Improving Image-Based Nutrition Estimation Through Multimodal Food-Item Verification and Recovery
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.
ProofPaper ↗
Key points
- 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.
- One whole-image review uses these verification results to identify unresolved gaps and omitted foods, triggering at most one targeted recovery pass.
- 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.
Sources (1)
- [1]Improving Image-Based Nutrition Estimation Through Multimodal Food-Item Verification and RecoveryarXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 8, 03:09 AM
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.
Single-image nutrition estimation can fail silently when visible foods are missed.
Extractive summary: sentences quoted from the sources.
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