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Dite-HRNet: Dynamic Lightweight High-Resolution Network for Human Pose Estimation
[article]
2022
arXiv
pre-print
A high-resolution network exhibits remarkable capability in extracting multi-scale features for human pose estimation, but fails to capture long-range interactions between joints and has high computational complexity. To address these problems, we present a Dynamic lightweight High-Resolution Network (Dite-HRNet), which can efficiently extract multi-scale contextual information and model long-range spatial dependency for human pose estimation. Specifically, we propose two methods, dynamic split
arXiv:2204.10762v3
fatcat:2lz7oxjp6zcmzbg4gzd3cejzq4