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In this paper, we introduce Recipe1M+, a new large-scale, structured corpusof over one million cooking recipes and 13 million food images. As the largestpublicly available collection of recipe data, Recipe1M+ affords the ability totrain high-capacity modelson aligned, multimodal data. Using these data, wetrain a neural network to learn a joint embedding of recipes and images thatyields impressive results on an image-recipe retrieval task. Moreover, wedemonstrate that regularization via the addition of a high-level classificationobjective both improves retrieval performance to rival that of humans andenables semantic vector arithmetic. We postulate that these embeddings willprovide a basis for further exploration of the Recipe1M+ dataset and food andcooking in general. Code, data and models are publicly available.

Recipe1M+: A Dataset for Learning Cross-Modal Embeddings for Cooking Recipes and Food Images