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- W4285308461 abstract "Human parsing is a subtask of semantic segmentation, which only segments the characters in the picture and ignores the background information. This technology has many application scenarios: such as pedestrian re-identification, smart home, human–computer interaction, etc. Due to the complexity of human semantic segmentation tasks, the existing network is not accurate enough. In view of this situation, this paper proposes a human parsing model based on deep learning to improve the accuracy of human body image semantic segmentation. The model includes three modules. In the human body feature extraction stage, this paper proposes an encoder module based on an improved residual network, which uses the residual network to continuously downsample the human body image; the decoder module uses bilinear interpolation and channel compression to continuously upsample the human body feature map; the encoder module and the decoder module merge the low-level and high-level features of the human body through the feature fusion module. The experimental results show that the details of the model proposed in this paper can process details better and effectively improve the accuracy." @default.
- W4285308461 created "2022-07-14" @default.
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- W4285308461 date "2022-01-01" @default.
- W4285308461 modified "2023-09-23" @default.
- W4285308461 title "ResNet-Based Multiscale U-Net for Human Parsing" @default.
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- W4285308461 doi "https://doi.org/10.1007/978-981-16-9735-7_6" @default.
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