7,392 research outputs found

    Hierarchical Attention Network for Visually-aware Food Recommendation

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    Food recommender systems play an important role in assisting users to identify the desired food to eat. Deciding what food to eat is a complex and multi-faceted process, which is influenced by many factors such as the ingredients, appearance of the recipe, the user's personal preference on food, and various contexts like what had been eaten in the past meals. In this work, we formulate the food recommendation problem as predicting user preference on recipes based on three key factors that determine a user's choice on food, namely, 1) the user's (and other users') history; 2) the ingredients of a recipe; and 3) the descriptive image of a recipe. To address this challenging problem, we develop a dedicated neural network based solution Hierarchical Attention based Food Recommendation (HAFR) which is capable of: 1) capturing the collaborative filtering effect like what similar users tend to eat; 2) inferring a user's preference at the ingredient level; and 3) learning user preference from the recipe's visual images. To evaluate our proposed method, we construct a large-scale dataset consisting of millions of ratings from AllRecipes.com. Extensive experiments show that our method outperforms several competing recommender solutions like Factorization Machine and Visual Bayesian Personalized Ranking with an average improvement of 12%, offering promising results in predicting user preference for food. Codes and dataset will be released upon acceptance

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

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    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.Comment: Submitted to Transactions on Pattern Analysis and Machine Intelligenc

