To address the problem of long-tail distribution for the
large vocabulary object detection task, existing methods
usually divide the whole categories into several groups and
treat each group with different strategies. These methods
bring the following two problems. One is the training inconsistency between adjacent categories of similar sizes,
and the other is that the learned model is lack of discrimination for tail categories which are semantically similar
to some of the head categories. In this paper, we devise
a novel Adaptive Class Suppression Loss (ACSL) to effectively tackle the above problems and improve the detection
performance of tail categories. Specifically, we introduce
a statistic-free perspective to analyze the long-tail distribution, breaking the limitation of manual grouping. According
to this perspective, our ACSL adjusts the suppression gradients for each sample of each class adaptively, ensuring
the training consistency and boosting the discrimination for
rare categories. Extensive experiments on long-tail datasets
LVIS and Open Images show that the our ACSL achieves
5.18% and 5.2% improvements with ResNet50-FPN, and
sets a new state of the art. Code and models are available
at https://github.com/CASIA-IVA-Lab/ACSL.