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- W4296340321 abstract "Measuring the similarity of human actions represented with human skeletons from motion capture plays an important role in classification, retrieval and analysis of actions. In this paper, a similarity measurement method of human action based on graph neural network is proposed. In this method, due to the introduction of graph convolution neural network, the dependence between adjacent joints in human skeleton can be obtained, which makes the expression of human action in a frame more accurate. In the further action similarity measurement, LSTM with self- attention is used to extract temporal feature of the human action sequence, and finally MMD-NCA is used to measure the similarity of action sequences. Experiments on public dataset verify the effectiveness of this method in action recognition and action similarity measurement." @default.
- W4296340321 created "2022-09-20" @default.
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- W4296340321 date "2022-07-26" @default.
- W4296340321 modified "2023-10-18" @default.
- W4296340321 title "Similarity Measurement Human Actions with GNN" @default.
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- W4296340321 doi "https://doi.org/10.1109/icivc55077.2022.9887189" @default.
- W4296340321 hasPublicationYear "2022" @default.
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