相关实验视频
Updated: May 8, 2025

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Deep Neural Networks for Image-Based Dietary Assessment
Published on: March 13, 2021
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IngredSAM:通过单个图像提示符进行开放世界的食品成分细分
Leyi Chen1, Bowen Wang2, Jiaxin Zhang3
1College of Food Science and Nutritional Engineering, China Agricultural University, Beijing 100083, China.
Journal of imaging
|December 27, 2024
概括
英格雷德SAM使用分段任何模型 (SAM) 和提示工程推进了食品成分语义细分. 这种新的方法在没有模型培训的情况下在开放世界的场景中取得了卓越的结果.
科学领域:
- 计算机视觉 计算机视觉
- 人工智能的人工智能
- 食品图像分析 食品图像分析
背景情况:
- 食品语义细分对于人工智能应用,如食品图像分析至关重要.
- 传统的监督方法与食品成分的复杂性和多样性作斗争.
- 现有的模型面临着各种外观和阶级不平衡的挑战.
研究的目的:
- 介绍IngredSAM,这是一个开放世界的食品成分语义细分的新方法.
- 扩展分段任何模型 (SAM) 的食品分析能力.
- 解决食品成分细分监督方法的局限性.
主要方法:
- 利用视觉基础模型 (VFMs) 和提示工程.
- 从清洁的图像提示中利用了区分和匹配的语义特征.
- 应用一次性,开放世界的细分,没有培训过程.
主要成果:
- 在食品成分细分方面取得了重大进展.
- 在FoodSeg103和UECFoodPix数据集上表现出卓越的表现,在最先进的方法上分别表现2.85%和6.01%.
- 在现实场景中成功生成了准确的细分面具.
结论:
- 英格雷德SAM代表了一次性,开放世界的食品成分细分的成功应用.
- 该框架提供了准确的细分,而不需要培训阶段.
- 为加强营养分析和消费者饮食趋势监测铺平了道路.
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