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

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

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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.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

arXiv AI

Jul 10

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

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.

By Qian Jiang, Zhecheng Shi, Jingpu Yang, Zirui Song, Miao Fang

llms agents multimodal benchmarks safety

arXiv AI

Jun 30

PlantExpertVQA: A Visual Question Answering Dataset for Benchmarking Vision-Language Models in Plant Science

arXiv:2508. 17117v3 Announce Type: replace-cross Abstract: Existing plant-disease datasets target classification and detection, leaving vision-language models unable to support interactive, reasoning-based diagnosis.

By Syed Nazmus Sakib, Nafiul Haque, Mohammad Zabed Hossain, Shifat E. Arman

llms nlp fine-tuning multimodal benchmarks

arXiv AI

Jun 9

NutriMLLM: Multimodal Large Language Models for Dietary Micronutrient Analysis

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.

By Runze Yan, Minxiao Wang, Jiaying Lu, Darren Liu, Xiao Hu, Hanqi Luo

llms diffusion fine-tuning multimodal benchmarks safety

arXiv Computer Vision

Sep 10

Vision-language models know more about agriculture than they show and rubric-grounded verifications close the gap

arXiv:2609.09417v1 Announce Type: new Abstract: Vision-language models (VLMs) show promise for agricultural classification, but zero-shot performance on disease, pest, damage, quality, and species id...

By Earl Ranario, Jared Smith, Lars Lundqvist, Urmil Jatin Chandarana

llms multimodal benchmarks

arXiv Computer Vision

Sep 23

BananaVLM: A Domain-Adapted Vision Language Model for Banana Crop Disease Diagnosis

arXiv:2609.25040v1 Announce Type: cross Abstract: Banana crop diseases threaten food security across the world, yet field diagnosis remains difficult because of limited expert access and visual simil...

By Sangam Kumar Jena, Pandarasamy Arjunan

llms fine-tuning multimodal benchmarks

arXiv AI

Sep 4

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

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.

By Bo Zeng, Linfeng Gao, Peiqin Lin, Yu Zhao, Mingyan Zeng, Yu Tong, Xintong Wang, Linlong Xu, Longyue Wang, Weihua Luo, Qinggang Zhang, Jinsong Su

llms multimodal benchmarks

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

PlantExpertVQA: A Visual Question Answering Dataset for Benchmarking Vision-Language Models in Plant Science

NutriMLLM: Multimodal Large Language Models for Dietary Micronutrient Analysis

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BananaVLM: A Domain-Adapted Vision Language Model for Banana Crop Disease Diagnosis

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