  Image segmentation in the urban scene has recently attracted much attention
due to its success in autonomous driving systems. However, the poor performance
of concerned foreground targets, e.g., traffic lights and poles, still limits
its further practical applications. In urban scenes, foreground targets are
always concealed in their surrounding stuff because of the special camera
position and 3D perspective projection. What's worse, it exacerbates the
unbalance between foreground and background classes in high-level features due
to the continuous expansion of the reception field. We call it Feature
Camouflage. In this paper, we present a novel add-on module, named Feature
Balance Network (FBNet), to eliminate the feature camouflage in urban-scene
segmentation. FBNet consists of two key components, i.e., Block-wise BCE(BwBCE)
and Dual Feature Modulator(DFM). BwBCE serves as an auxiliary loss to ensure
uniform gradients for foreground classes and their surroundings during
backpropagation. At the same time, DFM intends to enhance the deep
representation of foreground classes in high-level features adaptively under
the supervision of BwBCE. These two modules facilitate each other as a whole to
ease feature camouflage effectively. Our proposed method achieves a new
state-of-the-art segmentation performance on two challenging urban-scene
benchmarks, i.e., Cityscapes and BDD100K. Code will be released for
reproduction.
