95 research outputs found
When Kernel Methods meet Feature Learning: Log-Covariance Network for Action Recognition from Skeletal Data
Human action recognition from skeletal data is a hot research topic and
important in many open domain applications of computer vision, thanks to
recently introduced 3D sensors. In the literature, naive methods simply
transfer off-the-shelf techniques from video to the skeletal representation.
However, the current state-of-the-art is contended between to different
paradigms: kernel-based methods and feature learning with (recurrent) neural
networks. Both approaches show strong performances, yet they exhibit heavy, but
complementary, drawbacks. Motivated by this fact, our work aims at combining
together the best of the two paradigms, by proposing an approach where a
shallow network is fed with a covariance representation. Our intuition is that,
as long as the dynamics is effectively modeled, there is no need for the
classification network to be deep nor recurrent in order to score favorably. We
validate this hypothesis in a broad experimental analysis over 6 publicly
available datasets.Comment: 2017 IEEE Computer Vision and Pattern Recognition (CVPR) Workshop
Sparse Coding on Symmetric Positive Definite Manifolds using Bregman Divergences
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 areas. Unlike traditional sparse coding schemes
that work in vector spaces, in this paper we discuss how SPD matrices can be
described by sparse combination of dictionary atoms, where the atoms are also
SPD matrices. We propose to seek sparse coding by embedding the space of SPD
matrices into Hilbert spaces through two types of Bregman matrix divergences.
This not only leads to an efficient way of performing sparse coding, but also
an online and iterative scheme for dictionary learning. We apply the proposed
methods to several computer vision tasks where images are represented by region
covariance matrices. Our proposed algorithms outperform state-of-the-art
methods on a wide range of classification tasks, including face recognition,
action recognition, material classification and texture categorization
Building Neural Networks on Matrix Manifolds: A Gyrovector Space Approach
Matrix manifolds, such as manifolds of Symmetric Positive Definite (SPD)
matrices and Grassmann manifolds, appear in many applications. Recently, by
applying the theory of gyrogroups and gyrovector spaces that is a powerful
framework for studying hyperbolic geometry, some works have attempted to build
principled generalizations of Euclidean neural networks on matrix manifolds.
However, due to the lack of many concepts in gyrovector spaces for the
considered manifolds, e.g., the inner product and gyroangles, techniques and
mathematical tools provided by these works are still limited compared to those
developed for studying hyperbolic geometry. In this paper, we generalize some
notions in gyrovector spaces for SPD and Grassmann manifolds, and propose new
models and layers for building neural networks on these manifolds. We show the
effectiveness of our approach in two applications, i.e., human action
recognition and knowledge graph completion
Local Spherical Harmonics Improve Skeleton-Based Hand Action Recognition
Hand action recognition is essential. Communication, human-robot
interactions, and gesture control are dependent on it. Skeleton-based action
recognition traditionally includes hands, which belong to the classes which
remain challenging to correctly recognize to date. We propose a method
specifically designed for hand action recognition which uses relative angular
embeddings and local Spherical Harmonics to create novel hand representations.
The use of Spherical Harmonics creates rotation-invariant representations which
make hand action recognition even more robust against inter-subject differences
and viewpoint changes. We conduct extensive experiments on the hand joints in
the First-Person Hand Action Benchmark with RGB-D Videos and 3D Hand Pose
Annotations, and on the NTU RGB+D 120 dataset, demonstrating the benefit of
using Local Spherical Harmonics Representations. Our code is available at
https://github.com/KathPra/LSHR_LSHT
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