The DOI matches its Crossref record; 40 of the 40 references it cites were found in a registry or an index.
SubUNets: End-to-End Hand Shape and Continuous Sign Language Recognition
Abstract
We propose a novel deep learning approach to solve simultaneous alignment and recognition problems (referred to as “Sequence-to-sequence” learning). We decompose the problem into a series of specialised expert systems referred to as SubUNets. The spatio-temporal relationships between these SubUNets are then modelled to solve the task, while remaining trainable end-to-end. The approach mimics human learning and educational techniques, and has a number of significant advantages. SubUNets allow us to inject domain-specific expert knowledge into the system regarding suitable intermediate representations. They also allow us to implicitly perform transfer learning between different interrelated tasks, which also allows us to exploit a wider range of more varied data sources. In our experiments we demonstrate that each of these properties serves to significantly improve the performance of the overarching recognition system, by better constraining the learning problem. The proposed techniques are demonstrated in the challenging domain of sign language recognition. We demonstrate state-of-the-art performance on hand-shape recognition (outperforming previous techniques by more than 30%). Furthermore, we are able to obtain comparable sign recognition rates to previous research, without the need for an alignment step to segment out the signs for recognition.
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Cites
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Improving the Performance of Unimodal Dynamic Hand-Gesture Recognition With Multimodal Training
Abavisani, M., Joze, H. R. V., & Patel, V. M. (2019) · 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2019.00126 · Cited by 190
The FATE Landscape of Sign Language AI Datasets: An Interdisciplinary Perspective
Bragg, D., Caselli, N., Hochgesang, J. A., Huenerfauth, M., Katz-Hernandez, L., Koller, O., Kushalnagar, R., Vogler, C., & Ladner, R. E. (2021) · ACM Transactions on Accessible Computing · doi:10.1145/3436996 · Cited by 55
Self-citationRead 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
Aligning Subtitles in Sign Language Videos
Bull, H., Afouras, T., Varol, G., Albanie, S., Momeni, L., & Zisserman, A. (2021) · 2021 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv48922.2021.01135 · Cited by 30
Metadata
- Authors
- Camgoz, N. C., Hadfield, S., Koller, O., & Bowden, R.
- Venue
- 2017 IEEE International Conference on Computer Vision (ICCV)
- Publisher
- IEEE
- Published
- 2017-10
- Pages
- 3075-3084
- OpenAlex
- W2759302818
- Cited by
- 365
- References
- 40
- FWCI
- 17.52
- Type
- conference paper
- Language
- en
- Open access
- closed
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