FoodCHA reformulates food recognition as hierarchical decision-making with the Moondream-2B model, achieving 13.8%, 38.2%, and 153.2% precision gains in category, subcategory, and cooking style recognition over Food-Llama-3.2-11B on FoodNExTDB.
A closed-loop multi-agent system driven by llms for meal-level personalized nutrition management
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FoodCHA: Multi-Modal LLM Agent for Fine-Grained Food Analysis
FoodCHA reformulates food recognition as hierarchical decision-making with the Moondream-2B model, achieving 13.8%, 38.2%, and 153.2% precision gains in category, subcategory, and cooking style recognition over Food-Llama-3.2-11B on FoodNExTDB.