Deep learning for food computing

Today Deep learning (DL) is revolutionizing many fields of real problems, beating human performance in problems such as object recognition, lip reading, or cancer detection. However, food recognition and segmentation are still underexplored and underexploited. To address them, we develop highly robust DL algorithms employing Transfer learning, Transformers, Uncertainty modeling and Explainability among others.

Our team members are researching on various aspects of deep learning for food computing,

Food Image Analysis/ Food Recognition/ Food Volume Estimation

Our team specializes in developing advanced algorithms and models for accurately analyzing and recognizing food items in images. From identifying ingredients and dishes to assessing nutritional content, we're dedicated to leveraging computer vision and machine learning techniques to unlock insights in the food domain. Whether it's for dietary monitoring, recipe recommendation, or food industry applications, we're committed to providing cutting-edge solutions that enhance food-related processes and experiences.

Food and Nutrition Data

Our team specializes in collecting, organizing, and analyzing comprehensive datasets related to food composition, nutritional content, and dietary intake. Leveraging advanced data management techniques, we provide reliable and up-to-date information that supports research, health monitoring, and nutritional analysis. Whether it's for developing personalized diet plans, conducting epidemiological studies, or enhancing food product development, we're committed to delivering high-quality data that drives informed decision-making and promotes better health outcomes.