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Disambiguity and Alignment: An Effective Multi-Modal Alignment Method for Cross-Modal Recipe Retrieval.

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Summary

This study introduces a new method for cross-modal recipe retrieval that improves semantic alignment between food images and recipes. The approach addresses food image ambiguity, enhancing retrieval accuracy for better food computing applications.

Keywords:
cross-modal recipe retrievaldeep learningfood image ambiguitymulti-modal alignment

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Area of Science:

  • Food computing
  • Computer vision
  • Information retrieval

Background:

  • Cross-modal recipe retrieval is vital in food computing.
  • Existing methods lack intra-modal alignment, hindering semantic understanding.
  • Food image ambiguity is a critical, overlooked challenge.

Purpose of the Study:

  • To propose a novel Multi-Modal Alignment Method for Cross-Modal Recipe Retrieval (MMACMR).
  • To enhance semantic alignment by considering both inter-modal and intra-modal alignment.
  • To address the issue of food image ambiguity in retrieval models.

Main Methods:

  • MMACMR measures ambiguous food image similarity guided by corresponding recipes.
  • A cross-attention module is integrated between ingredients and instructions for enhanced recipe representation.
  • The method jointly optimizes inter-modal and intra-modal alignment.

Main Results:

  • Experiments conducted on the Recipe1M dataset.
  • The proposed MMACMR method significantly outperforms several state-of-the-art methods.
  • Demonstrated improved performance in commonly used evaluation criteria for cross-modal retrieval.

Conclusions:

  • MMACMR effectively enhances semantic alignment for cross-modal recipe retrieval.
  • The method successfully addresses food image ambiguity, improving model convergence.
  • This work offers a significant advancement in food computing and recipe retrieval systems.