A Self-supervised Pressure Map human keypoint Detection Approch: Optimizing Generalization and Computational Efficiency Across Datasets
This addresses the need for efficient and generalizable keypoint extraction in pressure map applications, though it appears incremental as it builds on existing self-supervised and model optimization techniques.
The paper tackled the problem of human keypoint detection from pressure maps, where RGB images are insufficient, by introducing a self-supervised method that reduces FLOPs by 5.96% and parameters by 1.11% compared to baselines.
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.