  The imbalanced distribution of long-tailed data presents a considerable
challenge for deep learning models, as it causes them to prioritize the
accurate classification of head classes but largely disregard tail classes. The
biased decision boundary caused by inadequate semantic information in tail
classes is one of the key factors contributing to their low recognition
accuracy. To rectify this issue, we propose to augment tail classes by grafting
the diverse semantic information from head classes, referred to as head-to-tail
fusion (H2T). We replace a portion of feature maps from tail classes with those
belonging to head classes. These fused features substantially enhance the
diversity of tail classes. Both theoretical analysis and practical
experimentation demonstrate that H2T can contribute to a more optimized
solution for the decision boundary. We seamlessly integrate H2T in the
classifier adjustment stage, making it a plug-and-play module. Its simplicity
and ease of implementation allow for smooth integration with existing
long-tailed recognition methods, facilitating a further performance boost.
Extensive experiments on various long-tailed benchmarks demonstrate the
effectiveness of the proposed H2T. The source code is available at
https://github.com/Keke921/H2T.
