The DOI matches its Crossref record; 64 of the 64 references it cites were found in a registry or an index.
Word-level Deep Sign Language Recognition from Video: A New Large-scale Dataset and Methods Comparison
Open accessAbstract
Vision-based sign language recognition aims at helping the deaf people to communicate with others. However, most existing sign language datasets are limited to a small number of words. Due to the limited vocabulary size, models learned from those datasets cannot be applied in practice. In this paper, we introduce a new large-scale Word-Level American Sign Language (WLASL) video dataset, containing more than 2000 words performed by over 100 signers. This dataset will be made publicly available to the research community. To our knowledge,it is by far the largest public ASL dataset to facilitate word-level sign recognition research. Based on this new large-scale dataset, we are able to experiment with several deep learning methods for word-level sign recognition and evaluate their performances in large scale scenarios. Specifically we implement and compare two different models,i.e., (i) holistic visual appearance based approach, and (ii) 2D human pose based approach. Both models are valuable baselines that will benefit the community for method benchmarking. Moreover, we also propose a novel pose-based temporal graph convolution networks (Pose-TGCN) that model spatial and temporal dependencies in human pose trajectories simultaneously, which has further boosted the performance of the pose-based method. Our results show that pose-based and appearance-based models achieve comparable performances up to 62.63% at top-10 accuracy on 2,000 words/glosses, demonstrating the validity and challenges of our dataset. Our dataset and baseline deep models are available at https://dxli94.github.io/WLASL/.
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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
Read and Attend: Temporal Localisation in Sign Language Videos
Varol, G., Momeni, L., Albanie, S., Afouras, T., & Zisserman, A. (2021) · 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr46437.2021.01658 · Cited by 39
ASL champ!: a virtual reality game with deep-learning driven sign recognition
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ASL Educators' Perspectives on AI for Enhancing Student Learning in American Sign Language Education
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Metadata
- Authors
- Li, D., Opazo, C. R., Yu, X., & Li, H.
- Venue
- 2020 IEEE Winter Conference on Applications of Computer Vision (WACV)
- Publisher
- IEEE
- Published
- 2020-03
- Pages
- 1448-1458
- ISSN
- 2472-6737 (ISSN-L)
- OpenAlex
- W3009828227
- Cited by
- 641
- References
- 64
- FWCI
- 52.64
- Type
- conference paper
- Language
- en
- Open access
- green
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