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.

翻译：长尾目标检测因其类别分布极度不平衡而面临巨大挑战。近期方法主要聚焦于分类偏差及其损失函数设计，却忽略了回归分支的微妙影响。本文证明回归偏差确实存在且会严重损害检测精度。现有方法无法处理回归偏差，本文假设针对稀有类别的类别特定回归头部是其主因。为此，我们提出了三种适配稀有类别的可行方案：添加类别无关分支、聚类头部及合并头部。所提方法在现有长尾检测方法基础上实现了持续而显著的性能提升，尤其在稀有类和常见类别中表现突出。该方法在大词汇量LVIS数据集上采用不同主干网络和架构均达到最优性能，并能良好泛化至更困难的评估指标、相对平衡的数据集以及掩码分支。这是首次揭示并探索修复长尾目标检测中回归偏差的开创性工作。