Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Uni-Sign: Toward Unified Sign Language Understanding at Scale
Authors: Zecheng Li, Wengang Zhou, Weichao Zhao, Kepeng Wu, Hezhen Hu, Houqiang Li
ICLR 2025 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments across multiple SLU benchmarks demonstrate that Uni-Sign achieves state-of-the-art performance across multiple downstream SLU tasks. |
| Researcher Affiliation | Academia | 1 Mo E Key Laboratory of Brain-inspired Intelligent Perception and Cognition, University of Science and Technology of China 2 Institute of Artificial Intelligence, Hefei Comprehensive National Science Center 3 University of Texas at Austin EMAIL EMAIL, EMAIL |
| Pseudocode | Yes | Algorithm 1 Pseudocode of the score-aware sampling strategy in a Py Torch-like style. |
| Open Source Code | Yes | Dataset and code are available at github.com/ZechengLi19/Uni-Sign. |
| Open Datasets | Yes | 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. Dataset and code are available at github.com/ZechengLi19/Uni-Sign. |
| Dataset Splits | Yes | For ISLR, we adopt WLASL (Li et al., 2020a) and MSASL (Joze & Koller, 2019) datasets for evaluation. For CSLR, we utilize CSL-Daily (Zhou et al., 2021). SLT task is conducted on the CSL-Daily, How2Sign (Duarte et al., 2021), and Open ASL (Shi et al., 2022) datasets. (Tables 4, 5, and 6 show results for 'Dev' and 'Test' splits for these datasets). |
| Hardware Specification | No | It was also supported by the GPU cluster built by MCC Lab of Information Science and Technology Institution, USTC, and the Supercomputing Center of the USTC. |
| Software Dependencies | No | We implement Uni-Sign using Py Torch (Paszke et al., 2019), employing m T5Base (Xue et al., 2021) as our pre-trained language model. |
| Experiment Setup | Yes | The detailed training recipe is presented in Table 2. Config Stage 1 Stage 2 Stage 3 optimizer Adam W base learning rate 3e-4 weight decay 1e-4 optimizer momentum β1, β2=0.9, 0.999 learning rate schedule cosine decay training epochs 20 5 20 batch size 16 4 8 gradient accumulation 8 8 1 |