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Cross-modal knowledge distillation for continuous sign language recognition.

Liqing Gao1, Peng Shi1, Lianyu Hu1

  • 1School of Computer Science and Technology, College of Intelligence and Computing, Tianjin University, Tianjin 300350, China.

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|August 7, 2024
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Summary
This summary is machine-generated.

This study introduces a novel cross-modal knowledge distillation method to improve continuous sign language recognition (CSLR). The approach effectively transfers multi-modal information, enhancing accuracy despite limited sign language datasets.

Keywords:
Attention mechanismCross-modalKnowledge distillationSign language recognition

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Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Natural Language Processing

Background:

  • Continuous Sign Language Recognition (CSLR) faces challenges due to limited large-scale datasets and frame-level annotations.
  • Existing deep learning models for CSLR often require extensive supervised information, which is scarce in current sign language data.
  • Inadequate supervision hinders the effective training of sign language recognition models.

Purpose of the Study:

  • To propose a novel cross-modal knowledge distillation method to address the limitations of data scarcity and inadequate supervision in CSLR.
  • To enhance the accuracy of sign language recognition by effectively transferring knowledge from teacher models to a student model.
  • To investigate the efficacy of multi-modal information transfer in improving CSLR performance.

Main Methods:

  • Developed a cross-modal knowledge distillation framework comprising two teacher models (Sign2Text dialogue and Text2Gloss translation) and one student model.
  • Teacher models provide information-rich soft labels to guide the training of the general sign language recognition student model.
  • The method leverages multi-modal information from sign language videos and corresponding text dialogues.

Main Results:

  • Extensive experiments were conducted on multiple benchmark datasets, including PHOENIX 2014T, CSL-Daily, and QSL.
  • The proposed cross-modal knowledge distillation method significantly improved sign language recognition accuracy.
  • The results demonstrate the effectiveness of transferring multi-modal information from teacher models to enhance the student model's performance.

Conclusions:

  • The proposed cross-modal knowledge distillation method is effective in improving continuous sign language recognition accuracy.
  • Transferring multi-modal information via knowledge distillation offers a viable solution to data scarcity and supervision limitations in CSLR.
  • This approach provides a promising direction for advancing sign language recognition technology.