# Sign language segmentation with temporal convolutional networks

arXiv: https://arxiv.org/abs/2011.12986 · HTML: https://mlsanity.com/paper/2011.12986/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Nov 2020.

## Built on by (most cited first, 14 of 14)

- [BBC-Oxford British Sign Language Dataset](https://mlsanity.com/paper/2111.03635/) (2021)
- [Linguistically Motivated Sign Language Segmentation](https://mlsanity.com/paper/2310.13960/) (2023)
- [Sign Segmentation with Changepoint-Modulated Pseudo-Labelling](https://mlsanity.com/paper/2104.13817/) (2021)
- [Hands-On: Segmenting Individual Signs from Continuous Sequences](https://mlsanity.com/paper/2504.08593/) (2025)
- [Fingerspelling Detection in American Sign Language](https://mlsanity.com/paper/2104.01291/) (2021)
- [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)
- [An Open-Source American Sign Language Fingerspell Recognition and Semantic Pose Retrieval Interface](https://mlsanity.com/paper/2408.09311/) (2024)
- [iLSU-T: an Open Dataset for Uruguayan Sign Language Translation](https://mlsanity.com/paper/2507.21104/) (2025)
- [The TUB Sign Language Corpus Collection](https://mlsanity.com/paper/2508.05374/) (2025)
- [SSL-SLR: Self-Supervised Representation Learning for Sign Language Recognition](https://mlsanity.com/paper/2509.05188/) (2025)
- [MHB: Multimodal Handshape-aware Boundary Detection for Continuous Sign Language Recognition](https://mlsanity.com/paper/2511.19907/) (2025)
- [SMART: MLLM-guided Temporal Alignment for Unifying Sign Language Recognition and Spotting](https://mlsanity.com/paper/2608.25493/) (2026)
- [Machine Translation for Sign Languages](https://mlsanity.com/paper/2610.00881/) (2026)

## Abstract (arXiv)

The objective of this work is to determine the location of temporal boundaries between signs in continuous sign language videos. Our approach employs 3D convolutional neural network representations with iterative temporal segment refinement to resolve ambiguities between sign boundary cues. We demonstrate the effectiveness of our approach on the BSLCORPUS, PHOENIX14 and BSL-1K datasets, showing considerable improvement over the prior state of the art and the ability to generalise to new signers, languages and domains.