Learning Structural Representations for Recipe Generation and Food Retrieval
This work addresses the challenge of handling complex, unstructured recipe data for applications in food-related AI, representing an incremental improvement with a novel method for a known bottleneck.
The paper tackles the problem of generating lengthy recipes and performing cross-modal food retrieval by learning unsupervised sentence-level tree structures from food images and recipes, achieving state-of-the-art performance on the Recipe1M dataset.
Food is significant to human daily life. In this paper, we are interested in learning structural representations for lengthy recipes, that can benefit the recipe generation and food cross-modal retrieval tasks. Different from the common vision-language data, here the food images contain mixed ingredients and target recipes are lengthy paragraphs, where we do not have annotations on structure information. To address the above limitations, we propose a novel method to unsupervisedly learn the sentence-level tree structures for the cooking recipes. Our approach brings together several novel ideas in a systematic framework: (1) exploiting an unsupervised learning approach to obtain the sentence-level tree structure labels before training; (2) generating trees of target recipes from images with the supervision of tree structure labels learned from (1); and (3) integrating the learned tree structures into the recipe generation and food cross-modal retrieval procedure. Our proposed model can produce good-quality sentence-level tree structures and coherent recipes. We achieve the state-of-the-art recipe generation and food cross-modal retrieval performance on the benchmark Recipe1M dataset.