  General movement assessment (GMA) of infant movement videos (IMVs) is an
effective method for the early detection of cerebral palsy (CP) in infants.
Automated body parsing is a crucial step towards computer-aided GMA, in which
infant body parts are segmented and tracked over time for movement analysis.
However, acquiring fully annotated data for video-based body parsing is
particularly expensive due to the large number of frames in IMVs. In this
paper, we propose a semi-supervised body parsing model, termed SiamParseNet
(SPN), to jointly learn single frame body parsing and label propagation between
frames in a semi-supervised fashion. The Siamese-structured SPN consists of a
shared feature encoder, followed by two separate branches: one for intra-frame
body parts segmentation, and one for inter-frame label propagation. The two
branches are trained jointly, taking pairs of frames from the same videos as
their input. An adaptive training process is proposed that alternates training
modes between using input pairs of only labeled frames and using inputs of both
labeled and unlabeled frames. During testing, we employ a multi-source
inference mechanism, where the final result for a test frame is either obtained
via the segmentation branch or via propagation from a nearby key frame. We
conduct extensive experiments on a partially-labeled IMV dataset where SPN
outperforms all prior arts, demonstrating the effectiveness of our proposed
method.
