  Long-tailed object detection faces great challenges because of its extremely
imbalanced class distribution. Recent methods mainly focus on the
classification bias and its loss function design, while ignoring the subtle
influence of the regression branch. This paper shows that the regression bias
exists and does adversely and seriously impact the detection accuracy. While
existing methods fail to handle the regression bias, the class-specific
regression head for rare classes is hypothesized to be the main cause of it in
this paper. As a result, three kinds of viable solutions to cater for the rare
categories are proposed, including adding a class-agnostic branch, clustering
heads and merging heads. The proposed methods brings in consistent and
significant improvements over existing long-tailed detection methods,
especially in rare and common classes. The proposed method achieves
state-of-the-art performance in the large vocabulary LVIS dataset with
different backbones and architectures. It generalizes well to more difficult
evaluation metrics, relatively balanced datasets, and the mask branch. This is
the first attempt to reveal and explore rectifying of the regression bias in
long-tailed object detection.
