MultiFoodhat: A potential new paradigm for intelligent food quality inspection
This addresses the need for more flexible and human-like food quality inspection without requiring large labeled datasets, though it appears incremental as it builds on existing vision-language and large language models.
The paper tackles the problem of limited generalization in food image classification by introducing MultiFoodChat, a dialogue-driven multi-agent reasoning framework for zero-shot food recognition, which achieves superior recognition accuracy and interpretability on multiple public datasets.
Food image classification plays a vital role in intelligent food quality inspection, dietary assessment, and automated monitoring. However, most existing supervised models rely heavily on large labeled datasets and exhibit limited generalization to unseen food categories. To overcome these challenges, this study introduces MultiFoodChat, a dialogue-driven multi-agent reasoning framework for zero-shot food recognition. The framework integrates vision-language models (VLMs) and large language models (LLMs) to enable collaborative reasoning through multi-round visual-textual dialogues. An Object Perception Token (OPT) captures fine-grained visual attributes, while an Interactive Reasoning Agent (IRA) dynamically interprets contextual cues to refine predictions. This multi-agent design allows flexible and human-like understanding of complex food scenes without additional training or manual annotations. Experiments on multiple public food datasets demonstrate that MultiFoodChat achieves superior recognition accuracy and interpretability compared with existing unsupervised and few-shot methods, highlighting its potential as a new paradigm for intelligent food quality inspection and analysis.