  In the real-world setting, data often follows a long-tailed distribution,
where head classes contain significantly more training samples than tail
classes. Consequently, models trained on such data tend to be biased toward
head classes. The medium of this bias is imbalanced gradients, which include
not only the ratio of scale between positive and negative gradients but also
imbalanced gradients from different negative classes. Therefore, we propose the
Gradient-Aware Logit Adjustment (GALA) loss, which adjusts the logits based on
accumulated gradients to balance the optimization process. Additionally, We
find that most of the solutions to long-tailed problems are still biased
towards head classes in the end, and we propose a simple and post hoc
prediction re-balancing strategy to further mitigate the basis toward head
class. Extensive experiments are conducted on multiple popular long-tailed
recognition benchmark datasets to evaluate the effectiveness of these two
designs. Our approach achieves top-1 accuracy of 48.5\%, 41.4\%, and 73.3\% on
CIFAR100-LT, Places-LT, and iNaturalist, outperforming the state-of-the-art
method GCL by a significant margin of 3.62\%, 0.76\% and 1.2\%, respectively.
Code is available at https://github.com/lt-project-repository/lt-project.
