2 citations · 2 across the 11 of their papers we have counts for
12 papers
Domain-Adaptive Data Assimilation for Global AI Weather Forecasting
Minseok Seo, Noah Brenowitz, Doyi Kim +2
AI weather forecasting models are commonly trained on the ERA5 reanalysis, which is unavailable in real time. Operational deployment therefore relies on initial conditions produced…
RainODE: Continuous-Time Precipitation Forecasting with Latent Neural ODEs
Yeeun Seong, Doyi Kim, Minseok Seo +1
In precipitation forecasting, not only accuracy but also temporal resolution is critical. However, increasing temporal resolution is constrained by observational limitations and th…
T-VSS: Test-Time Visual Subspace Steering for Adversarial Robustness of Vision-Language Models
Jaehyuk Jang, Minseok Seo, Seungju Cho +2
Vision-language models (VLMs) achieve strong zero-shot recognition, but they remain highly vulnerable to adversarial perturbations. Recent test-time adaptations improve robustness…
Query-Conditioned Test-Time Self-Training for Large Language Models
Chaehee Song, Minseok Seo, Yeeun Seong +2
Large language models (LLMs) are typically deployed with fixed parameters, and their performance is often improved by allocating more computation at inference time. While such test…
StreamTTO: Efficient Online Test-Time Optimization for Video Depth Completion
Minseok Seo, Wonjun Lee, Jaehyuk Jang +1
Monocular depth foundation models generalize across diverse scenes, but recovering accurate metric depth consistent with a target sensor remains challenging under sensor variation…
Station2Radar: query conditioned gaussian splatting for precipitation field
Doyi Kim, Minseok Seo, Changick Kim
Precipitation forecasting relies on heterogeneous data. Weather radar is accurate, but coverage is geographically limited and costly to maintain. Weather stations provide accurate…
Data-driven Precipitation Nowcasting Using Satellite Imagery
Young-Jae Park, Doyi Kim, Minseok Seo +2
Accurate precipitation forecasting is crucial for early warnings of disasters, such as floods and landslides. Traditional forecasts rely on ground-based radar systems, which are sp…
Masked Autoregressive Model for Weather Forecasting
Doyi Kim, Minseok Seo, Hakjin Lee +1
The growing impact of global climate change amplifies the need for accurate and reliable weather forecasting. Traditional autoregressive approaches, while effective for temporal mo…
ACE Metric: Advection and Convection Evaluation for Accurate Weather Forecasting
Doyi Kim, Minseok Seo, Yeji Choi
Recently, data-driven weather forecasting methods have received significant attention for surpassing the RMSE performance of traditional NWP (Numerical Weather Prediction)-based me…
Long-Term Typhoon Trajectory Prediction: A Physics-Conditioned Approach Without Reanalysis Data
Young-Jae Park, Minseok Seo, Doyi Kim +7
In the face of escalating climate changes, typhoon intensities and their ensuing damage have surged. Accurate trajectory prediction is crucial for effective damage control. Traditi…
Deterministic Guidance Diffusion Model for Probabilistic Weather Forecasting
Donggeun Yoon, Minseok Seo, Doyi Kim +2
Weather forecasting requires not only accuracy but also the ability to perform probabilistic prediction. However, deterministic weather forecasting methods do not support probabili…
Prototype-oriented Unsupervised Change Detection for Disaster Management
Youngtack Oh, Minseok Seo, Doyi Kim +1
Climate change has led to an increased frequency of natural disasters such as floods and cyclones. This emphasizes the importance of effective disaster monitoring. In response, the…