arXiv:2204.02803v2 Announce Type: replace-cross Abstract: Sign language recognition from monocular video or 2D pose sequences is challenging, both because 3D information must be inferred from 2D observations and because the signal is inherently spatiotemporal. Moreover, the large and continually growing vocabulary of signs in production settings makes conventional closed-set classification impractical: adding a class requires new labeled data and retraining. We propose a contrastive Transformer-based model that learns rich representations of body key-point sequences, enabling direct comparison
The paper leverages recent advancements in transformer architectures and contrastive learning, reflecting increased research into few-shot learning methods to address data scarcity in niche AI applications.
This development could significantly lower the barrier for deploying sign language recognition systems, making communication more accessible and opening new opportunities for human-computer interaction.
The proposed model offers a more practical approach to expanding sign language recognition vocabulary without extensive retraining, shifting from conventional closed-set classification to adaptable few-shot learning.
- · Deaf and hard-of-hearing communities
- · AI accessibility developers
- · Human-computer interaction researchers
- · Service providers for disabled communities
- · Traditional sign language recognition methods reliant on large datasets
- · Software requiring extensive retraining for new sign vocabularies
Improved accuracy and efficiency in sign language recognition systems tailored for diverse and evolving vocabularies.
Accelerated development of real-time translation tools and educational platforms for sign language learners and users.
Enhanced integration of sign language recognition into assistive technologies and mainstream devices, fostering greater inclusivity and digital accessibility.
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Read at arXiv cs.AI