface vehicle (ASV) to operate in narrow waterways without relying on GPS. The proposed method uses a camera and a light detection and ranging (LiDAR) sensor to detect navigable regions in the waterway. A deep lea

Cited by 17SourceScholar
2022

Intent Inference-Based Ship Collision Avoidance in Encounters With Rule-Violating Vessels

RA-L 2022

All vessels operating in a marine environment are required to comply with the international regulations for preventing collisions at sea (COLREGs), which provide the guidelines and evasive procedures required to resolve potential conflicts between vessels. However, not all vessels strictly abide by

Cited by 23SourceScholar
2021

Robust Data Association for Multi-Object Detection in Maritime Environments Using Camera and Radar Measurements

RA-L 2021

This letter presents a robust data association method for fusing camera and marine radar measurements in order to automatically detect surface ships and determine their locations with respect to the observing ship. In the preprocessing step for sensor fusion, convolutional neural networks are used t

Cited by 26SourceScholar
2019

Fusing Lidar Data and Aerial Imagery with Perspective Correction for Precise Localization in Urban Canyons

IROS 2019poster

This paper addresses a vehicle localization method that fuses aerial maps and lidar data in urban canyon environments where global positioning system (GPS) signals are inaccurate. The boundaries of buildings are extracted from the aerial map and they are matched to point cloud data provided by the l…

Cited by 14SourceScholar