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_19 focus on analyzing several possible preparations of a single dish, like chocolate chip cookie''. The authors design an interface that allows users to explore the similarities and differences between such recipes by visualizing the structural similarity between recipes as points in a space, in which clusters are formed according to how similar recipes are. Furthermore, they examine how cooking instructions overlap between two recipes to measure recipe similarity. Our work is of a different flavor, as the features they use to measure similarity are manually picked by humans, while ours are automatically learned by a multimodal network.
@cite_19
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Finding a right recipe that describes the cooking procedure for a dish from just one picture is inherently a difficult problem. Food preparation undergoes a complex process involving raw ingredients, utensils, cutting and cooking operations. This process gives clues to the multimedia presentation of a dish (e.g., taste, colour, shape). However, the description of the process is implicit, implying only the cause of dish presentation rather than the visual effect that can be vividly observed on a picture. Therefore, different from other cross-modal retrieval problems in the literature, recipe search requires the understanding of textually described procedure to predict its possible consequence on visual appearance. In this paper, we approach this problem from the perspective of attention modeling. Specifically, we model the attention of words and sentences in a recipe and align them with its image feature such that both text and visual features share high similarity in multi-dimensional space. Through a large food dataset, Recipe1M, we empirically demonstrate that understanding the cooking procedure can lead to improvement in a large margin compared to the existing methods which mostly consider only ingredient information. Furthermore, with attention modeling, we show that language-specific named-entity extraction based on domain knowledge becomes optional. The result gives light to the feasibility of performing cross-lingual cross-modal recipe retrieval with off-the-shelf machine translation engines. Human beings have developed a diverse food culture. Many factors like ingredients, visual appearance, courses (e.g., breakfast and lunch), flavor and geographical regions affect our food perception and choice. In this work, we focus on multi-dimensional food analysis based on these food factors to benefit various applications like summary and recommendation. For that solution, we propose a delicious recipe analysis framework to incorporate various types of continuous and discrete attribute features and multi-modal information from recipes. First, we develop a Multi-Attribute Theme Modeling (MATM) method, which can incorporate arbitrary types of attribute features to jointly model them and the textual content. We then utilize a multi-modal embedding method to build the correlation between the learned textual theme features from MATM and visual features from the deep learning network. By learning attribute-theme relations and multi-modal correlation, we are able to fulfill different applications, including (1) flavor analysis and comparison for better understanding the flavor patterns from different dimensions, such as the region and course, (2) region-oriented multi-dimensional food summary with both multi-modal and multi-attribute information and (3) multi-attribute oriented recipe recommendation. Furthermore, our proposed framework is flexible and enables easy incorporation of arbitrary types of attributes and modalities. Qualitative and quantitative evaluation results have validated the effectiveness of the proposed method and framework on the collected Yummly dataset. This paper deals with automatic systems for image recipe recognition. For this purpose, we compare and evaluate leading vision-based and text-based technologies on a new very large multimodal dataset (UPMC Food-101) containing about 100,000 recipes for a total of 101 food categories. Each item in this dataset is represented by one image plus textual information. We present deep experiments of recipe recognition on our dataset using visual, textual information and fusion. Additionally, we present experiments with text-based embedding technology to represent any food word in a semantical continuous space. We also compare our dataset features with a twin dataset provided by ETHZ university: we revisit their data collection protocols and carry out transfer learning schemes to highlight similarities and differences between both datasets. Finally, we propose a real application for daily users to identify recipes. This application is a web search engine that allows any mobile device to send a query image and retrieve the most relevant recipes in our dataset. 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.