Progressive Training Enabled Fine-Grained Recognition

Abstract

Organizing training samples in a meaningful order is beneficial for accelerating the convergence rate and enhancing the recognition performance in the CNN model. However, achieving reasonable sample ranking for fine-grained recognition datasets is very challenging because the intra and inter class relation in those datasets is opposite to that in public recognition datasets. In this paper, we propose a general framework for the progressive training of fine-grained recognition models. In particular, we first formulate the training subset selection as a group ranking-oriented submodular optimization problem, where the submodularity is adopted to evaluate the benefit of selected training subsets. This can give theoretical guidance for the consecutive discrimination of difficult and ordinary training subsets. Secondly, we design a training strategy to dynamically adjust the ratio of difficult and ordinary training subsets according to the recognition performance. Extensive experiments on CUB-200-2011 and Stanford Dogs datasets demonstrate that the proposed method outperforms the state-of-the-art curriculum learning methods.