Multi-modal Cooking Workflow Construction for Food Recipes

TL;DR

This paper introduces MM-ReS, a large-scale multi-modal dataset of food recipes with workflow graphs, and proposes a neural model that effectively combines images and text to construct cooking workflows, outperforming previous methods.

Contribution

The paper presents the first large-scale multi-modal dataset for cooking workflow construction and a neural model that leverages both images and text to improve workflow prediction.

Findings

Achieved over 20% performance improvement over baseline methods.

Created the first large-scale dataset with human-labeled workflow graphs.

Demonstrated the effectiveness of multi-modal information in cooking workflow understanding.

Abstract

Understanding food recipe requires anticipating the implicit causal effects of cooking actions, such that the recipe can be converted into a graph describing the temporal workflow of the recipe. This is a non-trivial task that involves common-sense reasoning. However, existing efforts rely on hand-crafted features to extract the workflow graph from recipes due to the lack of large-scale labeled datasets. Moreover, they fail to utilize the cooking images, which constitute an important part of food recipes. In this paper, we build MM-ReS, the first large-scale dataset for cooking workflow construction, consisting of 9,850 recipes with human-labeled workflow graphs. Cooking steps are multi-modal, featuring both text instructions and cooking images. We then propose a neural encoder-decoder model that utilizes both visual and textual information to construct the cooking workflow, which achieved over 20% performance gain over existing hand-crafted baselines.