What are two baseline methods?
Answer with content missing: (Table1 merged with Figure 3) Joint Neural
Embedding (JNE) and AdaMine
Direct computer vision based-nutrient content estimation is a demanding task, due to deformation and occlusions of ingredients, as well as high intra-class and low inter-class variability between meal classes. In order to tackle these issues, we propose a system for recipe retrieval from images. The recipe information can subsequently be used to estimate the nutrient content of the meal. In this study, we utilize the multi-modal Recipe1M dataset, which contains over 1 million recipes accompanied by over 13 million images. The proposed model can operate as a first step in an automatic pipeline for the estimation of nutrition content by supporting hints related to ingredient and instruction. Through self-attention, our model can directly process raw recipe text, making the upstream instruction sentence embedding process redundant and thus reducing training time, while providing desirable retrieval results. Furthermore, we propose the use of an ingredient attention mechanism, in order to gain insight into which instructions, parts of instructions or single instruction words are of importance for processing a single ingredient within a certain recipe. Attention-based recipe text encoding contributes to solving the issue of high intra-class/low inter-class variability by focusing on preparation steps specific to the meal. The experimental results demonstrate the potential of such a system for recipe retrieval from images. A comparison with respect to two baseline methods is also presented.
Social media and designated online cooking platforms have made it possible for large populations to share food culture (diet, recipes) by providing a vast amount of food-related data. Despite the interest in food culture, global eating behavior still contributes heavily to diet-related diseases and deaths, according to the Lancet BIBREF0. Nutrition assessment is a demanding, time-consuming and expensive task. Moreover, the conventional approaches for nutrition assessment are cumbersome and prone to errors. A tool that enables users to easily and accurately estimate the nutrition content of a meal, while at the same time minimize the need for tedious work is of great importance for a number of different population groups. Such a tool can be utilized for promoting a healthy lifestyle, as well as to support patients suffering food-related diseases such as diabetes. To this end, a number of computer vision approaches have been developed, in order to extract nutrient information from meal images by using machine learning. Typically, such systems detect the different food items in a picture BIBREF1, BIBREF2, BIBREF3, estimate their volumes BIBREF4, BIBREF5, BIBREF6 and calculate the nutrient content using a food composition database BIBREF7. In some cases however, inferring the nutrient content of a meal from an image can be really challenging - due to unseen ingredients (e.g. sugar, oil) or the structure of the meal (mixed food, soups, etc.). Humans often use information from diverse sensory modalities (visual, auditory, haptic) to infer logical conclusions. This kind of multi-sensory integration helps us process complex tasks BIBREF8. In this study, we investigate the use of recipe information, in order to better estimate nutrient content of complex meal compositions. With the aim to develop a pipeline for holistic dietary assessment, we present and evaluate a method based on machine learning to retrieve recipe information from images, as a first step towards more accurate nutrient estimation. Such recipe information can then be utilized together with the volume of the food item to enhance an automatic system to estimate the nutrient content of complex meals, such as lasagna, crock pot or stew. The performance of approaches based on machine learning relies heavily on the quantity and quality of the available data. To this end, a number of efforts have been made to compile informative datasets to be used for machine learning approaches. Most of the early released food databases were assembled only by image data for a special kind of meal. In particular, the first publicly available database was the Pittsburgh Fast-Food Image Dataset (PFID) BIBREF9, which contains only fast food images taken under laboratory conditions. After the recent breakthrough in deep learning models, a number of larger databases were introduced. Bossard et al. BIBREF10 introduced the Food-101 dataset, which is composed of 101 food categories represented by 101'000 food images. This was followed by several image-based databases, such as the UEC-100 BIBREF11 and its augmented version, the UEC-256 BIBREF12 dataset, with 9060 food images referring to 100 Japanese food types and 31651 food images referring to 256 Japanese food types, respectively. Xu et al. BIBREF13 developed a specialized dataset by including geolocation and external information about restaurants to simplify the food recognition task. Wang et al. BIBREF14 introduced the UPMC Food-101 multi-modal dataset, that shares the same 101 food categories with the popular Food-101 dataset, but contains textual information in addition. A number of studies have been carried out utilizing the aforementioned databases, mainly for the task of food recognition. Salvador et al. BIBREF15 published Recipe1M, the largest publicly available multi-modal dataset, that consists of 1 million recipes together with the accompanying images. The emergence of multi-modal databases has led to novel approaches for meal image analysis. The fusion of visual features learned from images by deep Convolution Neural Networks (CNN) and textual features lead to outstanding results in food recognition applications. An early approach for recipe retrieval was based on jointly learning to predict food category and its ingredients using deep CNN BIBREF16. In a following step, the predicted ingredients are matched against a large corpus of recipes. More recent approach is proposed by BIBREF15 and is based on jointly learning recipe-text and image representations in a shared latent space. Recurrent Neural Networks (RNN) and CNN are mainly used to map text and image into the shared space. To align the text and image embedding vectors between matching recipe-image pairs, cosine similarity loss with margin was applied. Carvalho et al. BIBREF17 proposed a similar multi-modal embedding method for aligning text and image representations in a shared latent space. In contrast to Salvador et al. BIBREF15, they formulated a joint objective function which incorporates the loss for the cross-modal retrieval task and a classification loss, instead of using the latent space for a multitask learning setup. To address the challenge of encoding long sequences (like recipe instructions), BIBREF15 chose to represent single instructions as sentence embedding using the skip-thought technique BIBREF18. These encoded instruction sentences are referred to as skip-instructions and their embedding is not fine tuned when learning the image-text joint embedding. In this study, we present a method for the joint learning of meal image and recipe embedding, using a multi-path structure that incorporates natural language processing paths, as well as image analysis paths. The main contribution of the proposed method is threefold: i) the direct encoding of the instructions, ingredients and images during training, making the need of skip instruction embedding redundant; ii) the utilization of multiple attention mechanisms (i.e. self-attention and ingredient-attention), and iii) a lightweight architecture.