Papers › Uni-Sign: Toward Unified Sign Language Understanding at Scale

Uni-Sign: Toward Unified Sign Language Understanding at Scale

25 Jan 2025arXiv:2501.15187archive 2025-07-28

Zecheng Li, Wengang Zhou, Weichao Zhao, Kepeng Wu, Hezhen Hu, Houqiang Li

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.

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Tasks

Computational EfficiencyGloss-free Sign Language TranslationSign Language RecognitionSign Language TranslationTransfer Learning

Datasets

Introduced by this paper, per the archive.

CSL-News

Results from the paper archive 2025-07-28

TaskDatasetModelMetricValueRank at snapshotLeaderboardReport
Sign Language Recognition CSL-Daily Uni-Sign Word Error Rate (WER) 26.0 #4 of 14 Archive leaderboard report
Sign Language Recognition MSASL-1000 Uni-Sign P-C Top-1 Accuracy 76.97 #1 of 2 Archive leaderboard report
Sign Language Recognition MSASL-1000 Uni-Sign P-I Top-1 Accuracy 78.16 #1 of 2 Archive leaderboard report
Sign Language Recognition WLASL-2000 Uni-Sign Top-1 Accuracy 63.52 #2 of 9 Archive leaderboard report
Sign Language Recognition WLASL100 Uni-Sign Official Test Split true #1 of 7 Archive leaderboard report
Sign Language Recognition WLASL100 Uni-Sign Top-1 Accuracy 92.25 #1 of 7 Archive leaderboard report

Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.

Methods

AttentionSoftmax

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