通过多模式大语言模型推理驱动的食物能量估计
1Department of Informatics, The University of Electro-Communications, 1-5-1 Chofugaoka, Chofu 182-8585, Tokyo, Japan.
Nutrients
|April 12, 2025
概括
多模大型语言模型 (MLLMs) 现在可以通过考虑食物体积,更准确地从图像中估计食物能量. 这一进步通过解决识别食物大小的局限性来改善饮食跟踪应用.
科学领域:
- 计算机视觉 计算机视觉
- 人工智能的人工智能
- 营养科学 营养科学
背景情况:
- 准确的基于图像的食物能量估计对于智能手机和AR设备上的饮食跟踪应用至关重要.
- 目前的深度学习方法在食品项目识别方面面临挑战,原因是广泛的数据注释要求.
- 多模大型语言模型 (MLLMs) 提供了潜力,但由于难以识别食物大小,难以准确估计食物能量.
研究的目的:
- 用多式大型语言模型 (MLLMs) 提高基于图像的食品能量估计的准确性.
- 解决MLLM中食品尺寸识别的局限性,以改善能量含量评估.
- 探索微调和体积感知推理策略,以更精确地监测饮食摄入量.
主要方法:
- 提出了两种新的方法:微调MLLM和使用体积意识推理与细粒度估计提示.
- 研究了低级适应 (LoRA) 的适应,以进一步提高食品能量估计性能.
- 使用Nutrition5k数据集进行实验验证和性能评估.
主要成果:
- 提出的微调和体积感知推理方法都显著提高了食品能量估计的准确性.
- 在Nutrition5k数据集上的实验结果证实了开发方法的有效性.
- 调整LoRA证明对提高整体食品能量估计能力产生了积极影响.
结论:
- 在基于图像的准确饮食评估方面,MLLM显示出显著的前景.
- 整合体积意识对于开发可靠的食品能量估计模型至关重要.
- 该研究强调了利用MLLM在营养应用中的有效策略.
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