Bridging the Gap: From Food Recognition to Accurate Weight Estimation

Accurate estimation of food weight is of utmost importance in the assessment of diet in nutrition, healthcare, and wellness. Although great progress has been achieved with machine learning and computer vision in image-based food recognition, the task of accurate weight estimation is still a challenging one. This review summarizes the current methodologies that address the gap, focusing on the reduction of errors and novel innovations. The review compares 2D and 3D computational methods, emphasizes the advantages of hybrid multimodal models, and assesses their strengths and weaknesses. The role of datasets and annotation techniques is analyzed in detail, highlighting the importance of having diverse and accurate datasets for model improvement. Standard evaluation metrics and benchmarks are addressed to evaluate accuracy and comparability across methods. Realism emerging trends of self-supervised learning, multimodal data integration, and real-time edge computing applications are searched for as answers to these practical constraints found in reality: variability in food, occlusion, environmental factors all challenge; solution proposed here. This review is aimed at outlining future research guidance by summarizing recent innovations, addressing gaps, and providing a roadmap for practical, scalable, and accurate dietary assessment systems.