Deep learning based food recognition has achieved remarkable progress in predicting food
types given an eating occasion image. However, there are two major obstacles that hinder …

In the field of fine-grained food recognition, subset learning-based methods offer a strategic
approach that groups classes into subsets to guide the training process. Our study …

Food image classification serves as a fundamental and critical step in image-based dietary
assessment, facilitating nutrient intake analysis from captured food images. However …

Food recognition plays a crucial role in several healthcare applications. Nevertheless it
presents significant computer vision challenges such as long-tailed and fine-grained …

Accurate recognition of nutritional components in food is crucial for dietary management and
health monitoring. Current methods often rely on traditional chemical analysis techniques …

Given the potential applications of generating recipes from food images, this area has
garnered significant attention from researchers in recent years. Existing works for recipe …

Deep neural networks represent a compelling technique to tackle complex real-world
problems, but are over-parameterized and often suffer from over-or under-confident …

Dietary assessment is essential to maintaining a healthy lifestyle. Automatic image-based
dietary assessment is a growing field of research due to the increasing prevalence of image …

Food-image recognition plays a pivotal role in intelligent nutrition management, and
lightweight recognition methods based on deep learning are crucial for enabling mobile …

Consumer behaviors and habits in food choices impact their physical health and have
implications for climate change and global warming. Efficient food image recognition can …