Summary of Signmusketeers: An Efficient Multi-stream Approach For Sign Language Translation at Scale, by Shester Gueuwou et al.
SignMusketeers: An Efficient Multi-Stream Approach for Sign Language Translation at Scale
by Shester Gueuwou, Xiaodan Du, Greg Shakhnarovich, Karen Livescu
First submitted to arxiv on: 11 Jun 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A persistent challenge in sign language video processing, including sign language to written language translation, is learning effective and efficient representations that preserve the attributes of these languages. Our proposed method focuses on the face, hands, and body posture of the signer, but instead of using off-the-shelf pose tracking models with inconsistent performance, we propose a self-supervised approach to learn complex handshapes and facial expressions. By learning from individual frames rather than video sequences, our approach is more efficient than prior work on sign language pre-training. Compared to a recent model that established a new state-of-the-art in sign language translation on the How2Sign dataset, our approach yields similar performance using less than 3% of the compute. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine trying to understand what someone is signing without knowing any sign language! To help with this problem, researchers are working on finding better ways to learn about sign languages. One idea is to focus on the important parts of a signer’s face and hands, rather than trying to track every movement. This helps us learn more efficiently and accurately about sign languages. Our new method does just that, and it works really well – almost as well as a recent state-of-the-art model, but using much less computer power. |
Keywords
» Artificial intelligence » Self supervised » Tracking » Translation
