In this paper, we introduce Recipe1M, a new large-scale, structured corpus of over one million cooking recipes and 13 million food images. As the largest publicly available collection of recipe data, Recipe1M affords the ability to train high-capacity models on aligned, multi-modal data. Using these data, we train a neural network to learn a joint embedding of recipes and images that yields impressive results on an image-recipe retrieval task. Moreover, we demonstrate that regularization via the addition of a high-level classification objective both improves retrieval performance to rival that of humans and enables semantic vector arithmetic. We postulate that these embeddings will provide a basis for further exploration of the Recipe1M dataset and food and cooking in general. Code, data and models are publicly available.
@cite_15 study the task of retrieving a recipe matching a corresponding food image. The authors find that although ingredient composition is important to the appearance of food, other attributes such as the manner of cutting and manner of cooking ingredients also play a role in forming the food's appearance. Given a food image, they attempt to predict ingredient, cutting and cooking attributes, and use these predictions to help retrieve the correct corresponding recipe. With our model, we attempt to retrieve the recipe directly, without first predicting attributes like ingredients, cutting and cooking attributes, separately. Furthermore, along with retrieving the recipe matching an image, our model also allow to retrieve the image matching a corresponding recipe.
@cite_15
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Food is rich of visible (e.g., colour, shape) and procedural (e.g., cutting, cooking) attributes. Proper leveraging of these attributes, particularly the interplay among ingredients, cutting and cooking methods, for health-related applications has not been previously explored. This paper investigates cross-modal retrieval of recipes, specifically to retrieve a text-based recipe given a food picture as query. As similar ingredient composition can end up with wildly different dishes depending on the cooking and cutting procedures, the difficulty of retrieval originates from fine-grained recognition of rich attributes from pictures. With a multi-task deep learning model, this paper provides insights on the feasibility of predicting ingredient, cutting and cooking attributes for food recognition and recipe retrieval. In addition, localization of ingredient regions is also possible even when region-level training examples are not provided. Experiment results validate the merit of rich attributes when comparing to the recently proposed ingredient-only retrieval techniques.