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arXiv · 2502.20171

Representing Signs as Signs: One-Shot ISLR to Facilitate Functional Sign Language Technologies

Toon Vandendriessche·Mathieu De Coster·Annelies Lejon·Joni Dambre
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Abstract

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.

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Toon Vandendriessche, Mathieu De Coster, Annelies Lejon, Joni Dambre. 2025-02-27. Representing Signs as Signs: One-Shot ISLR to Facilitate Functional Sign Language Technologies. https://arxiv.org/abs/2502.20171

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