The DOI matches its Crossref record; 35 of the 35 references it cites were found in a registry or an index.
Video-Based Sign Language Recognition Without Temporal Segmentation
Open accessAbstract
Millions of hearing impaired people around the world routinely use some variants of sign languages to communicate, thus the automatic translation of a sign language is meaningful and important. Currently, there are two sub-problems in Sign Language Recognition (SLR), i.e., isolated SLR that recognizes word by word and continuous SLR that translates entire sentences. Existing continuous SLR methods typically utilize isolated SLRs as building blocks, with an extra layer of preprocessing (temporal segmentation) and another layer of post-processing (sentence synthesis). Unfortunately, temporal segmentation itself is non-trivial and inevitably propagates errors into subsequent steps. Worse still, isolated SLR methods typically require strenuous labeling of each word separately in a sentence, severely limiting the amount of attainable training data. To address these challenges, we propose a novel continuous sign recognition framework, the Hierarchical Attention Network with Latent Space (LS-HAN), which eliminates the preprocessing of temporal segmentation. The proposed LS-HAN consists of three components: a two-stream Convolutional Neural Network (CNN) for video feature representation generation, a Latent Space (LS) for semantic gap bridging, and a Hierarchical Attention Network (HAN) for latent space based recognition. Experiments are carried out on two large scale datasets. Experimental results demonstrate the effectiveness of the proposed framework.
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Cites
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Jointly Modeling Embedding and Translation to Bridge Video and Language
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Cited by
Sign Language Recognition, Generation, and Translation: An Interdisciplinary Perspective
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Attention, please! A survey of neural attention models in deep learning
de Santana Correia, A., & Colombini, E. L. (2022) · Artificial Intelligence Review · doi:10.1007/s10462-022-10148-x · Cited by 298
Improving Sign Language Translation with Monolingual Data by Sign Back-Translation
Zhou, H., Zhou, W., Qi, W., Pu, J., & Li, H. (2021) · 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr46437.2021.00137 · Cited by 282
Self-citationAttention-Based 3D-CNNs for Large-Vocabulary Sign Language Recognition
Huang, J., Zhou, W., Li, H., & Li, W. (2019) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2018.2870740 · Cited by 232
Self-citationIncluding Signed Languages in Natural Language Processing
Yin, K., Moryossef, A., Hochgesang, J., Goldberg, Y., & Alikhani, M. (2021) · Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers) · doi:10.18653/v1/2021.acl-long.570 · Cited by 79
Metadata
- Authors
- Huang, J., Zhou, W., Zhang, Q., Li, H., & Li, W.
- Venue
- Proceedings of the AAAI Conference on Artificial Intelligence
- Published
- 2018-04-26
- Volume
- 32
- Issue
- 1
- ISSN
- 2159-5399 (ISSN-L)
- 2374-3468
- OpenAlex
- W2964253156
- Cited by
- 451
- References
- 35
- FWCI
- 36.08
- Type
- conference paper
- Language
- en
- Open access
- diamond
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