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- W4317664025 abstract "Skeleton-based gesture recognition (SHGR) is a very challenging task due to the complex articulated topology of hands. Previous works often learn hand characteristics from a single observation viewpoint. However, the various context information hidden in multiple viewpoints is disregarded. To resolve this issue, we propose a novel multi-view hierarchical aggregation network for SHGR. Firstly, two-dimensional non-uniform spatial sampling, a novel strategy forming extrinsic parameter distributions of virtual cameras, is presented to enumerate viewpoints to observe hand skeletons. Afterward, we adopt coordinate transformation to generate multi-view hand skeletons and employ a multi-branch convolutional neural networks to further extract the multi-view features. Furthermore, we exploit a novel hierarchical aggregation network including hierarchical attention architecture and global context modeling to fuse the multi-view features for final classification. Experiments on three benchmarked datasets demonstrate that our work can be competitive with the state-of-the-art methods." @default.
- W4317664025 created "2023-01-21" @default.
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- W4317664025 date "2023-01-21" @default.
- W4317664025 modified "2023-10-16" @default.
- W4317664025 title "MVHANet: multi-view hierarchical aggregation network for skeleton-based hand gesture recognition" @default.
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- W4317664025 doi "https://doi.org/10.1007/s11760-022-02469-9" @default.
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