Search Results for author:
Found 9 papers, 2 papers with code
Two-and-a-half Order Score-based Model for Solving 3D Ill-posed Inverse Problems
Score-based models have proven to be effective in addressing different inverse problems encountered in CT and MRI, such as sparse-view CT and fast MRI reconstruction.
Instance Image Retrieval by Learning Purely From Within the Dataset
Although achieving promising results, this approach is restricted by two issues: 1) the domain gap between benchmark datasets and the dataset of a given retrieval task; 2) the required auxiliary dataset cannot be readily obtained.
Triple-view Convolutional Neural Networks for COVID-19 Diagnosis with Chest X-ray
To address these issues, unlike traditional methods processing each CXR image from a single view, this paper proposes triple-view convolutional neural networks for COVID-19 diagnosis with CXR images.
Revisiting Metric Learning for SPD Matrix Based Visual Representation
The proposed method has been tested on multiple SPD-based visual representation data sets used in the literature, and the results demonstrate its interesting properties and attractive performance.
Exploiting Structure Sparsity for Covariance-based Visual Representation
A variety of methods have been proposed to boost its efficacy, with some recent ones resorting to nonlinear kernel technique.
Beyond Covariance: Feature Representation With Nonlinear Kernel Matrices
It proposes an open framework to use the kernel matrix over feature dimensions as a generic representation and discusses its properties and advantages.
HEp-2 Cell Image Classification with Deep Convolutional Neural Networks
Efficient Human Epithelial-2 (HEp-2) cell image classification can facilitate the diagnosis of many autoimmune diseases.
Learning Discriminative Stein Kernel for SPD Matrices and Its Applications
A comprehensive experimental study is conducted on a variety of image classification tasks to compare our proposed discriminative Stein kernel with the original Stein kernel and other commonly used methods for evaluating the similarity between SPD matrices.
A Fast Approximate AIB Algorithm for Distributional Word Clustering
Distributional word clustering merges the words having similar probability distributions to attain reliable parameter estimation, compact classification models and even better classification performance.
