Donghyun Kim

Gyusam Chang, Jiwon Lee, Donghyun Kim et al.

Recent advances in 3D object detection leveraging multi-view cameras have demonstrated their practical and economical value in various challenging vision tasks. However, typical supervised learning approaches face challenges in achieving satisfactory adaptation toward unseen and unlabeled target datasets (\ie, direct transfer) due to the inevitable geometric misalignment between the source and target domains. In practice, we also encounter constraints on resources for training models and collecting annotations for the successful deployment of 3D object detectors. In this paper, we propose Unified Domain Generalization and Adaptation (UDGA), a practical solution to mitigate those drawbacks. We first propose Multi-view Overlap Depth Constraint that leverages the strong association between multi-view, significantly alleviating geometric gaps due to perspective view changes. Then, we present a Label-Efficient Domain Adaptation approach to handle unfamiliar targets with significantly fewer amounts of labels (\ie, 1$\%$ and 5$\%)$, while preserving well-defined source knowledge for training efficiency. Overall, UDGA framework enables stable detection performance in both source and target domains, effectively bridging inevitable domain gaps, while demanding fewer annotations. We demonstrate the robustness of UDGA with large-scale benchmarks: nuScenes, Lyft, and Waymo, where our framework outperforms the current state-of-the-art methods.

Minjung Shin, Donghyun Kim, Jeh-Kwang Ryu

Questioning has become increasingly crucial for both humans and artificial intelligence, yet there remains limited research comprehensively assessing question quality. In response, this study defines good questions and presents a systematic evaluation framework. We propose two key evaluation dimensions: appropriateness (sociolinguistic competence in context) and effectiveness (strategic competence in goal achievement). Based on these foundational dimensions, a rubric-based scoring system was developed. By incorporating dynamic contextual variables, our evaluation framework achieves structure and flexibility through semi-adaptive criteria. The methodology was validated using the CAUS and SQUARE datasets, demonstrating the ability of the framework to access both well-formed and problematic questions while adapting to varied contexts. As we establish a flexible and comprehensive framework for question evaluation, this study takes a significant step toward integrating questioning behavior with structured analytical methods grounded in the intrinsic nature of questioning.

Donghyun Kim

In this paper, we present machine learning models based on random forest classifiers, support vector machines, gradient boosted decision trees, and artificial neural networks to predict participation in cancer screening programs in South Korea. The top performing model was based on gradient boosted decision trees and achieved an area under the receiver operating characteristic curve (AUC-ROC) of 0.8706 and average precision of 0.8776. The results of this study are encouraging and suggest that with further research, these models can be directly applied to Korea's healthcare system, thus increasing participation in Korea's National Cancer Screening Program.