# Sign Segmentation with Changepoint-Modulated Pseudo-Labelling

arXiv: https://arxiv.org/abs/2104.13817 · HTML: https://mlsanity.com/paper/2104.13817/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- Builds on 19 of its 62 references that are in the map.
- First posted on arXiv 28 Apr 2021.

## Builds on (most cited first, 19 of 19)

- [Quo Vadis, Action Recognition? A New Model and the Kinetics Dataset](https://mlsanity.com/paper/1705.07750/) (2017)
- [Domain-Adversarial Training of Neural Networks](https://mlsanity.com/paper/1505.07818/) (2015)
- [The Kinetics Human Action Video Dataset](https://mlsanity.com/paper/1705.06950/) (2017)
- [Do We Really Need to Access the Source Data? Source Hypothesis Transfer for Unsupervised Domain Adaptation](https://mlsanity.com/paper/2002.08546/) (2020)
- [TALL: Temporal Activity Localization via Language Query](https://mlsanity.com/paper/1705.02101/) (2017)
- [MS-TCN: Multi-Stage Temporal Convolutional Network for Action Segmentation](https://mlsanity.com/paper/1903.01945/) (2019)
- [Universal Source-Free Domain Adaptation](https://mlsanity.com/paper/2004.04393/) (2020)
- [Multi-Adversarial Domain Adaptation](https://mlsanity.com/paper/1809.02176/) (2018)
- [Asymmetric Tri-training for Unsupervised Domain Adaptation](https://mlsanity.com/paper/1702.08400/) (2017)
- [BSL-1K: Scaling up co-articulated sign language recognition using mouthing cues](https://mlsanity.com/paper/2007.12131/) (2020)
- [Every Moment Counts: Dense Detailed Labeling of Actions in Complex Videos](https://mlsanity.com/paper/1507.05738/) (2015)
- [Sign Language Recognition, Generation, and Translation: An Interdisciplinary Perspective](https://mlsanity.com/paper/1908.08597/) (2019)
- [Action Segmentation with Joint Self-Supervised Temporal Domain Adaptation](https://mlsanity.com/paper/2003.02824/) (2020)
- [An end-to-end generative framework for video segmentation and recognition](https://mlsanity.com/paper/1509.01947/) (2015)
- [Transferring Cross-domain Knowledge for Video Sign Language Recognition](https://mlsanity.com/paper/2003.03703/) (2020)
- [Quantitative Survey of the State of the Art in Sign Language Recognition](https://mlsanity.com/paper/2008.09918/) (2020)
- [Real-Time Sign Language Detection using Human Pose Estimation](https://mlsanity.com/paper/2008.04637/) (2020)
- [Source Free Domain Adaptation with Image Translation](https://mlsanity.com/paper/2008.07514/) (2020)
- [Sign language segmentation with temporal convolutional networks](https://mlsanity.com/paper/2011.12986/) (2020)

## Built on by (most cited first, 10 of 10)

- [SignAvatars: A Large-scale 3D Sign Language Holistic Motion Dataset and Benchmark](https://mlsanity.com/paper/2310.20436/) (2023)
- [BBC-Oxford British Sign Language Dataset](https://mlsanity.com/paper/2111.03635/) (2021)
- [Automatic dense annotation of large-vocabulary sign language videos](https://mlsanity.com/paper/2208.02802/) (2022)
- [A Tale of Two Languages: Large-Vocabulary Continuous Sign Language Recognition from Spoken Language Supervision](https://mlsanity.com/paper/2405.10266/) (2024)
- [Towards Online Continuous Sign Language Recognition and Translation](https://mlsanity.com/paper/2401.05336/) (2024)
- [Reconstructing Signing Avatars From Video Using Linguistic Priors](https://mlsanity.com/paper/2304.10482/) (2023)
- [Linguistically Motivated Sign Language Segmentation](https://mlsanity.com/paper/2310.13960/) (2023)
- [Hands-On: Segmenting Individual Signs from Continuous Sequences](https://mlsanity.com/paper/2504.08593/) (2025)
- [Segment, Embed, and Align: A Universal Recipe for Aligning Subtitles to Signing](https://mlsanity.com/paper/2512.08094/) (2025)
- [SignSparK: Efficient Multilingual Sign Language Production via Sparse Keyframe Learning](https://mlsanity.com/paper/2603.10446/) (2026)

## Abstract (arXiv)

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.