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Food is significant to human daily life. In this paper, we are interested inlearning structural representations for lengthy recipes, that can benefit therecipe generation and food cross-modal retrieval tasks. Different from thecommon vision-language data, here the food images contain mixed ingredients andtarget recipes are lengthy paragraphs, where we do not have annotations onstructure information. To address the above limitations, we propose a novelmethod to unsupervisedly learn the sentence-level tree structures for thecooking recipes. Our approach brings together several novel ideas in asystematic framework: (1) exploiting an unsupervised learning approach toobtain the sentence-level tree structure labels before training; (2) generatingtrees of target recipes from images with the supervision of tree structurelabels learned from (1); and (3) integrating the learned tree structures intothe recipe generation and food cross-modal retrieval procedure. Our proposedmodel can produce good-quality sentence-level tree structures and coherentrecipes. We achieve the state-of-the-art recipe generation and food cross-modalretrieval performance on the benchmark Recipe1M dataset.

Learning Structural Representations for Recipe Generation and Food Retrieval