对比统一环境下多种手势翻译模型，发现性能提升多来自实现细节而非算法创新。
Gloss-Free Sign Language Translation: An Unbiased Evaluation of Progress in the Field
- 统一代码库重现实验，固定预处理和训练设置确保公平比较
- 多数文献宣称的性能优势在一致条件下显著减弱甚至消失
- 开源代码库推动手势翻译研究透明化与可复现性
手势语言翻译（SLT）旨在自动将视觉手势视频转换为口语文本，反之亦然。近年来虽进展迅速，但性能提升的真实来源常不明确：是方法创新，还是因骨干网络、训练优化、超参数调优或评估指标计算方式不同所致？本文通过在一个统一代码库中重现实时下关键成果，系统研究了无词素（gloss-free）SLT模型。我们统一了预处理、视频编码器和训练配置，确保各模型在相同条件下对比。分析表明，在一致评估条件下，许多文献报道的性能提升明显减弱，说明实现细节和评估设置对结果影响重大。论文已公开代码库（https://github.com/ozgemercanoglu/sltbaselines），以促进SLT研究的透明度与可复现性。
原文摘要 · Abstract (English)
Sign Language Translation (SLT) aims to automatically convert visual sign language videos into spoken language text and vice versa. While recent years have seen rapid progress, the true sources of performance improvements often remain unclear. Do reported performance gains come from methodological novelty, or from the choice of a different backbone, training optimizations, hyperparameter tuning, or even differences in the calculation of evaluation metrics? This paper presents a comprehensive study of recent gloss-free SLT models by re-implementing key contributions in a unified codebase. We ensure fair comparison by standardizing preprocessing, video encoders, and training setups across all methods. Our analysis shows that many of the performance gains reported in the literature often diminish when models are evaluated under consistent conditions, suggesting that implementation details and evaluation setups play a significant role in determining results. We make the codebase publicly available here (https://github.com/ozgemercanoglu/sltbaselines) to support transparency and reproducibility in SLT research.
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