  The conventional detectors tend to make imbalanced classification and suffer
performance drop, when the distribution of the training data is severely
skewed. In this paper, we propose to use the mean classification score to
indicate the classification accuracy for each category during training. Based
on this indicator, we balance the classification via an Equilibrium Loss (EBL)
and a Memory-augmented Feature Sampling (MFS) method. Specifically, EBL
increases the intensity of the adjustment of the decision boundary for the weak
classes by a designed score-guided loss margin between any two classes. On the
other hand, MFS improves the frequency and accuracy of the adjustment of the
decision boundary for the weak classes through over-sampling the instance
features of those classes. Therefore, EBL and MFS work collaboratively for
finding the classification equilibrium in long-tailed detection, and
dramatically improve the performance of tail classes while maintaining or even
improving the performance of head classes. We conduct experiments on LVIS using
Mask R-CNN with various backbones including ResNet-50-FPN and ResNet-101-FPN to
show the superiority of the proposed method. It improves the detection
performance of tail classes by 15.6 AP, and outperforms the most recent
long-tailed object detectors by more than 1 AP. Code is available at
https://github.com/fcjian/LOCE.
