Unsupervised Domain Adaptation Learning for Hierarchical Infant Pose Recognition with Synthetic Data. (arXiv:2205.01892v1 [cs.CV])

Web: http://arxiv.org/abs/2205.01892

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 training. However, this domain mismatch
between real and …

arxiv cv data domain adaptation hierarchical learning synthetic data unsupervised