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. In addition%2C context-dependent adaptive topology as a neighborhood vertex information and attention mechanism leverages a model to better represent actions. In this paper%2C we propose self-attention GCN hybrid model%2C Multi-Scale Spatial-Temporal self-attention (MSST)-GCN to effectively improve modeling ability to achieve state-of-the-art results on several datasets. We utilize spatial self-attention module with adaptive topology to understand intra-frame interactions within a frame among different body parts%2C and temporal self-attention module to examine correlations between frames of a node. These two are followed by multi-scale convolution network with dilations%2C which not only captures the long-range temporal dependencies of joints but also the long-range spatial dependencies (i.e.%2C long-distance dependencies) of node temporal behaviors. They are combined into high-level spatial-temporal representations and output the predicted action with the softmax classifier.?quality=80&w=800)
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