Fine-Grained Visual Categorization using
Meta-Learning Optimization with Sample
Selection of Auxiliary Data
Abstract. Fine-grained visual categorization (FGVC) is challenging due
in part to the fact that it is often difficult to acquire an enough number
of training samples. To employ large models for FGVC without suffering from overfitting, existing methods usually adopt a strategy of pretraining the models using a rich set of auxiliary data, followed by finetuning on the target FGVC task. However, the objective of pre-training
does not take the target task into account, and consequently such obtained models are suboptimal for fine-tuning. To address this issue, we
propose in this paper a new deep FGVC model termed MetaFGNet.
Training of MetaFGNet is based on a novel regularized meta-learning
objective, which aims to guide the learning of network parameters so
that they are optimal for adapting to the target FGVC task. Based on
MetaFGNet, we also propose a simple yet effective scheme for selecting
more useful samples from the auxiliary data. Experiments on benchmark
FGVC datasets show the efficacy of our proposed method.