  The objective of this work is to find temporal boundaries between signs in
continuous sign language. Motivated by the paucity of annotation available for
this task, we propose a simple yet effective algorithm to improve segmentation
performance on unlabelled signing footage from a domain of interest. We make
the following contributions: (1) We motivate and introduce the task of
source-free domain adaptation for sign language segmentation, in which labelled
source data is available for an initial training phase, but is not available
during adaptation. (2) We propose the Changepoint-Modulated Pseudo-Labelling
(CMPL) algorithm to leverage cues from abrupt changes in motion-sensitive
feature space to improve pseudo-labelling quality for adaptation. (3) We
showcase the effectiveness of our approach for category-agnostic sign
segmentation, transferring from the BSLCORPUS to the BSL-1K and
RWTH-PHOENIX-Weather 2014 datasets, where we outperform the prior state of the
art.
