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AI model improves sign language retrieval in low-resource settings

By PulseAugur Editorial · [1 sources] ·

Researchers have developed a pose encoder for sign language retrieval that performs effectively even in low-resource dictionary settings. When trained on American Sign Language (ASL-Citizen) and applied to six other sign languages with minimal data, the encoder achieved retrieval accuracies between 36.4% and 80.7%. Key to its performance were the InfoNCE training objective and the blocking of attention between different body parts within the encoder. This approach significantly improved accuracy compared to existing methods, reaching up to 68.0% one-shot accuracy in some configurations. AI

IMPACT Enhances accessibility for deaf communities by improving sign language recognition technology, especially in regions with limited data.

RANK_REASON Academic paper detailing a new methodology for sign language retrieval. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

AI model improves sign language retrieval in low-resource settings

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Academic paper detailing a new methodology for sign language retrieval. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.AI TIER_1 English(EN) · Ushnish Sarkar, Vineet Kumar Rakesh, Bhaswar Chattopadhyay, Tapas Samanta ·

    Studies on Sign Language Retrieval in Low-Resource Dictionary Settings

    arXiv:2611.16621v1 Announce Type: new Abstract: Most sign languages have no annotated corpus, only small dictionaries with one or a few example videos per sign. This study examines how well a pose encoder trained on a single sign language serves such languages without any target-…

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