Continuous Sign Language Recognition Based on Cross-Resolution Knowledge Distillation
Author:
Funder
Development Project of Ship Situational Intelligent Awareness System
National Natural Science Foundation of China
Young Talent Fund of Association for Science and Technology in Shaanxi
Publisher
Springer Science and Business Media LLC
Link
https://link.springer.com/content/pdf/10.1007/s13369-024-09479-4.pdf
Reference60 articles.
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2. Adaloglou, N.; Chatzis, T.; Papastratis, I.; Stergioulas, A.; Papadopoulos, G.T.; Zacharopoulou, V.; Xydopoulos, G.J.; Atzakas, K.; Papazachariou, D.; Daras, P.: A comprehensive study on deep learning-based methods for sign language recognition. IEEE Trans. Multimedia 24, 1750–1762 (2021)
3. Wei, C.; Zhao, J.; Zhou, W.; Li, H.: Semantic boundary detection with reinforcement learning for continuous sign language recognition. IEEE Trans. Circuits Syst. Video Technol. 31(3), 1138–1149 (2020)
4. Cui, R.; Liu, H.; Zhang, C.: 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 (2017)
5. Wang, H.; Chai, X.; Chen, X.: A novel sign language recognition framework using hierarchical Grassmann covariance matrix. IEEE Trans. Multimedia 21(11), 2806–2814 (2019)
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