MetaFood Workshop

Introduction

Today, computer vision algorithms show near-perfect performance, better than human when there are clear, well curated and enough amount of data. However, there remains a substantial gap when it comes to applying state-of-the-art computer vision algorithms to food data, particularly when dealing with food in its natural, uncontrolled environment, often referred to as “data in the wild.” This gap stems from the inherent challenges in noisy, watermarked, and low-quality food data readily available on the internet. The MetaFood Workshop (MTF) invites the CVPR community to engage with the food domain-related challenges. These challenges provide not only a demanding, real testing environment for the development of robust computer vision algorithms, but also an exciting opportunity to develop new algorithms in the fields of food data analysis and food digitization.

Organizers

Keynote Speakers

Call for Papers

The MetaFood workshop papers will encompass a diverse array of computer vision topics related to food, including but not limited to:

• Food image/video generation and Generative AI
• Food video analysis and action recognition
• Food 3D model reconstruction
• Food portion/nutrition value estimation
• Food manipulation understanding
• Food image quality analysis/inspection
• Eating and cooking action recognition
• Multi-modal food data analysis
• Food ontologies and LLM-based models for food data analysis
• Visual question answering for food
• Food data analysis and uncertainty modeling
• Learning with noisy food labels
• Continual, self-supervised, semi-supervised, and unsupervised learning for food
• Food classification/detection/segmentation in 2D/3D