A Self-supervised Pressure Map human keypoint Detection Approch:
  Optimizing Generalization and Computational Efficiency Across Datasets
Chengzhang Yu Xianjun Yang Wenxia Bao Shaonan Wang Zhiming Yao
In environments where RGB images are inadequate, pressure maps is a viable
alternative, garnering scholarly attention. This study introduces a novel
self-supervised pressure map keypoint detection (SPMKD) method, addressing the
current gap in specialized designs for human keypoint extraction from pressure
maps. Central to our contribution is the Encoder-Fuser-Decoder (EFD) model,
which is a robust framework that integrates a lightweight encoder for precise
human keypoint detection, a fuser for efficient gradient propagation, and a
decoder that transforms human keypoints into reconstructed pressure maps. This
structure is further enhanced by the Classification-to-Regression Weight
Transfer (CRWT) method, which fine-tunes accuracy through initial
classification task training. This innovation not only enhances human keypoint
generalization without manual annotations but also showcases remarkable
efficiency and generalization, evidenced by a reduction to only $5.96\%$ in
FLOPs and $1.11\%$ in parameter count compared to the baseline methods.