MC-SLT: Towards Low-Resource Signer-Adaptive Sign Language Translation-Reference-Cited by-同舟云学术

MC-SLT: Towards Low-Resource Signer-Adaptive Sign Language Translation

Published:2022-10-10 Issue: Volume: Page:
ISSN:
Container-title:Proceedings of the 30th ACM International Conference on Multimedia
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Author:

Jin Tao¹,Zhao Zhou¹,Zhang Meng²,Zeng Xingshan³

Affiliation:

1. Zhejiang University, Hangzhou, China

2. Huawei Noah's Ark Lab, Shenzhen, China

3. Huawei Noah's Ark Lab, Hangzhou, China

Funder

National Natural Science Foundation of China

National Key R&D Program of China

Zhejiang Natural Science Foundation

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3503161.3548069

Reference42 articles.

1. Dzmitry Bahdanau , Kyunghyun Cho , and Yoshua Bengio . 2014. Neural machine translation by jointly learning to align and translate. arXiv preprint arXiv:1409.0473 ( 2014 ). Dzmitry Bahdanau, Kyunghyun Cho, and Yoshua Bengio. 2014. Neural machine translation by jointly learning to align and translate. arXiv preprint arXiv:1409.0473 (2014).

2. Learning to Forget for Meta-Learning

3. MetaHTR: Towards Writer-Adaptive Handwritten Text Recognition

4. SubUNets: End-to-End Hand Shape and Continuous Sign Language Recognition

5. Neural Sign Language Translation

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