no code implementations • 26 Jan 2024 • Hyeonwoo Cho, Chanmin Park, Jinyoung Kim, Won Hwa Kim
To deal with this problem, we propose to utilize cluster structure (i. e., {`Clean'} and {`Noisy'} regions within each cluster) in the target domain formulated by the source model.
no code implementations • 22 Jan 2024 • Jaeyoon Sim, Sooyeon Jeon, InJun Choi, Guorong Wu, Won Hwa Kim
As setting different number of hidden layers per node is infeasible, recent works leverage a diffusion kernel to redefine the graph structure and incorporate information from farther nodes.
no code implementations • CVPR 2023 • Deunsol Jung, Sanghyun Kim, Won Hwa Kim, Minsu Cho
The edge selection module selects relevant object pairs, i. e., edges in the scene graph, which helps contextual reasoning, and the quad attention module then updates the edge features using both edge-to-node and edge-to-edge cross-attentions to capture contextual information between objects and object pairs.
no code implementations • 9 Jun 2021 • Xin Ma, Won Hwa Kim
VCC takes advantage of distributions of local relationships of samples near the boundary of clusters, so that they can be properly separated and pulled to cluster centers to form compact clusters.
no code implementations • CVPR 2017 • Won Hwa Kim, Mona Jalal, Seongjae Hwang, Sterling C. Johnson, Vikas Singh
The adoption of "human-in-the-loop" paradigms in computer vision and machine learning is leading to various applications where the actual data acquisition (e. g., human supervision) and the underlying inference algorithms are closely interwined.
no code implementations • 3 Dec 2019 • Xin Ma, Guorong Wu, Won Hwa Kim
As there is significant interest in understanding the altered interactions between different brain regions that lead to neuro-disorders, it is important to develop data-driven methods that work with a population of graph data for traditional prediction tasks.
no code implementations • ICCV 2019 • Seong Jae Hwang, Zirui Tao, Won Hwa Kim, Vikas Singh
Such models may work for cross-sectional studies, however, they are not suitable to generate data for longitudinal studies that focus on "progressive" behavior in a sequence of data.
no code implementations • CVPR 2016 • Won Hwa Kim, Hyunwoo J. Kim, Nagesh Adluru, Vikas Singh
A major goal of imaging studies such as the (ongoing) Human Connectome Project (HCP) is to characterize the structural network map of the human brain and identify its associations with covariates such as genotype, risk factors, and so on that correspond to an individual.
no code implementations • ICCV 2015 • Won Hwa Kim, Sathya N. Ravi, Sterling C. Johnson, Ozioma C. Okonkwo, Vikas Singh
A variety of studies in neuroscience/neuroimaging seek to perform statistical inference on the acquired brain image scans for diagnosis as well as understanding the pathological manifestation of diseases.
no code implementations • CVPR 2015 • Won Hwa Kim, Barbara B. Bendlin, Moo. K. Chung, Sterling C. Johnson, Vikas Singh
Statistical analysis of longitudinal or cross sectionalbrain imaging data to identify effects of neurodegenerative diseases is a fundamental task in various studies in neuroscience.
no code implementations • CVPR 2013 • Won Hwa Kim, Moo. K. Chung, Vikas Singh
In this paper, we adapt recent results in harmonic analysis, to derive NonEuclidean Wavelets based algorithms for a range of shape analysis problems in vision and medical imaging.