從食物影像中準確估計其營養成分是一項具挑戰性的任務，原因包括真實世界中食物外觀的多樣性，以及缺乏具體的食材層級監督訊號。儘管近年已有方法透過結合深度資訊與食材名稱來提升預測表現，但大多忽略來自營養學中直觀且有價值的知識來源——即不同食材所固有的營養特性。為此，本研究提出一種利用大型多模態語言模型（LMM），如 GPT-4o，所隱含的營養知識來輔助營養預估的方法。經本研究實驗觀察到，雖然直接將 LMM 應用於食物營養預測的效果不佳，但它能夠生成富含語意的食材描述與營養分布，反映其潛在的營養知識。本研究因此設計一個基於知識增強的框架，藉由將 LMM 所生成的營養相關文字描述，透過跨模態注意力機制整合進視覺模型中。我們的方法包含一個結合三原色影像、深度資訊、與文字特徵的多模態融合架構，以及一個基於食材遮罩的資料增強策略，用以實現影像區域與文字描述的細粒度對齊（fine-grained alignment）。在 Nutrition5K、NutritionVerse-Real、和 NutritionVerse-Synth 三個標準資料集上的實驗結果顯示，本研究方法在所有評估指標上皆達到最新的最佳表現。進一步分析亦驗證了 LMM 所提供的營養知識對於提升預測準確度與語意對齊效果有關鍵性的貢獻。

Accurately estimating nutritional content from food images remains a challenging task due to the visual complexity of real-world dishes and the lack of explicit ingredient-level supervision. While recent methods have improved performance by incorporating depth information and ingredient names, they often overlook a rich and underutilized source of knowledge: the intrinsic nutritional characteristics of food items. In this work, we investigate the use of nutrition-related semantics embedded in large multimodal models (LMMs), such as GPT-4o, to enhance the task of nutrition estimation. We observe that although directly applying an LMM to nutrition estimation is not reliable, an LLM can provide ingredient-level semantic descriptions and qualitative nutrition profiles that reflect domain knowledge. We propose a knowledge-augmented framework that systematically integrates LMM-generated nutritional descriptions into a visual model through cross-modal attention mechanisms. Our method includes a multimodal feature fusion architecture that combines information of RGB and depth images with that in language, and a mask-based data augmentation strategy for fine-grained alignment between ingredient regions and textual descriptions. Experiments on Nutrition5K, NutritionVerse-Real, and NutritionVerse-Synth demonstrate that our approach achieves state-of-the-art performance across all benchmarks. Further analysis confirms that LMM-derived nutritional knowledge plays a critical role in improving prediction accuracy and semantic alignment.

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