Not All Pixels Are Equal: Learning Pixel Hardness for Semantic
  Segmentation
Xin Xiao Daiguo Zhou Jiagao Hu Yi Hu Yongchao Xu
Semantic segmentation has recently witnessed great progress. Despite the
impressive overall results, the segmentation performance in some hard areas
(e.g., small objects or thin parts) is still not promising. A straightforward
solution is hard sample mining, which is widely used in object detection. Yet,
most existing hard pixel mining strategies for semantic segmentation often rely
on pixel's loss value, which tends to decrease during training. Intuitively,
the pixel hardness for segmentation mainly depends on image structure and is
expected to be stable. In this paper, we propose to learn pixel hardness for
semantic segmentation, leveraging hardness information contained in global and
historical loss values. More precisely, we add a gradient-independent branch
for learning a hardness level (HL) map by maximizing hardness-weighted
segmentation loss, which is minimized for the segmentation head. This
encourages large hardness values in difficult areas, leading to appropriate and
stable HL map. Despite its simplicity, the proposed method can be applied to
most segmentation methods with no and marginal extra cost during inference and
training, respectively. Without bells and whistles, the proposed method
achieves consistent/significant improvement (1.37% mIoU on average) over most
popular semantic segmentation methods on Cityscapes dataset, and demonstrates
good generalization ability across domains. The source codes are available at
https://github.com/Menoly-xin/Hardness-Level-Learning .