Abstract Deep Convolutional Networks (ConvNets) are fundamental to, besides large-scale
visual recognition, a lot of vision tasks. As the primary goal of the ConvNets is to characterize …

Global covariance pooling in convolutional neural networks has achieved impressive
improvement over the classical first-order pooling. Recent works have shown matrix square …

By stacking layers of convolution and nonlinearity, convolutional networks (ConvNets)
effectively learn from low-level to high-level features and discriminative representations …

This paper proposes a new neural network based on SPD manifold learning for skeleton-
based hand gesture recognition. Given the stream of hand's joint positions, our approach …

Compared with global average pooling in existing deep convolutional neural networks
(CNNs), global covariance pooling can capture richer statistics of deep features, having …

Recently, plugging trainable structural layers into deep convolutional neural networks
(CNNs) as image representations has made promising progress. However, there has been …

Ultra-fine-grained visual categorization (ultra-FGVC) categorizes objects with more similar
patterns between classes than those in fine-grained visual categorization (FGVC), eg, where …

Face recognition under adverse conditions, such as low-resolution, difficult illumination, blur
and noise remains a challenging task. Among existing face recognition methods, Gabor …

In this paper, we propose a novel method for representation and classification of two-person
interactions from 3D skeleton sequences. The key idea of our approach is to use Gaussian …

Abstract Symmetric Positive Definite (SPD) matrices are ubiquitous in data analysis under
the form of covariance matrices or correlation matrices. Several O (n)-invariant Riemannian …