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Sharing food has become very popular with the development of social media.For many real-world applications, people are keen to know the underlyingrecipes of a food item. In this paper, we are interested in automaticallygenerating cooking instructions for food. We investigate an open research taskof generating cooking instructions based on only food images and ingredients,which is similar to the image captioning task. However, compared with imagecaptioning datasets, the target recipes are long-length paragraphs and do nothave annotations on structure information. To address the above limitations, wepropose a novel framework of Structure-aware Generation Network (SGN) to tacklethe food recipe generation task. Our approach brings together several novelideas in a systematic framework: (1) exploiting an unsupervised learningapproach to obtain the sentence-level tree structure labels before training;(2) generating trees of target recipes from images with the supervision of treestructure labels learned from (1); and (3) integrating the inferred treestructures with the recipe generation procedure. Our proposed model can producehigh-quality and coherent recipes, and achieve the state-of-the-art performanceon the benchmark Recipe1M dataset.

Structure-Aware Generation Network for Recipe Generation from Images