  Real-world data often follows a long-tailed distribution, where a few head
classes occupy most of the data and a large number of tail classes only contain
very limited samples. In practice, deep models often show poor generalization
performance on tail classes due to the imbalanced distribution. To tackle this,
data augmentation has become an effective way by synthesizing new samples for
tail classes. Among them, one popular way is to use CutMix that explicitly
mixups the images of tail classes and the others, while constructing the labels
according to the ratio of areas cropped from two images. However, the
area-based labels entirely ignore the inherent semantic information of the
augmented samples, often leading to misleading training signals. To address
this issue, we propose a Contrastive CutMix (ConCutMix) that constructs
augmented samples with semantically consistent labels to boost the performance
of long-tailed recognition. Specifically, we compute the similarities between
samples in the semantic space learned by contrastive learning, and use them to
rectify the area-based labels. Experiments show that our ConCutMix
significantly improves the accuracy on tail classes as well as the overall
performance. For example, based on ResNeXt-50, we improve the overall accuracy
on ImageNet-LT by 3.0% thanks to the significant improvement of 3.3% on tail
classes. We highlight that the improvement also generalizes well to other
benchmarks and models. Our code and pretrained models are available at
https://github.com/PanHaulin/ConCutMix.
