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- W4367016314 abstract "To solve the problems of high complex calculation and low recognition precision of similar actions in the high-resolution network (HRNet), we proposed an optimized HRNet based on attention mechanism. Firstly, the dilated convolution (DC) module is introduced into cross-channel sampling to obtain global features, which ensures that the feature map can cover all the information of the original image; Secondly, the channel attention Squeeze-and-Excitation (SE) module is employed in the process of cross-channel feature fusion to learn the correlations, improving the recognition precision without changing the parameter quantity and operation complexity; Finally, the experiment results on KTH dataset show that the proposed algorithm has a better performance." @default.
- W4367016314 created "2023-04-27" @default.
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- W4367016314 date "2023-02-24" @default.
- W4367016314 modified "2023-09-25" @default.
- W4367016314 title "An optimization high-resolution network for human pose recognition based on attention mechanism" @default.
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- W4367016314 doi "https://doi.org/10.1109/nnice58320.2023.10105762" @default.
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