A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2022; you can also visit the original URL.
The file type is application/pdf
.
Unsupervised Domain Adaptation Learning for Hierarchical Infant Pose Recognition with Synthetic Data
[article]
2022
arXiv
pre-print
The Alberta Infant Motor Scale (AIMS) is a well-known assessment scheme that evaluates the gross motor development of infants by recording the number of specific poses achieved. With the aid of the image-based pose recognition model, the AIMS evaluation procedure can be shortened and automated, providing early diagnosis or indicator of potential developmental disorder. Due to limited public infant-related datasets, many works use the SMIL-based method to generate synthetic infant images for
arXiv:2205.01892v1
fatcat:toseazbevvg4dbqxdaclzv47fy