7 citations · 13 across the 9 of their papers we have counts for
9 papers
Learning to Solve Generative ODEs Beyond the Linear Span
Sihyeon Kim, Seunghun Lee, Vikas Singh +1
Diffusion and flow generative models sample by integrating a learned ODE, but high quality still requires many sequential model evaluations. Solver learning reduces this cost by ad…
ST-VLM: Kinematic Instruction Tuning for Spatio-Temporal Reasoning in Vision-Language Models
Dohwan Ko, Sihyeon Kim, Yumin Suh +4
Spatio-temporal reasoning is essential in understanding real-world environments in various fields, eg, autonomous driving and sports analytics. Recent advances have improved the sp…
Constant Acceleration Flow
Dogyun Park, Sojin Lee, Sihyeon Kim +3
Rectified flow and reflow procedures have significantly advanced fast generation by progressively straightening ordinary differential equation (ODE) flows. They operate under the a…
DDMI: Domain-Agnostic Latent Diffusion Models for Synthesizing High-Quality Implicit Neural Representations
Dogyun Park, Sihyeon Kim, Sojin Lee +1
Recent studies have introduced a new class of generative models for synthesizing implicit neural representations (INRs) that capture arbitrary continuous signals in various domains…
Advancing Bayesian Optimization via Learning Correlated Latent Space
Seunghun Lee, Jaewon Chu, Sihyeon Kim +2
Bayesian optimization is a powerful method for optimizing black-box functions with limited function evaluations. Recent works have shown that optimization in a latent space through…
Semantic-Aware Implicit Template Learning via Part Deformation Consistency
Sihyeon Kim, Minseok Joo, Jaewon Lee +3
Learning implicit templates as neural fields has recently shown impressive performance in unsupervised shape correspondence. Despite the success, we observe current approaches, whi…
Self-positioning Point-based Transformer for Point Cloud Understanding
Jinyoung Park, Sanghyeok Lee, Sihyeon Kim +2
Transformers have shown superior performance on various computer vision tasks with their capabilities to capture long-range dependencies. Despite the success, it is challenging to…
Metropolis-Hastings Data Augmentation for Graph Neural Networks
Hyeonjin Park, Seunghun Lee, Sihyeon Kim +5
Graph Neural Networks (GNNs) often suffer from weak-generalization due to sparsely labeled data despite their promising results on various graph-based tasks. Data augmentation is a…
Point Cloud Augmentation with Weighted Local Transformations
Sihyeon Kim, Sanghyeok Lee, Dasol Hwang +3
Despite the extensive usage of point clouds in 3D vision, relatively limited data are available for training deep neural networks. Although data augmentation is a standard approach…