Object recognition techniques using convolutional neural networks (CNN) have achieved great success. However, state-of-the-art object detection methods still perform poorly on large vocabulary and long-tailed datasets, e.g. LVIS. In this work, we analyze this problem from a novel perspective: each positive sample of one category can be seen as a negative sample for other categories, making the tail categories receive more discouraging gradients. Based on it, we propose a simple but effective loss, named equalization loss, to tackle the problem of long-tailed rare categories by simply ignoring those gradients for rare categories. The equalization loss protects the learning of rare categories from being at a disadvantage during the network parameter updating. Thus the model is capable of learning better discriminative features for objects of rare classes. Without any bells and whistles, our method achieves AP gains of 4.1% and 4.8% for the rare and common categories on the challenging LVIS benchmark, compared to the Mask R-CNN baseline. With the utilization of the effective equalization loss, we finally won the 1st place in the LVIS Challenge 2019. Code has been made available at: https: //github.com/tztztztztz/eql.detectron2

本文提出了等化损失函数来解决卷积神经网络在大词汇和长尾数据集上的目标检测问题，通过简单的忽略稀有类别的梯度，保护稀有类别的学习不受到劣势影响，从而使得模型能够更好地学习稀有类别的物体的判别特征。通过实验证明，该方法在LVIS基准测试上，相对于Mask R-CNN基线，较为高效且能取得更好的检测效果。

长尾目标识别的均衡损失