# Food Ingredients Recognition through Multi-label Learning

arXiv: https://arxiv.org/abs/1707.08816 · HTML: https://mlsanity.com/paper/1707.08816/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Jul 2017.

## Built on by (most cited first, 6 of 6)

- [FoodLMM: A Versatile Food Assistant using Large Multi-modal Model](https://mlsanity.com/paper/2312.14991/) (2023)
- [A Comprehensive Survey of Image-Based Food Recognition and Volume Estimation Methods for Dietary Assessment](https://mlsanity.com/paper/2106.11776/) (2021)
- [Unifying Visual and Semantic Feature Spaces with Diffusion Models for Enhanced Cross-Modal Alignment](https://mlsanity.com/paper/2407.18854/) (2024)
- [Deep Image-to-Recipe Translation](https://mlsanity.com/paper/2407.00911/) (2024)
- [On the Cone Effect and Modality Gap in Medical Vision-Language Embeddings](https://mlsanity.com/paper/2603.17246/) (2026)
- [DishSeg24k: A Large-Scale Benchmark for Food Segmentation with Stochastic Expert Decoding](https://mlsanity.com/paper/2607.23070/) (2026)

## Abstract (arXiv)

Automatically constructing a food diary that tracks the ingredients consumed can help people follow a healthy diet. We tackle the problem of food ingredients recognition as a multi-label learning problem. We propose a method for adapting a highly performing state of the art CNN in order to act as a multi-label predictor for learning recipes in terms of their list of ingredients. We prove that our model is able to, given a picture, predict its list of ingredients, even if the recipe corresponding to the picture has never been seen by the model. We make public two new datasets suitable for this purpose. Furthermore, we prove that a model trained with a high variability of recipes and ingredients is able to generalize better on new data, and visualize how it specializes each of its neurons to different ingredients.