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- W3204377457 abstract "Accurate keypoint positioning is necessary for bottom-up multi-person pose estimation methods to handle scale variation and crowdedness. In this paper, we present DoubleHigherNet: a novel network learning scale-aware and precise heatmap representation for bottom-up process using double high-resolution feature pyramids and coarse-to-fine training. The two feature pyramids in DoubleHigherNet consists of 1/4 resolution feature and higher-resolution (1/2) maps generated by attention fusion blocks and transposed convolutions. Benefited by the training strategy, muti-resoltion and coarse-fine heatmap aggregation, the proposed approach is able to predict keypoints more accurately so as to perform better on difficult crowded scenes. DoubleHigherNet-w32 achieves competitive result on CrowdPose-test, surpassing all the top-down methods and bottom-up SOTA HigherHRNet-w32 (which possesses similar number of params with DoubleHigherNet-w32)." @default.
- W3204377457 created "2021-10-11" @default.
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- W3204377457 date "2021-09-01" @default.
- W3204377457 modified "2023-10-16" @default.
- W3204377457 title "DoubleHigherNet: Coarse-to-Fine Precise Heatmap Bottom- Up Dynamic Pose Computer Intelligent Estimation" @default.
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- W3204377457 doi "https://doi.org/10.1088/1742-6596/2033/1/012068" @default.
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