  Real-world data often have a long-tailed distribution, where the number of
samples per class is not equal over training classes. The imbalanced data form
a biased feature space, which deteriorates the performance of the recognition
model. In this paper, we propose a novel long-tailed recognition method to
balance the latent feature space. First, we introduce a MixUp-based data
augmentation technique to reduce the bias of the long-tailed data. Furthermore,
we propose a new supervised contrastive learning method, named Supervised
contrastive learning on Mixed Classes (SMC), for blended images. SMC creates a
set of positives based on the class labels of the original images. The
combination ratio of positives weights the positives in the training loss. SMC
with the class-mixture-based loss explores more diverse data space, enhancing
the generalization capability of the model. Extensive experiments on various
benchmarks show the effectiveness of our one-stage training method.
