Read and Attend: Temporal Localisation in Sign Language Videos
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The objective of this work is to annotate sign instances across a broad vocabulary in continuous sign language. We train a Transformer model to ingest a continuous signing stream and output a sequence of written tokens on a large-scale collection of signing footage with weakly-aligned subtitles. We show that through this training it acquires the ability to attend to a large vocabulary of sign instances in the input sequence, enabling their localisation. Our contributions are as follows: (1) we demonstrate the ability to leverage large quantities of continuous signing videos with weakly-aligned subtitles to localise signs in continuous sign language; (2) we employ the learned attention to automatically generate hundreds of thousands of annotations for a large sign vocabulary; (3) we collect a set of 37K manually verified sign instances across a vocabulary of 950 sign classes to support our study of sign language recognition; (4) by training on the newly annotated data from our method, we outperform the prior state of the art on the BSL-1K sign language recognition benchmark.
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The recent success of large language models and their multimodal derivatives demonstrates the potential of Transformers in multimodal foundation models.
“In particular, the recent success of large language models and their multimodal derivatives [82], [83], [84], [85], [86] further demonstrates the potential of Transformers in multimodal foundation models.”
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Imai, S., Kezar, L., Aichler, L., İnan, M., Walker, E., Wooten, A. K., Quandt, L. C., & Alikhani, M. (2026) · Language Resources and Evaluation Conference (LREC) · doi:10.63317/2wjnaaabgz4d
Lee, H., Park, M., Lee, Y.-E., & Park, J. (2026) · IEEE Conference on Artificial Intelligence (CAI) · doi:10.1109/cai68641.2026.11536421
SEN: Semantic-Enhanced Network for Gloss-Free Sign Language Translation
Shudong, L. I. U., Zhang, Y., Guo, Z., Zhang, Y., Gu, B., & Yu, Z. (2026) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2026.3724355
Nguyen, T. T. T., Nguyen, K. T. H., Pham, B. A., Nguyen, V. M. T., & Vo, K. H. (2025) · International Conference on Digital Technology in Education (ICDTE) · doi:10.1145/3796159.3796171
LexSign: Learning Sign Language from Lexical Descriptions
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- Authors
- Varol, G., Momeni, L., Albanie, S., Afouras, T., & Zisserman, A.
- Affiliations
- University of Oxford (GB)
- Venue
- 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
- Publisher
- IEEE
- Published
- 2021-06
- Pages
- 16852-16861
- ISSN
- 1063-6919 (ISSN-L)
- OpenAlex
- W3150074127
- Cited by
- 39
- References
- 56
- Citations by year
- 2026: 4 · 2025: 7 · 2024: 4 · 2023: 12 · 2022: 7 · 2021: 5
- FWCI
- 5.23
- Type
- conference paper
- Language
- en
- Open access
- green
- arxiv.org
- Licence
- Locations
- IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- HAL (submittedVersion)
- Funders
- Royal Society
- Topics
- Physical Sciences › Computer Science › Human-Computer Interaction › Hand Gesture Recognition Systems
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Human Pose and Action Recognition
- Social Sciences › Psychology › Developmental and Educational Psychology › Hearing Impairment and Communication
- Keywords
- sign language recognition, transformer attention, British Sign Language, video annotation
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
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