4 citations · 14 across the 16 of their papers we have counts for
14 papers · 1 filter
Wasserstein-Aligned Hyperbolic Multi-View Clustering
Rui Wang, Yuting Jiang, Xiaoqing Luo +3
Multi-view clustering (MVC) aims to uncover the latent structure of multi-view data by learning view-common and view-specific information. Although recent studies have explored hyp…
SMLNet: A SPD Manifold Learning Network for Infrared and Visible Image Fusion
Huan Kang, Hui Li, Tianyang Xu +4
Euclidean representation learning methods have achieved promising results in image fusion tasks, which can be attributed to their clear advantages in handling with linear space. Ho…
Riemannian Self-Attention Mechanism for SPD Networks
Rui Wang, Xiao-Jun Wu, Hui Li +1
Symmetric positive definite (SPD) matrix has been demonstrated to be an effective feature descriptor in many scientific areas, as it can encode spatiotemporal statistics of the dat…
DreamNet: A Deep Riemannian Network based on SPD Manifold Learning for Visual Classification
Rui Wang, Xiao-Jun Wu, Ziheng Chen +2
Image set-based visual classification methods have achieved remarkable performance, via characterising the image set in terms of a non-singular covariance matrix on a symmetric pos…
Collaborative Representation for SPD Matrices with Application to Image-Set Classification
Li Chu, Rui Wang, Xiao-Jun Wu
Collaborative representation-based classification (CRC) has demonstrated remarkable progress in the past few years because of its closed-form analytical solutions. However, the exi…
Riemannian Local Mechanism for SPD Neural Networks
Ziheng Chen, Tianyang Xu, Xiao-Jun Wu +3
The Symmetric Positive Definite (SPD) matrices have received wide attention for data representation in many scientific areas. Although there are many different attempts to develop…
Deep Manifold Prior
Matheus Gadelha, Rui Wang, Subhransu Maji
We present a prior for manifold structured data, such as surfaces of 3D shapes, where deep neural networks are adopted to reconstruct a target shape using gradient descent starting…
Label-Efficient Learning on Point Clouds using Approximate Convex Decompositions
Matheus Gadelha, Aruni RoyChowdhury, Gopal Sharma +5
The problems of shape classification and part segmentation from 3D point clouds have garnered increasing attention in the last few years. Both of these problems, however, suffer fr…
More About Covariance Descriptors for Image Set Coding: Log-Euclidean Framework based Kernel Matrix Representation
Kai-Xuan Chen, Xiao-Jun Wu, Jie-Yi Ren +2
We consider a family of structural descriptors for visual data, namely covariance descriptors (CovDs) that lie on a non-linear symmetric positive definite (SPD) manifold, a special…
Multiple Riemannian Manifold-valued Descriptors based Image Set Classification with Multi-Kernel Metric Learning
Rui Wang, XiaoJun Wu, Josef Kittler
The importance of wild video based image set recognition is becoming monotonically increasing. However, the contents of these collected videos are often complicated, and how to eff…
Grassmannian Discriminant Maps (GDM) for Manifold Dimensionality Reduction with Application to Image Set Classification
Rui Wang, Xiao-Jun Wu, Kai-Xuan Chen +1
In image set classification, a considerable progress has been made by representing original image sets on Grassmann manifolds. In order to extend the advantages of the Euclidean ba…
Riemannian kernel based Nyström method for approximate infinite-dimensional covariance descriptors with application to image set classification
Kai-Xuan Chen, Xiao-Jun Wu, Rui Wang +1
In the domain of pattern recognition, using the CovDs (Covariance Descriptors) to represent data and taking the metrics of the resulting Riemannian manifold into account have been…
Multiple Manifolds Metric Learning with Application to Image Set Classification
Rui Wang, Xiao-Jun Wu, Kai-Xuan Chen +1
In image set classification, a considerable advance has been made by modeling the original image sets by second order statistics or linear subspace, which typically lie on the Riem…
A Simple Riemannian Manifold Network for Image Set Classification
Rui Wang, Xiao-Jun Wu, Josef Kittler
In the domain of image-set based classification, a considerable advance has been made by representing original image sets as covariance matrices which typical lie in a Riemannian m…