A Study on pose-based deep learning models for gloss-free Sign Language Translation
Sign Language Translation (SLT) is a challenging task due to its cross-domain nature, different grammars and lack of data. Currently, many SLT models rely on intermediate gloss annotations as outputs or latent priors. Glosses can help models to correctly segment and align signs to better understand...
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| 主要な著者: | , , , , , |
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| フォーマット: | Artigo |
| 言語: | Inglês |
| 出版事項: |
Postgraduate Office, School of Computer Science, Universidad Nacional de La Plata
2024-10-01
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| シリーズ: | Journal of Computer Science and Technology |
| 主題: | |
| オンライン･アクセス: | https://journal.info.unlp.edu.ar/JCST/article/view/3480 |
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