A Multi-Task Learning Approach for Meal Assessment

TL;DR

This paper introduces a multi-task CNN model that uses a single RGB meal image to accurately and efficiently assess nutritional content, improving over existing methods in both accuracy and processing speed.

Contribution

The paper presents a novel multi-task learning CNN approach for meal assessment using only one RGB image, significantly enhancing accuracy and efficiency compared to prior methods.

Findings

Achieved outstanding performance in nutrient estimation

Demonstrated clear advantage over state-of-the-art methods in accuracy

Reduced processing time substantially

Abstract

Key role in the prevention of diet-related chronic diseases plays the balanced nutrition together with a proper diet. The conventional dietary assessment methods are time-consuming, expensive and prone to errors. New technology-based methods that provide reliable and convenient dietary assessment, have emerged during the last decade. The advances in the field of computer vision permitted the use of meal image to assess the nutrient content usually through three steps: food segmentation, recognition and volume estimation. In this paper, we propose a use one RGB meal image as input to a multi-task learning based Convolutional Neural Network (CNN). The proposed approach achieved outstanding performance, while a comparison with state-of-the-art methods indicated that the proposed approach exhibits clear advantage in accuracy, along with a massive reduction of processing time.