Summary of Gpt Sonograpy: Hand Gesture Decoding From Forearm Ultrasound Images Via Vlm, by Keshav Bimbraw et al.
GPT Sonograpy: Hand Gesture Decoding from Forearm Ultrasound Images via VLM
by Keshav Bimbraw, Ye Wang, Jing Liu, Toshiaki Koike-Akino
First submitted to arxiv on: 15 Jul 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); 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 Medium Difficulty summary: Large vision-language models (LVLMs), such as the Generative Pre-trained Transformer 4-omni (GPT-4o), are a type of multi-modal foundation model that has great potential to be used as powerful AI tools for various applications, including healthcare, industrial, and academic sectors. These foundation models perform well in general tasks but are limited in specialized tasks without fine-tuning. To address this challenge, we demonstrate that GPT-4o can decode hand gestures from forearm ultrasound data even with no fine-tuning, and improve with few-shot, in-context learning. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Low Difficulty summary: Imagine a super smart computer program that can help people in many different areas like healthcare and education. This program is called GPT-4o and it’s really good at understanding what people say and do. It can even learn new things without needing to be completely reprogrammed. In this paper, we show how GPT-4o can use special sensors to read hand movements from ultrasound data, which could help people with disabilities or in medical settings. |
Keywords
* Artificial intelligence * Few shot * Fine tuning * Gpt * Multi modal * Transformer
