Nutritional Studies and Diet

Neueste Paper

• TasteBench: Multimodal Benchmark for Sensory Prediction, from Molecules to Sustainable Foods

  Anna T. Thomas, Sohum Patnaik, Caroline Cotto, Benjamin Sanchez-Lengeling · 5. Oktober 2026

  Sustainable protein discovery lacks the fast computational proxies, analogous to molecular docking or density functional theory, that accelerate drug and materials discovery. Evaluating whether a novel food tastes like its animal-based target requires expensive human sensory panels, bottlenecking th…

• NutriVision: Ingredient-Conditioned Fusion and Prediction for Single-Image Food Nutrition Estimation

  Aman Kumar, Avinash Anand, Chaitanya Lakhchaura, Ashutosh Kumar, Akshita Abrol, Timothy Liu, Zhengkui Wang, Rajiv Ratn Shah · 29. September 2026

  Nutrition estimation is a fundamental task in consumer diet tracking, clinical dietetics, chronic disease management, sports and hospital nutrition, and broader food computing systems. The existing approaches have progressed along two largely separate axes, vision models that rely on calibrated RGB-…

• Nutri-ATLAS: Embodied Agent for Tabulated Lookup and Assistance for Smarter nutrition

  Uttej Kallakuri, Boxun Hu, Ankur A. Butala, Najim Dehak, Tinoosh Mohsenin · 29. September 2026

  Generative and Agentic IoT systems offer a promising foundation for digital healthcare applications that combine sensing, personalized reasoning, and autonomous interaction in real-world environments. Nutrition assistance is a natural use case, but existing Large Language Model (LLM)-based systems a…

• A framework for recipe data structure with applications for culinary and nutritional insights

  Mansi Goel, Sumit Bhagat, Saloni Srivastava, Malav Patel, Shlok Vinodkumar Mehroliya, Ganesh Bagler · 22. September 2026

  Cooking is a complex process that transforms raw ingredients into delicious and nutritious dishes, yet the recipes that encode this process remain largely free text; readable by people but not directly computable. Existing recipe collections capture fragments of this information, but no shared repre…

• A Conservative OCR-Enabled Workflow for R214 Sodium Screening of South African Packaged Foods

  Mayimunah Nagayi, Alice Scaria Khan, Tamryn Frank, Rina Swart, Clement Nyirenda · 16. September 2026

  Using food package images to monitor sodium and salt content against South Africa's R214 sodium limits is challenging when screening decisions require product identity, nutrition facts panel evidence, reporting basis, and category-specific thresholds. This study presents a conservative image-based w…

• Population-level measures of perceived food access reveal barriers beyond geographic proximity

  Teresa Groton, Benjamin rachunok · 14. September 2026

  Food access is multidimensional, but population-level measurement still relies heavily on geography because perceived dimensions of access are difficult to measure at scale. Here, we use 25,125 Google Maps reviews from 49 grocery stores in Raleigh, North Carolina, to measure five dimensions of food …

• CulturalMenuBench: Probing the Knowledge-Application Gap in Multimodal Culinary Reasoning

  Bo Zeng, Linfeng Gao, Peiqin Lin, Yu Zhao, Mingyan Zeng, Yu Tong, Xintong Wang, Linlong Xu, Longyue Wang, Weihua Luo, Qinggang Zhang, Jinsong Su · 4. September 2026

  Multimodal language models achieve near-ceiling scores on food recognition benchmarks, yet it remains unclear whether this success reflects genuine cultural understanding or mere visual matching. To probe this distinction, we introduce CulturalMenuBench, a benchmark of 4,870 items in 10 languages ac…

• Can We Trust AI Agents in the Supermarket? Sugar Content Inference from Product Images

  Jose Berengueres · 14. August 2026

  Nutritional labels are legally permitted to appear in very small print, reducing real-world readability and encouraging consumers to rely on 'AI nutrition lens' and vision-capable conversational agents for dietary guidance. We evaluate whether such AI-mediated advice can meaningfully substitute for …

• ReGraph: Learning to Generate Recipe Graphs from Food Images

  Guoshan Liu, Bin Zhu, Pengkun Jiao, Jingjing Chen, Chong-Wah Ngo, Yu-Gang Jiang · 10. August 2026

  Recent Large Multimodal Models (LMMs) have achieved impressive performance in recipe generation from food images.However, cooking is a structured transformation process in which ingredients undergo state changes through ordered actions,while free-form recipe language leaves the corresponding entitie…

• OliveGemma: A 3 Billion Visual Language Model for Recognising the Mediterranean & European Diet

  Dimitrios I. Zaridis, Traianos Tsiokris, Vasileios C. Pezoulas, Daphni Plati, Eugenia Mylona, Eleni Georga, Nikos Tsiknakis, Antonis Sakellarios, Dimitrios I. Fotiadis · 5. August 2026

  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. This study presents OliveGemma, a vision language model for recognising and reasoning abo…

• Leveraging AI for fine-grained food safety risk forecasting in sparse data conditions

  Dongqi Wang, Weiwei Chen, Han Zhou, Weihua Zhou · 4. August 2026

  Ensuring food safety represents a critical public health challenge, particularly when inspection resources are limited and regional sampling data are sparse. This study proposes a Transformer-based framework capable of forecasting fine-grained, city-level food safety risks by unifying over 11 millio…

• Food Image Segmentation with LLM-Derived Ingredient Labels and Multimodal Fusion

  Jui-Feng Chi, Wei-Ta Chu, Sheng-Long Lin · 29. Juli 2026

  Food image segmentation plays a vital role in health-related applications such as nutrition tracking and personalized health monitoring. However, existing models often underperform on visually similar ingredients and rare food categories. To address this issue, we propose two plug-and-play multimoda…

