Visual feature segmentation with reinforcement learning for continuous sign language recognition-Reference-Cited by-同舟云学术

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Visual feature segmentation with reinforcement learning for continuous sign language recognition

Published:2023-11-18 Issue:2 Volume:12 Page:
ISSN:2192-6611
Container-title:International Journal of Multimedia Information Retrieval
language:en
Short-container-title:Int J Multimed Info Retr

Author:

Fang Yuchun,Wang Liangjun,Lin Shiquan,Ni Lan

Funder

Natural Science Foundation of Shanghai

National Natural Science Foundation of China

Humanities and Social Science Research Program

Publisher

Springer Science and Business Media LLC

Subject

Library and Information Sciences,Media Technology,Information Systems

Link

https://link.springer.com/content/pdf/10.1007/s13735-023-00302-8.pdf

Reference43 articles.

1. Adaloglou N, Chatzis T, Papastratis I et al (2021) A comprehensive study on deep learning-based methods for sign language recognition. IEEE Trans Multimed. https://doi.org/10.1109/TMM.2021.3070438

2. Al-Ayyoub M, Nuseir A, Alsmearat K et al (2018) Deep learning for Arabic NLP: a survey. J Comput Sci 26:522–531

3. Cheng KL, Yang Z, Chen Q et al (2020) Fully convolutional networks for continuous sign language recognition. In: European conference on computer vision. Springer, pp 697–714

4. Cihan Camgoz N, Hadfield S, Koller O et al (2017) SubUNets: end-to-end hand shape and continuous sign language recognition. In: Proceedings of the IEEE international conference on computer vision, pp 3056–3065

5. Cui R, Liu H, Zhang C (2017) Recurrent convolutional neural networks for continuous sign language recognition by staged optimization. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 7361–7369

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