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In this paper, we are interested in modeling a how-to instructionalprocedure, such as a cooking recipe, with a meaningful and rich high-levelrepresentation. Specifically, we propose to represent cooking recipes and foodimages as cooking programs. Programs provide a structured representation of thetask, capturing cooking semantics and sequential relationships of actions inthe form of a graph. This allows them to be easily manipulated by users andexecuted by agents. To this end, we build a model that is trained to learn ajoint embedding between recipes and food images via self-supervision andjointly generate a program from this embedding as a sequence. To validate ouridea, we crowdsource programs for cooking recipes and show that: (a) projectingthe image-recipe embeddings into programs leads to better cross-modal retrievalresults; (b) generating programs from images leads to better recognitionresults compared to predicting raw cooking instructions; and (c) we cangenerate food images by manipulating programs via optimizing the latent code ofa GAN. Code, data, and models are available online.

Learning Program Representations for Food Images and Cooking Recipes