  Deep learning based food image classification has enabled more accurate
nutrition content analysis for image-based dietary assessment by predicting the
types of food in eating occasion images. However, there are two major obstacles
to apply food classification in real life applications. First, real life food
images are usually heavy-tailed distributed, resulting in severe
class-imbalance issue. Second, it is challenging to train a single-stage (i.e.
end-to-end) framework under heavy-tailed data distribution, which cause the
over-predictions towards head classes with rich instances and under-predictions
towards tail classes with rare instance. In this work, we address both issues
by introducing a novel single-stage heavy-tailed food classification framework.
Our method is evaluated on two heavy-tailed food benchmark datasets, Food101-LT
and VFN-LT, and achieves the best performance compared to existing work with
over 5% improvements for top-1 accuracy.
