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The Influence of Iconicity in Transfer Learning for Sign Language Recognition

arXiv – CS AI|Keren Artiaga, Conor Lynch, Haithem Afli, Mohammed Hasanuzzaman|
🤖AI Summary

Researchers examined transfer learning effectiveness for sign language recognition by comparing iconic signs between different language pairs (Chinese to Arabic and Greek to Flemish). The study achieved modest improvements of 7.02% for Arabic and 1.07% for Flemish using Google Mediapipe for feature extraction and neural network architectures.

Key Takeaways
  • →Transfer learning between sign languages showed improvement rates of 7.02% for Arabic and 1.07% for Flemish when using iconic signs.
  • →Google Mediapipe was successfully utilized as a feature extractor for spatial sign language information processing.
  • →The research used Multilayer Perceptron for spatial data and Gated Recurrent Unit for temporal information processing.
  • →Cross-linguistic similarities in iconic signs can enhance knowledge transfer effectiveness in sign language recognition.
  • →The study compared two distinct sign language pairs to validate the importance of iconicity in transfer learning.
#transfer-learning#sign-language#neural-networks#mediapipe#computer-vision#accessibility#nlp#machine-learning
Read Original →via arXiv – CS AI
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