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首页> 外文期刊>Journal of visual communication & image representation >Sign language recognition based on global-local attention
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Sign language recognition based on global-local attention

机译：Sign language recognition based on global-local attention

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摘要

Video-level sign language recognition is still a challenging task due to the influence of sign language-independentfactors and timing requirements. This paper constructs a sign language recognition framework based on globallocalfeature description, and proposes a three-dimensional residual global network model with attention layerand a local network model based on target detection. The global feature description is based on the whole videobehavior for time series modeling. The improved timing conversion layer is used to explore the timing informationof different periods and learn the video representations of different timings. In the local module the handis located through the target detection network to highlight its key role in the whole sign language behavior,which strengthens the category differences, and compensates the global network. Experiments on two wellknownChinese sign language datasets (SLR_Dataset and DEVSIGN_D) show that the proposed method canobtain higher recognition accuracy (respectively 89.2%, 91%) and better generalization performance.

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