    Computer vision beyond the visible : image understanding through language

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    In the past decade, deep neural networks have revolutionized computer vision. High performing deep neural architectures trained for visual recognition tasks have pushed the field towards methods relying on learned image representations instead of hand-crafted ones, in the seek of designing end-to-end learning methods to solve challenging tasks, ranging from long-lasting ones such as image classification to newly emerging tasks like image captioning. As this thesis is framed in the context of the rapid evolution of computer vision, we present contributions that are aligned with three major changes in paradigm that the field has recently experienced, namely 1) the power of re-utilizing deep features from pre-trained neural networks for different tasks, 2) the advantage of formulating problems with end-to-end solutions given enough training data, and 3) the growing interest of describing visual data with natural language rather than pre-defined categorical label spaces, which can in turn enable visual understanding beyond scene recognition. The first part of the thesis is dedicated to the problem of visual instance search, where we particularly focus on obtaining meaningful and discriminative image representations which allow efficient and effective retrieval of similar images given a visual query. Contributions in this part of the thesis involve the construction of sparse Bag-of-Words image representations from convolutional features from a pre-trained image classification neural network, and an analysis of the advantages of fine-tuning a pre-trained object detection network using query images as training data. The second part of the thesis presents contributions to the problem of image-to-set prediction, understood as the task of predicting a variable-sized collection of unordered elements for an input image. We conduct a thorough analysis of current methods for multi-label image classification, which are able to solve the task in an end-to-end manner by simultaneously estimating both the label distribution and the set cardinality. Further, we extend the analysis of set prediction methods to semantic instance segmentation, and present an end-to-end recurrent model that is able to predict sets of objects (binary masks and categorical labels) in a sequential manner. Finally, the third part of the dissertation takes insights learned in the previous two parts in order to present deep learning solutions to connect images with natural language in the context of cooking recipes and food images. First, we propose a retrieval-based solution in which the written recipe and the image are encoded into compact representations that allow the retrieval of one given the other. Second, as an alternative to the retrieval approach, we propose a generative model to predict recipes directly from food images, which first predicts ingredients as sets and subsequently generates the rest of the recipe one word at a time by conditioning both on the image and the predicted ingredients.En l'última dècada, les xarxes neuronals profundes han revolucionat el camp de la visió per computador. Els resultats favorables obtinguts amb arquitectures neuronals profundes entrenades per resoldre tasques de reconeixement visual han causat un canvi de paradigma cap al disseny de mètodes basats en representacions d'imatges apreses de manera automàtica, deixant enrere les tècniques tradicionals basades en l'enginyeria de representacions. Aquest canvi ha permès l'aparició de tècniques basades en l'aprenentatge d'extrem a extrem (end-to-end), capaces de resoldre de manera efectiva molts dels problemes tradicionals de la visió per computador (e.g. classificació d'imatges o detecció d'objectes), així com nous problemes emergents com la descripció textual d'imatges (image captioning). Donat el context de la ràpida evolució de la visió per computador en el qual aquesta tesi s'emmarca, presentem contribucions alineades amb tres dels canvis més importants que la visió per computador ha experimentat recentment: 1) la reutilització de representacions extretes de models neuronals pre-entrenades per a tasques auxiliars, 2) els avantatges de formular els problemes amb solucions end-to-end entrenades amb grans bases de dades, i 3) el creixent interès en utilitzar llenguatge natural en lloc de conjunts d'etiquetes categòriques pre-definits per descriure el contingut visual de les imatges, facilitant així l'extracció d'informació visual més enllà del reconeixement de l'escena i els elements que la composen La primera part de la tesi està dedicada al problema de la cerca d'imatges (image retrieval), centrada especialment en l'obtenció de representacions visuals significatives i discriminatòries que permetin la recuperació eficient i efectiva d'imatges donada una consulta formulada amb una imatge d'exemple. Les contribucions en aquesta part de la tesi inclouen la construcció de representacions Bag-of-Words a partir de descriptors locals obtinguts d'una xarxa neuronal entrenada per classificació, així com un estudi dels avantatges d'utilitzar xarxes neuronals per a detecció d'objectes entrenades utilitzant les imatges d'exemple, amb l'objectiu de millorar les capacitats discriminatòries de les representacions obtingudes. La segona part de la tesi presenta contribucions al problema de predicció de conjunts a partir d'imatges (image to set prediction), entès com la tasca de predir una col·lecció no ordenada d'elements de longitud variable donada una imatge d'entrada. En aquest context, presentem una anàlisi exhaustiva dels mètodes actuals per a la classificació multi-etiqueta d'imatges, que són capaços de resoldre la tasca de manera integral calculant simultàniament la distribució probabilística sobre etiquetes i la cardinalitat del conjunt. Seguidament, estenem l'anàlisi dels mètodes de predicció de conjunts a la segmentació d'instàncies semàntiques, presentant un model recurrent capaç de predir conjunts d'objectes (representats per màscares binàries i etiquetes categòriques) de manera seqüencial. Finalment, la tercera part de la tesi estén els coneixements apresos en les dues parts anteriors per presentar solucions d'aprenentatge profund per connectar imatges amb llenguatge natural en el context de receptes de cuina i imatges de plats cuinats. En primer lloc, proposem una solució basada en algoritmes de cerca, on la recepta escrita i la imatge es codifiquen amb representacions compactes que permeten la recuperació d'una donada l'altra. En segon lloc, com a alternativa a la solució basada en algoritmes de cerca, proposem un model generatiu capaç de predir receptes (compostes pels seus ingredients, predits com a conjunts, i instruccions) directament a partir d'imatges de menjar.Postprint (published version

    Inverse Cooking: Recipe Generation from Food Images

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    People enjoy food photography because they appreciate food. Behind each meal there is a story described in a complex recipe and, unfortunately, by simply looking at a food image we do not have access to its preparation process. Therefore, in this paper we introduce an inverse cooking system that recreates cooking recipes given food images. Our system predicts ingredients as sets by means of a novel architecture, modeling their dependencies without imposing any order, and then generates cooking instructions by attending to both image and its inferred ingredients simultaneously. We extensively evaluate the whole system on the large-scale Recipe1M dataset and show that (1) we improve performance w.r.t. previous baselines for ingredient prediction; (2) we are able to obtain high quality recipes by leveraging both image and ingredients; (3) our system is able to produce more compelling recipes than retrieval-based approaches according to human judgment. We make code and models publicly available.Comment: CVPR 201
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