A Self-supervised Pressure Map human keypoint Detection Approch: Optimizing Generalization and Computational Efficiency Across Datasets
<|reference_start|>A Self-supervised Pressure Map human keypoint Detection Approch: Optimizing Generalization and Computational Efficiency Across Datasets: 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.<|reference_end|>
@article{yu2024a,
    title={A Self-supervised Pressure Map human keypoint Detection Approch:
  Optimizing Generalization and Computational Efficiency Across Datasets},
    author={Chengzhang Yu and Xianjun Yang and Wenxia Bao and Shaonan Wang and
  Zhiming Yao},
    journal={arXiv preprint arXiv:2402.14241},
    year={2024},
    archivePrefix={arXiv},
    eprint={2402.14241},
    primaryClass={cs.CV cs.AI}
}