Learning User Preferences from Social Multimedia Analysis and Overview of the iFood2019Challenge

Abstract

There has been an explosion of multimodal content generated on social media networks in the last few years, which has necessitated a deeper understanding of social media content and user behavior. In the first part of the task, I will present a novel method that provides a unified framework for understanding content as well as modeling user preferences from noisy social media posts. I will discuss some applications in understanding food preferences and trends using this algorithm. I will then give an overview of the second large-scale food classification challenge in images (iFood challenge) held as part of the sixth Fine Grained Visual Classification Workshop at CVPR19. We introduce a new dataset of 251 fine-grained (prepared) food categories with 118K training images collected from the web, and human verified labels for both validation set (11K images) and the test set (12K images). 40 teams from academia and industry competed in this challenge with the top team obtaining a 5.6% top-3 error percentage, which is almost 2-points better than previous year challenge.