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 this https URL

我们提出了梯度感知的逻辑调整（GALA）损失和预测再平衡策略来解决长尾数据问题，并在多个数据集上实验证明了其有效性。

梯度感知的对数调整损失函数用于长尾分类器

Gradient-Aware Logit Adjustment Loss for Long-tailed Classifier

Recently proposed decoupled training methods emerge as a dominant paradigm
for long-tailed object detection. But they require an extra fine-tuning stage,
and the disjointed optimization of representation and classifier might lead to
suboptimal results. However, end-to-end training methods, like equalization
loss (EQL), still perform worse than decoupled training methods. In this paper,
we reveal the main issue in long-tailed object detection is the imbalanced
gradients between positives and negatives, and find that EQL does not solve it
well. To address the problem of imbalanced gradients, we introduce a new
version of equalization loss, called equalization loss v2 (EQL v2), a novel
gradient guided reweighing mechanism that re-balances the training process for
each category independently and equally. Extensive experiments are performed on
the challenging LVIS benchmark. EQL v2 outperforms origin EQL by about 4 points
overall AP with 14-18 points improvements on the rare categories. More
importantly, it also surpasses decoupled training methods. Without further
tuning for the Open Images dataset, EQL v2 improves EQL by 7.3 points AP,
showing strong generalization ability. Codes have been released at
this https URL

为解决不平衡梯度导致的长尾目标检测困难，本研究提出了一种新的均衡损失（EQL v2）。实验结果表明，EQL v2 在 LVIS 基准测试中表现优异，比起原 EQL 可提高 4 个点的总 AP 值和 14-18 个点的罕见类别 AP 值，并且甚至超过了分离式训练方法。