arXiv: cs.AI preprintsInternational6 November 2026
NutriVisRAG: Multimodal Exemplar Grounding for Nutrient Estimation with Vision-Language Models
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arXiv:2611.08054v1 Announce Type: cross Abstract: Diet is a major determinant of health, yet accurate dietary assessment from free-living meal images remains challenging. Existing retrieval-based Vision-Language Model (VLM) approaches to nutrient estimation typically rely on textual food representations, while, to our knowledge, direct grounding on visually similar annotated meals has not been investigated. We present NutriVisRAG, a retrieval-augmented generation framework that uses a vision encoder to retrieve similar meals with known nutritional annotations and provide them as multimodal in-
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