MTKGR: multi-task knowledge graph reasoning for food and ingredient recognition

Food and ingredient recognition emerges as a pivotal challenge in the domain of computer vision, particularly pertinent to multimedia systems applications. To exploit the intricate relationships between foods and their constituent ingredients, this paper introduces a novel approach termed Multi-Task Knowledge Graph Reasoning for Food and Ingredient Recognition (MTKGR). By integrating a multi-task convolutional neural network model with knowledge graph reasoning, MTKGR achieves significant breakthroughs in ingredient recognition on the ETH Food-101 and Ingredient-101 datasets, propelling the Micro-F1 and Macro-F1 scores to new state-of-the-art heights with improvements of 2.23