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.

该研究提出了一种新颖的自监督压力图关键点检测（SPMKD）方法，通过 Encoder-Fuser-Decoder 模型和 Classification-to-Regression Weight Transfer 方法，在不需要手动注释的情况下提高人体关键点泛化性能，同时显著减少计算和参数量。