GPT Sonograpy: Hand Gesture Decoding from Forearm Ultrasound Images via VLM
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arXiv
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| Autores principales: | , , , |
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| Formato: | Preprint |
| Publicado: |
2024
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| Materias: | |
| Acceso en línea: | |
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| _version_ | 1866929421434224640 |
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| author | Bimbraw, Keshav Wang, Ye Liu, Jing Koike-Akino, Toshiaki |
| author_facet | Bimbraw, Keshav Wang, Ye Liu, Jing Koike-Akino, Toshiaki |
| contents | Large vision-language models (LVLMs), such as the Generative Pre-trained Transformer 4-omni (GPT-4o), are emerging multi-modal foundation models which have great potential as powerful artificial-intelligence (AI) assistance tools for a myriad of applications, including healthcare, industrial, and academic sectors. Although such foundation models perform well in a wide range of general tasks, their capability without fine-tuning is often limited in specialized tasks. However, full fine-tuning of large foundation models is challenging due to enormous computation/memory/dataset requirements. We show that GPT-4o can decode hand gestures from forearm ultrasound data even with no fine-tuning, and improves with few-shot, in-context learning. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2407_10870 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | GPT Sonograpy: Hand Gesture Decoding from Forearm Ultrasound Images via VLM Bimbraw, Keshav Wang, Ye Liu, Jing Koike-Akino, Toshiaki Computer Vision and Pattern Recognition Artificial Intelligence Human-Computer Interaction Machine Learning Large vision-language models (LVLMs), such as the Generative Pre-trained Transformer 4-omni (GPT-4o), are emerging multi-modal foundation models which have great potential as powerful artificial-intelligence (AI) assistance tools for a myriad of applications, including healthcare, industrial, and academic sectors. Although such foundation models perform well in a wide range of general tasks, their capability without fine-tuning is often limited in specialized tasks. However, full fine-tuning of large foundation models is challenging due to enormous computation/memory/dataset requirements. We show that GPT-4o can decode hand gestures from forearm ultrasound data even with no fine-tuning, and improves with few-shot, in-context learning. |
| title | GPT Sonograpy: Hand Gesture Decoding from Forearm Ultrasound Images via VLM |
| topic | Computer Vision and Pattern Recognition Artificial Intelligence Human-Computer Interaction Machine Learning |
| url | https://arxiv.org/abs/2407.10870 |