Calorie Aware Automatic Meal Kit Generation from an Image
Ahmad Babaeian Jelodar, Yu Sun

TL;DR
This paper presents a novel pipeline that estimates calories and re-produces meals from a single image by predicting ingredients and their portions using a deep transformer model, aiding meal kit generation.
Contribution
The paper introduces a two-stage pipeline combining ingredient prediction and portion estimation with a transformer model for improved calorie estimation from images.
Findings
Portion estimation enhances calorie accuracy.
The pipeline effectively generates meal kits.
Using ingredients and portions improves calorie estimation.
Abstract
Calorie and nutrition research has attained increased interest in recent years. But, due to the complexity of the problem, literature in this area focuses on a limited subset of ingredients or dish types and simple convolutional neural networks or traditional machine learning. Simultaneously, estimation of ingredient portions can help improve calorie estimation and meal re-production from a given image. In this paper, given a single cooking image, a pipeline for calorie estimation and meal re-production for different servings of the meal is proposed. The pipeline contains two stages. In the first stage, a set of ingredients associated with the meal in the given image are predicted. In the second stage, given image features and ingredients, portions of the ingredients and finally the total meal calorie are simultaneously estimated using a deep transformer-based model. Portion estimation…
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Taxonomy
TopicsNutritional Studies and Diet · Culinary Culture and Tourism
