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Hybrid Decoding with Co-Occurrence Awareness for Fine-Grained Food Image Segmentation.

Shenglong Wang1, Guorui Sheng1

  • 1School of Computer Science and Artificial Intelligence, Ludong University, Yantai 264025, China.

Foods (Basel, Switzerland)
|February 13, 2026
PubMed
Summary
This summary is machine-generated.

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A new Hybrid Decoder for Food Image Segmentation (HDF) method improves food image analysis by effectively segmenting ingredients, crucial for dietary assessment and nutritional insights.

Area of Science:

  • Computer Vision
  • Artificial Intelligence
  • Biomedical Informatics

Background:

  • Fine-grained food image segmentation is critical for dietary assessment and nutritional analysis.
  • Challenges include ambiguous boundaries, similar food appearances, and complex meal layouts.
  • Current CNN, Transformer, and Mamba models struggle with local details and long-range context.

Purpose of the Study:

  • To develop a novel decoding framework, Hybrid Decoder for Food Image Segmentation (HDF), to enhance food image segmentation.
  • To address limitations of existing methods in capturing both fine-grained details and contextual dependencies.
  • To improve the accuracy of segmenting diverse food ingredients in complex meal images.

Main Methods:

  • Proposed HDF framework utilizing the MambaVision backbone.
Keywords:
deep learningfood co-occurrencefood computingfood image segmentation

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  • Employed a convolution-based Feature Pyramid Network (FPN) for multi-stage feature extraction.
  • Integrated a Cross-Layer Mamba module for efficient multi-scale feature fusion.
  • Incorporated an Attention Refinement module for global semantic context.
  • Introduced a Food Co-occurrence Module to learn category patterns and enhance semantics.

    • Main Results:

    • Achieved 52.25% mIoU on the FoodSeg103 benchmark.
    • Achieved 76.16% mIoU on the UEC-FoodPIX Complete benchmark.
    • Outperformed existing state-of-the-art methods on standard food segmentation datasets.

    Conclusions:

    • The HDF framework effectively addresses challenges in fine-grained food image segmentation.
    • The hybrid design and co-occurrence module significantly improve segmentation accuracy.
    • HDF provides a robust foundation for applications in dietary logging, nutritional estimation, and food safety.