Image Based Dietary Assessment

Image-based dietary assessment uses computer vision to analyze food images, aiming to automatically estimate nutritional intake, overcoming limitations of self-reporting methods. Current research focuses on improving food image classification accuracy through techniques like multi-modal contrastive learning, continual learning with compressed exemplars, and the use of generative models to augment training data, often employing deep neural networks and transformer architectures. This technology holds significant potential for improving dietary assessment accuracy and personalization, leading to more effective interventions for promoting healthier eating habits and managing diet-related diseases.