6 citations · 8 across the 7 of their papers we have counts for
7 papers
ProCompNav: Proactive Instance Navigation with Comparative Judgment for Ambiguous User Queries
Junhyuk Kwon, Seungjoon Lee, Hyejin Park +2
Natural-language instance navigation becomes challenging when the initial user request does not uniquely specify the target instance. A practical agent should reduce the user's bur…
VIRO: Robust and Efficient Neuro-Symbolic Reasoning with Verification for Referring Expression Comprehension
Hyejin Park, Junhyuk Kwon, Suha Kwak +1
Referring Expression Comprehension (REC) aims to localize the image region corresponding to a natural language query. Recent neuro-symbolic REC approaches leverage large language m…
Semantic Exploration with Adaptive Gating for Efficient Problem Solving with Language Models
Sungjae Lee, Hyejin Park, Jaechang Kim +1
Recent advancements in large language models (LLMs) have shown remarkable potential in various complex tasks requiring multi-step reasoning methods like tree search to explore dive…
Retrieval-Augmented Generation with Estimation of Source Reliability
Jeongyeon Hwang, Junyoung Park, Hyejin Park +3
Retrieval-Augmented Generation (RAG) is an effective approach to enhance the factual accuracy of large language models (LLMs) by retrieving information from external databases, whi…
MedBN: Robust Test-Time Adaptation against Malicious Test Samples
Hyejin Park, Jeongyeon Hwang, Sunung Mun +2
Test-time adaptation (TTA) has emerged as a promising solution to address performance decay due to unforeseen distribution shifts between training and test data. While recent TTA m…
Robustness Evaluation of Stacked Generative Adversarial Networks using Metamorphic Testing
Hyejin Park, Taaha Waseem, Wen Qi Teo +3
Synthesising photo-realistic images from natural language is one of the challenging problems in computer vision. Over the past decade, a number of approaches have been proposed, of…
Transfer Learning in Bandits with Latent Continuity
Hyejin Park, Seiyun Shin, Kwang-Sung Jun +1
Structured stochastic multi-armed bandits provide accelerated regret rates over the standard unstructured bandit problems. Most structured bandits, however, assume the knowledge of…