Representing Signs as Signs: One-Shot ISLR to Facilitate Functional Sign Language Technologies
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866929734491832320 |
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| author | Vandendriessche, Toon De Coster, Mathieu Lejon, Annelies Dambre, Joni |
| author_facet | Vandendriessche, Toon De Coster, Mathieu Lejon, Annelies Dambre, Joni |
| contents | Isolated Sign Language Recognition (ISLR) is crucial for scalable sign language technology, yet language-specific approaches limit current models. To address this, we propose a one-shot learning approach that generalises across languages and evolving vocabularies. Our method involves pretraining a model to embed signs based on essential features and using a dense vector search for rapid, accurate recognition of unseen signs. We achieve state-of-the-art results, including 50.8% one-shot MRR on a large dictionary containing 10,235 unique signs from a different language than the training set. Our approach is robust across languages and support sets, offering a scalable, adaptable solution for ISLR. Co-created with the Deaf and Hard of Hearing (DHH) community, this method aligns with real-world needs, and advances scalable sign language recognition. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2502_20171 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Representing Signs as Signs: One-Shot ISLR to Facilitate Functional Sign Language Technologies Vandendriessche, Toon De Coster, Mathieu Lejon, Annelies Dambre, Joni Computation and Language Computer Vision and Pattern Recognition Isolated Sign Language Recognition (ISLR) is crucial for scalable sign language technology, yet language-specific approaches limit current models. To address this, we propose a one-shot learning approach that generalises across languages and evolving vocabularies. Our method involves pretraining a model to embed signs based on essential features and using a dense vector search for rapid, accurate recognition of unseen signs. We achieve state-of-the-art results, including 50.8% one-shot MRR on a large dictionary containing 10,235 unique signs from a different language than the training set. Our approach is robust across languages and support sets, offering a scalable, adaptable solution for ISLR. Co-created with the Deaf and Hard of Hearing (DHH) community, this method aligns with real-world needs, and advances scalable sign language recognition. |
| title | Representing Signs as Signs: One-Shot ISLR to Facilitate Functional Sign Language Technologies |
| topic | Computation and Language Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2502.20171 |