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Updated: Sep 20, 2025

Deep Neural Networks for Image-Based Dietary Assessment
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Learning Structural Representations for Recipe Generation and Food Retrieval.

Hao Wang, Guosheng Lin, Steven C H Hoi

    IEEE Transactions on Pattern Analysis and Machine Intelligence
    |June 10, 2022
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    Summary
    This summary is machine-generated.

    This study introduces an unsupervised method to learn recipe structures, improving recipe generation and food retrieval. The approach creates sentence-level tree structures for cooking recipes, enhancing AI capabilities in food-related tasks.

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    Area of Science:

    • Artificial Intelligence
    • Computer Vision
    • Natural Language Processing

    Background:

    • Food is central to daily life, necessitating effective methods for recipe understanding and generation.
    • Existing vision-language models struggle with lengthy recipes and mixed-ingredient food images due to a lack of structural annotations.
    • Recipe generation and cross-modal retrieval tasks require sophisticated structural representations of cooking instructions.

    Purpose of the Study:

    • To develop a novel method for unsupervised learning of sentence-level tree structures in cooking recipes.
    • To generate structured recipe representations from food images without explicit annotations.
    • To integrate learned tree structures for improved recipe generation and food cross-modal retrieval.

    Main Methods:

    • An unsupervised learning approach was employed to derive sentence-level tree structure labels for recipes.
    • A model was developed to generate recipe trees from images, supervised by the learned structure labels.
    • The learned tree structures were integrated into the pipeline for recipe generation and food cross-modal retrieval.

    Main Results:

    • The proposed method successfully produces high-quality sentence-level tree structures for recipes.
    • Coherent and structurally sound recipes are generated by the model.
    • State-of-the-art performance was achieved on the Recipe1M dataset for recipe generation and food cross-modal retrieval.

    Conclusions:

    • Unsupervised learning of recipe structure is feasible and beneficial for AI tasks.
    • The integration of learned tree structures significantly enhances recipe generation and food retrieval.
    • The developed framework offers a promising direction for advancing AI in culinary applications.