The paper presents a framework that enhances single-image nutrition estimation by using multimodal large language models to inventory visible foods and verify both food identity and the suitability of proposed 2D regions for portion estimation. A whole-image review identifies gaps and triggers a single targeted recovery pass, after which recovered regions are re-verified and reconciled into a final item set. The approach, which requires no task-specific fine-tuning, improves item-level grounding, mass accuracy, energy accuracy, and item-identity precision and recall compared to a retrieval baseline.
arXiv:2607. 16514v1 Announce Type: cross Abstract: Image-based dietary assessment promises to replace costly, bias-prone manual recalls, but portion estimation remains a major blocker.
arXiv:2606. 08948v1 Announce Type: cross Abstract: Comprehensive estimation of dietary micronutrients from food images could improve clinical nutrition care, but training such models requires large multimodal datasets linking diverse foods to complete nutrient profiles.
arXiv:2607. 08423v1 Announce Type: new Abstract: The rapid integration of Large Vision-Language Models (VLMs) into critical infrastructure promises to revolutionize personalized healthcare and dietary management.
arXiv:2608. 03428v1 Announce Type: cross Abstract: Image based dietary assessment offers a scalable alternative to self reported food diaries, yet fine-grained food recognition remains challenging due to high intra-class variability and visually similar dishes.
The study introduces a multimodal framework that predicts postprandial glycemic response (PPGR) by combining image-derived macronutrient estimates with clinical variables and gut microbiome data. It jointly performs macronutrient estimation from meal images and glucose prediction, using an attention-based module to model interactions between dietary and host-specific information. Evaluated on a real-world dataset, the model outperforms existing PPGR baselines that use image-derived inputs and nearly matches methods relying on manually reported macronutrients.
arXiv:2607. 23273v1 Announce Type: cross Abstract: Computational nutrition needs precise ingredient data, but current databases are incomplete, inconsistent, and built for human reference rather than automated reasoning.
arXiv:2609.22171v1 Announce Type: cross Abstract: Precision healthcare, particularly for conditions like hypertension and cardiovascular disease, necessitates monitoring of dietary sodium intake. How...
Fine-grained food image retrieval is a key task in computational gastronomy, with applications in food traceability, dietary monitoring, and smart catering systems. Although hashing-based retrieval is attractive for large-scale search due to its storage efficiency and fast Hamming-distance computation, existing methods often perform poorly in fine-grained food scenarios, where subtle local semantics and frequency-sensitive visual cues are essential.
arXiv:2610.04425v2 Announce Type: replace-cross Abstract: Agricultural visual models are typically evaluated for either recognition or localization, but reliable diagnosis requires identifying what i...
arXiv:2606. 10120v1 Announce Type: cross Abstract: Postprandial hyperglycemia is a key risk factor for metabolic disorders; however, existing dietary guidance is often static, impractical, and insufficiently personalized, providing recommendations that are difficult to follow or not impactful.
arXiv:2609.22099v1 Announce Type: new Abstract: Cooking is a complex process that transforms raw ingredients into delicious and nutritious dishes, yet the recipes that encode this process remain larg...
CulturalMenuBench is a new benchmark comprising 4,870 culinary items in 10 languages across 18 regions, designed to test multimodal language models on tasks that combine dish recognition, step-by-step cooking images, ingredients, procedural text, and regional labels. The benchmark reveals a large knowledge‑application gap: models that score over 94% on standard multiple‑choice questions fall to at most 56% when attributing dishes to Chinese regional cuisines, indicating that cultural knowledge is present but not activated by visual input. Diagnostic analyses show that accuracy is driven by visual distinctiveness rather than cultural structure, and that removing sequential cooking images selectively harms process‑grounded tasks, confirming the need for procedural evidence.