  Different from large-scale classification tasks, fine-grained visual
classification is a challenging task due to two critical problems: 1) evident
intra-class variances and subtle inter-class differences, and 2) overfitting
owing to fewer training samples in datasets. Most existing methods extract key
features to reduce intra-class variances, but pay no attention to subtle
inter-class differences in fine-grained visual classification. To address this
issue, we propose a loss function named exploration of class center, which
consists of a multiple class-center constraint and a class-center label
generation. This loss function fully utilizes the information of the class
center from the perspective of features and labels. From the feature
perspective, the multiple class-center constraint pulls samples closer to the
target class center, and pushes samples away from the most similar nontarget
class center. Thus, the constraint reduces intra-class variances and enlarges
inter-class differences. From the label perspective, the class-center label
generation utilizes classcenter distributions to generate soft labels to
alleviate overfitting. Our method can be easily integrated with existing
fine-grained visual classification approaches as a loss function, to further
boost excellent performance with only slight training costs. Extensive
experiments are conducted to demonstrate consistent improvements achieved by
our method on four widely-used fine-grained visual classification datasets. In
particular, our method achieves state-of-the-art performance on the
FGVC-Aircraft and CUB-200-2011 datasets.