• Fine-Grained Food Image Understanding via Target-Aware Data Alignment

  Jui-Feng Chi, Wei-Lun Chu, Bruce Coburn, Jinge Ma, Fengqing Zhu · 29. Juli 2026

  Fine-grained food visual--semantic understanding requires models to capture subtle distinctions across ingredients, cooking methods, doneness, color, texture, and plate composition. Although CLIP-style vision-language models provide a natural framework for this task, their effectiveness is limited w…

• DishSeg24k: A Large-Scale Benchmark for Food Segmentation with Stochastic Expert Decoding

  Yilin Wang, Haochen Shi, Guanyu Chen, Weiqing Min, Jinkai Zheng, Chenggang Yan, Shuqiang Jiang · 28. Juli 2026

  Food segmentation is essential for applications such as intelligent catering, dietary assessment, and recommendation. However, existing benchmarks fail to capture the complexity of real-world dining scenes. The challenges of dense inter-dish overlap, fine-grained class similarity, and extreme long-t…

• Geometry-Enhanced Portion Estimation for Multimodal LLMs

  Lin Liao, Peng Li · 21. Juli 2026

  Image-based dietary assessment promises to replace costly, bias-prone manual recalls, but portion estimation remains a major blocker. Multimodal LLMs (MLLMs) recognize a wide range of foods zero-shot in uncontrolled photos, yet they are weak at portion estimation -- a gap we measure across the curre…

• OmniFood-Bench: Evaluating VLMs for Nutrient Reasoning and Personalized Health Advice

  Qian Jiang, Zhecheng Shi, Jingpu Yang, Zirui Song, Miao Fang · 20. Juli 2026

  The rapid integration of Large Vision-Language Models (VLMs) into critical infrastructure promises to revolutionize personalized healthcare and dietary management. However, in the domain of food systems, autonomous agents face a unique and persistent challenge: the "Systemic Information Asymmetr…

• Open-KNEAD: Knowledge-grounded Nutrition Estimation via Agentic Decomposition

  Bruce Coburn, Jingbo Yue, Jinge Ma, Siddeshwar Raghavan, Gautham Vinod, Fengqing Zhu · 15. Juli 2026

  Multimodal Large Language Models (MLLMs) are increasingly used for dietary assessment from meal images, where retrieval-augmented grounding was shown to sharpen nutrition estimates. However, we find this premise no longer holds for current MLLMs. A modern MLLM's direct estimate now matches or surpas…

• RFHNet: Relational and Frequency-Aware Hashing Network for Large-Scale Fine-Grained Food Image Retrieval

  Junsong Wang, Weiqing Min, Guorui Sheng, Tao Yao, Lili Wang, Shuqiang Jiang · 8. Juli 2026

  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 computati…

• Ingredient-Level Food Image Segmentation for Nutrition Awareness

  Jonesh Shrestha · 24. Juni 2026

  Food images often contain several visible ingredients, so assigning one dish label to an entire image hides important visual structure. This work studies ingredient-level semantic segmentation on FoodSeg103, where the model predicts an ingredient class for each pixel. Two SegFormer variants were fin…

• Food4All: An Agentic Framework and Benchmark for Food Resource Navigation with Adaptive User Understanding

  Yiyang Li, Weixiang Sun, Tianyi Ma, Kaiwen Shi, Zheyuan Zhang, Yanfang Ye · 11. Juni 2026

  Food assistance referral requires conversational agents to translate underspecified, often noisy help-seeking dialogues into locally valid resource recommendations. We present Food4All, an agentic food-resource referral framework and benchmark grounded in 686 structured Indiana food resources. Food4…

• MetaPlate: Counterfactual-Guided RAG-LLM Tool for Personalized Food Recommendation and Hyperglycemia Prevention

  Asiful Arefeen, Carol Johnston, Hassan Ghasemzadeh · 10. Juni 2026

  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. While recent advances leverage continuous glucose monito…

• NutriMLLM: Multimodal Large Language Models for Dietary Micronutrient Analysis

  Runze Yan, Minxiao Wang, Jiaying Lu, Darren Liu, Xiao Hu, Hanqi Luo · 9. Juni 2026

  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. We first show that existing multimodal large language models (MLLMs), including …

• FAM-Bench: A Multimodal Benchmark for Condition-Aware Food-as-Medicine Reasoning

  Mingyang Mao, Bhargav Rishi Medisetti, Utkarsh Grover, Tanvir Ibrahim, Wenyan Li, Tingting Zhang, Xiaomin Lin · 1. Juni 2026

  Food-as-Medicine requires models to reason beyond what a dish is or what nutrition it contains: they must decide whether a concrete food choice is appropriate for a specific health condition. Existing food AI benchmarks primarily evaluate dish recognition, recipe understanding, nutrient estimation, …

• An Explainable Unsupervised-to-Supervised Machine Learning Framework for Dietary Pattern Discovery Using UK National Dietary Survey Data

  Wing Yi Yu, Chun Yin Chiu · 12. Mai 2026

  Clinical dietary assessment can generate detailed but high-dimensional nutrient and food-group information that is difficult to translate quickly into counselling priorities. This paper proposes an explainable unsupervised-to-supervised machine learning framework for discovering, reproducing and int…

• CGU-ILALab at FoodBench-QA 2026: Comparing Traditional and LLM-based Approaches for Recipe Nutrient Estimation

  Wei-Chun Chen, Yu-Xuan Chen, I-Fang Chung, Ying-Jia Lin · 29. April 2026

  Accurate nutrient estimation from unstructured recipe text is an important yet challenging problem in dietary monitoring, due to ambiguous ingredient terminology and highly variable quantity expressions. We systematically evaluate models spanning a wide range of representational capacity, from lexic…