# Efficient Prompt Tuning for Hierarchical Ingredient Recognition

arXiv: https://arxiv.org/abs/2504.10322 · HTML: https://mlsanity.com/paper/2504.10322/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Apr 2025.

## Built on by (most cited first, 1 of 1)

- [ReGraph: Learning to Generate Recipe Graphs from Food Images](https://mlsanity.com/paper/2608.06917/) (2026)

## Abstract (arXiv)

Fine-grained ingredient recognition presents a significant challenge due to the diverse appearances of ingredients, resulting from different cutting and cooking methods. While existing approaches have shown promising results, they still require extensive training costs and focus solely on fine-grained ingredient recognition. In this paper, we address these limitations by introducing an efficient prompt-tuning framework that adapts pretrained visual-language models (VLMs), such as CLIP, to the ingredient recognition task without requiring full model finetuning. Additionally, we introduce three-level ingredient hierarchies to enhance both training performance and evaluation robustness. Specifically, we propose a hierarchical ingredient recognition task, designed to evaluate model performance across different hierarchical levels (e.g., chicken chunks, chicken, meat), capturing recognition capabilities from coarse- to fine-grained categories. Our method leverages hierarchical labels, training prompt-tuned models with both fine-grained and corresponding coarse-grained labels. Experimental results on the VireoFood172 dataset demonstrate the effectiveness of prompt-tuning with hierarchical labels, achieving superior performance. Moreover, the hierarchical ingredient recognition task provides valuable insights into the model's ability to generalize across different levels of ingredient granularity.