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NGQA: A Nutritional Graph Question Answering Benchmark for Personalized Health-aware Nutritional Reasoning [49.06840168630573]
Diet plays a critical role in human health, yet tailoring dietary reasoning to individual health conditions remains a major challenge. Nutrition Question Answering (QA) has emerged as a popular method for addressing this problem. We introduce the Nutritional Graph Question Answering (NGQA) benchmark, the first graph question answering dataset designed for personalized nutritional health reasoning.
arXiv Detail & Related papers (2024-12-20T04:13:46Z)
Optimizing Ingredient Substitution Using Large Language Models to Enhance Phytochemical Content in Recipes [0.0]
This study explores how large language models (LLMs) can be applied to optimize ingredient substitutions in recipes. We fine-tuned models, including OpenAI's GPT-3.5, DaVinci, and Meta's TinyLlama, using an ingredient substitution dataset. These substitutions led to the creation of 1,951 phytochemically enriched ingredient pairings and 1,639 unique recipes.
arXiv Detail & Related papers (2024-09-13T12:55:45Z)
RoDE: Linear Rectified Mixture of Diverse Experts for Food Large Multi-Modal Models [96.43285670458803]
Uni-Food is a unified food dataset that comprises over 100,000 images with various food labels. Uni-Food is designed to provide a more holistic approach to food data analysis. We introduce a novel Linear Rectification Mixture of Diverse Experts (RoDE) approach to address the inherent challenges of food-related multitasking.
arXiv Detail & Related papers (2024-07-17T16:49:34Z)
NutritionVerse-Direct: Exploring Deep Neural Networks for Multitask Nutrition Prediction from Food Images [63.314702537010355]
Self-reporting methods are often inaccurate and suffer from substantial bias. Recent work has explored using computer vision prediction systems to predict nutritional information from food images. This paper aims to enhance the efficacy of dietary intake estimation by leveraging various neural network architectures.
arXiv Detail & Related papers (2024-05-13T14:56:55Z)
NutritionVerse: Empirical Study of Various Dietary Intake Estimation Approaches [59.38343165508926]
Accurate dietary intake estimation is critical for informing policies and programs to support healthy eating. Recent work has focused on using computer vision and machine learning to automatically estimate dietary intake from food images. We introduce NutritionVerse- Synth, the first large-scale dataset of 84,984 synthetic 2D food images with associated dietary information. We also collect a real image dataset, NutritionVerse-Real, containing 889 images of 251 dishes to evaluate realism.
arXiv Detail & Related papers (2023-09-14T13:29:41Z)
Vision-Based Food Analysis for Automatic Dietary Assessment [49.32348549508578]
This review presents one unified Vision-Based Dietary Assessment (VBDA) framework, which generally consists of three stages: food image analysis, volume estimation and nutrient derivation. Deep learning makes VBDA gradually move to an end-to-end implementation, which applies food images to a single network to directly estimate the nutrition.
arXiv Detail & Related papers (2021-08-06T05:46:01Z)
Personalized Food Recommendation as Constrained Question Answering over a Large-scale Food Knowledge Graph [16.58534326000209]
We propose a novel problem formulation for food recommendation, modeling this task as constrained question answering over a large-scale food knowledge base/graph (KBQA) Besides the requirements from the user query, personalized requirements from the user's dietary preferences and health guidelines are handled in a unified way. Our approach significantly outperforms non-personalized counterparts.
arXiv Detail & Related papers (2021-01-05T20:38:16Z)
An Artificial Intelligence-Based System to Assess Nutrient Intake for Hospitalised Patients [4.048427587958764]
Regular monitoring of nutrient intake in hospitalised patients plays a critical role in reducing the risk of disease-related malnutrition. We propose a novel system based on artificial intelligence (AI) to accurately estimate nutrient intake.
arXiv Detail & Related papers (2020-03-18T15:28:51Z)
Cross-Modal Food Retrieval: Learning a Joint Embedding of Food Images and Recipes with Semantic Consistency and Attention Mechanism [70.85894675131624]
We learn an embedding of images and recipes in a common feature space, such that the corresponding image-recipe embeddings lie close to one another. We propose Semantic-Consistent and Attention-based Networks (SCAN), which regularize the embeddings of the two modalities through aligning output semantic probabilities. We show that we can outperform several state-of-the-art cross-modal retrieval strategies for food images and cooking recipes by a significant margin.
arXiv Detail & Related papers (2020-03-09T07:41:17Z)

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