  In this paper, we study the problem of class imbalance in semantic
segmentation. We first investigate and identify the main challenges of
addressing this issue through pixel rebalance. Then a simple and yet effective
region rebalance scheme is derived based on our analysis. In our solution,
pixel features belonging to the same class are grouped into region features,
and a rebalanced region classifier is applied via an auxiliary region rebalance
branch during training. To verify the flexibility and effectiveness of our
method, we apply the region rebalance module into various semantic segmentation
methods, such as Deeplabv3+, OCRNet, and Swin. Our strategy achieves consistent
improvement on the challenging ADE20K and COCO-Stuff benchmark. In particular,
with the proposed region rebalance scheme, state-of-the-art BEiT receives +0.7%
gain in terms of mIoU on the ADE20K val set.
