Cross-modal knowledge distillation for continuous sign language recognition
Author:
Funder
National Natural Science Foundation of China
Publisher
Elsevier BV
Reference63 articles.
1. Boháček, M., & Hrúz, M. (2022). Sign pose-based transformer for word-level sign language recognition. In Proceedings of the IEEE/CVF winter conference on applications of computer vision (pp. 182–191).
2. Camgoz, N. C., Hadfield, S., Koller, O., Ney, H., & Bowden, R. (2018). Neural sign language translation. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 7784–7793).
3. Camgoz, N. C., Koller, O., Hadfield, S., & Bowden, R. (2020). Multi-channel transformers for multi-articulatory sign language translation. In European conference on computer vision workshops (pp. 301–319).
4. Camgoz, N. C., Koller, O., Hadfield, S., & Bowden, R. (2020). Sign language transformers: Joint end-to-end sign language recognition and translation. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition (pp. 10023–10033).
5. Super-resolution guided knowledge distillation for low-resolution image classification;Chen;Pattern Recognition Letters,2022
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