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Found 11 papers, 0 papers with code
Multi-resolution Shape Analysis via Non-Euclidean Wavelets: Applications to Mesh Segmentation and Surface Alignment Problems
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.
Statistical Inference Models for Image Datasets With Systematic Variations
Statistical analysis of longitudinal or cross sectionalbrain imaging data to identify effects of neurodegenerative diseases is a fundamental task in various studies in neuroscience.
On Statistical Analysis of Neuroimages With Imperfect Registration
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.
Latent Variable Graphical Model Selection Using Harmonic Analysis: Applications to the Human Connectome Project (HCP)
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.
Conditional Recurrent Flow: Conditional Generation of Longitudinal Samples with Applications to Neuroimaging
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.
Multi-resolution Graph Neural Network for Identifying Disease-specific Variations in Brain Connectivity
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.
Online Graph Completion: Multivariate Signal Recovery in Computer Vision
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.
Separating Boundary Points via Structural Regularization for Very Compact Clusters
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.
Devil's on the Edges: Selective Quad Attention for Scene Graph Generation
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.
Learning to Approximate Adaptive Kernel Convolution on Graphs
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.
CNA-TTA: Clean and Noisy Region Aware Feature Learning within Clusters for Online-Offline Test-Time Adaptation
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.
