  Long-tail distribution is widely spread in real-world applications. Due to
the extremely small ratio of instances, tail categories often show inferior
accuracy. In this paper, we find such performance bottleneck is mainly caused
by the imbalanced gradients, which can be categorized into two parts: (1)
positive part, deriving from the samples of the same category, and (2) negative
part, contributed by other categories. Based on comprehensive experiments, it
is also observed that the gradient ratio of accumulated positives to negatives
is a good indicator to measure how balanced a category is trained. Inspired by
this, we come up with a gradient-driven training mechanism to tackle the
long-tail problem: re-balancing the positive/negative gradients dynamically
according to current accumulative gradients, with a unified goal of achieving
balance gradient ratios. Taking advantage of the simple and flexible gradient
mechanism, we introduce a new family of gradient-driven loss functions, namely
equalization losses. We conduct extensive experiments on a wide spectrum of
visual tasks, including two-stage/single-stage long-tailed object detection
(LVIS), long-tailed image classification (ImageNet-LT, Places-LT, iNaturalist),
and long-tailed semantic segmentation (ADE20K). Our method consistently
outperforms the baseline models, demonstrating the effectiveness and
generalization ability of the proposed equalization losses. Codes will be
released at https://github.com/ModelTC/United-Perception.
