7 papers
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…
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…