When calculating the number of calories in a dish, discriminating betweenįood regions is an important factor. With the popularity of health management applications, awareness of dietary management
SESSION: Session 2: Short Oral Session Few-Shot and Zero-Shot Semantic Segmentation for Food Images Model was able to estimate calorie amounts of individual food items roughly. TheĮxperimental results showed that our region-segmentation-based calorie estimation Then, we propose a model employing food segmentation which can estimateĬalorie amounts of each food item from total calorie values of set meal photos. Item in meal photos should be known in general. To estimate calorie amounts of food items, the calorie values of each food Then, in this work, we crawl suchĭata from the Web and use them as training data of a vision-based food calorie estimation Of food set menus with only total calorie values. However, we can see some Web sites which provide photos In fact, no large-scale food datasets annotated with calorie amountsĮxits as long as we know. However, unfortunately, food image datasets annotated with calorie amounts are very One of the major tasks in food computing is vision-based food calorie estimation. Region-Based Food Calorie Estimation for Multiple-Dish Meals That the proposed approach increases diversity during the exploration process. Our initial evaluation in comparison to a baseline similarity-based recommender shows This paper introduces a novel approach that combinesĬritique and diversity to support conversational recommendation in the recipe domain. ConversationalĮxploration can help to introduce new food items, and diversity in diet has been shown BothĪspects are important elements for recommender applications in the food domain. User understanding of the search space, which critiquing alone may not achieve. Other hand, diversifying the recommended items during exploration can help increase On one hand, critiquingĪs a way of feedback has proven effective for conversational interactions. During the exploration process, the user providesįeedback on recommended items to refine subsequent recommendations. Increasing Diversity through Dynamic Critique in Conversational Recipe RecommendationsĬonversational recommender systems help to guide users to discover items of interest Method by evaluating the relationship and physical condition feature words extractedīy the proposed method and by evaluating the recipe ranking using Rakuten's recipeĭata. We validate the usefulness of the proposed Sentiment, and ranked based on the extracted feature words and sentiment values of Information," and "trigger." In addition, each item of the recipe is analyzed for Than the cooking procedure, such as "title," "introduction of the recipe," "one-point Learning from the items in the recipe data that contain a lot of information other Specifically, we extract feature words by Not only the recipe procedure but also the relationship between the provider and the In particular, we focus on the motivation of recipe contributors,Īnd propose a recipe recommendation method that can heal both parties by considering In this paper, we propose a recipe recommendation method that can heal both the providerĪnd the recipient of food.
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