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SignVLM: a pre-trained large video model for sign language recognition.

Hamzah Luqman1

  • 1Information and Computer Science Department, King Fahd University of Petroleum and Minerals, SDAIA-KFUPM Joint Research Center for Artificial Intelligence, Saudi Arabia.

Peerj. Computer Science
|September 24, 2025
PubMed
Summary
This summary is machine-generated.

Sign language recognition (SLR) is improved by SignVLM, a pretrained vision model. SignVLM effectively handles low-resource sign languages, demonstrating strong performance across multiple datasets.

Keywords:
Arabic sign languageCLIPLarge vision modelsSign language recognitionSign language translation

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Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Human-Computer Interaction

Background:

  • Sign language recognition (SLR) is crucial for community inclusion of individuals with hearing impairments.
  • Developing effective SLR systems is hindered by a scarcity of annotated datasets, particularly for low-resourced sign languages.

Purpose of the Study:

  • To introduce SignVLM, a novel pretrained large vision model designed to enhance sign language recognition.
  • To investigate the efficacy of the contrastive language-image pre-training (CLIP) model for SLR tasks.

Main Methods:

  • Utilized a pretrained CLIP model to extract spatial features from sign video frames.
  • Employed a Transformer decoder for temporal learning within the SLR system.
  • Evaluated the SignVLM model on four diverse sign language datasets: KArSL, WLASL, LSA64, and AUTSL.

Main Results:

  • The proposed SignVLM model surpassed existing methods on the KArSL, WLASL, and LSA64 datasets.
  • Achieved competitive performance on the AUTSL dataset, indicating robustness.
  • Demonstrated effective generalization capabilities to new datasets with limited samples.

Conclusions:

  • SignVLM shows significant promise for advancing SLR, especially in low-resource scenarios.
  • The model's performance highlights the potential of large vision models in sign language processing.
  • The findings support the adaptability and effectiveness of SignVLM for diverse sign language recognition applications.