Efficient Human Pose Estimation by Learning Deeply Aggregated Representations

@article{Luo2021EfficientHP,
  title={Efficient Human Pose Estimation by Learning Deeply Aggregated Representations},
  author={Zhengxiong Luo and Zhicheng Wang and Yuanhao Cai and Guan'an Wang and Yan Huang and Liang Wang and Erjin Zhou and Tieniu Tan and Jian Sun},
  journal={2021 IEEE International Conference on Multimedia and Expo (ICME)},
  year={2021},
  pages={1-6}
}
In this paper, we propose an efficient human pose estimation network (DANet) by learning deeply aggregated representations. Most existing models explore multi-scale infonnation mainly from features with different spatial sizes. Powerful multi-scale representations usually rely on the cascaded pyramid framework. This framework largely boosts the performance but in the meanwhile makes networks very deep and complex. Instead, we focus on exploiting multi-scale information from layers with… 
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