Summary of T2s-gpt: Dynamic Vector Quantization For Autoregressive Sign Language Production From Text, by Aoxiong Yin et al.
T2S-GPT: Dynamic Vector Quantization for Autoregressive Sign Language Production from Text
by Aoxiong Yin, Haoyuan Li, Kai Shen, Siliang Tang, Yueting Zhuang
First submitted to arxiv on: 11 Jun 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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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 two-stage sign language production paradigm encodes sign language sequences into discrete codes and autoregressively generates sign language from text based on a learned codebook. The model addresses the issue of uneven information density in sign language by proposing a dynamic vector quantization (DVA-VAE) model that adjusts encoding length based on information density. A GPT-like model learns to generate code sequences and durations from spoken language text, demonstrating effectiveness on the PHOENIX14T dataset. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Sign language researchers are working on a new way to create sign language videos using computer algorithms. They’re developing a system that can understand what someone is saying and turn it into sign language. To make this happen, they need to be able to break down sign language into smaller parts and then put them back together again. The team has created a special kind of codebook that helps them do this. They’ve also made a model that can learn from text and generate the correct signs. This new method is being tested on a large dataset of German sign language videos. |
Keywords
» Artificial intelligence » Gpt » Quantization
