RoDE: Linear Rectified Mixture of Diverse Experts for Food Large Multi-Modal Models

TL;DR

This paper introduces Uni-Food, a comprehensive food dataset with nutritional info, and RoDE, a novel expert mixture model, to improve multi-task food analysis in large multi-modal models, demonstrating enhanced efficiency and performance.

Contribution

The paper presents Uni-Food, a large, detailed food dataset, and RoDE, a new expert mixture approach with linear rectification for efficient multi-task learning in food-related LMMs.

Findings

Uni-Food improves food data analysis capabilities.

RoDE enhances task efficiency and model performance.

Experimental results validate the effectiveness of RoDE in multi-task food analysis.

Abstract

Large Multi-modal Models (LMMs) have significantly advanced a variety of vision-language tasks. The scalability and availability of high-quality training data play a pivotal role in the success of LMMs. In the realm of food, while comprehensive food datasets such as Recipe1M offer an abundance of ingredient and recipe information, they often fall short of providing ample data for nutritional analysis. The Recipe1M+ dataset, despite offering a subset for nutritional evaluation, is limited in the scale and accuracy of nutrition information. To bridge this gap, we introduce Uni-Food, a unified food dataset that comprises over 100,000 images with various food labels, including categories, ingredients, recipes, and ingredient-level nutritional information. Uni-Food is designed to provide a more holistic approach to food data analysis, thereby enhancing the performance and capabilities of LMMs in this domain. To mitigate the conflicts arising from multi-task supervision during fine-tuning of LMMs, we introduce a novel Linear Rectification Mixture of Diverse Experts (RoDE) approach. RoDE utilizes a diverse array of experts to address tasks of varying complexity, thereby facilitating the coordination of trainable parameters, i.e., it allocates more parameters for more complex tasks and, conversely, fewer parameters for simpler tasks. RoDE implements linear rectification union to refine the router's functionality, thereby enhancing the efficiency of sparse task allocation. These design choices endow RoDE with features that ensure GPU memory efficiency and ease of optimization. Our experimental results validate the effectiveness of our proposed approach in addressing the inherent challenges of food-related multitasking.