研究人员正在探索利用大型语言模型（LLM）和先进的编码技术来改进手语翻译（SLT）的新方法。一种方法使用GPT-4o生成释义的目标句子，以增强训练数据，提高翻译质量，尤其是在词汇量稀疏的语言中。另一种方法FEA-SLT将面部表情作为语义锚点，以解决手动手语配置中的歧义，在无词汇方法中取得了最先进的成果。此外，SAGE框架引入了段感知视觉标记，通过缩短输入序列长度来创建更高效、可扩展的无词汇SLT模型。 AI
影响 LLM集成和高效编码的进步有望带来更准确、更具可扩展性的手语翻译系统。
排序理由 多篇研究论文介绍了手语翻译的新方法和框架。
AI 生成摘要 · Google Gemini · 来自 6 个来源。 我们如何撰写摘要 →
arXiv:2605.31393v1 Announce Type: cross Abstract: Sign language translation (SLT) remains constrained by limited paired sign-video/text corpora and heavy-tailed target vocabularies. We study target-side augmentation in which GPT-4o generates controlled paraphrase variants of refe…
Sign language translation (SLT) remains constrained by limited paired sign-video/text corpora and heavy-tailed target vocabularies. We study target-side augmentation in which GPT-4o generates controlled paraphrase variants of reference sentences while the sign input remains uncha…
arXiv:2601.03549v2 Announce Type: replace-cross Abstract: Sign Language Translation (SLT) is a challenging cross-modal task requiring joint modeling of manual articulations and non-manual signals. Existing gloss-free SLT methods effectively capture gestural dynamics but often und…
arXiv:2606.03788v1 Announce Type: new Abstract: Sign Language Translation (SLT) is typically evaluated with surface-form metrics such as BLEU and ROUGE, which reward lexical overlap but do not directly measure whether a translation preserves the meaning of the source sign sequenc…
Sign Language Translation (SLT) is typically evaluated with surface-form metrics such as BLEU and ROUGE, which reward lexical overlap but do not directly measure whether a translation preserves the meaning of the source sign sequence. This is in contrast with the final objective …
arXiv:2507.09266v2 Announce Type: replace Abstract: Gloss-free Sign Language Translation (SLT) has advanced rapidly, achieving strong performances without relying on gloss annotations. However, these gains have often come with increased model complexity and high computational dem…