259 citations · 295 across the 9 of their papers we have counts for
9 papers
InfiniMotion: Mamba Boosts Memory in Transformer for Arbitrary Long Motion Generation
Zeyu Zhang, Akide Liu, Qi Chen +5
Text-to-motion generation holds potential for film, gaming, and robotics, yet current methods often prioritize short motion generation, making it challenging to produce long motion…
Manifold Learning Benefits GANs
Yao Ni, Piotr Koniusz, Richard Hartley +1
In this paper, we improve Generative Adversarial Networks by incorporating a manifold learning step into the discriminator. We consider locality-constrained linear and subspace-bas…
Optimizing Over Radial Kernels on Compact Manifolds
Sadeep Jayasumana, Richard Hartley, Mathieu Salzmann +2
We tackle the problem of optimizing over all possible positive definite radial kernels on Riemannian manifolds for classification. Kernel methods on Riemannian manifolds have recen…
Kernel Methods on the Riemannian Manifold of Symmetric Positive Definite Matrices
Sadeep Jayasumana, Richard Hartley, Mathieu Salzmann +2
Symmetric Positive Definite (SPD) matrices have become popular to encode image information. Accounting for the geometry of the Riemannian manifold of SPD matrices has proven key to…
Kernel Methods on Riemannian Manifolds with Gaussian RBF Kernels
Sadeep Jayasumana, Richard Hartley, Mathieu Salzmann +2
In this paper, we develop an approach to exploiting kernel methods with manifold-valued data. In many computer vision problems, the data can be naturally represented as points on a…
Sparse Coding on Symmetric Positive Definite Manifolds using Bregman Divergences
Mehrtash Harandi, Richard Hartley, Brian Lovell +1
This paper introduces sparse coding and dictionary learning for Symmetric Positive Definite (SPD) matrices, which are often used in machine learning, computer vision and related ar…