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Sign Language Translation with Hierarchical Spatio-TemporalGraph Neural Network
Therefore, in this paper, these unique characteristics of sign languages are formulated as hierarchical spatio-temporal graph representations, including high-level and fine-level graphs of which a vertex characterizes a specified body part and an edge represents their interactions.
Embedding of FRPN in CNN architecture
The unfolding realized with this kind of iterative mechanism allows to simulate a deep neural network with any number of layers.
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