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.

本文提出了一种特定于手语的算法，以改善未标记的手语视频中手语分割的性能，该算法基于运动敏感特征空间中突变点的线索来提高伪标记的质量，在 BSLCORPUS 数据集与 BSL-1K 和 RWTH-PHOENIX-Weather 2014 数据集上，我们的方法优于先前的研究成果。

基于变点调制伪标签的符号分割