  Real-world data often follows a long-tailed distribution, which makes the
performance of existing classification algorithms degrade heavily. A key issue
is that samples in tail categories fail to depict their intra-class diversity.
Humans can imagine a sample in new poses, scenes, and view angles with their
prior knowledge even if it is the first time to see this category. Inspired by
this, we propose a novel reasoning-based implicit semantic data augmentation
method to borrow transformation directions from other classes. Since the
covariance matrix of each category represents the feature transformation
directions, we can sample new directions from similar categories to generate
definitely different instances. Specifically, the long-tailed distributed data
is first adopted to train a backbone and a classifier. Then, a covariance
matrix for each category is estimated, and a knowledge graph is constructed to
store the relations of any two categories. Finally, tail samples are adaptively
enhanced via propagating information from all the similar categories in the
knowledge graph. Experimental results on CIFAR-100-LT, ImageNet-LT, and
iNaturalist 2018 have demonstrated the effectiveness of our proposed method
compared with the state-of-the-art methods.
