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Cross-Modal Multivariate Pattern Analysis
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Cross modal recipe retrieval with fine grained modal interaction.

Fan Zhao1, Yuqing Lu2, Zhuo Yao2

  • 1Faculty of Printing, Packaging Engineering and Digital Media Technology &State Key Laboratory of Eco-hydraulics in Northwest Arid Region, Xi'an University of Technology, Xi'an, 710048, China. vcu@xaut.edu.cn.

Scientific Reports
|February 9, 2025
PubMed
Summary

This study introduces Fine-grained Modalities Interaction (FMI) for cross-modal recipe retrieval. FMI significantly improves matching food images with recipes and vice versa, outperforming existing methods.

Keywords:
Cross-modal recipe retrievalFeature embeddingModalities interactionRecipe1M dataset

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Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Machine Learning

Background:

  • Cross-modal recipe retrieval, matching food images to recipes and vice versa, is a growing research area.
  • Existing methods often lack fine-grained interaction between modalities, limiting multi-modal representation enhancement.
  • Advanced techniques have improved general benchmark performance, but deeper modality interaction is needed.

Purpose of the Study:

  • To enhance multi-modal representation by leveraging fine-grained modalities interaction for cross-modal recipe retrieval.
  • To introduce novel modules for enriching recipe components and visual representations.
  • To validate the proposed method's effectiveness on a standard dataset.

Main Methods:

  • Developed the cross-component multiscale recipe enriching (CCMRE) module using hierarchical recipe Transformers and fully convolutional operations.
  • Introduced the text-contextualized visual enhancing (TCVE) module within the image encoder for deeper visual representation relearning.
  • Utilized similarity between image local features and intermediate recipe representations for enhancement.

Main Results:

  • The proposed Fine-grained Modalities Interaction (FMI) method demonstrated superior performance on the Recipe1M dataset.
  • FMI achieved significant improvements over the baseline model, with +17.4 R@1 on the 1k test set and +20.5 R@1 on the 10k test set.
  • Ablation studies confirmed the effectiveness of the proposed CCMRE and TCVE modules.

Conclusions:

  • The FMI method effectively enhances multi-modal representations through fine-grained interaction, advancing cross-modal recipe retrieval.
  • The novel CCMRE and TCVE modules contribute to improved performance by enriching individual modalities and their interactions.
  • This research sets a new state-of-the-art for cross-modal recipe retrieval tasks.