  Food classification serves as the basic step of image-based dietary
assessment to predict the types of foods in each input image. However, food
image predictions in a real world scenario are usually long-tail distributed
among different food classes, which cause heavy class-imbalance problems and a
restricted performance. In addition, none of the existing long-tailed
classification methods focus on food data, which can be more challenging due to
the lower inter-class and higher intra-class similarity among foods. In this
work, we first introduce two new benchmark datasets for long-tailed food
classification including Food101-LT and VFN-LT where the number of samples in
VFN-LT exhibits the real world long-tailed food distribution. Then we propose a
novel 2-Phase framework to address the problem of class-imbalance by (1)
undersampling the head classes to remove redundant samples along with
maintaining the learned information through knowledge distillation, and (2)
oversampling the tail classes by performing visual-aware data augmentation. We
show the effectiveness of our method by comparing with existing
state-of-the-art long-tailed classification methods and show improved
performance on both Food101-LT and VFN-LT benchmarks. The results demonstrate
the potential to apply our method to related real life applications.
