Human pose estimation with 80% smaller model and 68% less CPU using STNet
Human pose estimation with %80 smaller model and 68% less CPU using STNet
Towards Simple and Accurate Human Pose Estimation with Stair Network
arXiv paper abstract https://arxiv.org/abs/2202.09115v1
arXiv PDF paper https://arxiv.org/pdf/2202.09115v1.pdf
In ... keypoint coordinates regression task. ... existing approaches adopt complicated networks with a large number of parameters, leading to a heavy model with poor cost-effectiveness in practice.
... To overcome ... develop a small yet discrimicative model called STair Network, which can be simply stacked towards an accurate multi-stage pose estimation system.
... composed of novel basic feature extraction blocks which focus on promoting feature diversity and obtaining rich local representations with fewer parameters
... introduce two mechanisms with negligible computational cost, focusing on feature fusion and replenish.
... 1-stage STair Network ... higher accuracy than HRNet by 5.5% on COCO test dataset with 80% fewer parameters and 68% fewer GFLOPs.
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