Do You Live a Healthy Life? Analyzing Lifestyle by Visual Life Logging

FoodAI: Food Image Recognition via Deep Learning for Smart Food Logging

An important aspect of health monitoring is effective logging of food consumption. This can help management of diet-related diseases like obesity, diabetes, and even cardiovascular diseases. Moreover, food logging can help fitness enthusiasts, and people who wanting to achieve a target weight. However, food-logging is cumbersome, and requires not only taking additional effort to note down the food item consumed regularly, but also sufficient knowledge of the food item consumed (which is difficult due to the availability of a wide variety of cuisines). With increasing reliance on smart devices,…

Measuring the Utilization of Public Open Spaces by Deep Learning: a Benchmark Study at the Detroit Riverfront

Physical activities and social interactions are essential activities that ensure a healthy lifestyle. Public open spaces (POS), such as parks, plazas and greenways, are key environments that encourage those activities. To evaluate a POS, there is a need to study how humans use the facilities within it. However, traditional approaches to studying use of POS are manual and therefore time and labor intensive. They also may only provide qualitative insights. It is appealing to make use of surveillance cameras and to extract user-related information through computer vision. This paper proposes a pr…

基于图像的饮食评估：一个健康饮食餐盘估算系统

Image-Based Dietary Assessment: A Healthy Eating Plate Estimation System

本文提出基于图像的饮食评估系统，通过分割、分类、营养评估和建议四步框架，依据 Harvard Healthy Eating Plate 判断餐食健康性。

Learning Cross-Modal Embeddings with Adversarial Networks for Cooking Recipes and Food Images

Food computing is playing an increasingly important role in human daily life, and has found tremendous applications in guiding human behavior towards smart food consumption and healthy lifestyle. An important task under the food-computing umbrella is retrieval, which is particularly helpful for health related applications, where we are interested in retrieving important information about food (e.g., ingredients, nutrition, etc.). In this paper, we investigate an open research task of cross-modal retrieval between cooking recipes and food images, and propose a novel framework Adversarial Cross-…

AI4Food-NutritionFW：一种面向饮食行为自动合成与分析的新型框架

AI4Food-NutritionFW: A Novel Framework for the Automatic Synthesis and Analysis of Eating Behaviours

本文提出AI4Food-NutritionFW框架，可配置合成饮食行为食物图像数据集，并基于多维健康指标自动评估饮食健康指数，准确率达99.53%。

利用自动个性化营养：基于营养分类法的食物图像识别基准与数据集

Leveraging Automatic Personalised Nutrition: Food Image Recognition Benchmark and Dataset based on Nutrition Taxonomy

本研究提出AI4Food-NutritionDB，首个结合食物图像与营养分类法的数据库，并提供标准基准与预训练模型，以支持个性化营养和食物识别研究。