A Few-Shot Approach to Sign Language Recognition: Can Learning One Language Enable Understanding of All?
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Publisher
Springer Nature Switzerland
Link
https://link.springer.com/content/pdf/10.1007/978-3-031-47637-2_11
Reference20 articles.
1. World Federation of the Deaf. https://wfdeaf.org/. Accessed 21 May 2023
2. Wang, Y., Yao, Q., Kwok, J.T., Ni, L.M.: Generalizing from a few examples: a survey on few-shot learning. ACM Comput. Surv. (csur) 53(3), 1–34 (2020). https://doi.org/10.1145/3386252
3. Oyedotun, O.K., Khashman, A.: Deep learning in vision-based static hand gesture recognition. Neural Comput. Appl. 28(12), 3941–3951 (2016). https://doi.org/10.1007/s00521-016-2294-8
4. Bantupalli, K., Xie, Y.: American sign language recognition using deep learning and computer vision. In: 2018 IEEE International Conference on Big Data (Big Data), pp. 4896–4899 (2018). https://doi.org/10.1109/BigData.2018.8622141
5. Nihal, R.A., Broti, N.M., Deowan, S.A., Rahman, S.: Design and development of a humanoid robot for sign language interpretation. SN Comput. Sci. 2(3), 1–17 (2021). https://doi.org/10.1007/s42979-021-00627-3
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