Improving Image-Based Nutrition Estimation Through Multimodal Food-Item Verification and Recovery

Abstract:Single-image nutrition estimation can fail silently when visible foods are missed. Even when a food is correctly identified, its proposed region may not support portion estimation. We propose a framework that uses multimodal large language models (MLLMs) to inventory visible foods and separately verify food identity and whether each proposed 2D region supports portion estimation. One whole-image review uses these verification results to identify unresolved gaps and omitted foods, triggering at most one targeted recovery pass. Recovered regions are re-verified without access to the recovery prompt, then reconciled into a final item set for nutrition estimation. The framework requires no task-specific fine-tuning. Matched evaluation on common valid-output samples shows that item-level grounding improves mass accuracy across all tested settings and energy accuracy relative to an adapted retrieval baseline, with item-identity precision and recall also improving, while post-recovery visual coverage is assessed separately at inference time without ground-truth annotations.

5 pages, 2 figures, 3 tables. Submitted to IEEE ICASSP 2027

Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI); Image and Video Processing (eess.IV)

arXiv:2610.11144 [cs.CV]

(or arXiv:2610.11144v1 [cs.CV] for this version)

https://doi.org/10.48550/arXiv.2610.11144 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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