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Found 6 papers, 0 papers with code
ConViViT -- A Deep Neural Network Combining Convolutions and Factorized Self-Attention for Human Activity Recognition
In this research, we propose a novel approach that leverages the strengths of both CNNs and Transformers in an hybrid architecture for performing activity recognition using RGB videos.
Magnifying Subtle Facial Motions for Effective 4D Expression Recognition
In this paper, an effective pipeline to automatic 4D Facial Expression Recognition (4D FER) is proposed.
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.
Sparse Coding of Shape Trajectories for Facial Expression and Action Recognition
The detection and tracking of human landmarks in video streams has gained in reliability partly due to the availability of affordable RGB-D sensors.
Coding Kendall's Shape Trajectories for 3D Action Recognition
Grounding on the Riemannian geometry of the shape space, an intrinsic sparse coding and dictionary learning formulation is proposed for static skeletal shapes to overcome the inherent non-linearity of the manifold.
