Summary of Signformer Is All You Need: Towards Edge Ai For Sign Language, by Eta Yang
Signformer is all you need: Towards Edge AI for Sign Language
by Eta Yang
First submitted to arxiv on: 19 Nov 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Computer Vision and Pattern Recognition (cs.CV); Computers and Society (cs.CY); Human-Computer Interaction (cs.HC); 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 novel approach to sign language translation is proposed, aiming to address the impracticality and unsustainability of current methods. Large Language Models (LLMs) and other sophisticated backbones have become staples in state-of-the-art (SOTAs), but their reliance on extensive datasets and computational resources hinders real-world applications. This research seeks to break away from the prevailing trend, eschewing external aids like NLP strategies and prior knowledge transfer, and instead focus on a ground-up architecture that achieves improvements without relying on external factors. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Sign language translation is facing a challenge because current methods are too resource-heavy. Large Language Models (LLMs) and other advanced tools require lots of data and computer power, making it hard to use them in real-life situations. This paper wants to change the way we approach sign language translation by starting from scratch and building something new, rather than relying on existing ideas and technology. |
Keywords
» Artificial intelligence » Nlp » Translation
