Food image classification models are crucial for dietary management applications because they reduce the burden of manual meal logging. However, most publicly available datasets for training such models rely on web-crawled images, which often differ from users' real-world meal photos. In this work, we present FoodLogAthl-218, a food image dataset constructed from real-world meal records collected through the dietary management application FoodLog Athl. The dataset contains 6,925 images across 218 food categories, with a total of 14,349 bounding boxes. Rich metadata, including meal date and time, anonymized user IDs, and meal-level context, accompany each image. Unlike conventional datasets-where a predefined class set guides web-based image collection-our data begins with user-submitted photos, and labels are applied afterward. This yields greater intra-class diversity, a natural frequency distribution of meal types, and casual, unfiltered images intended for personal use rather than public sharing. In addition to (1) a standard classification benchmark, we introduce two FoodLog-specific tasks: (2) an incremental fine-tuning protocol that follows the temporal stream of users' logs, and (3) a context-aware classification task where each image contains multiple dishes, and the model must classify each dish by leveraging the overall meal context. We evaluate these tasks using large multimodal models (LMMs). The dataset is publicly available at https://huggingface.co/datasets/FoodLog/FoodLogAthl-218.

We propose a recipe-oriented food logging method that records food by recipe, unlike the ordinary food logging method that records food by name. We also develop an application RecipeLog for this purpose. RecipeLog can create a "skeleton recipe," which is a standardized recipe representation suitable for estimating the nutritional value of a dish. This is represented by a list of ingredients linked to a Nutrition Facts table and a flow graph consisting of cooking actions such as cutting, mixing, baking, simmering, and frying the ingredients. The recipe log allows recipes to be written with fewer operations by editing only the differences from the already registered base recipe. Experiments have confirmed that the recipe log can effectively identify differences in recipes from household to household. The future work is to construct a multimedia recipe dataset consisting of structured recipes and their images using RecipeLog.

This paper presents a new food recording tool, FoodLog Athl, for the healthcare or physical enhancement of its users. Unlike existing food recording tools, we designed the system for dietitians or third parties who monitor the users. The tool not only supports the users by functions such as food image recognition, but also it helps the dietitians watch and communicate with users. Furthermore, it calculates nutritional values from food records - the use of the tool reduces the workload of dietitians and focuses their work on nutrition guidance.

Recently, meal management apps have allowed people to record food items and calories from photos automatically. These technologies include extracting food regions from photos of served meals, identifying the name of the food in each region, and calculating nutritional data. However, what you eat is not the only indicator that should be kept in the food record. How fast you eat and the order in which you eat is also significant information for dietary management. Therefore, we aim to construct a system that automatically generates a meal log from first-person videos that users capture of their eating behavior with a wearable camera. To tackle the complex problems that the data this system assumes contains, we constructed an eating behavior record dataset: 9.9 hours of first-person video that assume the natural diets of a user. To investigate the feasibility of our proposed system, we evaluated whether the first step, the detection of the meal area in the video during the meal, could be achieved with sufficient accuracy using this dataset. Using the limited number of frames assumed to be annotated by the user as training data, 30 frames were annotated for user-specific model training and four frames for online adaptation, resulting in detection accuracy of 72% for food regions. Our next goal is to create a multi-user dataset and service the application.

Since the development of food diaries could enable people to develop healthy eating habits, food image recognition is in high demand to reduce the effort in food recording. Previous studies have worked on this challenging domain with datasets having fixed numbers of samples and classes. However, in the real-world setting, it is impossible to include all of the foods in the database because the number of classes of foods is large and increases continually. In addition to that, inter-class similarity and intra-class diversity also bring difficulties to the recognition. In this paper, we attempted to solve these problems by using deep convolutional neural network features to build a personalized classifier which incrementally learns the user's data and adapts to the user's eating habit. As a result, we achieved the state-of-the-art accuracy of food image recognition by the personalization of 300 food records per user.

Currently, food image recognition tasks are evaluated against fixed datasets. However, in real-world conditions, there are cases in which the number of samples in each class continues to increase and samples from novel classes appear. In particular, dynamic datasets in which each individual user creates samples and continues the updating process often has content that varies considerably between different users, and the number of samples per person is very limited. A single classifier common to all users cannot handle such dynamic data. Bridging the gap between the laboratory environment and the real world has not yet been accomplished on a large scale. Personalizing a classifier incrementally for each user is a promising way to do this. In this paper, we address the personalization problem, which involves adapting to the user's domain incrementally using a very limited number of samples. We propose a simple yet effective personalization framework, which is a combination of the nearest class mean classifier and the 1-nearest neighbor classifier based on deep features. To conduct realistic experiments, we made use of a new dataset of daily food images collected by a food-logging application. Experimental results show that our proposed method significantly outperforms existing methods.