  Deep neural networks have exhibited remarkable performance in a variety of
computer vision fields, especially in semantic segmentation tasks. Their
success is often attributed to multi-level feature fusion, which enables them
to understand both global and local information from an image. However, we
found that multi-level features from parallel branches are on different scales.
The scale disequilibrium is a universal and unwanted flaw that leads to
detrimental gradient descent, thereby degrading performance in semantic
segmentation. We discover that scale disequilibrium is caused by bilinear
upsampling, which is supported by both theoretical and empirical evidence.
Based on this observation, we propose injecting scale equalizers to achieve
scale equilibrium across multi-level features after bilinear upsampling. Our
proposed scale equalizers are easy to implement, applicable to any
architecture, hyperparameter-free, implementable without requiring extra
computational cost, and guarantee scale equilibrium for any dataset.
Experiments showed that adopting scale equalizers consistently improved the
mIoU index across various target datasets, including ADE20K, PASCAL VOC 2012,
and Cityscapes, as well as various decoder choices, including UPerHead,
PSPHead, ASPPHead, SepASPPHead, and FCNHead.
