在婴儿哭声中进行疼痛分类的视觉语言模型
Anthony McCofie1, Abhiram Kandiyana1, Peter R Mouton2
1Computer Science and Engineering, University of South Florida, Tampa, Florida, USA.
概括
检测婴儿疼痛是一个挑战. 这项研究使用GPT-4(V) 和Mel光谱图以最小的数据准确检测婴儿疼痛,提高可解释性.
科学领域:
- 人工智能的人工智能
- 婴儿健康 婴儿健康
- 信号处理 信号处理
背景情况:
- 准确的婴儿疼痛检测是至关重要的,但具有挑战性.
- 传统的深度神经网络需要大量的数据集和计算能力,缺乏可解释性.
- 现有的方法在数据稀缺和透明度方面扎.
研究的目的:
- 为了引入一种新的,可解释的方法,用于婴儿疼痛检测,使用少数射击学习.
- 为了利用视觉语言模型 (GPT-4(V)) 与乳腺谱图进行增强的婴儿哭声分析.
- 减少对婴儿疼痛分类中广泛标记数据集的依赖.
主要方法:
- 使用了OpenAI的GPT-4 (V) 视觉语言模型.
- 采用了婴儿哭声的Mel谱图表示.
- 实施了几次射击提示策略来进行分类.
- 在USF-MNPAD-II数据集上验证了方法.
主要成果:
- 在婴儿疼痛检测中达到83.33%的准确性.
- 仅需要16个培训样本,与基线的4914个样本相比大幅减少.
- 与传统方法相比,证明了提高透明度和可解释性.
结论:
- 用视觉语言模型进行少数拍摄提示,为婴儿疼痛检测提供了一个有希望的解决方案.
- 这种方法大大减少了数据和计算需求.
- 代表了GPT-4o用于婴儿疼痛分类的新应用.
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