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20162023
most citedA Riemannian Network for SPD Matrix Learning

119 citations · 287 across the 16 of their papers we have counts for

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Showing 2016 · cs.CVShow all

5 papers · 2 filters

cs.CV2016★ 11 cited

Deep Learning on Lie Groups for Skeleton-based Action Recognition

Zhiwu Huang, Chengde Wan, Thomas Probst +1

In recent years, skeleton-based action recognition has become a popular 3D classification problem. State-of-the-art methods typically first represent each motion sequence as a high…

cs.CV2016★ 46 cited

Building Deep Networks on Grassmann Manifolds

Zhiwu Huang, Jiqing Wu, Luc Van Gool

Learning representations on Grassmann manifolds is popular in quite a few visual recognition tasks. In order to enable deep learning on Grassmann manifolds, this paper proposes a d…

cs.CV2016★ 1 cited

Geometry-aware Similarity Learning on SPD Manifolds for Visual Recognition

Zhiwu Huang, Ruiping Wang, Xianqiu Li +4

Symmetric Positive Definite (SPD) matrices have been widely used for data representation in many visual recognition tasks. The success mainly attributes to learning discriminative…

cs.CV2016★ 119 cited

A Riemannian Network for SPD Matrix Learning

Zhiwu Huang, Luc Van Gool

Symmetric Positive Definite (SPD) matrix learning methods have become popular in many image and video processing tasks, thanks to their ability to learn appropriate statistical rep…

cs.CV2016★ 6 cited

Cross Euclidean-to-Riemannian Metric Learning with Application to Face Recognition from Video

Zhiwu Huang, Ruiping Wang, Shiguang Shan +2

Riemannian manifolds have been widely employed for video representations in visual classification tasks including video-based face recognition. The success mainly derives from lear…