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  3. Igsmnet: Ingredient-guided Semantic Modeling Network For Food Nutrition Estimation.
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  3. Igsmnet: Ingredient-guided Semantic Modeling Network For Food Nutrition Estimation.

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IGSMNet: Ingredient-Guided Semantic Modeling Network for Food Nutrition Estimation.

Donglin Zhang1, Weixiang Shi1, Boyuan Ma1

  • 1School of Artificial Intelligence and Computer Science, Jiangnan University, Wuxi 214122, China.

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|November 13, 2025

View abstract on PubMed

Summary
This summary is machine-generated.

This study introduces an Ingredient-Guided Semantic Modeling Network (IGSMNet) for accurate food nutrition estimation. The novel approach enhances dietary analysis and public health by integrating ingredient and visual data for precise nutritional content prediction.

Keywords:
RGB-D fusionfeature learningfood nutrition estimationingredient-guided modeling

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Area of Science:

  • Computer Vision
  • Artificial Intelligence
  • Nutritional Science

Background:

  • Traditional food nutrition estimation is labor-intensive and lacks scalability.
  • Existing computer vision methods, including RGB and RGB-D approaches, struggle with visually similar foods and complex spatial-semantic relationships.
  • Accurate nutritional assessment is vital for dietary analysis and public health initiatives.

Purpose of the Study:

  • To develop an advanced food nutrition estimation method that overcomes limitations of current techniques.
  • To improve the accuracy of estimating nutritional content in complex food scenes.
  • To leverage ingredient information and spatial-semantic understanding for enhanced prediction.

Main Methods:

  • Proposed an Ingredient-Guided Semantic Modeling Network (IGSMNet) for food nutrition estimation.
  • Integrated an ingredient-guided module using a pre-trained language model and cross-modal attention to align ingredient and visual features.
  • Implemented an internal semantic modeling component with dynamic positional encoding and localized attention for enhanced structural and relational understanding.

    • Main Results:

    • Achieved state-of-the-art performance on the Nutrition5k dataset.
    • Reported low Percentage Mean Absolute Error (PMAE) values: 12.2% for Calories, 9.4% for Mass, 19.1% for Fat, 18.3% for Carbohydrates, and 16.0% for Protein.
    • Demonstrated consistent outperformance compared to existing baseline methods.

    Conclusions:

    • The IGSMNet effectively addresses challenges in complex food scenes for nutrition estimation.
    • The proposed method shows significant improvements in accuracy for estimating various nutritional components.
    • This work validates the effectiveness of integrating ingredient-specific and semantic spatial information for precise food nutrition analysis.