GSP-MC combines LLM-generated multipart sign descriptions with multi-positive contrastive learning to improve skeleton-based sign language recognition, reporting 97.1% on SLR-500 and 97.07% on AUTSL.
Title resolution pending
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
fields
cs.CL 1years
2025 1verdicts
CONDITIONAL 1representative citing papers
citing papers explorer
-
Generative Sign-description Prompts with Multi-positive Contrastive Learning for Sign Language Recognition
GSP-MC combines LLM-generated multipart sign descriptions with multi-positive contrastive learning to improve skeleton-based sign language recognition, reporting 97.1% on SLR-500 and 97.07% on AUTSL.