Ingredient Recognition

Ingredient recognition research focuses on automatically identifying and quantifying food ingredients within images and textual recipes, aiming to improve automated cooking systems and dietary assessment tools. Current approaches leverage deep learning models, particularly Convolutional Neural Networks (CNNs) for image analysis and Pre-trained Language Models (PLMs) for recipe text processing, often incorporating attention mechanisms and multi-label learning techniques to handle the complexity of diverse ingredients and presentations. These advancements are significant for developing intelligent kitchen robots, personalized recipe recommendation systems, and automated nutritional analysis, ultimately impacting both culinary technology and public health.