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Improving Signer Independent Sign Language Recognition for Low Resource Languages
We then evaluate the effect of transfer learning, with different levels of fine-tuning, on the generalisation of signer independent models, and show the effects of different input representations, namely variations in image data and pose estimation.
Towards the extraction of robust sign embeddings for low resource sign language recognition
However, due to a domain mismatch with their training sets and challenging poses in sign language, they lack robustness on sign language data and image-based models often still outperform keypoint-based models.
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