Integrating artificial intelligence with food category recognition has been a field of interest for
research for the past few decades. It is potentially one of the next steps in revolutionizing …

Large multimodal models (LMMs) extend large language models (LLMs) with multi-sensory
skills, such as visual understanding, to achieve stronger generic intelligence. In this paper …

Artificial intelligence (AI) use in diabetes care is increasingly being explored to personalise
care for people with diabetes and adapt treatments for complex presentations. However, the …

Background: Maintaining a healthy diet is vital to avoid health-related issues, eg,
undernutrition, obesity and many non-communicable diseases. An indispensable part of the …

Fine-grained and instance-level recognition methods are commonly trained and evaluated
on specific domains, in a model per domain scenario. Such an approach, however, is …

Food image classification is challenging for real-world applications since existing methods
require static datasets for training and are not capable of learning from sequentially …

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 …

Learning recipe and food image representation in common embedding space is non-trivial
but crucial for cross-modal recipe retrieval. In this paper, we propose a new perspective for …

With the development of deep learning technology, vision-based food nutrition estimation is
gradually entering the public view for its advantage in accuracy and efficiency. In this paper …

Despite the remarkable progress of Fine-grained visual classification (FGVC) with years of
history, it is still limited to recognizing 2 images. Recognizing objects in the physical world …