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Geometric Deep Neural Network Using Rigid and Non-Rigid Transformations for Human Action Recognition
Deep Learning architectures, albeit successful in mostcomputer vision tasks, were designed for data with an un-derlying Euclidean structure, which is not usually fulfilledsince pre-processed data may lie on a non-linear space. In this paper, we propose a geometry aware deep learn-ing approach using rigid and non rigid transformation opti-mization for skeleton-based action recognition.
KShapeNet: Riemannian network on Kendall shape space for Skeleton based Action Recognition
Deep Learning architectures, albeit successful in most computer vision tasks, were designed for data with an underlying Euclidean structure, which is not usually fulfilled since pre-processed data may lie on a non-linear space.
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