  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.
