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Generative Sign-Description Prompts with Multi-Positive Contrastive Learning for Sign Language Recognition.

Siyu Liang1,2,3, Yunan Li1,2,3, Wentian Xin4

  • 1School of Computer Science and Technology, Xidian University, Xi'an 710071, China.

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|October 16, 2025
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Summary
This summary is machine-generated.

This study introduces GSP-MC, a novel method for sign language recognition that uses generative large language models to precisely describe sign language. It achieves state-of-the-art accuracy, enhancing inclusive communication technologies.

Keywords:
contrastive learninggenerative large language modelmodality fusionsign language recognition

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Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Linguistics

Background:

  • Sign language recognition systems struggle with multimodal annotation for hierarchical semantics.
  • Current methods lack the ability to capture both sequential hand motions and concurrent non-manual cues effectively.

Purpose of the Study:

  • To propose GSP-MC, the first method integrating generative large language models (LLMs) into sign language recognition.
  • To develop a system capable of precise multipart descriptions and hierarchical semantic understanding of sign language.

Main Methods:

  • Utilized retrieval-augmented generation with domain-specific LLMs and expert-validated corpora.
  • Employed a dual-encoder architecture for bidirectional alignment of skeleton features with multi-level text descriptions (global, synonym, part) via probabilistic matching.
  • Combined global and part-level losses with KL divergence optimization for robust alignment and semantic capture.

Main Results:

  • Achieved state-of-the-art accuracy: 97.1% on Chinese SLR500 and 97.07% on Turkish AUTSL.
  • Demonstrated superior performance compared to existing methods (SSRL and SML).
  • Confirmed the cross-lingual potential of the GSP-MC approach.

Conclusions:

  • GSP-MC effectively bridges the gap in multimodal annotation for sign language recognition.
  • The method accurately captures sign semantics and detailed dynamics, enabling robust recognition.
  • This work advances inclusive communication technologies through improved sign language understanding.