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The recurrent three-dimensional convolutional architecture from [23]. As input, the network uses a dynamic gesture in the form of successive frames. It extracts local spatio-temporal features via a 3D Convolutional Neural Network (CNN) and feeds those into a recurrent layer, which aggregates activation across the sequence. Using these activations, a softmax layer then outputs probabilities for the dynamic gesture class.
