Search Results for author: Won Hwa Kim

Found 11 papers, 0 papers with code

CNA-TTA: Clean and Noisy Region Aware Feature Learning within Clusters for Online-Offline Test-Time Adaptation

no code implementations26 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.

Test-time Adaptation

Learning to Approximate Adaptive Kernel Convolution on Graphs

no code implementations22 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.

Devil's on the Edges: Selective Quad Attention for Scene Graph Generation

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.

Graph Generation Object +1

Separating Boundary Points via Structural Regularization for Very Compact Clusters

no code implementations9 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.

Clustering Deep Clustering

Online Graph Completion: Multivariate Signal Recovery in Computer Vision

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.

Active Learning Collaborative Filtering +1

Multi-resolution Graph Neural Network for Identifying Disease-specific Variations in Brain Connectivity

no code implementations3 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.

Graph Learning

Conditional Recurrent Flow: Conditional Generation of Longitudinal Samples with Applications to Neuroimaging

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.

Latent Variable Graphical Model Selection Using Harmonic Analysis: Applications to the Human Connectome Project (HCP)

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.

Model Selection

On Statistical Analysis of Neuroimages With Imperfect Registration

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.

Statistical Inference Models for Image Datasets With Systematic Variations

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.

Multi-resolution Shape Analysis via Non-Euclidean Wavelets: Applications to Mesh Segmentation and Surface Alignment Problems

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.

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