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cs.CV2026
VAUQ: Vision-Aware Uncertainty Quantification for LVLM Self-Evaluation
Seongheon Park, Changdae Oh, Hyeong Kyu Choi +2
Large Vision-Language Models (LVLMs) frequently hallucinate, limiting their safe deployment in real-world applications. Existing LLM self-evaluation methods rely on a model's abili…
cs.CV2025
GLSim: Detecting Object Hallucinations in LVLMs via Global-Local Similarity
Seongheon Park, Sharon Li
Object hallucination in large vision-language models presents a significant challenge to their safe deployment in real-world applications. Recent works have proposed object-level h…
cs.CV2025
GeoRanker: Distance-Aware Ranking for Worldwide Image Geolocalization
Pengyue Jia, Seongheon Park, Song Gao +2
Worldwide image geolocalization-the task of predicting GPS coordinates from images taken anywhere on Earth-poses a fundamental challenge due to the vast diversity in visual content…