{"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/uni-sign-toward-unified-sign-language","title":"Uni-Sign: Toward Unified Sign Language Understanding at Scale","arxiv_id":"2501.15187","date":"2025-01-25","proceeding":null,"authors":["Zecheng Li","Wengang Zhou","Weichao Zhao","Kepeng Wu","Hezhen Hu","Houqiang Li"],"abstract":"Sign language pre-training has gained increasing attention for its ability to enhance performance across various sign language understanding (SLU) tasks. However, existing methods often suffer from a gap between pre-training and fine-tuning, leading to suboptimal results. To address this, we propose Uni-Sign, a unified pre-training framework that eliminates the gap between pre-training and downstream SLU tasks through a large-scale generative pre-training strategy and a novel fine-tuning paradigm. First, we introduce CSL-News, a large-scale Chinese Sign Language (CSL) dataset containing 1,985 hours of video paired with textual annotations, which enables effective large-scale pre-training. Second, Uni-Sign unifies SLU tasks by treating downstream tasks as a single sign language translation (SLT) task during fine-tuning, ensuring seamless knowledge transfer between pre-training and fine-tuning. Furthermore, we incorporate a prior-guided fusion (PGF) module and a score-aware sampling strategy to efficiently fuse pose and RGB information, addressing keypoint inaccuracies and improving computational efficiency. Extensive experiments across multiple SLU benchmarks demonstrate that Uni-Sign achieves state-of-the-art performance across multiple downstream SLU tasks. Dataset and code are available at github.com/ZechengLi19/Uni-Sign.","url_abs":"https://arxiv.org/abs/2501.15187v2","url_pdf":"https://arxiv.org/pdf/2501.15187v2.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":"uni-sign-toward-unified-sign-language","repo_url":"https://github.com/zechengli19/uni-sign","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"computational-efficiency","task_name":"Computational Efficiency"},{"task_slug":"gloss-free-sign-language-translation","task_name":"Gloss-free Sign Language Translation"},{"task_slug":"sign-language-recognition","task_name":"Sign Language Recognition"},{"task_slug":"sign-language-translation","task_name":"Sign Language Translation"},{"task_slug":"transfer-learning","task_name":"Transfer Learning"}],"methods":[{"method_slug":"attention","method_name":"Attention"},{"method_slug":"softmax","method_name":"Softmax"}],"datasets_introduced":[{"slug":"csl-news","name":"CSL-News","full_name":""}],"methods_introduced":[],"results":[{"leaderboard":"/sota/sign-language-recognition-on-csl-daily","task":"Sign Language Recognition","dataset":"CSL-Daily","model":"Uni-Sign","rank_in_archive_order":4,"of":14,"metrics":{"Word Error Rate (WER)":"26.0"},"uses_additional_data":true},{"leaderboard":"/sota/sign-language-recognition-on-ms-asl","task":"Sign Language Recognition","dataset":"MSASL-1000","model":"Uni-Sign","rank_in_archive_order":1,"of":2,"metrics":{"P-C Top-1 Accuracy":"76.97","P-I Top-1 Accuracy":"78.16"},"uses_additional_data":true},{"leaderboard":"/sota/sign-language-recognition-on-wlasl-2000","task":"Sign Language Recognition","dataset":"WLASL-2000","model":"Uni-Sign","rank_in_archive_order":2,"of":9,"metrics":{"Top-1 Accuracy":"63.52"},"uses_additional_data":true},{"leaderboard":"/sota/sign-language-recognition-on-wlasl100","task":"Sign Language Recognition","dataset":"WLASL100","model":"Uni-Sign","rank_in_archive_order":1,"of":7,"metrics":{"Official Test Split":"true","Top-1 Accuracy":"92.25"},"uses_additional_data":true}],"syntology":{"syntology_url":"https://syntology.ai/paper/2501.15187","atlas_url":"https://app.syntology.ai/?focus=2501.15187","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2501.15187"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-25T09:33:49+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. 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