{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/bsl-1k-scaling-up-co-articulated-sign","title":"BSL-1K: Scaling up co-articulated sign language recognition using mouthing cues","arxiv_id":"2007.12131","date":"2020-07-23","proceeding":"ECCV 2020 8","authors":["Samuel Albanie","Gül Varol","Liliane Momeni","Triantafyllos Afouras","Joon Son Chung","Neil Fox","Andrew Zisserman"],"abstract":"Recent progress in fine-grained gesture and action classification, and machine translation, point to the possibility of automated sign language recognition becoming a reality. A key stumbling block in making progress towards this goal is a lack of appropriate training data, stemming from the high complexity of sign annotation and a limited supply of qualified annotators. In this work, we introduce a new scalable approach to data collection for sign recognition in continuous videos. We make use of weakly-aligned subtitles for broadcast footage together with a keyword spotting method to automatically localise sign-instances for a vocabulary of 1,000 signs in 1,000 hours of video. We make the following contributions: (1) We show how to use mouthing cues from signers to obtain high-quality annotations from video data - the result is the BSL-1K dataset, a collection of British Sign Language (BSL) signs of unprecedented scale; (2) We show that we can use BSL-1K to train strong sign recognition models for co-articulated signs in BSL and that these models additionally form excellent pretraining for other sign languages and benchmarks - we exceed the state of the art on both the MSASL and WLASL benchmarks. Finally, (3) we propose new large-scale evaluation sets for the tasks of sign recognition and sign spotting and provide baselines which we hope will serve to stimulate research in this area.","url_abs":"https://arxiv.org/abs/2007.12131v2","url_pdf":"https://arxiv.org/pdf/2007.12131v2.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"bsl-1k-scaling-up-co-articulated-sign","repo_url":"https://github.com/gulvarol/bsl1k","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok"}}],"tasks":[{"task_slug":"action-classification","task_name":"Action Classification"},{"task_slug":"keyword-spotting","task_name":"Keyword Spotting"},{"task_slug":"machine-translation","task_name":"Machine Translation"},{"task_slug":"sign-language-recognition","task_name":"Sign Language Recognition"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/sign-language-recognition-on-wlasl-2000","task":"Sign Language Recognition","dataset":"WLASL-2000","model":"I3D (pretraining: BSL-1K)","rank_in_archive_order":8,"of":9,"metrics":{"Top-1 Accuracy":"46.82"},"uses_additional_data":false}],"syntology":{"syntology_url":"https://syntology.ai/paper/2007.12131","atlas_url":"https://app.syntology.ai/?focus=2007.12131","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}