  Collecting fine-grained labels usually requires expert-level domain knowledge
and is prohibitive to scale up. In this paper, we propose Attribute Mix, a data
augmentation strategy at attribute level to expand the fine-grained samples.
The principle lies in that attribute features are shared among fine-grained
sub-categories, and can be seamlessly transferred among images. Toward this
goal, we propose an automatic attribute mining approach to discover attributes
that belong to the same super-category, and Attribute Mix is operated by mixing
semantically meaningful attribute features from two images. Attribute Mix is a
simple but effective data augmentation strategy that can significantly improve
the recognition performance without increasing the inference budgets.
Furthermore, since attributes can be shared among images from the same
super-category, we further enrich the training samples with attribute level
labels using images from the generic domain. Experiments on widely used
fine-grained benchmarks demonstrate the effectiveness of our proposed method.
