Boosting Long-tailed Object Detection via Step-wise Learning on
  Smooth-tail Data
Na Dong Yongqiang Zhang Mingli Ding Gim Hee Lee
Real-world data tends to follow a long-tailed distribution, where the class
imbalance results in dominance of the head classes during training. In this
paper, we propose a frustratingly simple but effective step-wise learning
framework to gradually enhance the capability of the model in detecting all
categories of long-tailed datasets. Specifically, we build smooth-tail data
where the long-tailed distribution of categories decays smoothly to correct the
bias towards head classes. We pre-train a model on the whole long-tailed data
to preserve discriminability between all categories. We then fine-tune the
class-agnostic modules of the pre-trained model on the head class dominant
replay data to get a head class expert model with improved decision boundaries
from all categories. Finally, we train a unified model on the tail class
dominant replay data while transferring knowledge from the head class expert
model to ensure accurate detection of all categories. Extensive experiments on
long-tailed datasets LVIS v0.5 and LVIS v1.0 demonstrate the superior
performance of our method, where we can improve the AP with ResNet-50 backbone
from 27.0% to 30.3% AP, and especially for the rare categories from 15.5% to
24.9% AP. Our best model using ResNet-101 backbone can achieve 30.7% AP, which
suppresses all existing detectors using the same backbone.